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Lenders Transformation Playbook: Bridging Strategy and Execution

Blanc Labs

Lenders Transformation Playbook: Bridging Strategy and Execution

Introduction

The “Lenders Transformation Playbook” serves as a comprehensive guide for financial institutions embarking on digital and operational transformation. In an era of rapid technological change and evolving customer expectations, lenders face increasing pressure to innovate and align business strategies with technology capabilities. This document provides a structured approach for bridging the gap between strategy and execution, ensuring that transformation efforts are targeted, phased, and measurable for sustainable growth.

Executive Summary

  • Strategic Alignment: The playbook outlines a transformation roadmap that integrates business objectives with technology capabilities, driving profitable growth, operational efficiency, and effective risk management.
  • Phased Approach: A structured, phased strategy breaks transformation into manageable steps, minimizing risk while steadily building necessary capabilities.
  • Detailed Initiatives and Costs: Each initiative is mapped out with its associated costs, dependencies, and timelines, enabling optimized resource allocation and ensuring maximum impact.
  • Current State Assessment and Target Setting: The document emphasizes evaluating the current state to set a clear, achievable target, supported by KPIs for tracking progress and outcomes.
  • Governance, Risk, and Compliance (GRC): GRC frameworks are integrated into the transformation process to align technology investments with regulatory compliance and business continuity.
  • Prioritization Framework: Initiatives are ranked using a Feasibility vs. Business Value Matrix, focusing on strategic value, ROI, and feasibility for streamlined execution.
  • Progress Measurement: A phased, metric-driven approach (Crawl, Walk, Run) measures success, with continuous stakeholder feedback for real-time adjustments and sustainable growth.

What Good Looks Like

A successful transformation roadmap provides a structured approach to achieving long-term business objectives. Key components include:

Clear alignment between business and technology strategies.

A phased approach to transformation, breaking down the journey into manageable initiatives.

A focus on prioritization, ensuring the highest impact projects are delivered first.

Phasing the transformation allows organizations to mitigate risks while steadily building capabilities. A good roadmap offers a structured sequence for initiatives based on their strategic relevance and feasibility, ensuring resources are optimized for maximum impact.

Chart 1

The strategic roadmap visually and functionally integrates these key elements:

  • Business Objectives: These are high-level goals the organization aims to achieve, such as Profitable Growth, Operational Efficiency, and Compliance and Risk.
  • Business IT / Capabilities: For each business objective, specific IT capabilities are identified that need to be developed or enhanced to achieve these objectives.
  • Strategic Actions: Aligned with each capability are strategic actions. These are specific activities or projects that need to be undertaken to develop the capabilities necessary for achieving the business objectives.
  • Strategic Roadmap: This section maps out the timing and sequence of initiatives derived from the strategic actions. It is presented in a timeline format across quarters or months. Each initiative is associated with a specific strategic action and is plotted against time to show when it will be initiated and its duration.
  • Cost: Each initiative on the timeline has an associated cost, which helps in budgeting and resource allocation planning.
  • Key Dependencies and Risks: Below the timeline, key dependencies and risks are illustrated. Dependencies show how certain initiatives are reliant on the completion of others, while identified risks might impact the execution of the initiatives.
  • Metrics / KPIs: Corresponding to the initiatives are metrics or KPIs that will be used to measure the success of each initiative, ensuring that each step taken aligns with the strategic goals and delivers the intended outcomes.

Three Steps Towards Creating a Transformation Roadmap

Developing a successful transformation roadmap requires a structured approach, ensuring that both business objectives and technology capabilities are fully aligned. This section outlines three critical steps to creating an effective roadmap, merging the analysis of the current state and the definition of the target state into a single step, as these are deeply interconnected processes.

Chart 2

Aligning Business Objectives with Technology Capabilities

In large-scale transformations, aligning business objectives with technology capabilities is a critical step. The success of a transformation initiative hinges on whether your technology strategy is effectively calibrated to deliver business value. This alignment is more than just matching technology solutions to business needs—it’s about creating a cohesive strategy that considers business vision, processes, architecture, and execution plans.

Establishing Strategic Linkage Between Business and Technology

Image 3

As shown in the diagram, lenders should align their technology investments with business strategy, ensuring each objective, whether focused on customer engagement, efficiency, or growth, is tied to relevant technology capabilities. By evaluating emerging technologies such as AI, cloud, and data analytics, lenders can ensure these investments are both strategically relevant and scalable, driving meaningful outcomes.

Translating Business Strategy into Operational Execution

A well-defined business strategy must translate into an executable plan, which is facilitated by enterprise architecture. The architecture serves as the “glue” that binds strategy with operational execution by guiding critical questions like:

Image 4

Governance, Risk and Compliance (GRC)

Governance, Risk, and Compliance (GRC) frameworks are crucial in aligning business objectives with technology capabilities in the highly regulated financial services industry. For lenders, GRC goes beyond regulatory compliance to enhance business continuity, risk mitigation, and operational integrity. GRC should be integral to the enterprise architecture, guiding technology selection and influencing architectural decisions to minimize vulnerabilities and ensure compliance from the outset.

Effective governance sets up processes, standards, and oversight to align technology investments with business goals, such as improving customer experience or boosting operational efficiency. By establishing clear roles and responsibilities through frameworks, it ensures accountability and facilitates informed decision-making and swift execution.

Risk management identifies and addresses threats like data breaches or system failures, crucial for safeguarding data privacy, transaction security, and service availability. Integrating risk management into technology planning helps lenders preempt regulatory risks and embed compliance into technology solutions from the start.

Compliance dictates specific technology adoptions, driving investments in data management and compliance-centric capabilities like automated reporting and audit trails. This ensures lenders meet regulatory demands efficiently and responsibly, particularly as they increasingly use AI and data analytics, adhering to privacy laws and ethical guidelines.

Assessing the Current State and Defining the Target State: Where We Are and Where We Want to Be

The transformation process begins by evaluating the current state of the organization’s technology capabilities and business alignment and setting a defined target state which the transformation aims to achieve. Utilizing tools like the Online Capability Maturity Assessment ensures that these assessments are thorough and accurately reflect the organization’s capability across various domains.

Assessing the Current State of Technology Capabilities

The first step is to gain a clear, objective understanding of where the organization stands today. This involves a thorough evaluation of current technology capabilities. The assessment phase serves as a diagnostic tool to identify existing gaps, inefficiencies, and misalignments that hinder the organization’s ability to achieve its goals. Capabilities will be organized into three domains:

Image 5

IT Operations
Applications & Data
Infrastructure & Security

Digital Organization: Evaluates the integration of digital technology into all areas of business, changing how operations are performed and value delivered.

Strategy & Innovation: Assesses how effectively the organization fosters innovation and implements strategic technology initiatives that align with business goals.

Technology Finance: Reviews financial management strategies related to technology investments to ensure cost-effectiveness and alignment with financial goals.

Project Portfolio Management: Analyzes the management of the entire portfolio of IT projects to ensure they deliver on intended business values and are aligned with strategic objectives.

Process Reengineering: Focuses on the analysis and redesign of workflows and processes within the organization to drive improvements in cost, quality, service, and speed.

App/SaaS Portfolio Rationalization: Assesses the use and effectiveness of applications and Software-as-a-Service solutions to identify overlap, reduce costs, and streamline software use.

Solution Delivery & Support: Examines how solutions are developed, delivered, and supported to meet user needs effectively and efficiently.

Enterprise & Data Analytics: Evaluates the capabilities in managing and leveraging data across the organization to support decision-making and achieve competitive advantage.

Data Center & Facilities Management: Reviews the management of physical data centers and related facilities to ensure they meet operational needs securely and efficiently.

Server & IaaS Management: Assesses the effectiveness of server management and Infrastructure-as-a-Service provisions to support scalability and business continuity.

Enterprise Security: Focuses on the policies, technologies, and controls deployed to protect data, applications, and the associated infrastructure from internal and external threats.

Networks & Communication Services: Reviews the infrastructure and services that support networking and communications to ensure they are robust, secure, and capable of meeting current and future demands.

The assessment incorporates detailed evaluations across these domains, utilizing metrics ranging from absent (0) to optimized (5), providing a clear picture of technology and business alignment.

Assess the Current State of Business Capabilities

To thoroughly assess the current state of business capabilities, an enterprise must first construct and examine its value chain. This involves a detailed analysis of all processes within the value chain to ensure they effectively support the organization’s strategic objectives. This assessment is critical for identifying efficiency gaps, opportunities for enhancement, and areas where the business may be at risk of falling behind market demands or technological advancements.

The construction of the value chain involves mapping out all key activities involved in creating and delivering the product or service to the customer. This includes everything from inbound logistics, operations, and outbound logistics to marketing, sales, and service. Each segment of the value chain is scrutinized to understand how individual activities contribute to overall value creation and competitive advantage.

Each of these business capabilities, and processes should be guided by key value drivers that align with the organization’s broader business objectives. These value drivers might include:

  • Cost Efficiency: Ensuring that processes are cost-effective and provide the best return on investment. Identifying areas where costs can be reduced without compromising quality is crucial.
  • Quality Enhancement: Maintaining or improving the quality of outputs to meet or exceed customer expectations. Processes should be assessed for their ability to consistently produce high-quality results.
  • Innovation and Adaptability: Gauging the organization’s capacity to innovate and adapt to changing market conditions. This includes the flexibility of processes to incorporate new technologies or methodologies.
  • Risk Management: Evaluating how risks are managed within the business processes. This includes assessing the robustness of the process against internal and external shocks and compliance with relevant laws and regulations.
  • Customer Satisfaction: Measuring how processes impact customer satisfaction and loyalty. This involves understanding customer needs and expectations and how well the business delivers on these.

Let’s take underwriting as an example:

To determine the maturity of the “Underwriting” business capability with regard to the key value drivers of cost efficiency and risk management, you can pose specific questions that probe the depth, effectiveness, and integration of current practices. These questions should help evaluate whether the underwriting processes are optimized to meet the organization’s goals and regulatory requirements effectively. Here’s a breakout of the questions can be asked for two key drivers:

Image 8

Defining the Target State: Where We Want to Be

After the current state has been assessed, the next step is to define the target state—the future vision for both technology and business capabilities. The target state represents the desired end-goal of the transformation process, and it serves as a guide for building the roadmap that will take the organization from its current state to this future vision.

Aligning the Target State with Strategic Objectives:

  • The future state should be directly aligned with the organization’s long-term business goals. Whether the objective is to drive revenue growth, improve customer satisfaction, or increase operational efficiency, every element of the target state should contribute to these outcomes.
  • Key Performance Indicators (KPIs) should be established to measure success. Examples of KPIs include reducing operational costs by 20%, increasing the speed of customer onboarding by 30%, or improving customer retention by 15%. These metrics ensure that progress toward the target state is measurable and aligned with business priorities.

Here is an example of the evaluations of the current state and the projection of the target state of technology capabilities:

Also, here is an example of the evaluation of the Underwriting business capability, under Cost Efficiency and Risk Management key drivers, and the target state.

Strategic Steps to Achieve Target State:

To strategically achieve the target state, the organization should implement several key measures. First, establishing interim goals is crucial; these act as milestones that progressively enable the achievement of target capabilities. This phased approach not only facilitates continuous assessment but also allows for necessary adjustments along the transformation journey. Secondly, priority setting is essential. The organization must focus on areas identified in the maturity assessment as needing the most improvement—these are the domains where enhancements can drive significant business value. Finally, effective resource allocation is paramount. Resources should be strategically deployed to areas that demand immediate attention and have the potential to make the most significant impact on reaching the target state. Collectively, these strategies ensure a structured and effective progression towards the envisioned future capabilities.

Sequencing initiatives based on Prioritization

In any large-scale transformation, successful execution depends heavily on the ability to effectively sequence initiatives. For lenders, this is particularly important given the complexities of aligning technology upgrades, process changes, regulatory requirements, and customer experience improvements. Developing a prioritization framework based on a feasibility versus business value matrix, along with other key factors, allows for focused and efficient execution of transformation efforts.

Feasibility vs. Business Value: A Practical Framework for Prioritization

Before delving into the detailed analysis with the Feasibility vs. Business Value Matrix, it’s crucial to first conduct a prefilter step. This preliminary assessment serves to screen initiatives by answering key questions related to their strategic alignment, optimization potential, technical feasibility, potential return on investment, and compliance considerations. By addressing these questions, we can effectively filter out initiatives that are misaligned with our strategic goals or lack viability, ensuring only the most promising initiatives proceed to the detailed evaluation phase. This streamlined approach helps prioritize resources efficiently and focus on initiatives that offer tangible benefits.

