| The final article in this three-part series argues that Africa should move beyond measuring AI readiness and begin measuring whether artificial intelligence is actually producing stronger firms, better institutions, and competitive advantage. Written by Roger Janito |
Africa is getting better at measuring its preparedness for artificial intelligence. Countries track broadband access, digital skills, computing capacity, regulatory frameworks, and the number of people trained. Governments announce AI strategies, launch innovation hubs, and support pilot programs. All of these things matter. But they can also create the impression of progress without answering the harder question: is AI actually making the economy more competitive?
A country can improve its digital infrastructure, pass an AI law, train thousands of people, and create a national AI council without producing many competitive companies or measurable gains in productivity. Activity is not the same as performance. This is why Africa needs an AI Competitiveness Scorecard.
Existing measures of AI readiness remain useful because they tell governments whether important foundations are in place. The next step is to measure what those foundations produce.
Michael Porter wrote in The Competitive Advantage of Nations more than three decades ago that “the only meaningful concept of competitiveness at the national level is national productivity.” (p. 6) In the AI era, that insight should guide how Africa measures progress. The objective is not simply to become more prepared for AI, but to use it in ways that raise productivity, strengthen companies and institutions, and create lasting economic value.
Measure conversion, not just readiness
The first generation of AI measurement has largely focused on inputs. How much connectivity is available? How many people have digital skills? Does the country have appropriate regulation? How much computing capacity exists?
An AI Competitiveness Scorecard would ask a different set of questions. Are firms using AI productively? Are successful pilots becoming contracts? Are young companies attracting the capital they need to grow? Are public institutions becoming more capable? Are AI-enabled companies reaching larger markets and creating value that remains in the economy?
In other words, the scorecard should measure the conversion of inputs into outcomes.
That distinction matters because two countries with similar levels of AI readiness can produce very different results. One may have strong infrastructure but weak adoption. Another may have fewer resources but institutions that procure useful solutions quickly, companies that respond to local demand, and investors willing to finance growth. Competitiveness lies in what countries do with the assets they have.
Five areas would provide a practical starting point.
The first is productive adoption. The measure should not simply count how many people have used an AI tool. It should ask whether firms are using AI to improve products, reduce costs, solve problems, and serve customers better. It should also examine whether adoption is spreading beyond large corporations and government agencies to smaller businesses and the informal economy.
The second is competitive companies. Are AI-enabled firms gaining paying customers, increasing revenues, converting pilots into contracts, and attracting appropriate capital? Are they surviving long enough to build durable businesses? The purpose is not to count startups, but to understand whether the environment is producing companies that can compete.
The third is execution capacity. Having an AI agency or policy is less important than whether institutions can implement what they announce. Can public agencies procure technology effectively, manage data, evaluate vendors, monitor results, and change course when programs do not work? Monitoring and evaluation belong here because implementation without feedback quickly becomes activity for its own sake.
The fourth is ecosystem performance. Article 2 of this series argued that Africa already possesses many of the ingredients of an AI ecosystem, but they often operate in isolation. A scorecard should therefore measure whether those connections are working. Does research reach companies? Do pilots lead to procurement? Does early traction attract finance? Can successful firms obtain talent, compute, and growth capital? Do exits and profitable companies recycle knowledge and capital into the next generation?
The fifth is value capture and market reach. Competitive economies should increasingly retain some of the value created by AI. That can be seen in growing revenues, investment, exports, intellectual property, successful companies and, where appropriate, expansion into regional and global markets. Not every useful AI company needs to export, but an economy that only consumes imported technology will struggle to build lasting competitive advantage.
Together, these five dimensions follow the path developed across this trilogy: productive adoption creates stronger firms, capable institutions and connected ecosystems help those firms grow, and successful companies generate economic value that can be reinvested.
A management tool, not another ranking
The purpose of the scorecard should not be to create another continental league table.
Rankings can attract attention, but they can also encourage governments to improve whatever raises the ranking rather than what improves the economy. The real value of an AI Competitiveness Scorecard would be diagnosis and course correction.
Suppose a country trains thousands of AI professionals but local companies cannot hire them. The scorecard should reveal the disconnect. If governments sponsor dozens of pilots but few become contracts, the weakness lies somewhere between innovation and procurement. If startups raise seed capital but cannot obtain growth finance, the financing ladder is incomplete. If AI adoption rises but productivity does not, policymakers and businesses should ask why.
Measurement becomes useful when it changes decisions.
For that reason, the scorecard should be simple enough to update regularly and practical enough to guide action. Africa does not need an elaborate reporting exercise that takes years to complete and is outdated by the time it is published.
There should be a common core that allows countries to learn from one another, but the framework should also respect national differences. Competitive advantage is not identical everywhere.
A country seeking to build strength in agricultural AI should measure whether farmers, agribusinesses, and related companies are becoming more productive. Another focused on financial services, healthcare, mining or logistics may need additional indicators suited to those sectors.
The scorecard should measure the strategy a country has chosen, not force every country into the same strategy.
From strategy to accountability
This trilogy began with a simple proposition: an Africa AI strategy is a means, while competitive advantage is the goal.
The second article argued that competitive companies do not emerge repeatedly from technology alone. They require ecosystems that connect problems to founders, founders to customers, customers to capital, and success to the next generation.
The final step is measurement.
A strategy provides direction. An ecosystem helps translate opportunity into companies and economic activity. A scorecard tells policymakers, businesses, and investors whether that process is actually producing results.
Africa’s AI debate has moved rapidly from awareness to readiness. It should now move from readiness to competitiveness, and from ambition to accountability.
The objective is not to measure more things. It is to measure the things that matter.
If competitive advantage is the goal, Africa must measure not how much AI activity it generates, but how much competitive capability it creates.
| Roger B. Jantio is an AI investor and strategic advisor focused on artificial intelligence, development finance, emerging markets, and strategic capital. He is the founder and CEO of Sterling Merchant Finance Ltd, a Washington-based merchant bank active across Africa for more than three decades, and General Partner of its affiliated investment funds.www.linkedin.com/in/roger-jantio-3262a113 |
Read Article 1of 3:
An Africa AI Strategy Is a Means. Competitive Advantage Is the Goal – iAfrica.com
Read Article 2 of 3:
Africa’s AI Challenge Is Not Technology. It Is Ecosystem Building
