The Framework Meets the Strategy. Here’s What the Next Twelve Months Need to Look Like.
If you've kept up, I respect your diligence to the importance of this nationally relevant topic. If you haven’t, you may want to. Here it is. The final and most important piece of this series. What follows is the bread baked out of that exercise.
Two weeks ago I argued the urgent need for a national AI framework. I presented my vision of that framework: the numbers, the scenarios, the case for why this moment matters more than most people realise. Last week I evaluated Ghana’s national intentions published as strategy: what (I think) it gets right, and the gaps that will determine whether this becomes Ghana’s defining decade or another bold launch that quietly fades. Like another community public toilet initiative.
This week I suggest a blueprint. And I do this under the backdrop of just having personally attended the unveiling of a declaration of intent by the Chinese government to turn the Quintiang district in Shanghai into a hub of industrial, embodied AI.
Here’s an excerpt of the launch speech:
Although we recognize that technological innovation is inherently high-risk, whether you are a leading enterprise or a startup, Qiantang will provide comprehensive support.
Whether you need policy support, capital, talent, or data – we will provide what is missing.
We want companies of every size to grow like a rainforest ecosystem: freely, mutually reinforcing one another, and together creating a powerful industrial cluster.
Second, we are building an orderly collaborative innovation platform.
Qiantang is already home to the National Artificial Intelligence Application Cooperation Center, which will serve as a national platform for:
- AI product validation
- * Product testing
- * Mutual recognition of domestic and international standards
In addition, Qiantang hosts:
- The China Cooperation Center of the National Comprehensive Pilot Zone
- * The Zhejiang Branch of the National Robotics Innovation Center
- * Eastern China’s largest high-level industrial innovation platform
Together, these institutions provide strong support for innovation.
We will fully integrate the:
- Industrial chain
- * Innovation chain
- * Talent chain
- * Capital chain
making Qiantang a national hub for technology transfer, commercialization, industrial validation, and talent development.
Third, we will support companies throughout their entire growth journey.
For entrepreneurs willing to explore uncharted territory, we will become genuine partners.
From proof-of-concept…
to pilot manufacturing…
to commercialization…
to large-scale growth…
we will face uncertainty together, share innovation risks, and help companies overcome the challenges of scaling.
We want entrepreneurs to focus on building businesses – not worrying about everything else.
Qiantang will provide:
- the best policies,
- * the best services,
- * and the best environment for innovation.
No gaps. They said what they would do, based on what they’ve done. There’s something regimented about it which removes the uncertainty of a path to execution.
I will attempt to do same starting with the RAI Authority, because everything we seek to do with AI as a country, seemingly depends on whether that institution works.
The mandate for the RAI as written is ambitious but historically impossible. To coordinate eight pillars, monitor every ministry, enforce data governance, engage the private sector, connect to international platforms, champion ethics, drive public sector adoption, all from day one? No institution survives that brief intact. I suggest it focuses on three things. At least for year one.
First, publish a public sequencing document within ninety days of the Authority’s establishment, naming the five priority sectors, the first three pilot projects within them, and a specific, dated milestone for each. The framework already tells you where to start: agriculture and public administration, because the data infrastructure already exists [to some extent] in both and the returns are evidence enough to build the political confidence that sustains everything that follows.
Second, stand up the National AI Fund’s first disbursement cycle and make the prioritisation criteria public before a single cedi moves. Ghana already has the GHS 5 billion seed commitment in the strategy. My framework’s financing architecture suggests how to sequence it: 40% from ICT budget reallocation, 30% from concessional finance, 20% from public-private co-investment, 10% from digital bonds. None of that requires new money. Well, maybe we rethink the digital bonds bit based on the current state of treasury bills. Regardless, we require a published allocation plan that investors, development partners, and ordinary Ghanaians can actually see.
Third, can we please settle the institution’s name, its legal mandate, and its reporting line, and publish all three? I made the point last week that a month after launch, the Authority does not yet have a settled identity. That cannot still be true by the time this piece reaches its first hundred readers.
Next, sector allocation.
I still strongly suggest that we focus on agric and start with cocoa. It’s not a difficult choice. Ghana already has the institutional infrastructure with COCOBOD [no side comments], the licensed buying company network, decades of yield and disease data. The framework shows that computer vision and predictive disease modelling applied to this existing infrastructure can raise effective yields by 8 – 12% and meaningfully cut the post-harvest losses that currently run as high as 30 – 50% across the value chain. It’s an eighteen-month pilot using technology that already exists, applied to a crop Ghana already understands better than almost anyone else in the world. Well, possibly except the Chinese at this point.
A successful cocoa AI pilot produces a believable, visible, farmer-facing proof point. Something a minister can point to in a press conference that isn’t a slide deck. Something international investors can look at and say, this country executes.
Public administration should be the second pilot, running in parallel. AI-driven fraud analytics in revenue collection will move non-oil tax receipts by roughly one percentage point of GDP within four years. In a country still rebuilding fiscal headroom after a severe debt crisis, that is the kind of result that makes the GHS 2.3 billion investment case practical rather than theoretical.
Two pilots. 18 months. Visible, measurable, fundable. That’s how we forge the credibility to subsequently tackle healthcare, financial services, and the creative economy with real momentum instead of starting all five at once and running out of attention/inspiration/money/zeal before any of them mature.
Next, money.
The Ghanaian government has committed $270 million as follows: $250 million for a national AI computing centre. $20 million for short to medium-term implementation.
My framework’s proposed full eight-year roadmap required GHS 2.3 billion in public investment; roughly $190 million at current exchange rates. Put those two numbers side by side and it becomes clear the government’s compute centre commitment alone exceeds the framework’s entire public investment case for the accelerated path. This is fantastic news, provided the money is sequenced correctly and not consumed entirely by infrastructure before any sector pilots see a cedi.
That is the catch. A $250 million compute centre is a long-term infrastructure play. It will not produce a believable cocoa pilot in eighteen months on its own. The $20 million implementation fund is where the early sector wins need to be drawn from. $20 million is a fraction of what the framework’s financing architecture allocates to early pilots in years one and two.
My ask here is that we publish how the $20 million is allocated, sector by sector, pilot by pilot, before the end of this year. Let’s show the public the spreadsheet behind it. It keeps everyone focused on the prize. Accountability is everything, as they say in the Mafia.
Let’s track exactly 4 things: the status of the two priority sector pilots against their dated milestones, the line-item disbursement of the $20 million implementation fund, the staffing and budget of the RAI Authority itself, and a single, consistent metric which could be jobs created attributable to AI adoption, sector by sector. It could be yield increase. Whichever. Let’s just name it, communicate it and track it.
Four numbers. Updated every quarter. Public.
‘Tisn’t a radical ask. Singapore does a version of this. The UK, for all its struggles with under-resourced AI governance bodies, [and heat] at least publishes what it knows. We don’t need to invent new accountability infrastructure. We don’t need a working committee.
So here is where this series ends, and here is where I hope it has had an impact on something or someone. Hopefully at the Responsible AI Authority. Ma’am/Sir, I do not envy your task.
Godspeed.
Until next time,
– Spyda