Image 11

Feasibility Factors
Business Value Factors
  • Technical Complexity
    Determine how challenging it is to implement the technology required. Initiatives involving advanced technology integration, significant infrastructure upgrades, or large-scale system migrations are generally considered less feasible.
  • Resource Availability
    Assess whether the organization has the necessary resources—people, time, skills, and budget—to execute the initiative. High feasibility is associated with initiatives that require minimal additional resources or leverage existing capabilities.
  • Organizational Readiness
    Consider how prepared the organization is for the change. Initiatives that align well with existing processes, skills, and culture are more feasible, while those requiring significant change management or reskilling are less so.
  • Dependency and Complexity
    Analyze whether the initiative depends on the completion of other projects or if it introduces significant cross-functional dependencies. High feasibility is associated with standalone initiatives or those with manageable dependencies.
  • Strategic Alignment
    Determine how closely the initiative aligns with the organization’s strategic priorities, such as enhancing customer experience, driving growth, or improving compliance. Initiatives that directly support key business objectives score high in business value.
  • Revenue Impact
    Evaluate the potential for increased revenue or cost savings. High-value initiatives typically offer substantial returns, either by generating new revenue streams, optimizing operations, or reducing risks.
  • Customer Experience Enhancement
    Consider how the initiative will affect customer satisfaction and loyalty. Projects that improve user journeys, reduce friction, or introduce new customer-facing features tend to have a high business value.
  • Risk Mitigation and Compliance
    Assess whether the initiative addresses critical regulatory requirements or mitigates significant risks. Initiatives that help the organization avoid penalties, improve data security, or ensure compliance with evolving regulations often deliver substantial business value.
  • Competitive Advantage
    Examine the extent to which the initiative provides differentiation in the market. High-value projects are those that give the organization a competitive edge, whether by introducing innovative products, improving service delivery, or optimizing go-to-market strategies.

To effectively prioritize initiatives using the Feasibility vs. Business Value matrix, it is essential to assign specific weights to each factor within both the feasibility and business value dimensions. Not all factors carry the same importance for every organization. By assigning weights to each factor—such as technical complexity, resource availability, revenue impact, or customer experience enhancement—organizations can create a more nuanced and tailored evaluation. Once weights are established, each initiative can be assessed by scoring it against these weighted factors. This scoring process allows for a more objective comparison of initiatives, making it easier to identify which projects should be prioritized based on their overall feasibility and potential business value. This approach not only brings clarity to the decision-making process but also ensures that resources are allocated to initiatives that best align with strategic goals while considering practical constraints. Each of the initiatives will have a Business Case, where evaluation will be placed.

Image 12

Once the initiatives are evaluated, they can be plotted in a scatter chart graph, where the initiatives will be classified into three zones:

Image 13

Initiatives to Pursue, with high ease of implementation and high business value; Initiatives to Explore, with high ease of implementation OR high business value; and Initiatives to Hold Off, with low ease of implementation OR low business value. Using this matrix allows lenders to visually map and rank initiatives, ensuring that resources are directed toward efforts that provide the greatest return on investment (ROI) while aligning with strategic objectives.

Balancing Quick Wins with Strategic Priorities

While the matrix provides a clear view of where to start, balancing quick wins with strategic priorities is key to sustaining momentum throughout the transformation. Here’s how:

  • Phased Approach: Start with quick wins that deliver early benefits. These successes build credibility and buy-in for the broader transformation, making it easier to secure resources and stakeholder support for more complex initiatives.
  • Dependency Mapping: Before initiating any strategic priority, map dependencies to understand how one initiative impacts others. For example, deploying an integrated loan origination platform may depend on completing data integration projects first.

 

Image 14

  • Risk Management: Prioritize initiatives that mitigate risks early on, such as addressing regulatory compliance issues or upgrading outdated systems that could lead to business disruptions.

Creating a Transformation Roadmap: Sequencing and Milestones

Once initiatives are prioritized, a transformation roadmap can be developed to sequence the initiatives over time. The roadmap should include:

  • Phases and Milestones: Break the transformation into phases, each with clear milestones. For example, Phase 1 might focus on foundational upgrades like data integration, while Phase 2 could introduce customer-facing digital services.
  • Resource Allocation and Timeline Planning: Align resources, including personnel, technology, and budget, with the planned initiatives. Ensure timelines are realistic and consider potential delays or risks.
  • Cross-Functional Coordination: Transformation often requires collaboration across different business units, including IT, operations, finance, and customer service. The roadmap should define how these units interact at each phase and clarify roles and responsibilities.

Measuring Progress – Tracking Success of Large-Scale Transformations

Measuring progress and tracking the success of large-scale transformations are critical to ensuring that the organization’s strategic changes yield the desired outcomes. Effective measurement and tracking not only provide a clear view of how well the transformation initiatives are performing but also help identify areas that need adjustment or further improvement.

Image 15

Establishing Clear Metrics and KPIs

Establish specific, measurable, achievable, relevant, and time-bound (SMART) metrics that align with the strategic objectives of the transformation. These could include operational efficiency metrics, customer satisfaction scores, system uptime, and financial metrics like ROI and cost savings.

Develop KPIs that are tailored to the specific goals of each transformation initiative. For example, if a project aims to enhance digital customer interactions, relevant KPIs might include digital engagement rates and digital sales conversion rates.

Phased Measurement Approach

This structured approach details the progression of architectural and operational enhancements over a 24-month period, segmented into three phases: Crawl, Walk, and Run. Each phase is defined by specific metrics that track the maturity and effectiveness of initiatives, starting from basic compliance and engagement, advancing through process optimization and governance, and culminating in technological refinement and redundancy reduction. This method ensures that improvements are sustainable and strategically aligned, facilitating continuous advancement in both technology and business processes.

chart 16

Stakeholder feedback

Stakeholder feedback plays a pivotal role in refining the transformation process. Gathering insights from internal stakeholders, including employees and management, provides a direct line of sight into the operational impacts and challenges of the transformation efforts. This internal feedback is critical for identifying unforeseen issues and areas needing improvement. Similarly, actively engaging with customers to obtain their perspectives on how changes affect their experience helps in adjusting strategies to better meet consumer demands, particularly for customer-facing innovations.

Learning, Adaptation and Documentation

Incorporating continuous learning and adaptation ensures the long-term success of transformations.

Regular data analysis allows for real-time adjustments and fine-tuning of strategies.

Transparent reporting and thorough documentation build trust and align stakeholders.

Sharing success stories reinforces the transformation’s value.

A structured framework to measure progress through ‘Crawl’, ‘Walk’, and ‘Run’ phases helps achieve immediate goals while supporting sustainable growth and adaptation.

Conclusion: Securing Success Through Strategic Transformation

Large-scale transformations require meticulous planning, execution, and continuous oversight, aimed not only at achieving short-term goals but at reshaping the organization for long-term success. A well-structured transformation roadmap helps align strategic actions with broader business objectives, ensuring each step contributes meaningfully. By adopting a phased approach—crawling before walking, and walking before running—organizations can manage the pace of change, solidifying capabilities before moving on to more complex initiatives, reducing risk, and enhancing the likelihood of success.

Continuous progress reviews, real-time monitoring, and stakeholder feedback are crucial for keeping the transformation relevant and responsive. Fostering a culture of learning and adaptation ensures that insights from each phase are integrated into future strategies. Transparent communication and documentation further support this adaptive approach, keeping stakeholders informed and engaged. Ultimately, transformation success lies not just in completing initiatives, but in positioning the organization to meet future challenges and seize new opportunities, ensuring sustainable growth and evolution.

Ruben Armas
Technology Strategy, Process and Data Specialist – Advisory Services

Ruben is a technology strategist with a passion for turning complex challenges into streamlined solutions. With over 8 years of experience in process optimization and business intelligence, he has led automation, data analytics, digital innovation, and technology transformation projects across the Financial, Insurance, and Healthcare sectors, consistently delivering results that drive growth and efficiency.

Prithvi Srinivasan
Managing Director – Advisory Services

Prithvi Srinivasan, the Managing Director for Advisory Services, brings extensive expertise in technology strategy and digital transformation to drive mission-critical programs and advance financial institutions with AI and automation. His ability to transform complex strategies into successful transformations underscores his commitment to innovation and client value.

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AI’s Mid-Market Makeover in Financial Services

Blanc Labs

AI’s Mid-Market Makeover in Financial Services

hero

Summary

Mid-sized financial services institutions (FIs) are facing significant challenges during this period of rapid technological change, particularly with the rise of artificial intelligence (AI). As customer expectations grow, smaller banks and lenders must stay competitive and responsive. Canada’s largest financial institutions are already advancing in AI, while many others remain in ‘observer’ mode, hesitant to invest and experiment. Yet, mid-sized FIs that adopt the right strategy have unique agility, allowing them to adapt swiftly and efficiently to technological disruptions—even more so than their larger counterparts.

This report explores how AI can tackle these challenges by transforming operations and generating substantial value across departments and business lines. We present a detailed approach for identifying, prioritizing, and implementing AI use cases, empowering mid-sized financial institutions to maintain a competitive edge in the fast-changing financial industry.

AI offers a major opportunity to enhance productivity, personalize customer interactions, improve decision-making, and create new business models. Successful AI integration requires a balanced, strategic approach that aligns with business goals and carefully evaluates potential risks.

Several challenges facing mid-sized financial services companies

In a time of rapid technological advancements and changing market conditions, mid-sized financial services companies are at a crossroads. They must meet rising customer expectations and deal with growing competition, presenting them with unique challenges that can either hinder their growth or drive them to seek new opportunities. Here are the top four challenges in this area:

Rising Customer Expectations

Competitive Pressures

Workforce Dynamics and People Constraints

Technology and AI Disruption

In this exploration, we look at how mid-sized FIs can use AI to overcome challenges and improve operations and customer engagement. In a following article, we will prioritize AI use cases to help these institutions make informed decisions. 

Creating Value Across All Departments

Amid the challenges faced by mid-sized financial firms, AI stands out as a key driver of progress. It enables transformative change, boosts operational efficiency, personalizes customer interactions, enhances decision-making, and fosters the development of new business models. By addressing these challenges, AI has the potential to create significant differentiation and impact every aspect of the business.

Here’s how AI can boost businesses:

Productivity Customer Decision making Business model
Increase Productivity and Efficiency Personalize Customer Experiences Augment Decision Making Invent New Business Models

Increase Productivity & Efficiency

AI-driven tools are revolutionizing operational workflows by streamlining processes and reducing manual intervention. Through intelligent document processing, regulatory compliance monitoring, and process automation, financial services firms can achieve unprecedented levels of productivity. By leveraging AI-powered copilots to augment humans in tasks like email management and coding modernization, organizations can optimize their day-to-day operations and enable their workforce to focus on higher-value activities.

Personalize Customer Experience

In today’s competitive market, personalization is key to customer satisfaction, and AI is at the forefront of this capability. With AI-powered sentiment analysis and recommendation engines, financial services companies can tailor services and products to meet individual customer needs more effectively. Furthermore, AI-driven customer engagement tools, from text-to-video communications to generative experiences, enable businesses to create seamless, personalized journeys that enhance customer loyalty and retention.

Augment Decision Making

AI augments decision-making processes by providing real-time, data-driven insights that enhance business strategies. From underwriting assistance to dynamic pricing and risk scoring, AI enables financial institutions and other businesses to make informed decisions faster and more accurately. By integrating AI in areas like fraud detection and smart financial advisory, companies can mitigate risks while optimizing their decision-making processes, leading to improved outcomes and competitive advantage.

Invent New Business Models

AI is not just about optimizing existing processes; it also drives the creation of entirely new business models. For example. the advent of AI-powered embedded finance and autonomous lending platforms demonstrates how AI can unlock new revenue streams and business opportunities. Through insights-as-a-service offerings and new partnership value streams, organizations can capitalize on their data, leverage AI to explore untapped markets, and invent business models that could disrupt traditional banks and lenders.

In a subsequent article, we’ll explore these topics in depth, breaking them down with detailed explanations and real-world use cases.

GenAI Value Creation: Aspiration vs. Reality

As financial institutions navigate the complex landscape of AI adoption, two distinct sentiments emerge, each underpinned by its own set of expectations and concerns.

On one hand, the optimists in the financial sector view GenAI as a revolutionary force capable of driving significant industry changes. This group is excited by the potential for:

True Hyper-personalization: Tailoring products and services to individual preferences and needs for every single customer.

Maximum Customer Experience: Leveraging conversational AI to improve service quality and user engagement across all channels.

Automation Everywhere: Implementing AI-driven processes to streamline operations and reduce the need for manual and paper-based work.

Giant Productivity Leaps: Increasing efficiency & effectiveness across all organizational levels, by augmenting employees to focus on the highest value activities.

Zero-Fraud Financial Systems: Using advanced AI algorithms to detect and prevent fraud in real-time.

These optimists are encouraged by projections suggesting that GenAI could unlock over $1 trillion in banking revenue by 2030[1], signaling a major shift in how financial services are structured and delivered. Moreover, 75% of executives anticipate that GenAI will drive disruptive changes within the industry over the next three years.[2]

Conversely, the skeptics approach GenAI with caution, emphasizing the potential pitfalls over the promised perks. Their concerns include:

Boom & Bust Cycles: Risk of overinvestments driving unsustainable growth, low ROI, and financial problems down the road, as was the case with several technology waves in the past decades.

Reliability Issues: Worries about the limitations and flaws of new GenAI technologies, and their impact on mission-critical financial processes where the room for error is very small.

Regulatory Constraints: Challenges to AI growth due to evolving, stringent regulations across local and federal jurisdictions in financial services.

