Inside The Trust Series: How Africa’s top leaders are thinking about trust in the age of AI

 Inside The Trust Series: How Africa’s top leaders are thinking about trust in the age of AI

There is a particular kind of conversation that only happens when the right people are in the room. Not a conference panel where everyone reads from prepared remarks, not a webinar where questions are filtered and softened. The kind of conversation where a senior executive from Wema Bank says trust cannot be assumed, it must be engineered, and the room goes quiet because everyone knows exactly what he means.

That was the energy at the Trust Series event this week, a closed gathering that brought together C-suite leaders, technologists, and founders under one theme: Scaling Trust in the Age of AI. Ajibade Laolu-Adewale, Chief Partnership and Ecosystems Officer at Wema Bank and Chairman of the Committee of e-Business Industry Heads (CeBIH), opened the session with a framing that set the tone for everything that followed. His argument was simple and sobering. AI expands capability, but it also introduces opacity. It improves efficiency, but raises accountability questions. And if innovation outpaces regulation, risk does not just increase, it multiplies.

He called for three deliberate actions across the industry: stronger governance, greater transparency in how AI-driven decisions are made, and deeper collaboration between banks, fintechs, regulators, and technology providers. Not because trust is a competitive advantage, but because it is shared infrastructure. When one part of the system fails, the ripple touches everyone.

It was the right frame for what came next.

The Problem Nobody Wants to Name Out Loud

As the panel moved into open discussion, something interesting happened. The conversation kept circling back to the same uncomfortable truth, approached from different angles by different speakers, but landing in the same place every time.

Atinuke O. Seun-Ajayi, Director at Sovereign Investments Group, a diversified holding company with eleven subsidiaries spanning real estate, hospitality, financial services, asset management and energy, put it in personal terms. She described being caught up in the Cambridge Analytica data scandal as a user, not a company, and how that experience fundamentally shifted how she thinks about data. She moved off WhatsApp, adopted Signal, and became deliberate about every platform she trusts with her information. Her point was not about paranoia. It was about the gap between perception and experience. Trust, she said, is given first. What you do with it determines whether it survives.

Bayo Oluwadairo, Director at Mobihive, brought the telecoms angle. Working across Africa and Asia with mobile network operators, he has seen how quickly AI tools get adopted without the governance infrastructure to match. His advice to executives is consistently the same: be deliberate. Know what you are feeding the system. Know what guardrails are in place. Because AI will not save you from bad inputs, it will just process them faster.

Onyekachi Agudosi, known as Red, a seasoned DevSecOps Leader, Cloud Architect and cybersecurity strategist, and creator of the ReconPro intelligence platform, took it even further. In a high-stakes environment, he argued, black box systems are not acceptable. AI needs guardrails. Companies need playbooks. And the organizations winning at this right now are the ones embedding those controls directly into their infrastructure, not bolting them on after the fact.

All of it kept pointing to the same gap. Not a technology gap. A data integrity gap.

Someone Had to Say It Directly

Lucky Johnson said it directly.

Johnson is the Founder and CEO of Pronalytics Limited, a technology company building what he describes as the infrastructure layer for continuous compliance, audit readiness, and operational trust across Africa. He was on the panel not as a sponsor but as a participant, and his contribution shifted the register of the entire conversation.

His argument: Nigeria does not have a technology problem. It has a data integrity problem.

The scenario he described will be familiar to anyone who has sat in a boardroom before a regulatory visit. The finance team believes their numbers are correct. The technology team believes the systems are working. The compliance team believes the reporting is clean. Then the auditor arrives and starts asking questions, and the scaffolding begins to shake. Not because anyone was dishonest, but because the underlying data was never properly reconciled in the first place.

He called it the trust gap. VAT inconsistencies on tracked transactions. WHT figures that do not align. PAYE records that live in a different version of reality from the ERP system. Every team operating in good faith within their own silo, with no single layer ensuring that the data actually holds together when looked at from the outside.

His conclusion was pointed. Scaling AI without trustworthy underlying data does not scale intelligence. It scales error. It scales inconsistency. It scales the conditions for audit panic.

Where TaxAnchor360 Fits

This is the problem Pronalytics built TaxAnchor360 to solve.

TaxAnchor360 is not a filing tool. It does not simply take your numbers and submit them to the regulator. What it does is sit as an intelligent layer between your existing infrastructure, whether that is an ERP system, a B2B upload, or another internal platform, and the point of submission. At that layer, it validates data, auto-reconciles figures, identifies revenue leakages, and surfaces tax exposures before they become liabilities.

The distinction matters. Most ERP systems were built for submission, not reconciliation. They will take your data and send it. TaxAnchor360 asks first whether the data is actually correct, whether it aligns, whether it will hold up if someone decides to dig deeper.

And then, even after submission, the platform enables continuous reconciliation. So when the audit comes, and in today's regulatory environment it will come, the business is not scrambling. The evidence is already there. The documentation is already organized. The answer to "can we go deeper?" is yes, because the foundation was built to support that question.

Johnson put it plainly during his panel contribution. You can have the best AI. You can have the best systems. But AI will only scale what you give it. If the underlying data is broken, the output will be broken at scale. The only real answer is to build the trust layer first.

Why This Conversation Matters

The Trust Series was not an academic exercise. The people in that room are making real decisions about infrastructure, about capital, about how African businesses will operate in the next five years. And the consensus that emerged, across banking, telecoms, cybersecurity, and enterprise, was that the gap between strategy and execution is not closing fast enough.

Companies are investing in AI. They are adopting new tools. But the foundational question, whether the data those tools are running on can actually be trusted, is being deferred. And that deferral has a cost. It shows up as compliance penalties. It shows up as audit exposure. It shows up as the quiet panic of realizing, too late, that the numbers do not hold.

Pronalytics is building at that exact intersection. Not because it is a compelling market thesis, though it is, but because the problem is real and the consequences of ignoring it are getting harder to absorb.

The room on Wednesday was full of people who understood that. Lucky Johnson was one of them. And Pronalytics was not there as a sponsor or a host. They were there because they belong in that conversation.

That is the difference.