Governing Artificial Intelligence in African Markets: Moving from Principles to Practice

WSO2 CEO Harry Ault stated that corporations need to gain a deeper understanding of how artificial intelligence (AI)-based systems behave and interact with all network components, rather than focusing solely on access to these networks for specific agents.

WSO2, an American company specializing in open-source software and founded in 2005, deals with software system integration, user identity security, and enterprise access.

According to Ault, although AI-based agents have been used for some time, most of them were applied in deterministic systems where security and management were built directly into the network, leaving agents with few opportunities to perform tasks outside established boundaries.

He noted that the shift to generative AI leads to more probabilistic scenarios where outcomes cannot be predicted, which poses a serious problem for production-level applications, and this transition is occurring across all enterprises.

To control such agents, companies must develop their own AI governance plans and create emulation gateways and authentication services, instead of using off-the-shelf solutions, thereby preserving intellectual property indefinitely.

Ault took over as CEO of WSO2 from founder and long-time leader Sandeep Iyeravaran, who retired in August of this year. Ault believes that all problems arising from AI, such as uncontrolled agent behavior or large language models generating responses irrelevant to their task, will be solved through 'open source and open standards,' allowing a larger number of people to collaborate on the same issue.

He added that the majority of revenue opportunities for almost all global technology and product companies come from AI-related demand, and this trend is likely to continue in the foreseeable future.

Speaking about growth, Ault emphasized the necessity of solving all existing AI issues before the company discusses budgets with other firms and offers them corresponding solutions.

He reported that in countries and regions such as the USA and India, the 'AI-first' approach has become standard for all companies. He specifically highlighted India, noting that the scale and value the technology can provide in that country may not be found in any other region.

The company, which serves about 950 clients in 92 countries worldwide, also collaborates with major government institutions, regulators, and financial organizations in India. Furthermore, he mentioned the strong development of founder-led product companies in India due to improvements in generative AI technologies, providing companies like WSO2 with many options for acquisition or merger with a parent company.

For a long time, the strategy for building a successful consumer brand was based on three pillars: an attractive product, a strong brand identity, and effective customer acquisition. Although these basic principles remain relevant, they are no longer the sole determining factor of success.

Today, the competitive battle is fought for tiny shares of consumer attention, where brands have only a few seconds to turn interest into a purchase. However, the moment the 'Buy Now' button is pressed marks the beginning of a second, more complex stage: ensuring fast delivery, an impeccable customer experience, and easy resolution of inquiries and returns—these are the moments that build loyalty.

Against the backdrop of endless channels and constantly changing marketing strategies, the true advantage has shifted towards operational activities that occur after the purchase. Artificial intelligence is rapidly transforming into this intelligent layer.

AI goes beyond simple chatbots or content generation; it transforms how brands forecast demand, distribute inventory, coordinate order fulfillment, interact with customers, and optimize operations in real time.

This shift occurs against the backdrop of the growing complexity of commerce itself. Consumers now find, evaluate, and purchase goods through a complex network of touchpoints—brand websites, marketplaces, fast delivery services, social commerce, and physical stores—often within a single purchasing cycle. Each such purchase is supported by a complex system of inventory nodes, warehouses, logistics partners, and interaction systems that must work in perfect synchronization.

For brands, this means receiving a huge volume of operational data. Every movement of a product, customer interaction, order update, warehouse scan, and delivery stage becomes a signal influencing subsequent business decisions. Simple online business quickly turns into a network of interconnected real-time decisions, each affecting the customer experience.

Until a few years ago, brands and marketplaces operated based on Systems of Record—a single source of operational truth recording transactions and managing inventory. SaaS platforms for e-commerce transformed them into Systems of Insight—real-time analytical dashboards allowing businesses to move from a reactive approach to data-driven decision-making.

Now, the next stage of this evolution has begun. Based on this rich data and technologically enabled processes, Systems of Record and Insight are becoming Systems of Intelligence, where AI does not just analyze information but actively suggests recommendations and executes decisions across all business operations.

Consider the example of a fashion retailer during a holiday sale. Thousands of orders may arrive simultaneously through the website, marketplaces, and fast delivery platforms. Instead of relying on manual rules or static workflows, AI can continuously assess product availability, warehouse capacity, delivery times, transportation costs, and customer location, automatically routing each order along the most efficient fulfillment path. It can also notice that a fast-selling item is about to run out in one city while inventory is in good shape in another, and arrange for transfer before the issue becomes apparent to the customer.

This is especially important in the omnichannel world, where customers expect product availability everywhere and through any purchase method. Fulfilling this promise requires more than just inventory visibility—it requires constant coordination between distribution, order orchestration, warehouse operations, logistics, and customer communication. AI provides this coordination at a scale and speed unattainable through manual decision-making.

