Insights

Building an AI Strategy Aligned to Vision 2030

· 3 min read

Line-art illustration of a compass and a layered roadmap rising toward a city skyline, in green and gold Najdi style
A mandate tells you to move. A strategy tells you where and in what order.

Vision 2030 and the national AI strategy put artificial intelligence near the top of the agenda for pretty much every organization in the Kingdom, public and private. The economic prize is real: PwC projects AI could add about $135bn to Saudi Arabia’s economy by 2030, the largest gain in the Middle East (PwC, via US ITA). That’s the good news. The awkward part? Leadership now wants “an AI strategy,” and a national ambition isn’t a plan you can hand to a team on Monday.

So let’s talk about what an AI strategy actually is, and how you build one that delivers instead of just satisfying the boss.

First, what it isn’t. It isn’t a list of AI projects. It isn’t picking a vendor platform. It isn’t a slide that says you’ll “embrace AI.” That’s all activity. A real strategy answers three questions: where will AI create the most value for us, what has to be true for it to work, and in what order do we move?

The building blocks.

  1. Value first, technology second. Find where AI could genuinely move the needle. Efficiency, better decisions, faster service, a capability you didn’t have. Then rank by value and feasibility, not by what’s trending on LinkedIn this week. This is where most programmes leak: Gartner expects at least 30% of generative AI projects to be abandoned after proof of concept, often for unclear business value (Gartner, 2024). A focused strategy says no to far more than it says yes to.
  2. Be honest about your data. AI runs on data, and in our experience almost everyone overestimates how ready theirs is. A credible strategy looks its data quality, access, and governance straight in the eye, and treats fixing the foundation as part of the plan, not something a different team will sort out later.
  3. Sort out who does what. Who builds, runs, and governs the AI? What’s in-house, what’s partnered? How do models get deployed, watched, and improved over time? Strategy with no operating model behind it is just a wish.
  4. Govern from the start. Responsible use, PDPL alignment, and risk management built in. In a regulated market, ungoverned AI rarely scales in our experience.
  5. Sequence it. Start with contained, high-value use cases that build capability and confidence, then expand. Prove value early. Don’t bet the whole budget on a multi-year programme before you’ve shipped a single thing.

Here’s the shape of it, start to finish.

flowchart LR
  A(["Vision 2030 mandate"]) --> B(["Rank use cases by value"])
  B --> C(["Check the data foundation"])
  C --> D(["Decide the operating model"])
  D --> E(["Ship a contained first win"])
  E --> F(["Measure, then expand"])
  F -.-> B
How a national mandate becomes a plan that ships value.

Align to Vision 2030 for real, not for show. The national agenda rewards genuine capability, local talent, and measurable outcomes. It doesn’t reward AI theatre. In our experience the organizations that win build capability that compounds over time, rather than chasing the next announcement.

Good AI strategy is focused, honest about the data and capability gap, governed by design, and sequenced to deliver something early and grow from there. That’s the whole thing.

Need an AI strategy that delivers, not just one that satisfies the mandate? SDCG helps organizations build focused, deliverable AI strategies aligned to Vision 2030, grounded in real value and an honest view of what it takes. We’re independent, so the plan is built around your outcomes, not a product we’re selling. Book a free 30-minute review.

Sources

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