Security and Privacy Risks: Increased risk of cyber threats, data privacy concerns, and potential bias as AI continues to handle and process more sensitive information on customers and employees.

Overpromise, Underdeliver: Many worry about the prospect of AI failing to meet the current hype, and see in it an over-marketed tech that is less revolutionary that it seems.

Interestingly, despite the much touted prospects of AI, a significant 66% of executives surveyed express ambivalence or dissatisfaction with their organization’s progress in AI integration, highlighting a gap between expectation and execution.[3]

Embracing AI Optimism with Prudence

Broadening Our AI Understanding
Evaluating AI’s Imperfections
Looking Beyond the Present
Avoiding Common Pitfalls

AI encompasses a wide array of tools and capabilities, including battle-tested solutions based on both predictive and generative AI. Recognizing the full range of AI technologies will enable us to leverage the most effective solutions for our specific needs.

Like humans, AI is not infallible—it makes mistakes. However, combining human oversight with GenAI’s capabilities could enhance our decision-making processes beyond what’s achievable by human efforts alone.

The rapid progress of AI means what’s cutting-edge today might be outdated tomorrow. We should consider the potential of future AI developments, much like how each model (e.g. GPT-4o) has expanded on the capabilities of its predecessors. Looking into the not-so-distant future, imagine what GPT-14 would be capable of vs. GPT-4.

AI isn’t a one-size-fits-all solution, and should not be treated solely as a technology problem. Rather than adopting AI for its own sake, it’s vital to focus on strategic AI use cases that align with our specific business goals.

The Spectrum of AI Adoption in Financial Services: Where Companies Stand

In the financial services sector, firms vary significantly in their approach and adoption of artificial intelligence. This diversity is best understood through four distinct groups: Observers, Experimenters, Implementers, and Innovators. Each category defines the group’s current engagement with AI technologies and also underscores its strategic vision and operational readiness for future disruptions.

 

new-chart

Observers

Many observers are choosing to wait for large, proven returns on investment (ROI) and clearer technology convergence before diving into AI. While this cautious stance lets them learn from early adopters, it risks putting these FIs behind competitors who are seizing AI-driven advantages now.

Experimenters

Experimenting FIs are cautiously dipping their toes into AI through small-scale projects and pilot programs, testing the waters to gauge capabilities and potential benefits without diving into full-scale implementation. This approach nurtures a culture of learning and adaptability, but without a comprehensive strategy, its impact and scalability may remain limited.

Implementers

Implementers are more advanced in their AI journey, embedding AI systems within specific departments and processes to generate tangible business value. These FIs are building significant AI capabilities and gaining advantages in differentiation and operational efficiency. However, their challenge is to scale these benefits across the entire organization to prevent siloed success and ensure widespread impact.

Innovators

Innovators are embedding comprehensive AI strategies across their business models, reshaping their operations and potentially revolutionizing the financial services industry for a powerful competitive edge. However, this ambitious approach comes with heightened risks and substantial investments, making the journey to success both challenging and uncertain.

Competitive AI Landscape: Lessons from Canada’s Big Financial Institutions 

As mid-sized FIs consider the question of AI, it’s crucial to recognize the significant strides made by Canada’s largest banks. Notably, three of the Big Five banks are ranked among the top ten globally for AI maturity [4], showcasing their leadership in using AI to transform financial services. Here’s how some of these banks are creating competitive advantage with AI: 

Ranked among the top three globally for AI maturity, RBC owns Borealis AI, a dedicated research and development institute. It has launched AI-driven products like Aiden for trading and NOMI for personal finance management, and utilizes AI to enhance internal processes and modernize legacy systems. 
With its AI R&D center, Layer 6, TD has grown its AI talent to over 200 professionals and is developing over 50 AI solutions across its business lines. It is the leading patent filer in AI among Canadian FIs (450+ AI-related patent filings) and uses predictive AI models to pre-approve mortgages in seconds, demonstrating a strong commitment to integrating AI across its operations.[5]
Winner of the Best Gen-AI Initiative by The Digital Banker 2024, CIBC has developed its own custom built ‘CIBA AI system to improve productivity and enable staff to focus on high-value activities. The introduction of tools like GitHub CoPilot boosts developer efficiency, and the bank plans to significantly expand its data and AI workforce by hiring 200+ related roles over the next 12 months.[6]
Scotiabank’s customer-facing AI chatbot handles 40% of queries with a 90% accuracy rate and has significantly sped up agent training. The bank has also launched a global AI platform to enhance customer insights across all touchpoints and employs large language models to improve employee experiences.[7]
BMO considers AI integral to its bank strategy. Its mobile app provides AI-driven personalized insights to improve customer engagement, its ‘Digital Workbench’ offers real-time analytics for commercial clients, and its contact centers’ service quality is boosted through NLP tech. Last year, it upskilled over 3,500 employees through learning & development programs centered on AI and cloud. Externally, BMO demonstrated commitment to AI by sponsoring the ‘Next AI accelerator.[8]

 

Strategic Imperative for Mid-Sized FIs 

The AI advancements by Canada’s largest banks illustrate a clear trend: AI is not just a technological upgrade but a strategic necessity. Mid-sized FIs must recognize the need to keep pace and innovate beyond traditional technologies and models. Key actions for staying ahead include: 

  1. Accelerated AI Adoption and Scale-Up: Mid-sized FIs need to prioritize their own AI roadmaps and seek opportunities to rapidly scale adoption of AI solutions across various departments. 
  2. Target High-Value AI Applications: Focus on areas where AI can quickly deliver the most significant returns, such as improving customer experience and increasing operational efficiency – before exploring how AI can enable the development of new financial products & services. 
  3. Invest in AI Talent and Partnerships: Expand internal capabilities by hiring AI specialists and consider partnerships with AI tech & service providers to tap into external expertise. 
  4. Monitor and Adapt Best Practices: Keep a close eye on industry leaders and continuously adapt their AI best practices to suit smaller scale operations and unique market demands. 

By embracing these strategies, mid-sized FIs can respond to the competitive pressures exerted by larger banks and carve out their own niche in the rapidly evolving financial landscape. 

Competitive AI Landscape: Lessons from Canada’s Big Financial Institutions 

As mid-sized FIs consider the question of AI, it’s crucial to recognize the significant strides made by Canada’s largest banks. Notably, three of the Big Five banks are ranked among the top ten globally for AI maturity [4], showcasing their leadership in using AI to transform financial services. Here’s how some of these banks are creating competitive advantage with AI: 

Ranked among the top three globally for AI maturity, RBC owns Borealis AI, a dedicated research institute. It has launched AI-driven products like Aiden for trading and NOMI for personal finance management, and utilizes AI to enhance internal processes and modernize legacy systems. 

With its AI R&D center, Layer 6, TD has grown its AI talent to over 200 professionals and is developing over 50 AI solutions across its business lines. It is the leading patent filer in AI among  Canadian FIs (450+ AI-related patent filings) and uses predictive AI models to pre-approve mortgages in seconds, demonstrating a strong commitment to integrating AI across its operations.[5]

Winner of the Best Gen-AI Initiative by The Digital Banker 2024, CIBC has developed its own custom built ‘CIBA AI system to improve productivity and enable staff to focus on high-value activities. The introduction of tools like GitHub CoPilot boosts developer efficiency, and the bank plans to significantly expand its data and AI workforce by hiring 200+ related roles over the next 12 months.[6]

Scotiabank’s customer-facing AI chatbot handles 40% of queries with a 90% accuracy rate and has significantly sped up agent training. The bank has also launched a global AI platform to enhance customer insights across all touchpoints and employs large language models to improve employee experiences.[7]

BMO considers AI integral to its bank strategy. Its mobile app provides AI-driven personalized insights to improve customer engagement, its ‘Digital Workbench’ offers real-time analytics for commercial clients, and its contact centers’ service quality is boosted through NLP tech. Last year, it upskilled over 3,500 employees through learning & development programs centered on AI and cloud. Externally, BMO demonstrated commitment to AI by sponsoring the ‘Next AI accelerator.[8]

 

Strategic Imperative for Mid-Sized FIs 

The AI advancements by Canada’s largest banks illustrate a clear trend: AI is not just a technological upgrade but a strategic necessity. Mid-sized FIs must feel the urgency to not only catch up but also innovate beyond traditional models to remain competitive. The following strategic actions are recommended: 

  1. Rapid AI Adoption and Scale-Up: Mid-sized FIs need to prioritize AI adoption and seek opportunities to scale AI solutions across their operations quickly. 
  2. Focus on High-Impact AI Applications: Identify areas where AI can create significant value, such as enhancing customer experience, improving operational efficiency, and developing new financial products. 
  3. Invest in AI Talent and Partnerships: Expand internal capabilities by hiring AI specialists and consider partnerships with AI tech & services firms to leverage external expertise. 
  4. Monitor and Adapt Best Practices: Keep a close eye on industry leaders and continuously adapt their AI best practices to suit smaller scale operations and unique market demands.

 

By embracing these strategies, mid-sized FIs can respond to the competitive pressures exerted by larger banks and carve out their own niche in the rapidly evolving financial landscape. 

Conclusion

The financial services industry is at a turning point, especially for mid-sized FIs. Adopting AI is no longer a choice but a necessity to remain competitive and meet the growing demands of the market. With extensive experience working with mid-sized FIs, Blanc Labs recognizes the transformative potential AI brings—from boosting operational efficiency to tailoring customer experiences and introducing new business models. As technology continues to advance at a rapid pace, it’s crucial that these institutions take decisive action to leverage these tools. By embracing AI now, your organization can gain a competitive advantage, fuel growth, and position itself for long-term success in an increasingly fast-paced industry. 

The next article in this series will explore real-world examples of AI in action within financial services and offer a structured approach to identifying and prioritizing the most impactful AI applications for mid-sized FIs.

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How Open Banking Will Empower Small Businesses in Canada

Financial Services | Consumer Driven Banking | Open Banking

How Open Banking Will Empower Small Businesses in Canada

June 6, 2024
small business owners benefitting from open banking technology

As the old saying goes, “cash is king” and with 85% of business owners saying they’re looking for faster and easier access to capital, it seems like this is still truer than ever in today’s economy. Factors like high inflation and slow lending processes are making it harder for small businesses to access growth and operating capital, while needing to respond to challenges and opportunities in an ever-evolving business landscape. Open Banking can help address some of these challenges. In this guide, we discuss how.

What is Keeping Small Businesses From the Capital They Need?

To understand how open banking helps small businesses access capital faster, we need to look at the challenges they currently face.

Margins Under Pressure

Small businesses find accessing capital difficult when lenders tighten their belts or become risk-averse. An unfavorable credit market can result from multiple factors, such as high interest rates or a global financial crisis.

Inflation is a major contributor to tightening the credit market. It increases the cost of labor, raw materials, and operating expenses. In fact, 49% of respondents cited the cost of goods as the biggest inflation-related concern in an Equifax survey.

If small businesses can’t pass on the increased costs to customers because of competitive pressures or customer resistance, inflation can put pressure on profit margins. This translates to a lower net income percentage and cash generation—banks don’t like to see either when lending to a business.

Slow Lending Processes

Banks require applicants to submit tax returns, financial assets, a business plan, and various other documents. Excessive paperwork leaves room for errors and missing information. This is a major reason small businesses don’t get loan approval and are often hesitant to apply at all.

Stringent Know Your Customer (KYC) checks and rigorous anti-money laundering regulations significantly slow down loan approvals. Banks in Canada take risk assessment seriously. While there’s a good reason for it, analyzing credit history, projecting cash flows, and valuing collateral take time.

Even after going through the process, the number of small businesses that secure funding is often as low as 13.5%. Moreover, applicants who do make it through the approval phase might receive approval for an amount lower than requested.

Lack of Complete Information

Banks often don’t receive complete information from applicants. This can skew their decisions. For example, an applicant’s business might have changed significantly since they filed their last return.

Suppose your business recently secured a huge long-term contract with a multinational corporation. The bank might not see it on your previous return and won’t factor it into their assessment. That’s why banks need access to real-time data when making a lending decision.

Some banks rely on the borrower’s personal credit score to make lending decisions. This means you may find getting loan approval more difficult if you belong to Black or Hispanic communities where low credit scores and credit invisibility are common.

How will Open Banking Technology Transform the Lending Process?

Open banking, or “consumer-driven banking,” is about to revolutionize how small businesses secure debt capital. A whitepaper by Equifax explains:

“Two use cases stand out among those lenders already using Open Banking: payment initiation and more accurate consumer lending decisions. Not far behind, at 24%, are organizations that are using Open Banking to improve estimation or verification of consumer income.”

Open banking is not available in Canada yet, but work is underway to implement it as soon as possible. Open banking will transform the lending process in the following ways, making it easier for small businesses to access debt capital from financial institutions.

Easy Access to Data for Lenders and Small Businesses

Banks rely on financial data to make lending decisions. Open banking does a great job of mobilizing financial data, and here’s how it helps both small businesses and lenders:

Offers Banks a Thorough Credit Picture

Open banking technology connects small businesses and financial institutions via a third-party provider—usually fintech companies and alternative lenders. Third-party providers have access to a wealth of financial information, including transaction history, income, and the applicant’s creditworthiness. This gives lenders ample data to assess a loan application.

