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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The exact figures vary considerably by crop, country and measurement method. The discrepancy is itself revealing: In many markets, we do not know precisely where losses occur or which interventions would have the greatest effect.

This post-harvest crisis is often presented as an infrastructure problem. Africa does need more warehouses, refrigerated vehicles, processing facilities and reliable electricity, but beneath these physical constraints lies another, less visible problem: an information deficit.

Under the , African leaders have set a goal of reducing post-harvest losses by 50% by 2035. Meeting that ambition will require continued investment in storage, energy, transport and processing. But building more infrastructure without improving the flow of information risks reproducing today鈥檚 inefficiencies at a larger scale.

Data drought impacts food security

Agricultural value chains frequently lack timely, reliable data about what has been harvested, where it is stored, how quickly it must reach market and where demand exists. Without this visibility, businesses cannot co-ordinate transport, plan processing capacity or intervene before produce spoils. Infrastructure is essential, but it cannot deliver its full value when decisions are based on incomplete or outdated information.

What we often call food loss is therefore also the result of disconnected decisions. A processor may not know that a harvest is arriving early. A logistics provider may lack the information needed to allocate refrigerated transport, and a warehouse operator may identify a temperature breach only after a shipment has deteriorated. Farmers without access to market signals may sell immediately after harvest, when prices are lowest, because they cannot assess whether storage would produce a better return.

Artificial intelligence can help close these gaps by turning fragmented operational data into co-ordinated action. Yield forecasts can give processors and distributors earlier visibility of expected supply. Connected sensors can monitor temperature and humidity during storage and transport. Predictive models can identify spoilage risks, while intelligent planning systems can match products with available warehouse capacity, vehicles, processing facilities and buyers.

Building a connected agri value chain

The greatest opportunity, however, comes from connecting these capabilities across the value chain. A temperature alert has limited value if it does not trigger a logistics response. A yield forecast cannot prevent waste if procurement, labour and processing plans remain unchanged. Traceability data will not unlock premium export markets unless it flows into quality management, compliance reporting and financial systems.

Enterprise technology plays a critical role by linking procurement, inventory, manufacturing, quality, logistics, finance and traceability, intelligent business systems to create a shared operational picture from farm gate to retailer. AI then helps organisations anticipate disruptions, co-ordinate responses and continuously adjust plans as conditions change.

These systems can support practical decisions: redirecting vulnerable produce to the nearest processor, prioritising a refrigerated shipment, adjusting warehouse labour ahead of a delivery, or identifying a quality risk before goods reach an export customer. Over time, this moves agricultural supply chains from reacting to losses toward preventing them.

Technology tailored to local conditions

We can already see elements of this model taking shape. are making refrigeration more accessible to small-scale producers in Nigeria. Digital sourcing platforms are helping incorporate farmers into formal value chains; one in partnership with was designed to connect 850 000 Nigerian maize producers with buyers and services. The next step is to bring infrastructure, farm-level information and enterprise operations together as one connected system.

Local conditions and infrastructure must be considered. Many smallholders do not own smartphones or have dependable connectivity. Successful solutions will therefore need offline capabilities, simple mobile interfaces and trusted intermediaries who can capture information and deliver insights locally. Technology should accommodate the realities of African agriculture rather than assume every farmer is continuously connected.

Better visibility over agricultural value chains also offers benefits beyond food security. Traceable, well-managed supply chains can strengthen access to finance, support sustainability reporting and help agricultural businesses meet the quality and compliance requirements of high-value export markets. Reducing losses improves margins without requiring more land, water or agricultural inputs, delivering both an economic and an environmental opportunity.

Africa needs an intelligent, connected value chain capable of seeing problems early and co-ordinating action across organisational boundaries.

When physical infrastructure is combined with trusted data and AI-enabled operations, post-harvest loss becomes a preventable failure that businesses, governments and technology partners can address together.

Shiraz Khota

Managing Director: Emerging Africa

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From the rise of the Autonomous Enterprise to the case for embedding AI at the very core of business operations, the world of work is undergoing seismic shifts that are fundamentally altering what it means to work inside an enterprise.

The question organisations now face is not whether AI will reshape the workforce, but whether they are ready to lead that transition in a way that genuinely empowers their people.

At 麻豆原创 Sapphire 2026, under the banner of The Beginning of Better, 麻豆原创 made its most consequential set of workforce-related announcements to date. Taken together, they represent a clear and considered answer to that question.

