Trustrorthy AI
Knowledge Base

AI Strategy Framework

An organization’s AI strategy ought to be constructed 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.

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 IT ecosystem overall. Reference our guiding principle that 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.

Figure 2: 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.

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

  • 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”?

  • 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?

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

  • 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?

  • 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.

Figure 3: 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.

Look no further to understand how significantly we’ve expanded our model of AI strategy from this white paper’s first edition to this second edition. The first edition discussed the pillars Data Consolidation, Data Readiness, incremental AI, Differential AI, and Scaling AI. This second edition preserves and expands “Scaling AI” as its own pillar, preserves “Incremental AI” and “Differential AI” as dimensions in the new and much expanded “Workloads” pillar, and preserves “Data Distribution” whilst changing “Data Readiness” to “Core Platform Services” as part of the new and expanded “Ecosystem Architecture” pillar. Moving parts to be sure, but we thought it helpful to map the evolution of AI strategy from early to late 2024.

Figure 4: Core Platform Services (formerly “Data Readiness”), Data Distribution, Incremental AI, Differential AI, and Scaling AI have been revised and reworked from this white paper’s first to second edition.

We’ll explore each pillar and its dimensions in the subsections that follow.