According to Gartner, more than 40% of agentic AI projects will be cancelled before the end of next year. One of the main reasons, says the research house, is unclear business value. Organisations are deploying autonomous systems because of FOMO and without defining measurable outcomes. If anything, agents get pointed at whatever is loudest in the organisation, which is often an executive with a preference. 鈥淭he agent will follow its instructions to the letter,鈥� says Jan van Niekerk, senior vice president: Data and AI strategy at Netsurit.

鈥淚f the instructions were never tied to strategy, that obedience is the problem, not the safeguard.鈥� What gets automated in those conditions is what Van Niekerk calls 鈥渧anity work鈥�, the visible tasks that show the organisation is 鈥渄oing鈥� AI without changing what it costs to run.

But is any of this a technology problem? South African enterprises license the same frontier models as everyone else and pre-built agents now ship for processes that have been documented for decades. Van Niekerk says enterprise AI maturity runs through four stages, from personal AI, to workflow AI, and process AI, to being AI-native. 鈥淢ost South African enterprises are stuck at personal AI, where a tool gets handed to everyone and the world is expected to change, with no P&L impact to show for it,鈥� he says. He adds that markets like the US, the UK and parts of the Gulf have more enterprises operating at process level, mapping and redesigning end-to-end work before deploying agents and assigning ownership of the value.

Agentic AI promised to free staff up for other tasks, which is easy to report, but essentially meaningless on its own. An agent that removes six hours of work a week from a finance team has changed nothing until somebody decides what those hours can be used for. 鈥淗ours saved is a vanity metric unless the capacity is redeployed,鈥� says Van Niekerk.

His advice is to baseline the process first, measuring the capacity that鈥檚 freed up, and then tracking what it was redirected to, such as more customers served, a cost line reduced or increased revenue. 鈥淚f all you can report after 90 days is 鈥榯ime saved鈥�, you haven鈥檛 proven value, you鈥檝e proven activity,鈥� he says.

Nazia Pillay, managing director, 麻豆原创 Southern Africa, says measurable improvement should be evident within months when an enterprise picks a well-defined process. The quickest payback comes from work that is repetitive, time-consuming and governed by clear rules. 鈥淪tarting with the outcome, rather than the technology, makes it easier to demonstrate value and decide where to expand next,鈥� she says.

Van Niekerk says there are a few signs to look out for if a process is not ready for an agent. One would be if staff members can’t describe it end-to-end, without contradicting one another. Another is if the exceptions outnumber the rule, or the handoffs between people and systems are undocumented and the real knowledge is in somebody’s head or inbox. Lastly, if there is no single owner or baseline metric. 鈥淎gent readiness isn’t a property of the process, it’s the output of having mapped it, where needed, and redesigned it first,鈥� he says. A great example of this is credit decisioning and fraud monitoring, where the rules are already written down, which is why banks have deployed agents here first. 鈥淲here there is a decision that needs to be made, and it’s an explainable decision, that’s where agents really work well,鈥� says David Pretorius, customer advisor manager at SAS.

Pretorius believes one problem enterprises are least prepared for is agent containment, or defining the limits on what an agent can reach, change or break once it starts acting on systems rather than answering questions. An agent that reasons, retries and chains calls can act on systems. 鈥淭he model is the engine of the vehicle and the harness around it is the steering, the brakes, the dashboard, the seatbelts, everything around it, keeping it contained,鈥� says Pretorius. 鈥淚t should have a bounded blast radius.鈥� If a deployment causes problems, it should be cheap to fix and quickly reversible, which implies project management rather than model selection. This is also true of policy, because a document written for chatbots will have little defence against agents acting autonomously. 鈥淭he objective is not to require human approval for every routine action, but to establish clear thresholds so that agents can handle predictable work efficiently while people retain control over sensitive decisions, exceptions and consequences,鈥� says Pillay.

Agent density is already a capacity metric in infrastructure planning, but Pretorius says that the enterprise version will rather count agents against people. 鈥淏y 2030, the question will be how dense is your agent landscape,鈥� he says. 鈥淎nd how does that ratio sit with regards to the amount of people you have, and what is the payoff rate?鈥� Getting to this kind of number means knowing what each agent is for, and who owns it, which most enterprises haven鈥檛 yet considered. This is why Van Niekerk鈥檚 recommendation is to treat agents as organisational assets, with ownership tied to a role.

