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Enterprise AI strategy needs more than a promising model: it needs a clear business goal, workable data and processes, and people responsible for oversight. In a TechRadar Pro opinion piece published August 25, 2026, Aiimi CTO Paul Maker casts IT, Legal, and Compliance as “protectors of the realm”—not obstacles to progress, but partners who can help connect AI to real workflows and risks.
What the House of the Dragon analogy means for AI strategy
Maker’s fantasy metaphor is about operationalizing AI. IT understands systems and integration; Legal and Compliance help identify obligations and risks. Involving them early can make a proposed use case more practical to deploy and oversee. It does not guarantee a safe outcome, and it is Maker’s argument rather than proof that governance teams always improve results.
The useful distinction is between governance that shapes a solution and a late-stage review that discovers unresolved issues. Cross-functional input belongs in design and deployment, with ongoing owners after launch. That is different from treating every risk question as a reason to stop.
Maker’s article proposes an AI-supported data classification and governance scenario: compliance and legal expertise would inform how classification requirements are handled, while IT would help integrate the capability into existing workflows. Treat this as an illustrative proposal, not a validated product recommendation.
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Start with the business outcome, not the model
Microsoft defines an AI strategy as “a coordinated plan for using AI to reach specific organizational goals.” Its guide organizes that strategy around five connected threads: vision, data, people, processes, and governance. In practice, that means a use case should be tied to a business priority and a way to judge whether it helped—not selected simply because a technology is available.
Deloitte similarly recommends linking AI initiatives to corporate strategy and KPIs, with leaders across divisions involved. These approaches help distinguish a useful experiment from a disconnected demonstration.
- State the objective. Identify the business problem, the people affected, and the change you want to see.
- Choose a feasible use case. Check whether the data, systems, skills, and workflow needed to deliver it are available or can realistically be built.
- Set success measures. Define the relevant business outcome and how it will be measured before deployment.
- Assign cross-functional owners. Give business, IT, Legal, and Compliance roles in design, launch decisions, and ongoing oversight appropriate to the use case.
Ask questions that improve decisions
Executives often want to know when generative AI will match their best employees, whether it is accurate enough to create value, whether the CIO is moving fast enough, and what competitors are doing. Harvard Business Review authors Bharat N. Anand and Andy Wu suggest reframing the decision around what the organization can do effectively now, despite limitations, and where it can create competitive advantage.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThat reframing avoids waiting for a hypothetical perfect system while still requiring a credible case for use. For each proposed application, ask:
- What task or outcome will change, and for whom?
- What level of accuracy is sufficient for that task, and what happens when the system is wrong?
- How sensitive is the data, who can access it, and what compliance obligations apply?
- Does the AI fit the workflow people actually use, including review or escalation when needed?
- Who monitors performance and is accountable for changes after launch?
- Would building, buying, or partnering best balance control, speed, cost, and differentiation?
Microsoft’s guide supports evaluating build, buy, and partner options against tradeoffs such as control, speed, cost, and differentiation. The right choice depends on the use case and requires current evaluation; a general strategy cannot establish which supplier or implementation is best.
Balance speed with the cost of error
There is no universal setting for how quickly an organization should adopt AI. The practical comparison is the cost of an error in a particular workflow against the cost of waiting or delaying a useful change. A low-consequence assistive task and a decision with serious effects on people or operations do not call for identical review.
Governance is therefore not simply a brake or a permission step. It is part of deciding what to deploy, with what controls, and how to respond when performance or circumstances change. The sources support combining business fit with governance, but do not establish one formula that resolves the tradeoff for every organization.
Make adoption part of the operating plan
Deployment is not the end of the strategy. OpenAI’s business guide describes five principles for organizational adoption: communication, role-specific training, experimentation, scaling, and acceleration, alongside governance. Together, they point to a practical sequence: explain why a use case is being introduced, help affected people learn how to use it, test it in context, expand only when the case supports doing so, and maintain oversight as usage grows.
OpenAI’s guide, published December 16, 2025, also presents three market figures: 5.6× growth since 2022 in frontier-scale AI model releases, a 280× reduction in the cost to run GPT-3.5-class models in 18 months, and adoption 4× faster than desktop internet. These are figures reported by OpenAI; the page information available here is not enough to assess their underlying methodology. They are context for a changing market, not a substitute for evaluating a specific business case.
Build an AI strategy that can operate
House of the Dragon supplies a memorable image, but the strategy lesson is practical: connect AI work to business priorities, make sure its data and workflows are fit for purpose, involve the functions that understand systems and obligations, and name owners for outcomes and oversight. IT, Legal, and Compliance can help make a plan workable; responsibility for success remains broader than any three teams.
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