The next frontier is Agentic AI. Unlike traditional automation, which follows predefined scenarios, Agentic AI can reason about multiple variables, propose the best next course of action, and autonomously execute routine operational decisions within established business boundaries. Whether prioritizing high-value orders during peak demand, responding to supply chain disruptions, or identifying bottlenecks in fulfillment before they cause problems, these systems go beyond supporting human decisions and begin to actively shape them.

This marks a fundamental shift in how brands approach growth. Customer acquisition remains critical, but sustainable growth increasingly depends on what happens after demand is generated. Instead of increasing staff every time order volumes surge, companies invest in intelligent platforms that allow them to scale without proportionally increasing complexity. The goal is no longer just to process more orders, but to make better operational decisions—faster and more consistently.

E-commerce support platforms with deep integration into brand data and operational processes are already building this future. AI-focused SaaS tools are ceasing to be mere software—they are becoming operational partners that continuously improve business functioning.

Tomorrow's market leaders may not be the brands with the largest marketing budgets. They will be the brands whose technology allows them to operate smarter, adapt faster, and make better decisions every day. Their competitive advantage will increasingly depend on the intelligence behind every business decision they make.

The author shares impressions from visiting their rural hometown in Limpopo, where interactions with friends from their roots differ from those with urban residents. Recently, the topic of artificial intelligence (AI) has become very relevant, causing people to worry about how this technology will affect their lives, for example, whether it will lead to job losses.

The questions the author receives boil down to two main aspects: whether AI can help people find jobs and how it will facilitate the use of public services. The author notes that explaining technological concepts in simple language, especially to those concerned about securing food, is a difficult task. He emphasizes that citizens must participate in decision-making processes that affect their lives.

Many official statements about AI swing between excessive promises and strong fears. Some claim that AI will solve all problems, while others create the impression that people have no influence on the future. Instead of informing people how AI can help them, where mistakes might occur, and how to make it useful, the discussion often devolves into fear-mongering.

This approach only increases anxiety about AI, causing people to miss even the most basic information about the tool. They do not know that AI is software trained on examples to perform specific tasks. They are also unaware that AI can help write letters, translate words, sort records, or detect something unusual. For instance, small businesses can use AI for inventory planning, and a nurse can use it to reduce paperwork and increase time spent with patients.

There are also drawbacks that need to be known. Some AI tools can provide answers that sound convincing but contain fabricated facts. They may misinterpret questions or struggle with local languages, as well as reproduce unfair ideas from training materials. This is comparable to how a person confidently giving directions might lead to a dead end if they don't know the road; AI is capable of something similar, so important answers always require verification.

Risks increase when decisions concern health, income, or a person's access to services. Furthermore, there is the problem of incorrect data. If an AI system uses records, for example, about municipal bill payments, without proper verification, it might send an incorrect bill faster, leaving the family with the same problem but accelerating the error. Accurate records, clear division of responsibilities, and a willingness to correct mistakes are required; buying a smart system does not replace this human work.

Leaders are obliged to maintain record accuracy, respond to complaints, and fix shortcomings. An automated response like 'Your inquiry is important to us' is meaningless if no one provides assistance. Citizens expect a reply, a fair explanation, and the ability to correct an error; the phrase 'The system failed' should never end the dialogue, as someone must be held accountable.

Jobs also require an honest approach. AI can transform workflows and reduce the need for certain tasks, but it can also improve people's work and open up new opportunities. The population needs to be prepared for these changes. While benefits for everyone cannot be guaranteed, decisions can be made that support workers and create new chances. Employers must inform employees about changes in advance and involve them in the process. Government, businesses, and universities must promote the acquisition of useful skills, and training should give people more chances to earn money, improve their work, or develop their business.

Accessibility is equally important. A service may function perfectly on an expensive smartphone but be useless to someone who cannot afford the internet. If the system does not understand the language the person speaks, it creates an additional barrier. Help must be available to both a grandmother in the village and a young applicant in a settlement, and services must remain open to those who need help in person. Ensuring personal information protection is also vital; before entering identification numbers, medical records, or private documents into an AI tool, responsible parties must know who and how can access them.

For all decision-makers in the field of AI, three questions should guide any major proposal: what problem will it solve, how will we know it works, and who will answer if something goes wrong. It must start with a clear need, test the idea on a small scale, listen to users, and verify results and full costs before scaling up. Sometimes a simpler change can solve the problem better.

These discussions should take place not only at conferences but also in community halls, villages, schools, and workplaces. People who use public services daily often know where problems arise, and their experience should influence solution development. They should be invited to test new services and provide feedback, as a plan that looks good in a meeting might feel different to the person using it.

The author hopes that conversations about AI will focus on whether it brings visible improvements to people's lives: shorter queues, correct billing, strengthening small businesses, or more time for patient care. This is the goal to strive for. South Africa should explore AI with optimism, asking questions without fear, and assessing progress by how people's lives improve. Good leadership, honest work, and caring for others remain indispensable, as no machine can take on these responsibilities.