How Senior Officials Should Think About AI Roadmaps

In an era where artificial intelligence (AI) is touted as both an existential opportunity and threat, government agencies and non-governmental non-profits face an increasingly urgent need to prepare—not just for AI adoption, but for long-term stewardship. One of the most important but often misunderstood tools in this journey is the AI roadmap.

An AI roadmap is not a technology document. It is a strategic instrument for systems change, accountability, and capability-building. When done well, it becomes a tool to reshape institutions, empower employees, and serve the public more effectively. When done poorly, it becomes a buzzword-laden wish list, destined for a shared drive and forgotten.

This blog post is for senior officials and decision-makers in ministries, public health agencies, educational institutions, nonprofit foundations, and other mission-driven organizations who are either creating or overseeing AI roadmaps. We aim to provoke, challenge, and inspire new thinking about what truly effective AI roadmaps should include—and how to evaluate them.

AI Roadmaps Should Start with Institutional Friction, Not Technical Capacity

Most roadmaps begin with technology inventories. But the best ones begin by mapping organizational friction—where decisions bottleneck, where frontline staff feel stuck, where data goes unused. Friction is where AI delivers value, because it provides leverage.

Start your roadmap by asking:

• Where are decisions made too slowly, too often, or too inconsistently?
• What repeatable processes create staff burnout or bottlenecks?
• What predictable patterns go undetected because they span departments or timeframes?

If you cannot identify the human or institutional friction points, you’re not ready to invest in AI.

Don’t Just Ask “What Can We Automate?” Ask “What Should We Augment?”

Public and non-profit missions are rarely just about efficiency. They’re about legitimacy, equity, and service. That means AI should be thought of not as a replacement for humans, but as an augmentation of judgment.

Ask:

• Where could decision-makers benefit from simulations or counterfactual modeling?
• What information could be surfaced at just the right time to improve judgment?
• How do we reduce cognitive overload instead of just process load?

AI should make people more trusted and capable—not more obsolete.

Every AI Roadmap Needs a Public Legitimacy Chapter

If you’re a government agency or NGO, your authority comes from trust. If AI is perceived as opaque, biased, or extractive, you risk losing public legitimacy.

A roadmap should include:

• Explainability commitments: What must every AI system be able to explain to a citizen or clinician?
• Auditability requirements: How do we detect model drift, misuse, or discrimination?
• Participation plans: How will stakeholders (especially the vulnerable) co-design or evaluate the AI tools?

Legitimacy is a feature—not a nice-to-have addendum.

Roadmaps Should Include Red Teaming and Counter-Roadmaps

A truly strategic roadmap should include its own adversary. Build a small internal or contracted “red team” that tries to poke holes in every step:

• What could go wrong if this roadmap succeeds on paper but fails in practice?
• Where are we relying too much on a single vendor, model, or framework?
• How would a journalist or regulator interpret this roadmap two years from now?

This stress-testing function should be funded, formalized, and respected.

Roadmaps Should Specify When NOT to Use AI

One of the most credible things your AI roadmap can do is include a list of use cases where AI is not appropriate:

• Where outcomes are legally or ethically sensitive
• Where explainability cannot be achieved
• Where stakes are too high for prediction errors
• Where public trust is fragile and cannot be risked

The willingness to define “No-Go Zones” signals maturity and discipline.

Hiring the Right Contractors and Reviewers

Many roadmaps fail not because of intent, but because the teams advising them are too narrow in scope. Here’s how to build a credible advisory ecosystem:

• Cross-functional teams: Every roadmap review team should include clinicians, social scientists, ethicists, and public sector operators—not just data scientists.
• Country-specific expertise: For nations like KSA, localization is essential. AI must reflect local language, policy, law, and workflow realities.
• Neutrality: Avoid AI advisors who are also selling productized platforms. Strategic guidance should be free from implementation bias.
• Procurement awareness: Choose reviewers who understand public sector budgeting, procurement rules, and the timeline constraints of government cycles.

How to Tell If a Roadmap Is Useful

Use the following criteria to judge roadmap quality:

• Actionability: Does it clearly identify roles, milestones, funding sources, and metrics?
• Alignment: Is it tied to the institution’s mission and regulatory obligations?
• Breadth: Does it cover governance, trust, and training—not just models and data?
• Flexibility: Can it accommodate shifts in regulation, workforce, or political climate?
• Testability: Can you evaluate its success with real-world pilots in six months?

A roadmap that sounds good but cannot be implemented in 90 to 180 days is not a roadmap. It’s a strategy paper.

Roadmaps Are Living Documents, Not PR Pieces

The worst AI roadmaps are those that are launched in press releases and never updated. The best roadmaps are:

• Version-controlled and publicly posted
• Updated quarterly with execution status
• Backed by dashboards, data catalogs, and ethics review logs

Treat your roadmap like a product that matures over time—not a policy that gathers dust.

Examples From the Field

I have conducted AI readiness and roadmap validation exercises across the Gulf and in multiple jurisdictions. For example:

• In collaboration with a Gulf-region health authority, I supported a triage optimization roadmap that aligned AI risk scoring with SFDA SaMD pathways, integrated into their EMR system, and accounted for health literacy across rural regions.
• We supported a major nonprofit research institution by red-teaming their AI-driven diagnostics pipeline, helping to create governance frameworks, vendor selection checklists, and public-facing explainability protocols.

Use my RAISE Framework

To make AI roadmap reviews more repeatable, I use a framework called RAISE:

• Readiness: Are we operationally, ethically, and technically ready?
• Architecture: Is our AI stack modular, explainable, and interoperable?
• Impact: Do use cases map to measurable outcomes with equity in mind?
• Safety: Is there a governance structure to detect harm or failure?
• Execution: Are implementation timelines, ownership, and funding clearly defined?

I use RAISE to lead structured evaluations of public-sector AI strategies and ensure alignment with national and international best practices.

Checklist: Is Your AI Roadmap Ready? [Coming Soon]

We’re preparing a self-assessment checklist that will let institutions rapidly assess their AI roadmap’s completeness across clinical, regulatory, and operational criteria.

AI will not save your institution. But a well-designed, trust-grounded, and stakeholder-tested roadmap will make your institution more capable, more adaptable, and more aligned with the public interest in the age of algorithmic governance.

If you are a senior official, your roadmap is your legacy. Make sure it’s more than a PDF. Make sure it works.