From Johannesburg: Five Signals on Where Agentic AI Meets Enterprise Banking

Last week I was in Johannesburg for Mastercard's "AI: Intelligence in Action" summit at the Four Seasons Westcliff (tagline: From Insight to Impact). Nobody there needed convincing that AI matters. The guest list had executives from Nedbank, Old Mutual's new OM Bank, Standard Bank South Africa and Microsoft, plus a handful of companies like us that build the infrastructure connecting AI to actual financial services.

I came to demo two conversational AI experiences we built on Blits.ai for Mastercard, and the demos are further down. Most of this post comes from the conversations between sessions, which told me more than either demo did. I wrote them up as five signals.

The room was split between production and pilots

Some organizations in the room already have AI in production, answering customer queries, generating recommendations, running back office jobs. Others have built AI systems and can't yet say with confidence what those deliver. Both groups have access to roughly the same models and tools, so I doubt capability is what separates them. What the second group is missing is trust, both the organizational kind (do we believe the outputs?) and the customer's (do I feel safe letting this thing act for me?).

Agent adoption is moving from demos to durable workflows. The teams that have made that move know, for each agent, what it does, what data it reads, who reviews it and who answers for it when it drifts. How many agents they run matters a lot less.

We spend a lot of our own time on evaluation for the same reason. Every agent we build at Blits.ai goes through extensive eval cycles: we classify failure modes, stress test edge cases and measure where the AI breaks and why. A demo shows the happy path. What a bank needs to know is what the agent does with messy input, an ambiguous question or incomplete data, and you only find that out by testing for it.

The banks that are ahead had sorted out ownership first. That was the fault line in Johannesburg.

The technology isn't the bottleneck anymore. Governance is. Who owns this agent, what is it allowed to do, and where does a human step in?

Agentic payments are real, and so is the credibility layer

Mastercard was there to push agentic commerce at scale. Their Agent Suite (launched earlier this year, now positioned as "built for African scale") is designed for AI agents that authenticate, transact, and settle, and recommending products is only the first step of that. They also built a credibility layer for payment confirmations that an agent initiates. In that step the human confirms intent before any funds move. I'd guess the tech could run autonomous transactions already. The trust infrastructure around it hasn't caught up, and nobody in the room pretended otherwise.

The "From Hype to High Impact" panel had Chipo Mushwana (Nedbank), Ethel Nyembe (OM Bank) and Steve Barker (Standard Bank) on stage. Chipo talked about dual service, meaning the bank has to serve people directly and also serve the AI agents acting for them. Abstract, until you remember Nedbank has 3 million active users on its Money App and sells 90,000 products a month through digital channels. Her team is building for this now and trust is what keeps her up at night. She wants banking to get more personal without getting opaque (once a customer can't understand why the agent did what it did, you've lost them).

Ethel joined OM Bank in January to build the product stack from scratch for what Old Mutual calls its "single largest strategic investment," so she gets to put AI in at the foundation of a bank designed in 2026 instead of bolting it on. Even she made a point of saying the customer has to feel in control, with intelligence everywhere and autonomy handed out carefully. Steve's angle was volume. Standard Bank processes more payments than any other institution on the continent and at that size a small trust failure cascades, so he wanted to talk about keeping trust when millions of interactions a day each carry the bank's reputation. Whether agents work at all was not his worry.

Three banks at three stages of AI maturity, and all three treated the credibility layer as part of the product.

Agentic payments are being built now. But nobody is shipping "fully autonomous" to production. The credibility layer is the product.

Building AI is done, self learning comes next

One theme kept surfacing in side conversations. Many companies have built their AI systems and the initial build is behind them. They have stopped asking how to get AI working and started asking how to make it better over time without rebuilding it every quarter.

I've been calling this the self learning pivot. Most first generation enterprise AI was static (train a model, deploy it, hope it holds). For the second generation the bar is improvement on the go, so the system learns from interactions, updates its knowledge base from real usage and adapts to shifts in customer behavior without anyone running a full retraining cycle.

I find it useful to think in eras. Era 1 was the model race (2023 to 2024, who has the best benchmark). Era 2 was the interface race (2024 to 2025, agent scaffolding and tooling). Era 3, where we are now, is about persistence and memory, a layer that is always on. Models are turning into a commodity, and what you own is the context, the memory and the feedback loop.

For banks in South Africa this is pressing. Standard Bank processed roughly €8.8 trillion in payments last year and invested over €1.2 billion in technology, which is production at continental scale and well past any pilot. Whether to use AI is settled for them. What they need is AI that gets smarter from every one of those interactions. For the leaders in that room the build phase is over, and the test now is whether your AI learns from last Tuesday or starts from zero every morning.

What the room wanted from our two demos

We showed two demos in the "Moment of Action" session. The first, the Mastercard WhatsApp Travel Assistant, puts the full travel booking flow inside a WhatsApp conversation: flights, hotels, restaurants, visa info and payments, all in the customer's own language. That means the language people really use, with the shortcuts and slang (someone typing on WhatsApp at 10pm writes very differently from how they'd write an email), and getting it right is what makes a bot feel like a local assistant instead of a translation layer. The feature people picked up on most was card intelligence. The agent knows the user's full Mastercard portfolio and brings up the right card at the right moment: World card for lounge access before the flight, Bonvoy card for hotel points, Cashback card for dining, Rewards card for points redemption against the total.