Third-party apps use application programming interfaces (APIs) to access real-time financial data from multiple sources, including the applicant’s bank account. This eliminates the need for manual documentation and verification, minimizes administrative load, and streamlines the lending process.

Drives Fairer Outcomes for Applicants With Low Credit Visibility

Access to more accurate data also drives fairer outcomes for a diverse range of applicants. This is especially important for Canada, where 12.2% of small business owners or primary decision-makers belong to a visible minority.

Many of these owners, especially those who recently migrated to Canada, don’t have enough financial history that banks can use to assess their creditworthiness. Open banking can help these applicants by enabling them to supplement loan credit checks with transaction data.

Enables Small Businesses to Seek Funding More Strategically

Open banking products give small businesses a holistic view of their financial position. For example, small businesses can monitor their cash flows and profitability during times of economic turmoil and apply for a loan once their financial statements project greater resilience.

In countries where open banking has already been implemented, businesses have realized various benefits, including greater visibility over cash flow. The UK’s Open Banking Implementation Entity interviewed 900 SMEs, of which 77% reported that open banking provided them with better visibility of their financial position.

Streamlined Application and Approval Process

Funding that doesn’t arrive on time is often futile. If you need access to quick cash during a high-growth phase or cash crunch, you probably don’t want to wait weeks until, and if, the lender approves your application. Not to mention, the loan application process itself could take up to a week, depending on the lender you’re dealing with.

Open banking automates various tasks in the lending process, such as:

Customer Data Aggregation

Open banking APIs enable applicants to share financial data with the lender via the open banking app. There’s no need for applicants to submit documents and statements manually. Once data flows in through the API, advanced data analytics tools and algorithms categorize, structure, and analyze the data to assess creditworthiness.

Cash Flow Analysis

Your cash flow statement may give lenders an idea of your business’s ability to generate cash. However, analyzing the sources and applications of cash using a cash flow statement requires manual work.

Instead of manually going through the cash flow statement, lenders can run the data through an algorithm or data analytics tool. They can apply advanced data analytics techniques to customer’s cash flow data sourced through open banking to understand the customer’s sources of income, recurring expenses, and spending habits.

Integration with Loan Origination Systems

Open banking enables seamless integration with the lenders’ loan origination systems, facilitating automated data transfer. This means underwriters, loan officers, and decision-makers have all the data they need readily available, resulting in faster approvals.

Greater Transparency and Competition

Here’s how open banking enhances transparency and competition: 

Makes  Shopping for Loans Easier 

Open banking offers small businesses greater transparency over prevailing rates.  With better data portabilitybusiness owners could use a third-party  app to compare interest rates, terms, and features of a loan product across multiple lenders without having to approach them individually. 

Increases Efficiency and Innovation 

Open banking levels the playing field by lowering the entry barrier for new financial services startups. Open banking has been instrumental in facilitating new lending models like peer-to-peer (P2P) lending platforms and digital lending marketplaces where you can explore a wide range of financing options. 

 

Open Banking for Small Businesses: Immediate and Long-Term Benefits

Here are some benefits of open banking for small businesses: 

Faster Access to Capital

Small businesses often face various challenges when accessing traditional financing options like bank loans, thanks to stringent eligibility criteria and lengthy approval processes. According to Ellie Mae, the average time to close a loan from the day of application to the disbursement of funds is 52 days. 

Open banking makes it easier for small businesses to access capital via alternative financing options. Countries that have already implemented open banking are seeing massive benefits. For example, Infosys built an open banking solution for a non-banking financial company (NBFC) in India. The solution helped the NBGC disburse funds faster while remaining 100% compliant. 

Helps Build Long-Term Financial Fortitude

Financial prudence offers various long-term benefits. Many small businesses aren’t profitable and deal with cash flow challenges. Using open banking solutions to track financial data across multiple accounts can add financial fortitude to your business, which is critical to getting the best loan terms and quick approval. 

Open banking software can even help reduce costs. A survey reveals that open banking helps businesses save up to 150 hours and 36 minutes each year—a little over four full working weeks. This translates to greater profitability and favorable changes in key metrics like the interest coverage ratio, eventually improving the odds of getting a loan approved. 

Tailored Financial Products

Open banking offers personalized financial products and services that cater to a small business’s specific needs. 

Suppose a small business uses an open banking app that monitors the business’s cash flow. The app keeps forecasting cash flows in real-time and alerts you when it sees a possible cash crunch coming. 

It also recommends a working capital loan, a list of lenders offering great terms, and a repayment schedule so the business owner or decision-maker can understand the impact on cash flows. 

Fosters Innovation and Competition

Greater access to financial data gives fintechs the ammunition to develop unique solutions and  tailored offerings that address specific needs insmall business finance. 

Mastercard is already making strides towards innovating unique solutions. The company has partnered with payments platform Dwolla to help users securely send funds without sharing account or routing numbers. 

It’s also collaborating with the fintech company Jack Henry, which services small community and regional banks, to provide account holders with the ability to see all of their financial information across multiple accounts (including those outside their primary financial institution) in one place. 

 The UK has already seen a massive range of innovative solutions powered by open banking. For example, the UK has apps to help people easily pay tax—the UK government received over £10.5 billion in taxes through open banking payments, according to a report by the Joint Regulatory Oversight Committee published in February 2023. 

Empowers Customers

One of the most significant aspects of open banking is customer empowerment. Open banking promotes transparency and accountability in the financial ecosystem by putting control of data into the customers’ hands and letting them decide whether they want to share their data with a third party. Customers can choose to share data with lenders that offer the most competitive loan terms, driving competition among lenders. 

Open Banking Strategy and Implementation 

Financial Institutions can benefit significantly from creating an open banking ecosystem for small and medium-sized businesses.  The global impact potential of Open Banking adoption across all sectors of banking  has been estimated at between 1 and 5 % of GDP according to McKinsey. But getting started can be challenging. To ensure a successful outcome, a strategic and methodical approach is crucial. Here are some key steps to effectively implement open banking for your institution: 

  1. Define Clear Use Cases: Start by identifying customer’s needs and how open banking can enhance your current SMB service offerings. Evaluate how these initiatives align with your overall business strategy in unlocking new opportunities for growth.  
  2. Develop a Partnership Roadmap: Open banking is an ecosystem that thrives on collaboration. Map out potential partnerships with other financial institutions and third parties. Focus on enhancing data utilization and creating customer offerings. While choosing partners, make sure they align with your technology capabilities and business goals.  
  3. Standardize and Manage APIs: APIs should be managed with a comprehensive framework which includes protocols for access, security, maintenance, and governance. This framework will be critical in ensuring that your open banking ecosystem runs smoothly and securely, which complying to data protection standards.  

Are you ready to implement Open Banking Technology?

Partner with Blanc Labs to build an open banking platform or solution that will help you innovate, stay ahead of the curve, and thrive as the open banking ecosystem matures.  

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Align, Assemble, Assure: A Framework for AI Adoption

Align, Assemble
Assure

Executive Summary 

In the rapidly evolving landscape of artificial intelligence (AI), enterprises face significant challenges in differentiating their AI capabilities to achieve strategic objectives. This article provides a comprehensive approach to organizing internal structures across business, technology, and governance/risk/compliance domains to build a robust and differentiated AI capability. 

The first part of the article emphasizes the importance of Stakeholder Alignment. To ensure success, AI initiatives must align closely with the organization’s strategic objectives and core values. This alignment ensures that AI projects not only drive innovation but also resonate with the organizational ethos and mission. By integrating AI principles and strategies, enterprises can foster a culture that supports and accelerates AI adoption. 

The second section delves into the Operating Model, which is crucial for driving AI innovation and efficiency. This involves defining the ideal team structures and identifying the essential capabilities required across people, processes, and technology. A robust operating model includes a well-defined AI platform that supports strategic objectives and maximizes returns. It also emphasizes the importance of ideation, experimentation, and the productionalization of AI projects to ensure they deliver tangible business value. Effective resource allocation and management are key to maximizing the returns on AI investments. 

The third critical aspect covered is Governance, Risk, and Compliance. As AI technologies advance, so do the associated risks and regulatory requirements. It is imperative for enterprises to identify and manage potential risks linked to AI projects proactively. Establishing comprehensive compliance mechanisms ensures that AI applications adhere to all regulatory and ethical standards, thereby safeguarding the organization against legal and ethical pitfalls. 

To address these multifaceted challenges, this article introduces the Align | Assemble | Assure (AAA) Framework for AI adoption within enterprises. 

  1. Align: This phase focuses on ensuring that AI initiatives are in sync with the strategic objectives and core values of the enterprise. By developing a clear AI strategy and set of principles, organizations can provide a coherent direction for AI adoption that supports broader business goals. 
  2. Assemble: This phase is about building the necessary capabilities across the organization. It involves organizing teams effectively and ensuring the right mix of talent, processes, and technology. By developing an AI use case portfolio, roadmap, business case, and budget, enterprises can prioritize AI investments and drive projects that offer the highest return on investment (ROI). 
  3. Assure: The final phase focuses on risk management and compliance. This involves assessing AI risks comprehensively and ensuring compliance with internal policies and external regulations. Implementing an AI risk management framework and compliance measures ensures that AI initiatives are secure, ethical, and compliant with all necessary standards.

In the next section, we will explore in depth how the Blanc Labs AAA Framework for AI Adoption can be implemented using actionable and measurable steps.

Align

First, ensure that all AI initiatives are aligned with the company’s strategic objectives and core business values. This involves understanding the business landscape and determining how AI can solve existing problems or create new opportunities. By focusing on alignment, the enterprise ensures that every AI project drives meaningful impact and contributes positively to the overarching goals of the organization. 

Here’s how you can effectively execute this alignment:

Identify Strategic Objectives

Business Goals: Begin by clearly understanding the core goals of the organization. What are the key performance indicators (KPIs) or business outcomes that matter most? How can AI contribute to these areas? Thinking through this at the beginning of the process will help you make the right choices pertaining to resources, time and effort, resulting in the long-term success of your AI strategy.  

Value Alignment: Ensure that the planned AI initiatives resonate with the company’s values and culture. People are a consequential pillar in AI adoption. Aligning your goals and vision will establish a clarity of purpose, which will in turn drive employee motivation and participation. 

Stakeholder Engagement

Collaboration: Engage with stakeholders across various departments to gather insights and identify needs that AI can address. This includes executives, operational staff, and IT teams. AI can improve processes across the mortgage value chain, starting with lead generation and pre-approval at the loan origination stage, going all the way up to default management. Examples of how AI can add value include automating routine tasks to allow employees to concentrate on strategic activities, helping underwriters make complex decisions faster by analyzing research and data; using data to tailor customer experiences, which can boost sales, customer retention, and engagement; and developing comprehensive AI-driven services like chatbots or specialized products for small businesses, which can increase both new and existing revenue streams. 

Feedback Loops: Establish continuous communication channels to keep all stakeholders informed and involved in the AI integration process. This helps in adjusting strategies as needed based on real-world feedback and evolving business needs.

Market and Competitive Analysis

Benchmarking: Analyze competitors and industry standards to understand where AI can provide a competitive edge or is necessary to meet industry benchmarks. 

Innovative Opportunities: Identify gaps in the current market that AI could fill, potentially opening new business avenues or improving competitive positioning.

Risk Assessment and Mitigation

Identifying Risks: Part of alignment involves understanding the potential risks associated with AI deployments, such as data privacy issues, biases in AI models, or unintended operational impacts. 

Mitigation Strategies: Develop strategies to mitigate these risks upfront, ensuring that the AI initiatives proceed smoothly and with minimal disruption.

Scalability and Sustainability

Future-proofing: Consider how the AI initiatives align with long-term business strategies and technological advancements. Ensure that the solutions are scalable and adaptable to future business changes and technological evolution.

Regulatory Compliance

Legal and Ethical Considerations: Ensure that all AI deployments follow relevant laws and ethical guidelines, which is particularly important in industries like healthcare, finance, and public services. 

Assemble 

Next, assemble a dedicated cross-functional AI Center of Excellence (CoE). This centralized team should consist of experts in AI, data science, ethics, compliance, and business operations. The CoE acts as the hub for AI expertise and collaboration within the company, enabling the standardization of tools, techniques, and methodologies. It also facilitates the pooling of resources and knowledge, ensuring that AI projects across the organization benefit from a consistent approach and high levels of technical and ethical oversight. 

To delve deeper into the “Assemble” part of the “Align, Assemble, Assure” framework using the people, process, technology, and organization structure components, we need to consider how each of these elements supports the creation of a robust AI capability within an enterprise. 

People 

Assembling the right talent is critical. This includes hiring and nurturing: 

  • AI Specialists: Data scientists, machine learning engineers, and AI researchers who can develop and optimize AI models. 
  • Technology Experts: Cloud architects, cyber security engineers, solution architects, software developers and testers who can build and support AI applications 
  • Business Domain Experts: Project managers and business analysts who understand the specific challenges and opportunities within the industry and can ensure that AI solutions are relevant and impactful.
  • Support Roles: Risk, legal and compliance officers to oversee AI projects and ensure they align with business and regulatory requirements. 

