Trustrorthy AI
Knowledge Base
AI Maturity Model
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.
We’ve developed the AI Maturity Model shown below to accompany the AI Strategy Framework previously discussed at length.
Figure 31: The AI Maturity Model allows organizations to assess their maturity or readiness for AI across each of the five pillars and twenty-five dimensions.
In the model, each dimension is reviewed with cognizant stakeholders - and your AI Center for Enablement team, we 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.
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.
Figure 32: An example of the AI Strategy Framework with each dimension and pillar scored using the AI Maturity Model.
It’s early days 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:
o Strategy and Vision = 3
o Ecosystem Architecture = 3.4
o Workloads = 2.6
o Responsible AI = 1.2
o Scaling AI = 2.6
• Aggregate Maturity (the average of all dimensions) = 2.56, so, Disarray
Incidentally, we believe that any organization that achieves 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 our recent work with organizations around the world, a Pillar Maturity of 1.2 for Responsible AI 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.
We 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. In general, there are three reasons to update a dimension’s maturity assessment (up or down):
• When just starting out on your AI journey. It’s hard to know what to do next when you don’t have a firm understanding of where you are. We recommend assessing all dimensions in a single round;
• At a regular cadence that makes sense for your organization. This could be quarterly or half-yearly. It may also make sense to re-assess a different pillar each month to produce rolling maturity updates;
• When a compelling event occurs, which might include big Microsoft product updates, your organizations M&A events or major internal re-orgs, following an incident, major platform expansions in the organization, etc.
If opting for a six-month regular re-assessment cadence, consider coinciding these with major Microsoft product announcements like Microsoft Ignite.
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.