From process operators to leaders of judgment

The Autonomous Enterprise framework introduced at Sapphire is built on an explicit design principle: humans must remain at the centre, not as operators of process, but as leaders of judgment, strategy and change. As AI assistants take on the coordination work that has long consumed employee time and attention, people are freed to focus on the decisions, relationships and creative problem-solving that machines cannot replicate.

Joule Work, 麻豆原创鈥檚 reimagined AI engagement layer, illustrates this shift vividly. Rather than navigating multiple systems and entering data across screens, employees can now describe what they need to accomplish in plain language 鈥� fill an open headcount, manage my team鈥檚 time-off backlog 鈥� and Joule orchestrates the right workflows, data and agents to make it happen.

The model of work is shifting from task navigation to intent-driven interaction, reducing operational friction and returning cognitive bandwidth to the things that matter most.

Planning for a workforce that includes people and agents

One of the most strategically significant themes at Sapphire was how organisations plan and design for a workforce that now includes both human employees and AI agents executing tasks in concert.

 reveals that 62% of C-suite executives are dissatisfied with how well people data connects to business performance, making it harder to translate strategy into workforce action. 麻豆原创鈥檚 new AI-enabled workforce planning capability directly addresses this, connecting financial data from 麻豆原创 Cloud ERP, contingent workforce data from 麻豆原创 Fieldglass, and people and skills data from 麻豆原创 SuccessFactors into a unified foundation for decision-making. Planning shifts from a periodic exercise to a continuous discipline, with AI agents able to model scenarios, simulate KPI impacts and recommend actions in real time.

New organisational modelling capabilities within 麻豆原创 SuccessFactors Employee Central allow leaders to explore alternative structures, evaluate changes to roles and reporting lines, and understand implications before they are implemented, supporting thoughtful, data-informed change rather than disruptive restructuring.

Ongoing efforts to close skills gap

Across every industry and every region, leaders are confronting the same reality: new roles are emerging, new competencies are required, and the processes that have defined work for decades are being reimagined at speed by generative AI.

Traditional approaches to workforce development, such as annual training cycles, standalone learning systems, and scheduled courses, cannot alone keep pace. The recently unveiled Workforce Upskilling Assistant offers a new capability that delivers personalised, AI-driven learning directly within the flow of daily work, whether that is in Microsoft Teams, Slack or 麻豆原创 SuccessFactors itself.

By converting organisational content into adaptive micro-learning and embedding timely nudges into workflows, the assistant brings the right learning to the right person at the right moment instead of when a calendar event dictates it. Organisations that embed learning into culture and workflows with AI-powered tools are significantly ahead of peers, and .

Trust is the foundation

None of this transformation succeeds without the trust of the workforce it is designed to serve. Accenture found that , while significant proportions report burnout and anxiety about the future of their roles.

Here, the emphasis that humans remain firmly in control reflects a commercial and human reality that enterprise leaders across our region understand well: technology that erodes trust fails. Technology that builds trust, endures.

The organisations that will lead this era are those that invest as intentionally in workforce readiness, reskilling and governance as they do in AI platforms and automation.

The Autonomous Enterprise is, at its heart, a vision for human progress, one where intelligent systems handle the operational burden and people are empowered to pursue the work that only they can do. 麻豆原创 is committed to helping organisations across the region build enterprises where that potential is fully realised.

The Beginning of Better starts with the people inside it.

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Companies are reviewing their ERP strategies as they balance digital transformation goals with tighter budgets, operational complexity and the need to demonstrate measurable business value, they say.

Andrew Strachan, director of sales at Spinnaker Support Southern Africa,  companies across Africa are reassessing ERP support strategies as decision-makers seek to modernise their environments while managing costs.

“Enterprise software decisions are not like other technology decisions,” said Strachan. 鈥淭imelines are longer, dependencies run deeper and the cost of reversing course 鈥� if reversing course is even possible 鈥� is often measured in years and very substantial sums.”

According to Strachan, the relationship between a major ERP vendor and its customers rests on more than product capability alone. “It is built on trust: trust in the roadmap, trust in the commercial model and trust that the terms you commit to today will not be materially redefined once your business is locked in.”

Gerhard Alberts, head of customer evolution at 麻豆原创 Africa, said trust is ultimately measured by business outcomes.

