Foundational considerations for your enterprise AI Strategy

Discussing AI, I have often thought about the fable of the boiled frog, whereby a frog placed in boiling water jumps out, but a frog placed in warm water that is gradually heated lacks awareness of his impending demise until it is too late. The Economist summed it up nicely in an article last year, Your employer is (probably) unprepared for artificial intelligence:

Robert Gordon of Northwestern University has argued that the ‘great inventions’ of the 19th and 20th centuries had a far bigger impact on productivity than more recent ones. The problem is that as technological progress becomes more incremental, diffusion also slows, since companies have less incentive and face less competitive pressure to upgrade.

It is yet unknown if artificial intelligence is more akin to the “great inventions” of the 19th and 20th centuries, or if it will ultimately represent another more incremental evolution of existing capabilities. The former - as seems more likely given the immense investments being made today - will present significant challenges to nearly every organization that, having become accustomed to incremental change, is suddenly faced with a “great inventions” caliber paradigm shift that AI seems to portend.

So it is that I've been spending significant time and mental energy thinking about a proper “AI strategy” for organizations that wish to escape the sad fate of the frog, or that of the world’s organizations being left behind by the pace of technological change. If you've not read my recent LinkedIn article, The "AI Strategy Framework" guides your organization's journey in the Age of AI, take a moment to do so before you go further here.

Artificial Intelligence vs. Generative Artificial Intelligence

Let’s start by defining what we’re talking about when we discuss “artificial intelligence.” There’s an argument that we could trace the lineage of AI back to the Turing Machine of the 1940s, and then pull that string through the history of computing to include such logic-based “if this, then that” applications all the way through robotic process automation (RPA) technologies of recent years. These technologies lacked real intelligence, given that they were rather extraordinarily intelligent machines created by extraordinarily intelligent people. These machines were designed to make more efficient (or make possible) the intelligence of their creators at scale. 

Generative AI is what we have in mind when we think about artificial intelligence today. Here we define 'AI' as the ability of the machine to think independently of its creators or of the parameters its creators have set forth. Generative AI describes the ability of AI to generate unique and original responses - be they textual, imagery, or in some other medium - based on its index of accumulated knowledge. In other words, for the machine to assimilate data in unpredictable ways that conjure new responses, rather than to simply navigate a logical process for which it has been pre-programmed.

It's worth noting that we’ve also seen what we call “multi-modal” AI capabilities markedly mature in 2024. Multi-modal extends previously existing abilities to learn from and generate new text, imagery, video, and other mediums as separate scenarios such that single models can understand and generate across multiple modes. This is a significant development in the ability of an AI workload to wrap its inorganic head around a vastly expanded variety of mediums, which in turn expands both its comprehension and its generative abilities.

The reality of AI in today's organizations

All of this is new. It is groundbreaking. Historically speaking, we are in a very different era in late 2024 than we were two years ago, a fact that causes me to encounter two foundational realities time and again.

You are (probably) not ready; almost nobody is

First, very few - if any - organizations are truly prepared to make the most of the AI wave crashing on their shore. Very few have done the hard work to build the kind of proper, modern data platform required to make AI work at scale across their organization. For this reason, nearly everyone’s “AI strategy” will at this point look very much like most everyone else’s AI strategy, as organizations across the economy and around the world scurry to get their house in order.

Future-ready, not future-proof

Second, nobody truly knows exactly what a mature AI capability will look like or exactly how this will play out in practice. Today we’re at a point with AI reminiscent of where we were with the “world wide web” in the late 1990s when organizations rushed to digitize their physical identity. Back then we - predictably - experienced a lot of websites that looked like someone had relocated their back-of-the-phonebook advertising to a screen. We went through a similar chain of events with the advent of the smartphone when developers first tried to cram desktop apps into a smaller form factor, and then again with smart watches when developers tried to cram smartphone apps onto our wrists. Comparably, many today are busy making yesterday’s business processes incrementally more efficient by grafting AI onto them. Each of these cases illustrates our tendency to transpose legacy paradigms to new technologies. That is, at least until we gain a sufficient understanding of the new capability to grasp how transformative it really is… and how best to exploit it.

Guiding principles for AI in your organization

Any future-ready AI strategy must be flexible, meaning it is able to absorb tomorrow what we don’t fully grasp today. Your strategy should also offer immediate value to the organization beyond specific AI-driven workloads because the nature and value of these workloads will remain unclear for some time. For example, “data readiness” is indispensable to your AI strategy because it is likely to yield better AI workloads in the future, and because it offers real value in terms of security, accuracy, discoverability, and analytical integrity separate and apart from AI today.

This explains my fondness for the phrase future-ready, and why I cringe when I hear people say “future-proof.” The former describes a cloud ecosystem built with modern technologies using best practices that are most likely to absorb whatever future innovations come our way. The latter is unachievable in all circumstances.

RAG, and how generative AI acts on enterprise data

Let’s establish a basic understanding of how AI uses and acts on enterprise data. We will define 'enterprise data' as data that is proprietary to a specific organization, kept and (I certainly hope!) secured inside the boundary of the organization’s data estate.

You may be familiar with the term “RAG”, an acronym for “retrieval augmented generation”. While this is not the sole means through which AI acts on organizational data - and new and evolving patterns now emerge regularly - RAG represents a good baseline for the general concept through which nearly all AI workloads essentially augment an existing model with an organization’s proprietary data.

In the top-right of the diagram we’re looking at various data sources sitting in a modern data platform (Azure SQL, OneLake, and Blob Storage are shown top to bottom for representative purposes). Blob Storage is a highly efficient way to store unstructured data, that is, files, images, videos, documents, etc. In this simple scenario we’ll say that unstructured data is drawn from Blob. 

These data sources are indexed by Azure AI Search (formerly called “Cognitive Search”), which also provides an enterprise-wide single search capability. Moving to the far left we see an application user experience (UX) e.g., a mobile, tablet, or web app that provides an end user the ability to interact with our workload.

The application sitting beneath the UX queries the knowledge contained in Cognitive Search’s index (as derived from the data sources on the right). It then passes that prompt and knowledge to Azure AI services to generate an appropriate response to be fed back to the user.

CIOs and enterprise architects need not be experts in the technical mechanics of AI to formulate and execute an effective AI strategy. That said, it is critical that leaders driving this strategy must understand this basic concept of how institutional AI - that is to say, AI workloads specific to your organization - both requires and acts on enterprise data.   

Without that data, it’s just AI, unspecific to the organization it is serving.