AI Adoption: A Use Case Maturity Model for Enterprises

In the current AI era, the speed of innovation far exceeds the speed of adoption.  As a result, both vendors and prospective customers have a common need for a framework to gauge where any given enterprise stands today with respect to AI, where it thinks it needs to get to, and what kind of steps it would take to do so.

To set the stage properly, recall that there are three major strands of AI in the market today—predictive, generative, and agentic.  The first has been with us for decades and is not part of the current adoption challenge.  Generative, on the other hand, is although it has clearly crossed the chasm as witnessed by widespread consumer adoption of LLMs.  The question it poses for enterprise adoption is economic: what use cases should be prioritized and where will the ROI come from?  Agentic AI, on the other hand, is still on the Early Market side of the chasm.  The question it poses is what use cases would warrant immediate adoption before it goes into general availability.

With this context in mind, below is a first cut at a maturity model focused on deploying generative and agentic AI.  The use cases mentioned are meant to be representative, not exhaustive.  I like to call such models stairways to heaven.  The goal is to help prospective customers better assess what stair should be their ultimate goal, what stair they find themselves on today, and what stair they should focus on to get their next set of returns.

1. AI-augmented Decision Support.  Because it is inherently human-in-the-loop and augments an already established practice, it is relatively low-risk and easy to adopt.  The value is based on improving decision-making by giving access to data that might otherwise be neglected. ROI will be gated by how much first-party data beyond the public LLMs can be included. 
2. Intelligent Robotic Process Automation.  RPA has been around for more than a decade as a departmental productivity improvement tool based on knowledge workers taking a low-code approach to automating time-consuming routine workflows.  Adding AI to the mix allows RPA to expand its reach into processes that drag human decision-makers back into the workflow, such as classifying, routing, or prioritizing, all according to what, in the end, are deterministic algorithms.
3. Intelligent Document Processing.  There is a trove of trapped value in approval processes that require multiple reviews to complete.  To the extent that these reviews are algorithmic, they can simply be replaced.  Those that require judgment can be accelerated by pre-processing the documentation to extract and foreground the relevant information.  The ROI comes from accelerated decision-making, particularly relevant for deal desks and loan approvals.
4. Enterprise Knowledge Management.  In any consultative practice, there is a trove of trapped value in its legacy of presentations and reports.  They are all on file somewhere, but navigating them is such a chore nobody bothers to.  Running this data set through an LLM and putting an “Ask Me Anything” button in front of it not only eliminates drudgery but improves the quality of work products dramatically.  This is the first deeply compelling ROI case in this maturity model.
5. Customer Support Agents.  As more and more work goes virtual, staying connected to the customer becomes increasingly important.  Human agents shine where empathy and creative intelligence are needed, but wait times can be painful, and there are numerous transactions that can be better handled via self-service.  Additionally, AI agents can learn as they go with any gains in expertise becoming immediately available across the entire customer service footprint.  The ROI here comes not just from labor reduction, but from faster response times, higher customer satisfaction, and the opportunity to redeploy human resources to higher-value tasks.
6. Service Request Triage and Routing.  At this stair, AI adoption transitions from improving the productivity of individuals to that of entire systems.  That makes the risk bar much higher but also the ROI from successful implementations much greater.  Here third-party enterprise application vendors have a big advantage over in-house teams as they can leverage decades of process learning even as they revamp the way that knowledge is put to work.  The gains come from faster mean time to repair, less scrap and rework, and optimal prioritization of scarce resources. 
7. Workflow Reengineering.  On this stair the business objective escalates from automating processes that are context (industry-standard non-differentiating) to enabling those that are core (competitively differentiating).  This is strictly DIY and cannot be outsourced to a third-party vendor for the simple reason that they would then be free to sell the same thing to your competition.  The key lesson here is make sure you are allocating your DIY resources to core and not to context.  Should you fail to do so, not only will you be missing present opportunities, you will also have committed yourself long term to maintaining applications that do not differentiate and whose costs cannot be amortized across a base of multiple customers.
8. Autonomous Decision Systems.  This is the holy grail of AI.  As digital transformations continue to expand their footprints, the overall web of interrelated systems increases in both scope and complexity.  We humans are simply not suited to monitoring such systems, so we must assimilate more and more AI into our enterprise architecture, whether we like it or not.  The focus of most of this work will be on mission-critical context (work that does not differentiate but carries severe penalties for making mistakes).  Because the risks are so material, the natural tendency is to invest scarce expert resources regardless.  Autonomous decision systems can solve for this conundrum provided they can be deployed in stages that allow for incremental learning as well as damage control along the way.

Eight stairs may be too many, but this is just a first cut, so I am very much looking forward to future iterations that make it more fit for purpose.

That’s what I think.  What do you think?