鈥淲hile customers rightly expect transparency and choice, the greater risk in today’s competitive environment is often the cost of inaction,” he said. “Ultimately, success is measured not by the technology deployed, but by the operational improvements, agility and competitive advantage it enables.鈥�

Alberts said the pace of technological change means companies must adopt relevant technologies to remain competitive, and that customers must be equipped with the most relevant tools to navigate a shifting market. “By not innovating, customers run the risk of attracting direct and potential costs in terms of system and infrastructure stability, security and compliance. This results in an inability to stay competitive across multiple markets due to a lack of agility.鈥�

Strachan said new platforms and regular vendor upgrade announcements are adding pressure on companies already facing constrained budgets, skills shortages, infrastructure complexity and currency pressures.

鈥淚n South Africa and across the African market, large technology decisions are made under intense scrutiny,” he said. “Boards and executive teams are balancing growth ambitions against currency pressure, constrained capital budgets, skills shortages, infrastructure complexity and the need to show clear business value from every major investment. In that context, organisations cannot afford to keep revisiting foundational ERP decisions every few years because the vendor鈥檚 commercial priorities have changed.鈥�

Migration is not modernisation

Courtney Hounsell, client experience manager at Microsoft managed partner Braintree, said AI is reshaping the role of ERP platforms while exposing the limitations of legacy systems. He said outdated ERP infrastructure is hindering AI adoption because many companies mistake cloud migration for digital modernisation.

鈥淢igration is not modernisation,” Hounsell said. “Unfortunately, many companies have merged the two, and the result is a gap that鈥檚 creating incoherence and a lack of visibility, constraining their ability to benefit from AI and its capabilities.”

He explained that cloud migration has been sold as digital modernisation, but this only changes the address of the system. Real modernisation, he said, changes architecture, data models, integration logic and the capacity to generate value. “Companies want their modernisation projects to actively reduce other costs, show long-term ROI and empower their staff with systems that work faster and smarter. So when migration and modernisation are confused, companies end up paying cloud prices for on-premises problems and arrive in the AI era without the foundations required to ensure AI can function effectively.”

Hounsell said the gap between technology investment and business outcomes is widening, leaving companies with limited AI capability and poor data visibility. He cited McKinsey’s “The State of AI in 2025” report, based on a survey of 2 000 executives in 105 countries, which found that 88% of companies have adopted AI in at least one business function.

He said on-premises ERP systems struggle to support AI because data must be extracted, cleaned and transferred before analysis.

鈥淥n-premises ERP systems are struggling uphill within the AI economy,” Hounsell said. “They hold their data in local databases, and to make this usable for AI, it has to be extracted, cleaned and pushed somewhere accessible. Without real-time data pipelines feeding into core systems, AI deployments remain siloed and limited.鈥�

Braintree said ERP modernisation also delivers financial benefits, citing the Forrester Total Economic Impact study of Dynamics 365 Business Central, which found that companies could achieve a 265% ROI, with productivity gains of 12.5% in operations, 15% in sales and 15.6% in finance.

The study also found that companies that modernised successfully avoided more than $30 000 a year in third-party consulting fees, while cloud ERP platforms reduce infrastructure costs and receive automatic updates, including new AI capabilities.

Braintree argued that choosing the right migration partner is as important as selecting the ERP platform itself.

Alberts added that technical debt continues to grow as companies rely on siloed data, ageing infrastructure and extensive custom code, while shortages of legacy skills compound the problem.

鈥淥ver time, doing nothing to save money becomes the most expensive system to maintain, together with the inability to adapt, which has the potential to result in the demise of some organisations,鈥� he said.

Arthur Goldstuck, CEO of World Wide Worx,  after 麻豆原创 Sapphire 2026 that 麻豆原创 CEO Christian Klein described ERP as the “brain” of every business in the age of agentic AI. Goldstuck quoted Klein: 鈥淔or over 15 years, we have been developing an ERP with incredibly deep process and data domain know-how. On top of that, all your governance requirements and customer-specific extensions are stored in the ERP. The ERP is the trusted system of execution running your company.鈥�

According to Garth Ridgway, senior director for 麻豆原创 at NTT DATA, misconceptions about implementation timelines, affordability and suitability for mid-market companies continue to delay modernisation.

“Many executives still assume ERP transformation requires years of disruption and significant upfront investment,” Ridgway said. “In reality, modern cloud ERP platforms have evolved considerably, enabling organisations to adopt best-practice processes more rapidly, scale according to business needs and realise value far sooner than was historically possible.”

