"AI Strategy Framework" guides your organization's journey in the Age of AI

The below contains content sourced from our recent paper, Crafting your Future-Ready Enterprise AI Strategy, e2. Delve into the full whitepaper to read more around this topic, learning about Foundational concepts for AI tech and your AI strategy, the AI Strategy Framework’s five pillars - Strategy and Vision, Ecosystem Architecture, Workloads, Responsible AI, Scaling AI and the AI Maturity Model for assessing readiness, targeting AI investments, reducing risk, and exploiting the opportunities that AI offers.

I have spent the last two years crisscrossing the world helping C-level leaders craft their AI strategies, building the actionable roadmaps and technical architectures necessary to bring those strategies to fruition.

It has become obvious how difficult so many organizations are finding it to actually craft and execute their AI strategy, in part because of the (often) decades they’ve spent kicking their proverbial data can down the road, in part because it turns out that enterprise-grade AI really does require the adoption of ecosystem-oriented architecture to truly scale, but largely because many organizations have no idea where to start. Many lack the wherewithal to really assess where they stand on day one, and to identify areas where they must mature to get to day 100 (and beyond).

We’ve learned a great deal about maturity and readiness for - and responsibility to the ethics of - AI over the past year, as well. It’s now time for a proper model through which organizations may realistically assess their readiness to adopt and scale artificial intelligence, and then identify specific areas to invest time, talent, and funding along their journey.

This AI Strategy Framework guides organizations as they construct their AI strategy atop five pillars, each with five dimensions to be considered, matured, and regularly evaluated. This model has the benefit of shaping (a) how you evaluate your organization’s maturity, risks, and opportunities in AI at any point in time - including when just getting started - and (b) how you organize your strategy to mitigate those risks, seize those opportunities, and mature the organization’s use of AI over time.

Use these five pillars to evaluate an organization’s maturity, risks, and opportunities in AI at any point in time. Then build your strategy to mitigate those risks, seize those opportunities, and mature the organization’s use of AI over time.​

Incidentally, because AI depends on a sound technical foundation in terms of data estate, application portfolio, governance, security, etc., those who embrace this model will find that they significantly mature the strategic architecture of their cloud ecosystem overall. This is important, because your investments in AI ought to offer immediate value to the organization beyond specific AI-driven workloads. In other words, invest in AI such that the investment pays off in other ways, as well.

These pillars address five broad questions that an organization ought to continually ask itself:

• Strategy and Vision: Are our investments in AI strategically driven by a coherent vision for how we wish to use it rather than driven by the arrival of the latest trend or “shiny object”?

• Ecosystem Architecture: Do we build AI capabilities atop a solid ecosystem-oriented architecture across our IT estate rather than grafting AI capabilities onto a fragmented IT estate that will be difficult to maintain in the future?

• Workloads: Have we effectively balanced AI’s risk and reward across incremental, extensible, and differential workloads?

• Responsible AI: Do we embrace the principles of “responsible AI” (RAI), and – importantly - are we doing the never-ending hard work of making those principles actionable in our organization?

• Scaling AI: Are we positioned to scale AI across the organization, including our ability to manage and govern AI and the data upon which it relies?

These broad questions offer helpful guidance, but on their own lack the specificity that a truly actionable strategy requires. Each pillar, therefor, is supported by five component dimensions.

Each pillar is supported by five component dimensions that offer greater specificity through which AI maturity can be measured. Low maturity dimensions generally indicate risks to be mitigated, whilst higher maturity dimensions generally indicate strengths or opportunities to be leveraged.

The AI Strategy Framework offers a comprehensive blueprint through which organizations craft their future-ready enterprise AI strategy. Equally important is our ability to assess an organization’s maturity or readiness for artificial intelligence, both in beginning to craft its strategy and regularly as it travels its roadmap.

So combine the framework with the AI Maturity Model shown below. In the model, each dimension is reviewed with cognizant stakeholders - and your AI Center for Enablement team, I hope - to reach consensus on which maturity level and description fits best at the time of review. These ratings align to the 5-point scale shown, with "Strategic" (5) being the most mature and "Unaware" (1) being the least.

The AI Maturity Model allows organizations to assess their maturity or readiness for AI across each of the five pillars and twenty-five dimensions.

Apply the model to each dimension to determine each dimension’s maturity relative to the others.

More mature dimensions are assets to be leveraged across the organization. They are also indicators of success that justify investment, in other words, where an investment has sufficiently matured a dimension and effectively lowered corporate risk. Less mature dimensions represent organizational risk and opportunity to unlock new capabilities, and should generally be a focus of investment.

Undertaking this assessment as you begin formulating your AI strategy promotes informed decisions as to which dimensions ought to receive early attention and be included in your actionable roadmap.

Let’s work through a practical example.

An example of the AI Strategy Framework with each dimension and pillar scored using the AI Maturity Model.

It’s early days in this example, and we’re just beginning to craft our AI strategy. We’ve worked through the dimensions one by one, giving a score to each. The diagram above reflects this, using averages to produce Pillar Maturity scores of:

• Strategy and Vision = 3

• Ecosystem Architecture = 3.4

• Workloads = 2.6

• Responsible AI = 1.2

• Scaling AI = 2.6

...and an Aggregate Maturity (the average of all dimensions) = 2.56, so, Disarray.

Incidentally, I've found that any organization achieving a score of 2.56 in 2024 should count itself lucky. Most are even less future-ready for AI. It’s also worth noting that, based on my recent work with organizations around the world, a Pillar Maturity of 1.2 for Responsible AI (RAI) is not hyperbole; most organizations are woefully unprepared for RAI.

Apply this guidance when choosing which dimensions focus on in your actionable roadmap:

• Scores of less than "3" are high risk / high opportunity, so address these immediately;

• Scores of "3" are both a risk and opportunity for the organization, so address these when possible;

• Scores greater than "3" are lower risk and areas of strength, so protect them.

The model provides a common standard for assessing AI maturity and readiness, but it cannot be used on its own absent the insight and judgement that comes from professional expertise. The model is best used as a tool in the hands of experienced practitioners, not as a formulaic shortcut. In fact, Microsoft partners that take AI seriously should develop questions and methods that they can use to facilitate such assessments. Customers ought to challenge any Microsoft partner claiming expertise here to demonstrate it accordingly.

I recommend some ground rules when using this model:

• Round down when undecided between two maturity levels - It is better to overestimate risk than to ignore it;

• There is no shame in “Disarray” - It is better to admit where you are and fix it than to hope things magically improve;

• “Proactive” is a high bar to achieve - It means that you’ve planned and committed resources to evolving as AI technology and your business drivers change;

“Strategic” is an even higher bar. Don’t award yourself lightly.

The pace at which an organization re-assesses itself is important. Too infrequent assessments can result in bad data that could skew risk management and resource allocation, whilst assessing too frequently can waste a lot of time in pursuit of only marginally more current results. Rigorously applying this model and re-assessing yourself on a regular basis will not only equip you to keep the strategy fresh and relevant but will also demonstrate progress - and help to justify investment - from a less to a more mature state.

And remember: Your organization is (probably) not ready for AI, because almost none are.

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.

Be one of the few.