Agentic AI: A Practical Guide for African Business Leaders

October 14, 2026
AI Insights
7 min read

By GAA Editorial Team

Search interest in agentic AI has grown quickly over the past year. One 2026 search demand study puts “agentic AI” at about 110,000 US searches a month, up 39%, with searches for “AI agents for business” up 210% (AI Search Demand Report 2026). Executives in Lagos, Nairobi and Johannesburg are asking the same question as their peers in London and New York: what is an AI agent, and should we be using one?

This guide answers that question in plain terms. It draws on recent research from McKinsey and BCG and puts it in the context of African businesses, where costs, data and talent look different from the markets most of that research comes from.

From chatbots to agents: what changed

Most companies met generative AI through a chat window. Someone types a question, the tool drafts an answer, and a person decides what to do with it. That is a copilot. It helps one person at a time, and that person stays in charge of every step.

An agent goes further. McKinsey describes agents as systems that combine a large language model with memory, planning and connections to other software, so that they can take a goal, break it into tasks and act on company systems with limited human input (McKinsey, 2025).

Here is the difference in practice. A copilot drafts a reply to a customer complaint. An agent reads the complaint, checks the order in your system, issues a refund within an agreed limit, updates the customer record and sends a confirmation. A person reviews the exceptions.

That shift from “helps me write” to “does the work and reports back” is why agentic AI is getting so much attention.

Why leaders are paying attention now

The first wave of generative AI has disappointed many companies. McKinsey calls this the gen AI paradox. In its 2025 analysis, more than 78% of companies were using generative AI in at least one business function, yet more than 80% said it had made no material contribution to earnings.

McKinsey’s explanation is useful. Company-wide chat tools spread small gains thinly across many staff, so the benefit is hard to see in the accounts. The use cases that could move the numbers, those built into specific business processes, mostly stayed stuck in pilots. McKinsey estimates that about 90% of these function-specific use cases never got past the pilot stage.

Agents are seen as a way out of that trap, because they are built around a process rather than a person. Adoption is moving. McKinsey’s 2026 State of AI survey of 1,719 respondents in 97 countries found that 40% of large organisations are now scaling AI agents somewhere in the business, up from 27% a year earlier. Across companies of all sizes the figure is closer to two in ten (McKinsey, 2026).

BCG sees the same trend. In its 2025 study of 1,250 senior executives, agents produced about 17% of the value companies got from AI in 2025, and BCG expects that share to reach 29% by 2028. The firms BCG calls “future-built”, the top 5%, were putting about 15% of their AI budgets into agents, and around a third of them were already using agents (BCG, 2025).

There is still a gap between ambition and what happens at work. In BCG’s 2025 survey of more than 10,600 workers, only 13% said agents were part of their daily workflows, and only about a third said they understood how agents work (BCG AI at Work, 2025).

What this means for African businesses

McKinsey estimates that generative AI could add between $61 billion and $103 billion a year in economic value across Africa. The largest pools are in retail, telecoms, packaged consumer goods, mining, heavy industry and energy, and banking. It also found that more than 40% of the African organisations it surveyed were already experimenting with or implementing generative AI (McKinsey, 2025).

Several features of African markets make agents especially relevant:

  • High volumes, thin margins. Banks, fintechs, telcos and distributors handle very large numbers of small transactions. Reconciliation, dispute handling, KYC checks and collections follow clear rules, which makes them a good fit for agents with human review.
  • Scarce specialist talent. Many firms struggle to hire and keep experienced analysts and engineers. Agents that take on routine workflow steps free those specialists for the judgement calls.
  • Customers on many channels. Customers reach companies through WhatsApp, phone calls, USSD and social media. An agent that can sort requests across channels, and hand over to a person when it should, can cut response times without a large hiring drive.
  • Cost pressure. Currency swings raise the cost of imported software and services, so AI spending has to show up in operating results, not just in demos.

There are real constraints too. Data is often scattered across spreadsheets, legacy core systems and paper files. Power and internet reliability vary. Nigeria’s Data Protection Act 2023, Kenya’s Data Protection Act 2019 and South Africa’s POPIA all limit how customer data can be processed. An agent is only as good as the data and systems it can reach, and only as safe as the rules it runs under.

Where to start: five practical steps

1. Pick a process, not a tool. Choose one workflow that is high in volume, rule-based and painful today. Loan application checks, supplier invoice matching, field sales reporting and customer refund requests are good candidates. Map the steps, who does them and how long each takes.

2. Redesign the work before you automate it. McKinsey’s 2026 survey found that about three-quarters of AI high performers had fundamentally redesigned their workflows, compared with about a quarter of other companies. Putting an agent into a broken process gives you a faster broken process.

3. Set the limits of autonomy. Decide what the agent can do on its own, what needs approval and what it must never do. A refund agent might approve claims under ₦50,000 on its own and send the rest to a supervisor. Write these rules down and log every action the agent takes.

4. Fix the data it depends on. Agents need clean, current data and access to the right systems. The first weeks of an agent project often go on connecting systems and cleaning records. Budget for that time.

5. Measure business results. Track cost per transaction, turnaround time, error rates and customer satisfaction, not the number of users or prompts. McKinsey found that AI high performers were twice as likely as other companies to have defined processes for measuring the impact of AI, and twice as likely to have senior leaders who are committed to it.

McKinsey’s advice to chief executives is direct: end the open-ended experimentation phase, set up proper governance for AI, and launch a small number of “lighthouse” projects that change a whole workflow from start to finish. For a mid-sized African company, that could mean one agent project in operations and one in customer service, each with a named business owner.

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The leadership questions

Agents raise questions about leadership more than technology. Who is accountable when an agent gets something wrong? How do you reassure staff who are worried about their jobs? BCG’s worker survey found that 41% of respondents feared their roles could disappear within the next decade. And how do you keep customers’ trust when software, not a person, is acting for your company?

The companies getting value from AI treat these as management questions from the first day. They bring in risk, HR and operations early. They tell staff plainly what will change and what will not. They retrain people whose tasks shift, instead of leaving them to work it out alone.

If you are a senior leader working through these decisions, our Lead with AI programme covers how to set direction on AI, judge where it creates value and lead your people through the change. If your priority is turning pilots into measurable returns, Make AI Pay shows how to choose the right use cases, build the business case, redesign processes and run a pilot that pays.

The bottom line

Agentic AI is not a reason to buy a new tool this quarter. It is a reason to look hard at the processes that drive your costs and your customer experience, and ask which of them could run differently. Firms that start with one well-chosen workflow, clear rules and honest measurement will learn faster than those still running scattered pilots. In markets where margins are thin and talent is scarce, that learning is a real advantage.

Ready to move from pilots to results? Make AI Pay is a self-paced programme on finding the right AI use cases, building the business case and running a pilot that delivers measurable value.

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