The MTN MoMo Agent starts from a telco service interaction. A customer opens a chat with a complaint ("my data ran out too fast"). Where a typical bot would return an FAQ answer and stop, the agent diagnoses the issue and resolves it. Then, because it can see wallet balance, spending patterns and payday cycle, it moves on to personalized offers: a better data bundle, a savings goal, a cross border remittance, scam protection, and an autonomous "Smart Auto Top-Up" that watches the balance and tops up before it ever hits zero.

The Travel Assistant got the strongest reaction. People wanted to try it as a live onboarding experience, not as a demo. Most of their questions were about how deep the personalization goes: whether the agent can ask detailed questions upfront and listen to the user while also drawing on historical data the bank already has, and whether it can infer preferences from past behavior and keep new answers in memory for later conversations.

I was surprised by where the audience drew the line on autonomy. They wanted the agent to handle the tedious parts, like finding the best flight for this particular trip, comparing card benefits and calculating loyalty point redemptions. The actual vacation decisions they wanted to keep. The human picks the destination and the hotel vibe and the agent does the legwork. I'd treat that as the design brief: do the boring work very well, then get out of the way for the decisions that matter to the user.

For the banks, travel was only the example. They were interested in the pattern behind it, where one conversation can expand usage of featured partners, surface in bank perks and loyalty programs and still give the user something of real value. The idea of card intelligence in chat carried over immediately to dining, shopping and insurance.

Language and culture got more attention than I expected. We support over 100 languages on the Blits.ai platform and test them continuously, and it matters more than most people think. A bot that speaks to customers in their own language, with the right tone and cultural register, comes across as a helper. A bot that answers a banking customer in Johannesburg or Cairo or Riyadh in generic US English feels like a foreign object that doesn't understand their world. The Travel demo running in English, Afrikaans and isiZulu was the moment people in the room leaned in. When an agent sounds like it belongs in the market it serves, trust tends to follow.

Where to point banking AI agents

Everything I heard in Johannesburg pointed the same way. The technology works and the models are capable, and what all of us are still working out is where to point them.

I've started calling this the dispatch problem, because most organizations have bought AI agents and never figured out what to point them at. I'd call that a context matching problem more than a failure of AI. The same agent architecture that resolves a data complaint for a MoMo user in Soweto can book a holiday to Mauritius for a traveler from Johannesburg; the platform underneath is identical, only the context differs.

On the platform side we have been building toward this at Blits.ai for a while. It is secure and certified for financial services, and it supports self learning, so agents improve from real conversations instead of waiting for the next manual update cycle. It can redirect to real humans when the situation calls for it, and it runs on WhatsApp, in app, voice, digital human or whatever channel the market requires.

That's half of it. Delivering projects across the Middle East, Africa and Europe has taught us that most of the effort sits before the agent is built. You have to decide which use cases will return ROI, what data you already have and what shape it needs to be in, and what the AI actually needs to perform well as opposed to what is just noise.

Most organizations sit on years of customer data, transaction histories, product catalogs and CRM records, and none of that is context yet. An agent works best with structured, relevant, well scoped information that maps to a specific customer moment, and a data dump gives it the opposite. Whether you end up with a generic chatbot or an agent that helps depends mostly on whether someone sat down and worked out which data matters for this use case, how to make it accessible and what the agent should do when the data is incomplete. The model is a smaller factor than most people assume.

A big part of my own time goes into testing exactly this. I take a client's raw knowledge base, run it through evaluation cycles, classify where and why the AI fails, then split and restructure the data until the agent performs. It is slow, detailed work, and it decides whether the agent answers a customer's question correctly or gives them a wrong answer with full confidence. I'd count the data pipeline as part of the product, as much as the conversation is.

Our project experience across banking verticals helps here. We work with clients on structuring the problem before building anything: picking the three or four use cases that will deliver measurable value first, mapping the data requirements, designing the conversation architecture and setting up the feedback loops that let the system learn and improve. The platform handles execution. Deciding what it should execute is where we spend most of our time with clients.

The fast movers are pulling away from everyone else. Standard Bank invested over €1.2 billion in technology last year, Nedbank runs hackathons with AI on its banking APIs, and Ethel Nyembe at OM Bank is building an entirely new bank's product stack with AI in it from day one. These institutions stopped piloting some time ago and are compounding now.

So whether AI works in banking is settled, as far as that room was concerned. The open points are whether you have the right context for each use case, a trust framework that lets agents act, and a feedback loop that makes them better every day. That is what people in Johannesburg were working on, and to me it felt like the start of AI that banks can actually trust, which is a different thing from AI in banking.

Blits.ai is an enterprise conversational AI platform. We build, deploy, and manage AI agents for financial institutions across the Middle East, Africa, and Europe. If you're working on agentic AI for financial services, let's talk.

Updated on 12 October 2026.