 

Process 

Establishing clear processes ensures AI projects are executed efficiently and effectively: 

Development Lifecycle:  

Define a standard AI project lifecycle, from ideation and data collection to model training and deployment. Here is a detailed breakdown of the various stages of the project lifecycle, including challenges, solutions and how to measure impact: 

Evaluation and Planning: Define critical performance indicators to measure success. Detail both functional and non-functional requirements, outline the necessary output formats, develop a comprehensive monitoring strategy, and define AI literacy requirements for the system. Additionally, determine the necessary data inputs and set clear criteria for explanations. Select the appropriate Generative AI technology, determine how it will be customized, and outline a high-level support and accountability framework. Evaluate and address the risks associated with these activities proportionally, documenting significant effects and strategies for risk mitigation. 

Pro Tip: When setting up your evaluation framework, maintain a balance between technical precision and flexibility. This allows your team to adapt quickly to new insights or changes in technology without compromising the system’s integrity or performance. Regularly revisit and refine your performance metrics and requirements to ensure they remain aligned with your strategic goals and the evolving landscape of AI technology. 

Data Set Up: Access, clean, and transform data to ensure it is high-quality, well-understood, and relevant for the specific use case. Address data privacy, security, legal, and ethical concerns by putting in place robust safeguards and compliance measures for both the input and output of data, making sure that data owners have approved its use. Set up strict guardrails for the input of information to third-party generative tools and the output from the solution. This includes establishing processes for refining or filtering the output before it reaches the end user, and setting clear restrictions on how the output can be utilized. 

Pro Tip: Emphasize the importance of automation in cleansing and transforming your data. This not only saves time but also reduces human error, ensuring consistent data quality. Additionally, continually update and refine your data governance policies and generative AI guardrails to keep pace with technological advancements and evolving regulatory landscapes. This proactive approach will help maintain the integrity and security of your data, enhancing overall trust in your AI solutions. 

Development: Start by selecting the optimal tool that matches your requirements in terms of size, language capabilities, and pre-trained features, along with the most appropriate integration method, whether it’s an API or an on-premises solution. Customize your tool using various advanced techniques such as fine-tuning, which involves teaching the tool to perform new or improved tasks like refining output formats, and Retrieval Augmented Generation (RAG), which enhances prompt responses and outputs by incorporating external knowledge sources. Develop and refine a systematic approach to prompt engineering to create, manage, and continuously improve prompts. Focus on refining the solution to boost performance, accuracy, and efficiency through methods like adjusting parameters, tuning hyperparameters, improving data quality, conducting feature engineering, and making architectural adjustments. 

Pro Tip: Prioritize the scalability and adaptability of your tools and methods. As you refine and expand your AI applications, ensure that the tools you select can evolve with your needs and can integrate new features or data sources seamlessly. Regularly revisit your prompt engineering and customization strategies to keep them aligned with the latest advancements in AI technology, thus maintaining your competitive edge and maximizing the effectiveness of your solutions. 

Implementation and Management: Integrate the AI solution into the operational applications, ensuring seamless transition into production environments. Assign clear ownership to oversee the solution’s lifecycle. Develop a support structure that includes performance-based Service Level Agreements (SLAs) and operational guidelines aimed at fulfilling the established non-functional requirements, and introduce practices for consistent data management. Enhance organizational understanding and capability through targeted AI training programs. Ensure diligent registration of any new use of AI. Complete all the necessary documentation to provide clear explanations and transparency regarding the AI solution’s functionalities and decisions. 

Pro Tip: Establish a feedback loop between the operational performance and the development teams. This ensures that any insights gained from real-world application can be swiftly acted upon to refine the solution. Regularly updating your integration practices and operational protocols in response to these insights will help you maintain high standards of performance and reliability, ensuring that your AI system remains robust and effective in ever-changing environments. 

Monitoring and Maintenance: Implement continuous monitoring of the deployed AI solution to assess its performance, accuracy, overall impact (including any unintended biases), usage, costs, and data management practices. Promptly investigate and resolve any issues or anomalies that arise during operation. Utilize the insights gathered from the monitoring process to regularly update, maintain, and enhance the solution. Maintain vigilant oversight of any updates made to the AI system. Ensure all new applications of the AI are properly registered. Occasionally, updates pushed by the supplier might require a comprehensive review due to potential significant changes. Provide ongoing reporting to highlight the benefits and value added by the AI solution. 

Pro Tip: Consider implementing automated tools that can alert you to anomalies in real-time. Regularly scheduled reviews of the system’s outputs and operations can help preempt problems before they escalate, ensuring that the AI continues to operate efficiently and effectively. Moreover, documenting every adjustment and update not only aids in compliance and governance but also provides valuable historical data that can inform future enhancements and deployments. 

The Role of AI Quality Assurance and Testing

AI development demands rigorous and continuous testing. The role of Quality Assurance (QA) and Testing is to assess the relevance and effectiveness of the training data, ensuring it performs as intended. This process starts with basic validation techniques, where QA engineers select portions of the training data for the validation phase. They test this data in specific scenarios to evaluate not only the algorithm’s performance on familiar data but also its ability to generalize to new, unseen data. Evaluation metrics such as accuracy, precision, recall, and the F1 score are defined based on the specific use case, recognizing that not all metrics are appropriate for every scenario.

If significant errors are detected during validation, the AI must undergo modifications, similar to traditional software development cycles. After adjustments, the AI is retested by the QA team until it meets the expected standards. However, unlike other software, AI testing by the QA team does not conclude after one cycle. QA engineers must repeatedly test the AI with various datasets for an indefinite period, depending on the desired thoroughness or available resources, all before the AI model goes into production.

During this repetitive testing phase, also known as the “training phase,” developers should test the algorithm on various fronts. Notably, QA teams will need to focus not on the code or algorithm itself but on whether the AI fulfills its intended function. QA engineers for AI testing will primarily work with hyperparameter configuration and training data, using cross-validation to ensure correct settings.

The final focus is on the training data itself, evaluating its quality, completeness, potential biases, or blindspots that might affect real-world performance. To effectively address these issues, QA teams need access to representative real-world data samples and a deep understanding of AI bias and ethics. This comprehensive approach will help them pose critical questions about the AI’s design and its ability to realistically model the scenarios it aims to predict, ensuring robustness and reliability across various applications.

 

 

Technology

To deliver comprehensive and effective AI solutions, enterprises need to establish robust technology and tooling capabilities within their AI platforms. These capabilities span several domains, each critical for developing, deploying, and managing Gen AI, Cognitive AI and machine learning (ML) models.

Gen AI Development is foundational for creating full-featured generative AI applications. Enterprises must have the capability to customize and deploy models to meet specific business needs. This includes fine-tuning model inputs and engineering prompts to enhance performance and relevance. Additionally, managing the lifecycle of models during development is crucial, ensuring that models are continuously improved and updated. Orchestrating various AI models allows enterprises to integrate multiple AI systems seamlessly, optimizing the overall functionality and efficiency of the AI platform.

Gen AI and ML Operations focus on the day-to-day management and fine-tuning of AI models once they are in production. This involves managing the lifecycle of models and their versions to maintain consistency and reliability. Effective access management to models ensures that only authorized personnel can modify or utilize these models, enhancing security. Fine-tuning ML models and their training is essential to adapt to new data and evolving business requirements. Furthermore, managing data sources efficiently ensures that the models are fed with accurate and relevant data, which is vital for generating reliable outputs.

Gen AI and ML Governance is essential for maintaining the integrity and compliance of AI systems. Enterprises need to monitor models continuously to ensure they produce accurate and appropriate responses. Safeguarding models from inappropriate or malicious inputs is critical to prevent misuse and potential harm. Compliance with legal and regulatory frameworks is another significant aspect, ensuring that AI operations adhere to the necessary standards and regulations. Managing the data leveraged by models helps maintain data privacy and security, which is paramount in today’s regulatory environment.

Cognitive AI Capabilities are designed to mimic human cognitive functions, providing advanced interactions through APIs and SDKs. These capabilities include understanding and translating languages, recognizing images and sounds, and extracting and summarizing data. By integrating these cognitive functions, enterprises can enhance their AI applications to provide more natural and intuitive interactions, thereby improving user experience and engagement.

Establishing these comprehensive technology and tooling capabilities within an enterprise AI platform is crucial for developing robust, secure, and compliant AI solutions. By focusing on these areas, enterprises can harness the full potential of AI, driving innovation and achieving strategic business objectives.

Organization Structure 

Effective organization structure facilitates AI adoption and integration: 

  • Centralized AI Unit: An AI Center or hub, possibly under a Chief AI Officer, to centralize expertise and provide leadership. 
  • Cross-functional Teams: Integration of AI teams with other business units to promote collaboration and ensure AI solutions meet business needs.  
  • Change Management: Structures to support change management processes, helping the workforce adapt to new technologies and methods introduced by AI. 

Assure 

Finally, implement robust governance to assure the safety, compliance, and ethical integrity of AI deployments. This includes setting up frameworks for ongoing monitoring and evaluation of AI systems, ensuring they adhere to regulatory requirements and ethical standards. The governance process should also involve stakeholder engagement to maintain transparency and address any concerns related to AI projects. 

Responsible AI 

Responsible use of AI will require a commitment to ethical guidelines that prioritize: 

Transparency & Explainability: Create AI systems that are both transparent and clear in their functioning. As an organization, you should be able to pinpoint and clarify AI-driven decisions and outcomes. This will be especially important in the context of customers and stakeholders.  

Compliance: Designate responsible individuals for AI systems and ensure that the systems comply with regulatory standards. Conduct frequent audits to ensure the accuracy of your AI solutions, handle unforeseen outcomes, and check that the solutions fulfill legal and regulatory requirements. 

Equity and Diversity: Design, develop and deploy AI in an ethical, fair and inclusive manner. Proactively strive to identify and address biases that can emerge from training data or decision-making algorithms.  

Data Privacy: It’s imperative to safeguard personal data and prioritize individuals’ well-being, preventing any potential harm. Commit to respecting privacy rights, secure personal data, and mitigate potential risks and negative impacts on customers by obtaining consent and implementing responsible data practices.  

Security: Create AI solutions that can withstand attacks, malfunctions and manipulation. Implement safeguards to secure AI data, services, networks and infrastructure to prevent unauthorized access, breaches or data manipulation.  

Examples of Prohibited Use Cases: 

Using a risk assessment framework guided by the ethical AI principles mentioned above, here is an example of use cases that will not pass:  

  • Assessing, categorizing, or evaluating individuals based on their biometric information, personal attributes, or social behavior. 
  • Using subliminal, manipulative, or misleading techniques to sway an individual’s behavior. 
  • Taking advantage of people’s vulnerabilities (e.g., their age, disability, or social/economic status). 
  • Emotion recognition, as defined by your legal department.  
  • Collecting biometric data indiscriminately. 
  • Generating false content that seems to represent a particular person.

 

See Also: Artificial Intelligence and Data Act Canada

The proposed Artificial Intelligence and Data Act (AIDA) aims to establish standards for the responsible design, development, and deployment of AI systems, ensuring they are safe and non-discriminatory. This legislation will require businesses to identify and mitigate the risks of their AI systems, and to provide transparent information to users.

Under AIDA, the level of safety obligations for AI systems will depend on the associated risks, and businesses will need to adhere to new regulations across the design, development, and deployment stages. The government is working to create regulations that align with existing standards, aiming to facilitate compliance for businesses. A new AI and Data Commissioner will monitor compliance to ensure AI systems are fair and non-discriminatory. By introducing this law, Canada is among the first countries to propose AI regulation, aiming to balance innovation with safety, and ensuring international competitiveness while considering the needs of all stakeholders.

For more information, view the full act.

 

The diagram above shows how you can create your own AI risk assessment framework. Risk can be measured along four factors: personal information, decision-making, bias and external access.

In Conclusion

With the principle of “Align, Assemble, Assure,” the enterprise can methodically approach AI integration, ensuring that the efforts are strategically sound, well-supported by a specialized team, and maintained under stringent ethical and regulatory standards. This principle not only fosters innovation and efficiency but also builds trust and reliability in AI applications across the business.

Take the first step towards transforming your business with AI

Prithvi Srinivasan
Managing Director – Advisory Services

Prithvi Srinivasan, the Managing Director for Advisory Services, brings extensive expertise in technology strategy and digital transformation to drive mission-critical programs and advance financial institutions with AI and automation. His ability to transform complex strategies into successful transformations underscores his commitment to innovation and client value.

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BPI in Banking and Financial Services in the US & Canada

Financial Services | Business Process Improvement | Enterprise Automation

BPI in Banking and Financial Services in the US & Canada

April 23, 2024
Business Process Improvement in Financial Services in US & Canada

Banking and financial services are evolving at immense speed. Transitioning from traditional, paper-based processes to digital and AI-powered processes is critical to survival. That’s why you need to think about business process improvement in banking and financial services.

