AI Strategy and Vision

This blog is an excerpt from the co-authored whitepaper, Crafting your Future-Ready Enterprise AI Strategyaiming to support organizations on their journey in the Age of AI.

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 and its related technologies become more pivotal to the success of an organization - see our earlier statistics regarding productivity and Investment - 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, our Strategy and Vision pillar sets forth five dimensions which begins with vision, extends to creating the actionable roadmap and architecture necessary to actualize that vision, and finally establishes the programmatic elements necessary to drive that vision to fruition. These dimensions help organizations formulate and take action on their big ideas.​

Strategy and Vision is thus the first indispensable pillar of any ​
future-ready enterprise AI strategy. Looking for the place to start with your AI ambitions? This is it.

Executive Vision​

We’ve tried in vain over the years to accommodate shortcuts demanded by various organizations with whom we’ve worked. Alas, we’ve reached 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.​

We’ve tried in vain over the years to accommodate shortcuts demanded by various organizations with whom we’ve worked. Alas, we’ve reached 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.​

Finally, a well-crafted executive vision ought to go beyond headline aspirations to describe what we 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. 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:

Figure 1: These five top-line priorities are representative samples similar to those that we see many organizations priorities as part of their early AI strategy.

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 earlier. 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.

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