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The next phase of artificial intelligence (AI) should be shaped with Africa, not simply about or for the continent, according to 麻豆原创’s Director of Global Government Affairs and CSR for Africa, .

His comments follow the launch of the , announced yesterday by international leaders to expand access to AI, strengthen trust and increase its social and economic impact.

The commission brings together representatives from governments, businesses and international organisations to identify practical ways to unlock AI’s potential while promoting equitable access to the technology.

Its inaugural meeting will take place during the International Telecommunication Union’s (ITU) AI for Good Global Summit, from 7鈥�10 July in Geneva, Switzerland.

The summit forms part of Digital Week, running from 6鈥�10 July, alongside the first UN-mandated Global Dialogue on AI Governance and the WSIS Forum 2026.

Geness, who will participate in discussions at the summit, said Africa should approach global AI governance with a clear agenda centred on economic growth and inclusion.

“Africa must meet that room with clarity, not caution. Our agenda should be simple and bold: AI governance that expands prosperity.

“That means compute access, skills investment, trusted data systems, open standards, local-language innovation, accountable public procurement, and regulation that protects people without suffocating entrepreneurs.

“It means turning the African Union’s Continental AI Strategy from a document into national roadmaps, investment pipelines and regional co-operation. This is technology diplomacy: 54 nations aligning where they can, rather than negotiating as 54 separate voices. This is where I hope to add value.”

Africa’s representation on the commission has been strengthened by the appointment of Rwandan President Paul Kagame as co-chair alongside Salesforce Chair and CEO Marc Benioff.

According to the ITU, the commission aims to promote equitable access to AI and help narrow the global digital divide.

The organisation said an estimated 2.2 billion people remain offline, leaving around a quarter of the world’s population excluded from AI-driven opportunities.

“A key focus of the AI for Good Global Commission will be to bridge digital divides and help ensure that AI becomes a tool for solving global challenges, not deepening inequalities,” the ITU said.

President Kagame said technology should reduce inequality and broaden access to AI’s benefits.

“One thing is certain: technology is supposed to be a force for good, and we have a responsibility to use it accordingly. Let us work together to reduce inequality and allow more of our citizens to benefit from the good AI can deliver to all of us.”

Benioff said AI’s economic potential depends on maintaining public trust.

The promise of AI is built not only on incredible opportunities for economic growth, but on the foundation of trust required for our shared success.”

ITU Secretary-General Doreen Bogdan-Martin, vice-chair of the commission, said collaboration across sectors would be essential to ensure AI benefits people globally.

“No organisation can single-handedly put AI at the service of all humanity. It will take collective leadership and the combined expertise of partners across sectors to ensure AI benefits everyone, everywhere.”

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AI is reshaping enterprise support and changing how solution providers think and operate, says Stefan Steinle, executive VP and head of global听customer听support at 麻豆原创, who adds that support has evolved into a strategic driver of business value.

Support in this context refers to services, management and resource allocation used to troubleshoot, address issues and optimise infrastructure within an organisation.

鈥淭he focus is on value generation and customer health,鈥� says Steinle. 鈥淪upport is central to driving business outcomes with AI-integrated platforms. Support teams are becoming more embedded in business strategy. They provide visibility into system performance, user behaviour and operational trends.鈥�

According to Steinle, AI-assisted toolchains and intelligent automation allow organisations to anticipate issues before they arise, streamline performance and improve long-term agility and resilience.

This is an entirely different approach to traditional support execution and application.

鈥淭raditionally, support operated on a break-fix model 鈥� issues were addressed when disruption occurred. AI changes this dynamic entirely. Through machine learning, pattern recognition and real-time data analysis, AI can continuously monitor systems, detect anomalies and predict potential failures before they impact operations. This enables preventive intervention rather than reactive troubleshooting.鈥�

AI is also improving decision-making, adds Steinle. 鈥淚t surfaces insights from vast datasets, identifies optimisation opportunities and recommends actions that improve performance, reduce costs and enhance user experience. In this way, enterprise support evolves from a technical necessity into a value-generating function that actively contributes to business strategy.鈥�

Shift from break-fix significant

AI expert and founder of AIforBusiness.net Johan Steyn says the shift from break-fix to strategic value driver is one of the most significant 鈥� and under-appreciated 鈥� transformations AI has enabled in organisations.