Business process improvement comes with a price tag. You need experts on your team, a culture centered on continuous improvement, and a powerful set of automation tools. But these tools offer extensive benefits as well. For example, Accenture estimates that automation and augmentation can help save financial services firms $140 billion and banks $70 billion.

Revolutionizing Banking and Financial Services Through Business Process Improvement

Banking and financial services firms have grown tremendously through business process improvement. Look back at your own organization’s past. You likely had to radically redesign processes to bring about any revolutionary change in your organization. From developing a mobile app for your bank to building your ATM network, all changes were a result of improved, more efficient processes.

The next leg of growth in banking and financial services requires using technologies like AI and natural language processing (NLP) to reduce costs, increase efficiency, and improve customer experience.

These technologies will help you stay ahead of the curve and improve your workflow. However, implementing technologies like AI, Gen AI, and Robotic Process Automation (RPA) requires experience and expertise, so find the right partner for smooth implementation and minimal disruption.

Let’s now dive into how you can plan to improve business processes to prepare your financial institution for the future.

BPI vs BPM

Business Process Improvement (BPI) and Business Process Management (BPM) are two distinct but complementary approaches within organizational process optimization. BPI is primarily concerned with improving the efficiency and effectiveness of specific processes. It involves identifying, analyzing, and enhancing existing business operations to reduce costs, improve service delivery, or increase quality. On the other hand, BPM takes a broader view, focusing on the entire set of processes within an organization. It integrates all aspects of process management—from design and modeling to monitoring and optimization—with the goal of aligning processes with the strategic objectives of the organization. While BPI zooms in on process improvements, BPM includes holistic management of all processes to ensure coherence and alignment with overall business goals. 

Financial Processes That Need Improvement

There’s plenty of room for improvement in financial processes as we know them today. AI and RPA technologies have opened doors to a whole universe of process improvements that were previously unimaginable. Here are some examples of processes you should consider working on when implementing BPM:

Customer Onboarding

Onboarding sets the stage for your relationship with the client. Customers who have a positive experience when opening their bank account are 73% more likely to become the bank’s promoters.

Improving your onboarding process by implementing a fully digitized workflow eliminates the need to manage piles of paperwork, errors, and delays. A frictionless onboarding experience doesn’t just attract new customers; it also builds a solid reputation and loyalty among customers.

Customer Support

The modern customer wants their bank or financial institution to respond within seconds and be available 24/7. Not delivering on these expectations is essentially the same as handing your clients to competitors.

Intelligent Digital Assistants (IDAs), next-gen chatbots powered by AI, “are fully equipped with natural language understanding which aids in understanding and retaining context for polished conversations while carrying out a variety of tasks to fulfill a user’s requirements.” They act as “knowledgable bankers” who not only understand the unique journeys of customers, but are also able to recommend products, offers and next steps based on their knowledge of a customer’s past behavior and financial history.

Risk and Compliance Management

Tailoring business processes to minimize risks and comply with industry regulations helps prevent penalties as well as lower your compliance team’s workload. For example, you can create a digital workflow that provides real-time (or frequent) tracking for compliance requirements as well as detailed compliance reports.

Analytics can play a key role in bolstering your bank’s or financial institution’s risk assessment capabilities. AI-powered systems can ingest massive amounts of data your organization generates to identify patterns. The system can detect anomalies and predict risks using this data.

Loan Approvals

Loan origination systems are often plagued by labor-intensiveness, errors, and a ton of paperwork. Automating your loan origination system can transform your efficiency and the customer’s experience.

If your organization currently uses traditional processes, consider completely reengineering the process. Modernizing your lending process, right from eligibility checks to approval, is important not to develop a competitive advantage, but to survive in an environment where quick approvals and disbursals are the norm.

 

Implementing Business Process Management in Banking and Financial Services

Changing and managing business processes is easier said than done. Your team might need to unlearn and relearn. You might have to deal with reluctance and proactively offer training to help them get up to speed.

However, there’s an even greater challenge — implementing the right set of technologies and building the right infrastructure. That’s why you need a partner who has experience helping banks and financial services firms implement technologies to improve business processes.

With that in mind, here’s an overview of how you can implement business process management:

Step 1: Identify Key Processes

The first step is to identify key processes within your bank or financial institution that could use some improvement. The process could be onboarding, loan processing, risk assessment, or customer service.

Consider asking employees — people on the ground executing these processes — how you can improve. Identify their pain points and bottlenecks and prioritize areas for optimization based on your analysis.

For example, if your bank’s loan origination process is lengthy and has high error rates, fixing that should be your priority over streamlining your support processes.

Step 2: Map Your Processes

A process map gives you an overview of the entire workflow and the steps involved in the process, including stakeholders, decision points, and the flow of information. A visual representation of your process helps you understand its current state, identify inefficiencies, and find opportunities to improve the process.

Step 3: Optimize Processes

Once you’ve identified inefficiencies and redundancies, it’s time to eliminate them. The exact solutions to implement depend on the challenge you’re addressing. However, automation and standardization are the first steps towards optimizing processes.

If you’re thinking to yourself, “We’ve already automated repetitive tasks,” that’s not enough. Use AI to automate some tasks you previously thought were best handled by humans.

For example, don’t use scripted chatbots that make customers feel like they’re talking to a robot. Find a partner who can develop a chatbot powered by technologies like AI and Gen AI. AI-powered chatbots can personalize conversations and interact just like humans.

If you’re not using AI, you’re already late to the party — Bank of America has been using its AI chatbot, Erica, since 2016. Bank of America claims that Erica has been able to provide more than 98% of its clients with the answers they need.

Step 4: Monitor and Improve

Optimizing business processes is not a one-off task. It’s an ongoing journey. Measure the results your optimization efforts yield with process metrics like cycle time, error rates, and customer satisfaction.

You can use AI-powered data analytics to gain deeper insights into process performance and find areas that require further improvement.

For example, if you recently optimized your loan origination process, use data analytics to monitor processing time.

Has it improved to the extent you expected? If not, use data to find out why. Is the optimized process still prone to errors? Or is your team still going through the learning curve?

 

The Role of Automation in Improving Financial Processes

Automation plays multiple roles in improving banking and financial processes. Here’s a quick overview of why automation is vital to your business process improvement initiatives:

  • Say goodbye to repetitive tasks and errors: You don’t need to spend thousands of dollars on data entry, reconciliations, and reports. Automation can perform those tasks more efficiently than humans and doesn’t make mistakes. Moreover, software allows you to keep costs under control even as you continue to scale.
  • Enforce standardized processes: You can create all the SOPs (standard operating procedures)you want. But there will always be someone trying to take a shortcut. Automation helps you achieve consistency in executing standardized processes — you can impose rules through your automated system to eliminate deviations from a standardized process.
  • Greater efficiency: Automation helps you multiply productivity. The more productive your bank or financial institution is, the more loans you can process, accounts you can reconcile, reports you can generate, and customers you can onboard without compromising on accuracy.
  • Improved customer experience: One of the biggest roles of automation in business process improvement is tailoring your processes to meet the customers’ needs. Automation allows you to personalize processes, eliminate errors, offer convenience, and deliver fast experiences at scale.
  • Deeper insights: Automating data collection and reporting helps you derive value out of the massive volume of data financial institutions generate. AI-powered analytics help you spot patterns and detect outliers. For example, if there’s fraudulent activity in a customer’s account, AI can pick up on that. Analytics also helps you make data-driven decisions, predict customer behavior, and assess risk exposure.
  • Improved customer experience: Automation allows you to improve customer experience across channels. Your AI chatbot can deliver personalized responses, analytics can help you predict customer behavior, and streamlined onboarding helps you make a great first impression.

Revolutionize Financial Services with Business Process Improvement

Let’s face it. Redesigning business processes comes with risk. However, the risk-reward ratio is in your favor when you use the right technologies and work with a team of experts. Strategic business improvement can transform your customer experience and generate massive cost savings for your business. 

Blanc Labs is committed to helping partners succeed through the use of cutting-edge technologies. Our approach can help you redesign processes in a way that fits your business’s needs. 

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What is the role of a Business Process Improvement Specialist? 

February 16, 2024
Business Process Improvement Specialist in Canada

What is the role of a Business Process Improvement Specialist in Canada?

Business process improvement (BPI) involves analyzing current processes, identifying room for improvement, and redesigning processes to make them more efficient. The problem? Analyzing processes, using data to find inefficiencies, and redesigning and implementing processes with minimal disruption requires skill and experience. That’s where a business process improvement specialist steps in. 

You might tackle various challenges when working to improve your processes. According to Capgemini, the most prominent business process management challenges are functional silo culture and fragmented budgets. 

Redesigning processes while tackling these challenges can be overwhelming, but specialists are equipped with the experience needed to make the transition smoother. In this guide, we discuss the business process improvement workflow and how a specialist can make things easier for you.

The Need for Business Process Improvement before Automation

You’re investing in automation to improve business processes. Before you put money on the table, it’s vital to learn how this investment will impact your business’s efficiency and bottom line.

At its core, business process improvement is about driving efficiency and improving performance through streamlined operations. A critical analysis of business processes and workflows enables you to identify bottlenecks and inefficiencies, allowing you to focus efforts on automating the right processes.

Business process improvement is undeniably mission-critical to make your business future-ready, but it’s also important to:

  • Develop a competitive advantage: Prioritizing continuous improvement and standardizing processes can help build competitive advantages like cost leadership, differentiated products or services, better quality, scalability, and agility.
  • Improve efficiency: Zeroing in on processes you can automate or streamline improves efficiency and output. For example, you can use technologies like intelligent document processing (IDP) to streamline invoice processing. This frees up your finance team to focus on more strategic tasks like cash flow analysis.
  • Elevate customer experience: Modern customers have some ground rules. They want fast responses, consistent experiences, and personalized interactions. Modern lenders provide hassle-free digital experiences to SMB clients, where document collection is a single step and approvals take hours and not weeks.  

“The fact is that consistency on the most common customer journeys is an important predictor of overall customer experience and loyalty. Banks, for example, saw an exceptionally strong correlation between consistency on key customer journeys and overall performance in customer experience.”

The Three Cs of customer Satisfaction, McKinsey & Company

  • Foster a culture of innovation: Striving for continuous improvement encourages critical thinking, offers your team a platform for experimentation, and rewards innovation. For example, if you’re a financial institution trying to automate your loan origination process, a culture of innovation would encourage employees to take charge of every step, from process analysis to pilot implementation.

Business Process Improvement Workflow

Before you take the first step, see if you can get a business process improvement specialist on board.

Their experience and expertise go a long way in minimizing workflow disruption and efficiently designing and executing new workflows. With that in mind, let’s talk about how you can improve business processes.

Process Mapping

Process mapping involves creating a visual representation of your current business processes. You map every step, interaction, and decision point in your workflow from start to finish when mapping a process.

This map gives you a starting point to understand how the current process works. Identifying bottlenecks or ways to make a process more efficient is easier when you can view the process rather than just verbally discussing the steps in a process.

Root Cause Analysis

Time to play detective. Root cause analysis involves digging deeper into finding the underlying reasons behind a specific problem or inefficiency. For example, if you’re a financial company experiencing delays in processing customer payments, here’s what finding the root cause might look like:

  • Why are employees encountering difficulties processing the payments? Probably because employees are spending time fixing errors in payment records.
  • Why are there discrepancies in the payment records? Maybe the outdated system is causing technical problems or employees need more training.
  • Why are we using outdated systems and not investing in training? Lack of budget.

 

This is how root cause analysis takes you to a problem’s roots. In the example above, the finance team could request additional funds in the next quarter to invest in a system upgrade and training to process customer payments faster.

Blanc Labs’ Business Process Improvement Framework

Cause and Effect Analysis

Use techniques like fishbone or Ishikawa diagrams for a visual representation of the cause-and-effect relationship between factors impacting your business processes and problems.

Categorize problems into multiple buckets like people, processes, equipment, and external factors. This will help you gain insights into various elements that impact your business processes and prioritize the ones with the biggest positive impact.

Statistical Process Control

Numbers don’t lie — statistical process control (SPC) helps you understand your process through numbers. SPC enables you to identify trends, detect abnormalities in the process, and make data-driven decisions to improve your process.

Design of Experiments

Once you’ve identified a problem, you need a systematic and structured way to identify potential improvements and test them. Experimentation helps you do exactly that.

It’s the same as stress-testing your processes under various scenarios. You design an experiment, manipulate relevant variables, and measure their impact on the business process. This enables you to understand the impact of a change before you fully implement it, allowing you to minimize disruption.

How BPI and Intelligent Automation can improve the Borrowing Experience for Small Business Owners

BEFORE
AFTER

A Frustrating Lending Experience

Small business owner_loan approval

Alex applies for a loan with a traditional commercial lender to expand her cafe into a local chain. The onboarding process is frustrating and time-consuming, with lengthy paperwork and no guidance. The lender’s inaccurate reporting and static risk assessment results in conservative loan terms that fail to acknowledge her business’s recent growth and the positive trends in the local market. This jeopardizes Alex’s ability to secure an ideal property for her new location and erodes her confidence in the lender’s support for her business growth. Throughout the process, Alex experiences an impersonal service, devoid of the understanding and support crucial for an entrepreneur.

