Crafting the Executive Vision and an Actionable Roadmap for your AI Strategy

Note: This article is next in a series that builds upon its predecessors listed below. I suggest you give them a read, particularly the first one on the list:

• The "AI Strategy Framework" guides your organization's journey in the Age of AI
• Foundational considerations for your enterprise AI Strategy

There’s an incredibly important transition in the broad information technology space that is often lost in the furor and excitement over generative AI.

You see, since IT time immemorial most chief information officers and those in similar roles have been called on by their organizations to essentially function as superintendents of utility companies. Their charge has been to keep the phones ringing, the emails sending and receiving, and to prevent data from leaking.

AI is upending this paradigm, even though many still don’t yet realize it. As AI, the extraction of value from data, and related technologies become more pivotal to the success of an organization, technology leaders are finding that they must transition from being superintendents of utility companies to being strategic leaders of the organizations they serve.

But crafting, executing, and making smart investments in scalable cloud and AI strategy is hard. Leading strategically - and empowering your people to implement the vision - can seem overwhelming.

Simply “wanting AI” doesn’t cut it. So, the AI Strategy Framework begins with the Strategy and Vision pillar that sets forth five dimensions beginning with vision, extending to creating the actionable roadmap and architecture necessary to actualize that vision, and finally establishing the programmatic elements necessary to drive that vision to fruition. These dimensions help organizations formulate and take action on their big ideas.

Organizations ought candidly evaluate their position across each of these dimensions using the AI Maturity Model, and craft their strategy and roadmap such that they take action to improve their maturity in each.

I'll spend the rest of this piece covering the first two - Executive Vision and Actionable Roadmap - and will discuss the others in future articles.

Executive Vision

I've tried in vain over the years to accommodate shortcuts demanded by various organizations with which I’ve worked. Alas, I reach the same conclusion each time: Technology adoption fails when not driven by executive vision. Adopting AI is simply too challenging for most organizations to do when absent of long-term vision supported from top-down. You simply must define the organizational direction of travel for AI at the CXO level.

This is the stuff of many, many business leadership books written over the years, so I don’t want to be too prescriptive here. Executive vision can take many forms, but the bottom line is that your executive vision for AI (or any technology) must frame everything that follows so that it is crystal clear why the organization is embracing this technology and what the organization collectively aspires to achieve from its adoption.

I've helped many organizations craft their vision for AI. The anonymized aspirations below provide a great example of a top-level executive vision at a real-world enterprise firm.

Notice that our vision is aspirational, succinctly describing not just what we hope to achieve with artificial intelligence, but what we hope to be as an organization that has embraced artificial intelligence. Further, only two of our five aspirations explicitly mention AI at all. This is important: I often hear folks talk of AI as if it were a product, but it’s not a product at all. AI is quickly being woven through nearly every aspect of our work lives (and our lives in general), and it equally depends on the proper functioning of other domains including data, applications, technical governance, business process, digital culture, and the mission of the organization itself (“improving client engagement”, in the case of the example above).

Finally, a well-crafted executive vision ought to go beyond headline aspirations to describe what I call “targeted outcomes”, which is to say, to define the outcomes the organization hopes to achieve in actualizing its aspirations. Think of targeted outcomes as adding specificity to your aspirations, not necessarily hard, quantifiable specificity, but a clear articulation of what it means to (for example) “Extract increasing value from our data using responsible, safely leveraged artificial intelligence”:

• The data platform offers a mastered single source of truth for the most mission critical data domains;
• Data is addressable by AI and aggregated from different sources as part of our data platform;
• AI is deployed consistently and with governance guardrails in place;
• "Low-hanging fruit" (incremental) AI capabilities quickly deliver lower-risk capabilities to our colleagues;
• We pursue a risk-sensitive portfolio of "differential AI" customized for the firm.

Whatever your executive vision, it is important to lead with it, to prioritize the AI investments that best align to it, and to evangelize it such that colleagues both in IT and the wider business understand the all-important “why”.

Actionable Roadmap

Strategy without action is like the rule of law on a deserted island. Irrelevant, even to the birds.

The trick to making strategy relevant is to pair it with an actionable roadmap, really the actions, activities, even full-blown projects that will be undertaken to actualize our aspirations and achieve our targeted outcomes.

There’s an old adage attributed to American General and later President Dwight D. Eisenhower that “plans are useless, but planning is indispensable”. Take it to heart. Firm roadmaps quickly grow obsolete even under stable conditions, and the only thing stable about the evolution of AI is its acceleration. An actionable roadmap for your AI strategy that runs more than 12 to 24 months into the future is far too long. And we’re only able to achieve that level of durability by taking to heart our first principles:

• AI strategy should 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. ?In other words, make investments in modern data platform technology that will pay dividends not just in AI but in analytics, business intelligence, search, etc.;
• AI strategy must be flexible: able to absorb tomorrow what we don’t fully grasp today. It’s wise to plan 24 months in advance, but it is equally unwise to assume that you’ll not be regularly revising those plans as things evolve.

Start by formulating up to five big priorities, inspired of course by your executive vision. If, for example, you have established five aspirations as part of your vision, try first to devise one major priority aligned with each aspiration. For example, referring to the executive vision shared earlier, we might establish the following topline priorities.

I'll restate them here in case the words in the diagram are a bit too small:

• Digital Enablement: Undertake an "Enablement" program to empower colleagues with AI skills and AI-infused ways of working.
• Cloud Ecosystem: Build a composable cloud ecosystem integrating data, apps, AI, and governance guardrails.
• Modern Data Platform: Build a modern, Fabric-centric data platform to hydrate AI, analytical, and other data workloads.
• Power Platform: Fully embrace Power Platform and composable dev to integrate app, data, automaton, and AI capabilities.
• Key AI Workloads: Deploy high-impact AI-infused business workloads that deliver rapid business value to both operational and support teams.

Then, add specificity to these priority buckets with 3-5 milestones that the organization will achieve in the next 18 (give or take) months. It’s helpful to break these down into three horizons of three to six months each, and be prepared to drastically rework the milestones in the third horizon given that they’re likely at least 12 months out.

Finally, keep in mind that you are likely to uncover specific actions or milestones you need to undertake simply by evaluating where the organization is in each of the twenty-five AI maturity dimensions outlined in my previous article. For example, if you assess early on that the organization is particularly immature in the dimensions of “AI Development Tools” and “Digital Literacy”, it’s wise to prioritize milestones that are likely to close those maturity gaps as part of your actionable roadmap. Finally, invest in your stakeholder relationships to ensure that your roadmap is mapped back to those stakeholders, clear feedback loops are in place, and updates are shared so that you bring colleagues on the proverbial journey.