He says agents need to be registered, and their purpose, permissions and prompts documented. 鈥淚n a business of 10 000 people running 10 agents each, agent lifecycle management becomes as fundamental as identity management is today.鈥�

BETTER TOOLS, SAME RULES

FNB parent FirstRand went from dabbling with agentic AI to scaling it inside six months, says Kevin Mitchell, chief digital officer. Project scoping took 30 minutes, compared to two weeks.

No exceptions

Every control built up over decades of regulated delivery still applies once agents enter the pipeline, he says. Segregation of duties, risk review and release management all need to be included in the production workflow. All code, whether written by a staff member or generated, must still be reviewed before it鈥檚 committed.

Keep the estate closed

Pay attention to data flows; a tool that sends customer information to an unintended location will be flagged by risk and compliance. Running agentic tooling in a closed environment isolates the prompts, code and data. Mitchell says the data must be kept under the bank鈥檚 jurisdiction. Usage telemetry will also show who is actually working with the tools, and for what purpose.

Build it into the SDLC

An AI program that runs alongside a development process should be included in the development lifecycle at the outset. Mitchell says AI agents should span ideation, product development, risk, legal and operations, rather than only being used by a single team. Bring the risk and legal people in early, he advises, because teams that meet an agent for the first time at go-live may become a bottleneck.

Tie tokens to outcomes

Consumption dashboards will tell you an agent is busy with a task, but not whether it鈥檚 providing value for money. Mitchell suggests linking token spend to specific backlog items and features. He says the bank is already tracking these metrics, including time-to-production.

Plan for the sceptics

The appetite for agentic usage inside a large engineering team will not be uniform, and a rollout should assume that enthusiasm will wane. One group at FNB queued for access immediately, while another team said they didn鈥檛 trust a coding agent to fulfil tasks as well as they could. The latter group needs evidence rather than encouragement, says Mitchell. The argument he makes is that the tooling has changed, not the responsibility. 鈥淗umans are ultimately accountable. You just have a better tool,鈥� he says.

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• Around 48% of employees think that organisation will replace human managers with AI in the next five years
• 61% of employees expect that Ai will improve the quality of people decisions
• Employees expect that Ai will enable a 266% improvement in productivity within 5-years
• And 39% of workers skill sets will be outdated or transformed by 2030

鈥�Lots of companies making very quick decisions to get rid of an entire area of their workforce only to just hire them back a few months later because they realised they weren鈥檛 ready for it鈥�, says Megan Fife, Strategy Lead, 麻豆原创 Success Factors.

鈥淪o our focus is understanding what that transformation means for HCM and how we can help you navigate it and lead it. 

Megan showed the three main pillars that 麻豆原创 we鈥檙e focussing on as they built out their HR AI  strategy. 

Three Core Pillars of AI Adoption in Organisations:

1. The first one is elevating the human advantage. Here people are expected to do more with AI now.
2. The next pillar is redesigning work for impact. This is a really big and key pillar to AI adoption in HR, and this one certainly impacts HR across the board.
   1. Because now your company is looking to you to come up with a plan for the future. Addressing what happened in 2025, with lots of companies who featured in news headlines for making lots of cuts or alternatively making fast decisions around AI adoption too quickly. And your company does not want that to be their headline in 2026.聽
   2. More and more it鈥檚 looking like skills intelligence is going to be the future of planning your workforce.
      1. Truly understanding what skills do your employees have today?聽
      2. What skills do you need for the future?聽
      3. And how are you going to get there?聽
   3. And it鈥檚 likely going to be a mix of upskilling the workforce that you have, but also you may be hiring short-term or contract workers for things that may be a focus for the next one to two years.
3. 聽And then finally there may be some specific skills that you know that you are hiring for outside of your organisation.

HR Needs a Comprehensive AI Plan

There are lots of different opinions about AI, but the important thing is that because it is disrupting the way that people and organisations work, there is more focus now than ever on HR to figure out what the plan is moving forward and how businesses are going to manage their people. This includes making sure that you have the right tools to deal with this disruption.

Expanding on the Three Pillars

At the start of AI adoption, a lot of the AI use cases were around employee self-service, manager self-service, and really empowering them to get things done in a way that makes sense to them and is easy and quick. According to Fife, what 麻豆原创 is also looking at is how they can make sure that HR and administrative jobs are made easier by leveraging AI to really understand what some of the issues are and then make recommendations on how to fix them. 