Small business owner_loan approval

A Transformative Lending Experience

Small business owner_lending

Six months later, Alex chooses a lender with a digital platform and automated loan decisioning for her next expansion. With streamlined onboarding, automated data extraction, and intelligent chatbot, the experience is transformative. Real-time updates, automated tracking, intelligent document processing, and dynamic risk assessment make loan processing efficient and user-friendly. Alex secures her loan promptly and finds the perfect location for her new café. The chatbot also offers personalized customer experience, making Alex feel valued.

Small business owner_lending

The Role of a Business Process Improvement Specialist

Process mapping, analysis, and re-engineering require a specialist. A specialist has the knowledge and experience required to minimize workflow disruption and effectively realize process efficiencies. Here’s how a business process improvement specialist helps:

Pain Point Discovery

Think of business process improvement specialists as Sherlock Holmes. They come in, zoom into your processes, and find pain points that hinder efficiency.

They don’t just observe. They conduct interviews and surveys, ask the right questions, and listen attentively to discover areas that are inefficient, error-prone, and not aligned with organizational goals. The discovery phase is vital — it sets the stage for targeted improvements.

Process Analysis

When the specialist finds room for improvement in a process, they pull up their sleeves and start dissecting it.

They map every step, decision point, and interaction in your process and use techniques like value stream mapping, data analysis, and flowcharting to take a closer look at how the process currently operates and where bottlenecks and inefficiencies lie. This lays the groundwork for informed and data-backed decision-making and process design.

Opportunity Analysis

Insights from process analysis equip the specialist to shift focus to opportunity analysis. They consider the benefits of addressing a pain point and improving the process versus the cost — do the monetary benefits of redesigning the process justify (and exceed) the cost of making those improvements?

Cost-benefit analysis requires input from multiple experts. For example, if a process redesign could improve customer experience, what would be the benefit, in monetary terms, of improved customer experience? The specialist might need insights from multiple teams like sales and accounting to understand the monetary impact.

They also consider the risks of redesigning the process. Will the redesign require significant changes? How long will it take the employees to get through the learning curve and achieve full efficiency? The specialist understands these risks and identifies ways to mitigate them.

Implementation Design

After factoring in the costs and risks associated with redesigning a process and verifying the viability of a process redesign, the specialist transitions into creating and implementing a more efficient process design.

The redesigned process might involve one or multiple changes like a more efficient workflow, new technology, or a redesigned organizational structure. For example, the specialist might use enterprise automation solutions to automate repetitive tasks, potentially multiplying your team’s productivity or include GenAI to enable the next level of customer service.

The specialist develops a detailed implementation plan including the details of redesign, clear timelines, responsibilities, and performance metrics. They collaborate with stakeholders during this phase to ensure buy-in and alignment with organizational objectives.

Business Process Improvement Integration

Process improvement isn’t a one-off activity. To truly reap the benefits of process improvement, the specialist must integrate the improvement initiatives into the organization’s culture.

The specialist can help you develop change management strategies to inculcate a culture rooted in continuous improvement; this is vital to engaging employees across the organization and encouraging them to contribute ideas that drive change.

For example, the specialist can train teams to build applications quickly using low-code platforms. This enables teams to solve problems independently and quickly. Of course, there are many ways to develop a culture of continuous improvement depending on factors like industry. For example, Bill Keen, CEO of Keen Wealth Advisors, explains:

By owning mistakes and building processes to prevent the mistakes that do occur from happening again, you will build trust among your customers and a culture of continuous improvement among your staff.

With training and efficient feedback mechanisms, you can sustain process improvements for a long time and ingrain them in your company’s DNA.

Business Process Improvement with Blanc Labs

We firmly believe that transformation happens over time and that process improvement is an ongoing journey. Our approach helps you practically redesign processes effectively and consistently, and in a way that fits your business’s unique needs.

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Canadian IT services firms offer a strategic opportunity for US Banks and FIs

Insight | Banking Automation | IT Management | Nearshore IT Support

Canadian IT services firms offer a strategic opportunity for US Banks and FIs

January 29, 2024
Understanding the Strategic Advantages of Canadian Nearshore IT Support for U.S. Banks_Blanc Labs

In today’s challenging environment, U.S. bank IT departments are under unprecedented pressure to deliver more with limited resources. Cost-effective Canadian nearshore IT support, especially from technology-rich regions like Toronto, are emerging as a compelling opportunity for U.S. banks seeking to transform their technology infrastructure and enhance operational efficiency.

Toronto: Access to World-Class Technology Talent

Toronto is recognized globally as a Canadian center for technology and fintech companies. This world-class and fast growing city with 6.5 million citizens in 2023 is a dynamic and creative urban environment that at this point has become a self-sustaining financial technology hub, making it an ideal partner for U.S. banks seeking talent to drive their transformation roadmaps further and faster.

The region is home to top-tier universities like the University of Toronto, Waterloo and York University are renowned for their research and technology programs, providing a steady stream of skilled graduates and fostering a culture of innovation. Additionally, Toronto hosts the MaRS Discovery District, one of the world’s largest urban innovation hubs. MaRS provides a platform for tech start-ups and entrepreneurs, further solidifying Toronto’s status as a leader in technological development and innovation.

 

Proximity and Time Zone Alignment

Toronto’s geographical proximity, where almost any major U.S. city can be easily reached with a short, direct flight, offers significant benefits, including time zone alignment for real-time collaboration and agile project management. This is particularly advantageous compared to offshoring and other options. The ease of travel not only strengthens business relationships but also ensures effective communication and alignment, which are critical for the success of complex IT projects.

Cost-Effectiveness

Choosing Toronto for nearshore IT support allows U.S. banks to maintain high service standards while managing operational costs. The significant savings, often ranging from 30-50%, are a result of the current exchange rate differences between the U.S. and Canada, coupled with the inherent efficiencies in project management and execution. These financial benefits can be redirected towards other areas of growth and innovation.

Regulatory and Cultural Alignment

The U.S. and Canada share many regulatory and cultural similarities, which simplifies compliance standards and business practice alignment. This is vital for U.S. banks to minimize legal and operational risks in the highly regulated banking sector. 

Customized and Scalable Solutions

Nearshore IT support in Toronto offers tailored, scalable solutions to meet specific banking needs. This flexibility ensures IT services adapt to the bank’s growth and changing priorities, promoting long-term sustainability. Moreover, these solutions are market-proven and highly relevant for U.S. banks, having been successfully implemented and delivering tangible results in similar banking environments.

Enhanced Security

Canadian nearshore IT support provides enhanced security, ensuring compliance with North American data protection laws. This safeguards sensitive information and customer data. Look for a SOC2 certification, which signifies a firm that can adapt to the stringent security requirements, a crucial aspect for U.S. banks in safeguarding their operations and customer information.

Looking for a Canadian tech partner? Let us help.

For U.S. banks striving to stay ahead in innovation, cost-effective Canadian nearshore IT support is a compelling strategic choice. Partnering with Toronto-based firms, such as Blanc Labs, maximizes these benefits, leading to improved efficiencies, fostering innovation, and enhancing customer satisfaction, significantly contributing to the bank’s success.

Author

Les Riedl
US Business Advisor

Les Riedl, Managing Principal of 10XBizDEV, is dedicated to connecting US Banks with innovative, best-in-class solutions and services that contribute to their success. His extensive experience as CEO and board member in fintech and financial services consulting provides valuable insights into the sector’s evolving challenges and opportunities. As US advisor for Blanc Labs, Les is instrumental in introducing their world-class capabilities to the American market.

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Document Processing before LLMs

Document processing primarily relies on rule-based systems and keyword matching, which can be effective for structured or even semi-structured documents with predictable formats. However, this approach often struggles with unstructured data, where variability and complexity are high. In contrast, Large Language Models (LLMs) bring a transformative approach to document understanding. They leverage advanced natural language processing (NLP) techniques, enabling them to comprehend context, semantics, and nuanced language variations in documents.

In the ever-evolving world of data science and enterprise automation, the explosive growth of unstructured data generated by companies has been a major challenge for data scientists. To give you a sense of scale, recent studies show we’re likely to witness a surge from 33 zettabytes in 2018 to a predicted 175 zettabytes by 2025. Furthermore, according to Gartner, unstructured data currently represents an estimated 80 to 90 percent of all new enterprise data.  Unstructured data can include conversations through e-mail or text messages, but also social media posts, blogs, video, audio, call logs, reviews, customer feedback, and replies in questionnaires. This trend spotlights an urgent need for more sophisticated tools to create value from this burgeoning data deluge.

Our team has over 5 years working with various OCR and NLP technologies, including having developed and training models in-house.  Don’t get me wrong, IDP  tech has come an extremely long way and the tools have gotten tremendously powerful. Libraries such as Amazon Textract (among many others) provide ML engineers a powerful suite of tools to accelerate the speed and quality of applying intelligent document processing to automation scenarios.

However, there are still limitations to how IDP can be adopted to a range of automation scenarios that we encounter in enterprise environments.

Think of traditional models document processing tech as a diligent yet somewhat myopic librarian, meticulously following rules but often missing the bigger picture. In contrast, Large Language Models (LLMs) are like Sherlock Holmes — insightful, context-aware, omnipresent, and adept at deciphering the most cryptic of texts.

This results in several key benefits and improvements:

Enhanced Comprehension

Traditional Method: Typically relies on keyword spotting and pattern recognition. For example, extracting dates or specific terms from structured forms.

LLMs Approach: Goes beyond mere pattern recognition. It interprets language nuances and intent, essential in contexts like financial and legal document analysis where the meaning of clauses and data can be complex.

Flexibility with Unstructured Data

Traditional Method: Struggles with documents like unstructured emails or reports, often leading to high error rates or the need for manual intervention.

LLMs Approach: Excel in handling unstructured formats. For instance, in customer service, LLMs can analyze and respond to diverse customer queries that vary in structure and content, easily extract information from employment letters or mortgage commitment statements.

Dealing with unstructured data, which includes everything from casual emails to social media chatter, videos, and customer feedback, is not a trivial matter. This kind of data resists neat categorization and defies traditional database structures, posing significant challenges in analysis and comprehension. Here’s where Large Language Models show their mettle, adeptly navigating this complex, non-uniform data and unlocking valuable insights that conventional methods might miss.

Adaptive Learning

Traditional Method: Updating rule-based systems for new formats or languages is time-consuming and resource-intensive.

LLMs Approach: Can continuously learn from new data, adapting to changes in language usage or document formats without extensive manual reprogramming.

Error Reduction

Traditional Method: Prone to errors in cases of ambiguous or context-heavy information, resulting in lower reliability.

LLMs Approach: Their deep contextual understanding leads to more accurate data extraction and interpretation, crucial in high-stakes industries like legal, financial and healthcare.

A Practical Example

ChatGPT 4, without any specific fine tuning or pre-training is able to easily extract information from a document it has never seen before. It understands the context and you can simply query in a natural way for data points that you are interested in:

*Chat gpt extracts information_Blanc Labs

* Note: we take data privacy and PII seriously (see below) and created “spoof” documents for the purposes of this demonstration.

For instance, by asking direct questions such as ‘What is the policy end date?’ or ‘By what margin has the insured amount varied?’, it promptly delivers precise information with perfect accuracy.

This scenario offers the opportunity to further explore solutions that leverage the unique capabilities of LLM’s for the purposes of intelligent document processing and automation.

Generative AI-Powered Extraction and Comparison of Insurance Policy Documents

In the previous section, we delved into the capabilities of Language Learning Models (LLMs) in streamlining the extraction of key information from various document types. To demonstrate this further, we now present a practical application of our system.

The first part of the demonstration involves uploading the initial insurance policy, which acts as our benchmark document. Watch how the system seamlessly processes this document, effortlessly extracting critical details such as the policy number, coverage specifics, the insured party’s information, and other essential data.

Next, we upload a second document representing a modification in the policy. It not only extracts pertinent information from the new document but also conducts an intelligent comparison with the original policy. Notice how the system highlights the changes in the date and insurance limit. This comparative analysis is vital to ensure comprehensive and accurate updates of all modifications and their implications.

To enhance the efficiency of such systems, integration with existing databases and cloud storage services is key. Utilizing APIs, these systems can automatically retrieve documents from various sources such as cloud storage (like AWS S3, Google Cloud Storage), internal databases, or even directly from email attachments. This integration enables real-time processing and updates, ensuring that the latest documents are always analyzed and compared.

The Role of Retrieval-Augmented Generation (RAG) in LLMs

For more context specific answers and solutions, Retrieval-Augmented Generation represents a significant advancement in the capabilities of LLMs. It’s another step forward on this never-ending roller coaster!

