There is a difference between chasing a trend and deploying a tool. In African private equity, that difference will show up in returns.
Everyone in private equity is talking about AI right now. Fund managers are adding it to their investment theses. Pitch decks have whole slides dedicated to it. LPs are asking about AI exposure in every due diligence conversation.
Here is the honest truth about AI and private equity in Africa: the biggest opportunity is not owning AI companies. It is using AI to make every company you already own significantly better.
That is the distinction that matters. And most funds are missing it.
The Thesis Trap
There is a version of the AI conversation happening in African PE right now that goes something like this: Africa has a large, young, digitally active population. AI adoption is accelerating. Therefore, investing in AI-enabled businesses in Africa is a generational opportunity.
All of that is true. And it is also what every other fund is saying.
When every fund is chasing the same thesis, the thesis stops being an edge. It becomes a crowded trade with compressed entry multiples and a lot of funds competing for the same deals.
We are not uninterested in AI-enabled businesses. Several are in our pipeline. But we are not positioning AI as our investment angle. We are deploying it as an operational tool. And that is a very different conversation.
What Using AI Actually Looks Like Inside a Portfolio Company
The mid-market African businesses we invest in share a common profile. Revenue exists. Demand is real. The founder has built something from nothing through sheer determination.
But the operational infrastructure is almost always underdeveloped.
Reporting is manual and delayed. Financial visibility is limited to what the accountant produces at month end, often weeks after the period has closed. Sales pipelines exist in someone’s head or in a spreadsheet that nobody fully trusts. Margin analysis is rarely done at all because nobody has the time or the tools.
This is where AI changes everything.
Not AI as a strategic direction. AI as a practical operational tool deployed on day one of ownership.
- Real-time financial reporting. We use AI-assisted tools to build reporting systems that surface problems in real time rather than four weeks after the fact. A business that knows its cash position daily makes better decisions than one that finds out monthly.
- Margin leakage identification. AI can analyse transaction data at a granularity that manual processes cannot match. In one recent pipeline review we identified margin leakage in a target business that the founders had not seen in five years of operation. That kind of insight changes what you pay for a business and how you run it post-acquisition.
- Sales pipeline intelligence. We use AI tools to help management teams understand their conversion rates, their customer acquisition costs, and where deals are being lost in the pipeline. For businesses that have been running on intuition, this is transformative.
- Leadership decision support. We use AI to synthesise market data, competitor intelligence, and operational metrics into formats that management teams can actually use in weekly operating cadences. Less noise. More signal.
None of this is exotic. None of it requires a dedicated data science team or a seven-figure technology budget. These are tools available today, deployable in weeks, and capable of producing operational improvements that show up directly in EBITDA
The Returns Question
Private equity returns are driven by three things: what you pay going in, what you build while you own it, and what you receive going out.
AI does not change what you pay. It changes what you build.
A business that has real financial visibility, operational discipline, and data-driven decision-making is a different business from the one you acquired. It grows faster. It retains more margin. It attracts better leadership. And when it comes time to exit, it commands a higher multiple because the next buyer can see exactly what they are getting.
That is the AI thesis that matters in African PE. Not the sector exposure. The operational leverage.
Why This Matters More in Africa Than Anywhere Else
In mature markets, the businesses that PE funds acquire often already have operational infrastructure. ERP systems, financial controllers, structured reporting. The value creation lever is usually financial engineering, geographic expansion, or M&A.
In African mid-market PE, the operational starting point is lower. The businesses are real. The markets are real. But the infrastructure is raw.
That means the operational improvement available is larger. And AI amplifies operational improvement.
A fund that enters an African mid-market business with the operational tools, the team capability, and the AI-assisted systems to build what is missing is not competing with the same playbook as everyone else. It is playing a different game entirely.
What We Are Not Doing
We are not putting AI on a slide in our pitch deck and calling it differentiation.
We are not telling founders their businesses need to become AI companies.
We are not making investment decisions based on AI exposure as a criterion.
What we are doing is treating AI the same way a good operator treats any tool. You use it where it works. You measure whether it is producing results. And you deploy it in service of the one thing that actually matters in this business: building companies that create real, sustainable value in African markets.
The funds that figure this out operationally, not just rhetorically, are the ones that will define what African PE returns look like over the next decade.
We intend to be one of them.
Education
-MBA (Strategic Management) – University of East London
-Strategic Communications – The London School of Economics
- Why We Are Not Investing in AI. We Are Using It – April 30, 2026
- Operational Private Equity: The Key to Unlocking Value in Africa – August 18, 2025
- Why a Clear Investment Thesis Matters for African Private Equity – July 25, 2025