The next piece or pillar is redesigning work for impact. This is a really big one. And this really impacts HR across the board, comments Fife.

鈥淏ecause now your company is looking to you to come up with a plan for the future. .. and I don鈥檛 want to be in the headlines.

麻豆原创鈥檚 focus is to assist to create a plan 鈥渟o that we鈥檙e not in there and we鈥檙e future-proofing our workforce, She says. 

Skills Intelligence The Future

According to Fife, more and more it鈥檚 looking like skills intelligence is going to be the future of planning of workforces. Truly understanding what skills do your employees have today, and what skills will you need for the future? And how are you going to get there? 

The new buzzword of 2026 is autonomous: Autonomous payroll, autonomous HR. However, there still has to be a human ultimately in the loop and making the decision. And developing the right skills is going to play a key role in determining success in an AI driven economy.

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From the need for more sustainable alternatives to the way we do business to the incomparable impact of AI on every facet of our personal and professional lives, African executives face a challenging time ahead, says at 麻豆原创 Africa.

What is in store for African enterprises in 2024? The global economy continues to be in a state of flux as geopolitical conflict, supply chain pressures, inflation, technology-led disruption and changing consumer needs put pressure on companies and decision-makers.

From the desperate need for more sustainable alternatives to the way we do business to the incomparable impact of AI on every facet of our personal and professional lives, business leaders face a challenging time ahead. By taking stock of emerging trends, however, business leaders may be better able to ride the waves of change and steer their companies through turbulent waters.

These are the key trends that African enterprises should take heed of in 2024:

AI adoption

received an astonishing amount of hype in 2023, and was a bolt of lightning that made every business and technology leader sit up and take notice. Today, it is not a matter of if businesses will adopt AI, but rather how quickly they can deploy and enjoy AI-driven benefits.

In 2024, business leaders will look beyond the hype and seek AI solutions that deliver real-life use cases and benefits. At an operational level, organisations may quickly realise that the quality of their AI outcomes depend largely on the quality of their data.

Technology providers will be indispensable in this conversation, thanks to the huge volumes of data and transactions running through their systems and broader business network. Business leaders will leverage the technology providers鈥� expertise to access the data they need, visualise it, and take decisive actions to drive successful outcomes.

Sustainability centre stage

As the world continues to warm and the consequences of climate change hit home for billions of people, will take centre stage in the business and technology world.

In 2024, the energy industry will shed its view of the customer as simply a commodity user in favour of the prosumer model. Industry leaders will aim to not only secure loyalty and create retail-like experiences, but encourage users to make active contributions to the stability and health of the grid.

This may involve keeping customers informed of how they can make the best, most sustainable decisions over energy use, or see consumers feed excess energy, for example from rooftop solar, into the grid to stabilise supply and contribute clean energy.

In addition, mounting pressure from regulators, consumers and investors will see organisations build more sustainable supply chains with lower carbon emissions and greater use of circular economy principles. This will require extensive innovation in data and analytics to provide business leaders with end-to-end visibility over their environmental impact across the broader supply chain.

Those companies that do succeed in leveraging technology to build truly eco-conscious supply chains will not only unlock new sources of innovation and revenue, but secure long-term profitability for years to come.

Procurement advances

In the rush for digitalisation, enterprises will continue to enhance their procurement processes, driving the need for more intuitive user interfaces. Procurement is evolving from a tactical to a strategic function to drive greater value throughout the organisation.

AI and analytics will be deployed to support procurement professionals as they seek ways to spend more time on value-adding tasks. In 2024, the demand for technologies that enable value-based procurement will increase, enhancing the organisation鈥檚 ability to leverage procurement to deliver value in other parts of the business, such as sustainability.

Resilient supply chains

Global supply chains are still under severe pressure, thanks to a combination of geopolitical disruption, inflation, environmental factors and the ongoing ripple effects of the pandemic. In 2024, expect a surge in supply chain technology investments as organisations seek greater visibility, agility and risk resilience.

In particular, AI holds huge promise for its ability to enhance organisations鈥� ability to sense, recognise and react to disruptions and opportunities in new and exciting ways. The key to unlocking AI鈥檚 benefits hinges on connected data and collaboration throughout the organisation.

Business leaders must lead the charge by ensuring AI is integrated with every layer of the supply chain network to achieve measurable business outcomes. This will unleash a new era of hyper-predictive and efficient logistics, revolutionising the way goods flow from factory floor to consumers.

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