The Keys to the Secret Garden: Unlocking the Potential of AI in the Enterprise
At this point, enterprise generative AI applications for  software development, marketing, customer/employee service , and product design are proliferating as the standout use-cases early adopters are focusing on. From our position working with clients to develop and enable enterprise data + AI solutions, we’ve gained perspective on several of the foundational characteristics enterprises will need to develop to realize on the promise of this 3rd wave of the human-computer revolution.
But First, a Disclaimer
Blanc Labs focuses on the financial services and healthcare industries. We see both sectors as having massive potential in terms of the benefits offered through the application of these transformative technologies. Both healthcare and financial services are hugely complex, highly regulated, and depending on who you speak to, resistant to change. We believe the stage is set for a broader divergence of winners and losers: organizations that build capability and competency in adopting new tools and leverage the power of their enterprise data will stand to reap the benefits of their investments.
âïž Cloud Infrastructure Only âïž
Enterprise Platforms are a Lynchpin
If we consider that having completed a cloud migration or cloud native approach is table stakes for AIÂ enablement within an organization, the next big question will be where your enterprise data resides. The organization’s technology infrastructure and enterprise platform ecosystem will in many ways determine what toolsets may be available to facilitate experimentation and development of AI enabled use cases.
If you are a CIO, CTO or CDO, start by taking stock of the platforms that house your enterprise data and evaluate the toolsets offered by these power technology-ecosystem juggernauts. Microsoft and AWS are emerging as clear leaders in terms of providing their customers powerful tools to apply ML and AI to their enterprise data, but Salesforce, SAP, Pega and OpenText – amongst many others will offer tooling to access, manage and apply AI to enterprise data.
It is impossible to predict who the big winners and losers of this space will be, partially because they are such massive and well entrenched players but keep an eye on this space over the next few years.
The đż is in the Data [Taxonomy]
Building an enterprise data model across a complicated operating environment like a bank is no small feat. Standardizing data definitions between different lines of business in a bank involves tackling the complexity of enterprise (+legacy) platforms used to store and handle data. This complexity extends to accommodating the diverse needs of various stakeholder groups with distinct preferences for accessing, manipulating, and analyzing data.
In many ways, risk is a unifying theme and responsibility within regulated industries like the banking sector. We see conformance to risk measurement and regulatory reporting requirements as a lightning rod for action amongst FI’s to get their “data house” in order but proactive approaches amongst banking and technology executives are few and far between.
In a recent interview with Tech Exec magazine, VP of Data and Adaptive Intelligence at Munich Re Canada Branch (Life), Lovell Hodge encapsulated the shift in how progressive organizations are thinking about their data:
“Over the years, there has been a realization that data has an inherent information value to the organization. As the years go by, weâre rapidly producing more data at an increasingly and somewhat alarming rate. So, what a lot of folks in the industry have realized is that there is not a huge space between theoretical concepts around information from data, and how they can use that information to simplify internal processes and also benefit clients.
So, youâre seeing that interdependence starting to mature. And because of that, many organizations are looking at their data not as just something they have to store, but as an asset they can leverage. And once you start thinking in that sort of way, then you start to formulate methods by which you can gain value from data, and it becomes an important part of your corporate strategy.”
App Design and Development Now Occurs ‘In the Data’
Historically, software design and development occurred in a sandbox that was devoid of enterprise data. In traditional dev environments we piped in dummy data to test parameters and during QA tested connectivity and security. But with the explosion of sophisticated ML and AI tools, application development will very much occur at the nexus of querying, manipulating, analyzing and visualizing proprietary data sets.
There is a tremendous amount of opportunity for these new tools to democratize data for business users through the use of conversational/natural language query tools to conduct analysis and derive insights from enterprise data sets.
Another big change will be the blurring of the lines between the role of developers and data scientists and in many cases, these resources will be working directly alongside each other. Technical resources will require access to live data sets to facilitate model training and to leverage powerful data analytics toolsets. This is going to require a significant shift (from reactive to proactive) in terms of access management and data security.
Firms that work with external partners will require them to adhere to an extremely high standard of data security protocols i.e., ISO and SOC 2 compliance certification with regular audits.
Specific sector, domain, and knowledge of business context has always counted for a lot but increasingly developers won’t be able to rely on detailed functional, technical and design requirements documentation to define the specs of what they are developing. It will require a much more experimental, test and learn approach. Toolsets are only going to get more user friendly to work with but developing high impact apps leveraging advanced AI/ML techniques will require a detailed and nuanced understanding of user needs, workflow, data inputs, command prompts, prompt modelling strategies, and a continuous improvement approach to get to a place of lasting and tangible impact for businesses and teams.
In Summary:
There is much still to be learned through this large-scale adoption of a suite of technologies that are evolving at such a rapid pace. This is by no means a complete list of the things business teams and leaders need to consider as they look to capture competitive advantage from these powerful new tools. At Blanc Labs, we are excited about the future and look forward to working with companies to develop their capabilities to create business value by harnessing the tremendous potential of their enterprise data.
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