  • Enhanced Accuracy and Relevance: RAG combines the generative power of LLMs with information retrieval, pulling in relevant data or documents to provide contextually accurate responses. This is particularly beneficial for financial analysis and reporting, where accuracy is paramount.
  • Dynamic Data Integration: Unlike traditional LLMs, RAG can integrate real-time data, offering dynamic responses to financial queries. This is essential in finance, where market conditions and regulatory environments are constantly evolving.
  • Customized Financial Advice: RAG’s ability to retrieve and process vast amounts of data allows for highly personalized financial advice, tailored to individual customer profiles and market conditions.
  • Improved Compliance and Risk Management: In the regulatory-heavy landscape of financial services and healthcare industries, RAG can efficiently process and cross-reference internal and external data sources including detailed regulations and requirements. We believe that there is a huge opportunity to automate regulatory, risk, and compliance checklists to reduce complex manual efforts that exist in regulated industries.

Potential limitations of deploying LLM’s for document processing (at scale)

While LLMs are highly likely to revolutionize document processing with,  it is important to consider potential limitations as well. LLMs are like a double-edged sword, powerful in processing vast amounts of data but requiring careful handling to address privacy concerns and manage computing resources.

  • Privacy and Personal Identifiable Information (PII): LLMs are capable of processing vast amounts of data, including confidential or proprietary information as well as PII data.  Organizations must work within data security frameworks and engineer solutions that have data security at the core of how they are designed and deployed.  As a SOC2 Certified company, this is an area of key focus for our teams and we have implemented robust data handling and processing protocols to ensure that all PII is managed securely and in compliance with privacy regulations.
  • Computing Power and Cost: The potential impact offered by powerful LLMs to read and extract data from large volumes of unstructured data is reliant on the on substantial computing power required to run these models.  We are still in the early stages of enterprise adoption of generative AI technologies and the economics of leveraging these toolsets at an enterprise scale are fairly dynamic.  We expect a lot to change over the next few years but in the meantime, we are actively working with clients to understand the business drivers of using LLM’s to automate processes.  With our deep background in intelligent document processing we’ve become experts at crafting solutions that optimize model efficiency without compromising performance. We employ techniques like model pruning, efficient data processing pipelines, and cloud-based solutions that balance computational demands with cost-effectiveness.

Conclusion

LLMs and RAG are the vibrant threads bringing new patterns of efficiency, accuracy, and innovation. We’ve journeyed from the meticulous yet narrow pathways of traditional methods to the expansive highways of AI-driven solutions. This evolution isn’t just a step forward; it’s a quantum leap into a future where data isn’t just processed but understood, where advice isn’t just given but tailored, and where compliance isn’t just followed but mastered.

The advancements in unstructured data analytics signal a critical shift in our approach to data. It’s not just about the volume; it’s about the untapped potential that lies within. This raises a compelling question: how can we leverage unstructured data to gain a deeper understanding of our customers, societal trends, and the world at large? The key lies in harmonizing cutting-edge AI tools like Large Language Models with human insight, transforming this wave of data into insightful and actionable knowledge.

Harness the power of AI

At Blanc Labs, we specialize in tailoring AI solutions to the specific needs of the Canadian financial and healthcare sector. Our expertise in AI, automation & digital product development positions us to assist in harnessing the power of LLMs and other AI technologies. We provide customized solutions for intelligent document processing, intelligent automation, and enhancing customer experiences, ensuring compliance with industry standards and regulations.

Explore how Blanc Labs can assist your organization in navigating and succeeding in the digital era.

Author

Luciano Lera Bossi
BPI & Intelligent Automation Lead

Luciano Lera Bossi is a skilled Engineer with 15+ years of success in tech, specializing in Intelligent Automation, Low Code/No Code and Agile Project Management. He enables effective communication between technical and business stakeholders, resulting in seamless project outcomes.

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Digital Transformation Vs. IT Modernization: What’s the Difference?

Technology | Digital Transformation | IT Management

Digital Transformation Vs. IT Modernization: What’s the Difference?

July 11, 2023
Digital Transformation

In today’s dynamic business landscape, organizations face numerous challenges such as economic volatility, supply chain complexities, evolving work styles, competing for talent, fickle consumer trends, market commoditization, sustainability concerns, and more. As stated in Forrester Research’s 2021 report, “In the 2020s, the one constant will be disruption.” CIOs play a crucial role in navigating these challenges and driving business transformation. The ability to embrace disruption will set leaders apart from laggards in this ever-changing landscape.

In this context, it is important for technology executives to make the choice between modernizing legacy processes or instituting a complete transformation of their systems. Yet, many organizations struggle to understand the difference.

In this article, we explore both digital transformation and modernization and why transformation is mission-critical for modern businesses.

Digital Transformation Vs. IT Modernization

Digital transformation has a broader scope and touches  almost every facet of a business. IT modernization’s scope is limited to upgrading existing systems.

Don’t get us wrong. Modernizing your tech stack  is just as critical to your business as transformation. The 2023 CEO Priorities survey shows that 51% of CEOs think low digital maturity/technical capabilities are barriers to creating business value with AI. As companies try to achieve digital maturity, they’ll go through multiple IT modernization iterations.

Let’s dig into the differences between digital transformation and IT modernization.

Digital Transformation in Practice

A digital transformation is meant to change or evolve some of the DNA of an organization such that a company is better equipped to compete in a constantly evolving  environment where customer expectations are changing at breakneck speed.  Technology can and should play a central role in a digital transformation but technology alone will not equip a company to operate effectively in this “new normal”, this requires a careful choreography of people, processes, and technology.

In addition to being better equipped to meet evolving customer needs and expectations, digital transformation can be your doorway to scaling your business by entering new markets, eliminating or minimizing process inefficiencies, and becoming more agile.

Domino’s is an excellent example of how digital transformation can change your company’s growth trajectory. Back in the day, the pizza chain was a brick-and-mortar restaurant that promised to deliver pizzas in under 30 minutes.

Competitors started offering a similar service. So Patrick Doyle, the CEO of Domino’s in 2011, decided to create an app that allowed customers to order pizza within seconds. Currently, Domino’s is testing delivery via autonomous vehicles to stay ahead of delivery services like DoorDash and Uber Eats.

Essentially, digital transformation involves a fundamental shift in various business aspects, like internal processes and your business model. The goal is to achieve sustained growth and become a more competitive business.

Modernization in Practice

Modernization is often confused with digital transformation, but they are not the same. Modernization focuses on building on top of existing systems. For example, you could implement cloud computing and AI without changing legacy processes.

Suppose you’re a bank that is looking to improve its mortgage origination process. You currently require staff to enter customer details manually. Over the next quarter, you want to implement an automated loan origination system that eliminates almost all the mundane tasks in the origination process.

For this, you’ll use technologies like AI, natural language processing (NLP), optical character recognition (OCR), and robotic process automation (RPA) through third-party integration or by building a new solution. These technologies will enable you to automate tasks like document processing and customer onboarding.

These technologies can help you re-engineer the origination process to make it more efficient—without disturbing your underlying mortgage process or the business model.

Why Digital Transformation Matters

Digital transformation is right for you if you’re in a rapidly evolving market and want to stay ahead of the competition.

Since digital transformation involves a more comprehensive change in your business’s model and operational processes, it can be resource and time-intensive, especially in the absence of first-hand experience on the multi-faceted nature of transforming an organization.  Let’s go back to our mortgage example to understand the difference between transformation and modernization.  Consider if the mortgage lender wanted to move from being a traditional, broker-based lender to a digital direct lender and the relative time, cost and complexity required to stand up a whole new business model.

IT modernization is relatively less resource and time-intensive. Modernization is an excellent place to start if you’re struggling to meet customer expectations or efficiency targets because of outdated technology.

Revamping your technological systems will help you reduce costs, operate more efficiently, and scale your business.

Digital transformation matters to two types of businesses:

  • Businesses that want to stay ahead of the curve by implementing the latest technologies in a fast-evolving market.
  • Businesses that need to survive in a market where other businesses failed to implement new technologies on time.

Digital transformation isn’t easy for either of these businesses to implement without an experienced team—only about 30% of companies navigate digital transformation successfully.

However, once you’ve successfully completed digital transformation, your business stands to benefit on multiple fronts.

The pandemic caused the most recent surge in digital transformation. Businesses were forced to adapt to an environment where employees prefer remote or hybrid work models, but they benefited greatly from the changes that followed.

That’s exactly why more businesses are excited about digital transformation. In fact, according to the World Economic Forum, the combined value of digital transformation to society and industry will exceed $100 trillion by 2025.

Here are some benefits of digital transformation that highlight why it can be a game changer for your business this year:

Improves Customer Experience

Customer expectations have risen significantly over the past decade. They prefer businesses that offer a great experience in all customer-facing aspects.

Enable  Data-Driven Decision Making

Data analytics is one of the most critical capabilities you gain from digital transformation. With modern tech, it is typically much easier to securely access and manipulate data that can be used to unlock valuable insights about customers, competitors, and internal processes.

Better Resource Management

Digitally-powered businesses use hundreds of apps and store data in multiple places. The lack of interoperability and data silos can make business hard. Digital transformation involves integrating these digital tools, allowing you to consolidate dispersed data sets and apps.

Improves Team Collaboration

Adding the right digital collaboration tools to your toolkit can be critical to building a collaborative environment in the workplace. They allow teams to collaborate outside the office space or country. If your team is spread across time zones, you can use async communication tools for better collaboration.

Increases Scalability

Digital transformation gives you the flexibility to grow faster. It can help reduce time to market and make product or service improvements faster. You can also serve clients in a larger geographic area and communicate with your clientele more effectively.

What Does it Take to Create Digital Transformation?

Digital transformation needs more than just technical expertise. It’s a massive undertaking where multiple factors contribute to your success.

Here are things to focus on when transforming your business digitally:

Digital First Culture

Transforming your business requires agility and an attitude to pursue the latest technologies proactively. Building a digital-first culture, where employees proactively look for opportunities to use digital solutions to help the business achieve its goals, is critical to successfully transforming your business.

Culture is a critical part of Gartner’s ContinuousNEXT approach to digital transformation. The Gartner experts who coined the term cite culture as a major barrier — 46% of CIOs identify culture as the most significant barrier to digital transformation.

report by MIT and Capgemini explains that focusing on the following seven cultural attributes can be vital to building a digital-first culture:

  • Innovation: The prevalence of behaviors that support taking risks, thinking disruptively, and exploring new ideas.
  • Digital-first mindset: A mindset where everyone, by default, thinks of digital solutions first.
  • Customer-centricity: The use of digital tools to improve customer experience, expand the customer base, and co-create new products.
  • Open culture: The extent of partnership with third parties, including vendors and customers.
  • Agility and flexibility: Decision-making speed, dynamism, and your company’s ability to adapt to evolving market demands and technologies.
  • Data-driven decision-making: The use of data to make better decisions.
  • Collaboration: The strategic creation of teams to make the best use of the company’s human resources.

Change Management

Often, the most overlooked aspect of a digital transformation is the amount of change management and capability development required to enable team members to excel in their new or evolved roles. One takeaway that we’ve learned from our experience is the need to have a strong leadership commitment and a high level of conviction and alignment across the senior leadership team on the transformation objectives, priorities, and plan to undertake the transformation effort at an organization. .

When communicating a change, remember that the benefits of change may be evident to you. Yet, your team might see change as a signal to step out of a workflow they’re comfortable with and learn new skills.

Resistance will snowball when one employee who feels that way communicates with colleagues. Change management can help prevent or at least minimize this resistance to change.

Here are three critical elements of managing change:

  • Plan before you start: Change management should start before digital transformation. Risk analysis and assessing the organization’s readiness are great ways to lay the foundation for your change management strategy. They involve identifying organizational attributes like culture and characteristics of change.
  • Change management: This is where you go from planning to execution. You keep communicating your digital transformation plans, address concerns regarding the plan, and train employees.
  • Feedback: Change management doesn’t end after executing your change management strategy. Seek feedback from employees about the change. If you see reluctance, take corrective actions to ensure resistance doesn’t snowball.

Data-Driven Organization

Digital transformation is highly data-driven. Managing data — storing and processing data and ensuring data integrity — are one of the first things you’ll need to work on when you start the digital transformation process.

Data provides a unique insight into your operations. Once you’ve achieved a higher level of operational transparency and gained a deeper understanding of core business processes using data, you’ll be able to identify barriers to digital transformation. Data will also enable you to improve and optimize your transformation strategy continuously.

Companies like Airbnb and Amazon hold massive volumes of data. These companies are great examples of data-driven organizations. This data allows them to personalize customer experiences and stay one step ahead of the competition.

Emphasis on Quality

When you’re adamant about delivering quality, you’ll need to ask yourself — is the business prepared to ensure a high level of quality during the digital transformation journey?

Going all in at once is a bad idea. Digital transformation requires an incremental approach where you plan, test, and validate ideas every step of the way. This will allow you to stay prepared for potential pitfalls and optimize your strategy as you go. You’ll avoid complete disasters, which can result in loss of money, trust, and reputation.

Taking an incremental approach makes your digital transformation journey smoother and ensures you maintain high quality throughout the transformation process.

Begin Your Digital Transformation Journey

Digital transformation can be challenging to navigate without an experienced partner.

At Blanc Labs, we understand each organization has unique needs. We offer advisory and consulting services to gain a complete understanding of your most pressing challenges and help you start your digital transformation journey.

Book a discovery call with us today to learn more.