鈥淔or years, IT support was seen as a cost centre: reactive, ticket-driven and measured by how quickly problems were resolved. AI has fundamentally changed that equation. Predictive diagnostics, intelligent triage and automated resolution are not just making support faster 鈥� they are making it anticipatory. The best organisations are no longer waiting for things to break; they are using AI to understand system behaviour patterns and intervene before users are even aware of a problem,鈥� says Steyn.

The current status is one of rapid but uneven adoption, he adds.

Rise of autonomous enterprise

AI-assisted toolchains and intelligent automation are transforming ERP systems from static platforms into adaptive, self-improving environments.

According to Steinle, the shift is being driven by predictive maintenance capabilities, where AI analyses historical and real-time data to forecast failures and enable pre-emptive repairs. AI is also accelerating automated root cause analysis by identifying issues across complex system landscapes more quickly and accurately.

In addition, self-healing capabilities are enabling some system problems to be resolved automatically without human intervention, while AI-driven process optimisation tools continue to identify operational inefficiencies and recommend improvements.

The result is ERP systems that become more intelligent over time, enabling organisations to respond more rapidly to change, scale operations more effectively and strengthen operational resilience.

鈥淭he integrated toolchain breaks silos and drives collaboration between business and IT. You can confidently modernise your ERP landscapes while staying agile in today鈥檚 competitive climate,鈥� says Steinle.

鈥淎I plays a key role in enhancing capabilities by reducing errors and speeding up transitions. Take AI-powered change point detection as an example, where fundamental shifts in system downtimes can be found reliably and hence resolved faster. At the same time, AI-generated requirements reduce manual effort and accelerate the implementation process.鈥�

People and culture

However, Steinle warns that technology alone is not enough.

鈥淲hile technology is at the core of transformation, you can still fall short when it comes to outcomes if your people are not on board. When you continue to operate in old ways, the AI copilots or the automations are pointless. You get the same issues, but with more dashboards.鈥�

He adds that culture and leadership are critical to successful AI adoption. 鈥淎s they say, culture is what people do when no one is watching. Establishing this culture is what leadership is ultimately about. How motivated are your people? Are they as excited as you are about new innovations?鈥�

Diverse teams are also essential in solving problems within complex ERP environments.

Nazia Pillay, MD for 麻豆原创 southern Africa, adds: 鈥淭he next phase of AI adoption is about execution. Organisations are looking for trusted data foundations, strong governance and practical business use cases that can deliver measurable value.鈥�

Early stages

According to Steyn, larger, more mature organisations are already seeing measurable gains 鈥� reduced mean time to resolution, lower support costs and IT teams freed up for higher-value work.

鈥淏ut many organisations, particularly in markets like South Africa, are still in early stages, often constrained by legacy infrastructure and skills gaps. The outlook, however, is clear: AI-augmented support will become the baseline expectation, not a competitive differentiator,鈥� he continues.

Organisations that invest in this transition thoughtfully 鈥� with the right human capability alongside the technology 鈥� will build a genuine and lasting operational advantage, adds Steyn.

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Companies across sectors are racing to embed AI into operations, but proving return on investment (ROI) remains a key hurdle.

While 84% of businesses investing in AI听, only a fraction have achieved sustained value at scale. For South African executives navigating budget constraints and talent shortages, the question is not 鈥渟hould we invest in AI?鈥� but 鈥渉ow can we prove it works?鈥�

According to Dumi Moyo, marketing director for Africa at 麻豆原创, there鈥檚 good news: AI doesn鈥檛 have to be a leap of faith.

鈥淲ith the right strategy and the right tools, AI deployments deliver measurable ROI quickly across a range of business functions. One of the most effective ways to get there is by embedding AI directly into business applications, avoiding costly integrations and accelerating time-to-value.鈥�

Embedded AI upends traditional implementations. Instead of requiring custom data pipelines, standalone platforms, and complex integrations, embedded AI delivers intelligence directly into the flow of work.

鈥淎 native approach means faster deployment, less disruption, and clearer outcomes,鈥� says Moyo. 鈥淔or example, 麻豆原创 has built more than 295 AI-powered scenarios into business applications that span supply chain, procurement, finance, customer experience and HR, empowering business users to complete navigational and transactional tasks up to 90% faster.鈥�

听Strategy for AI ROI

A recent study of 1600 businesses in eight countries听听found that businesses investing in AI expect on average a 16% ROI in 2025, nearly doubling to 31% in 2027.

Moyo says businesses typically have high expectations of the ROI they will receive from AI projects, with nearly half of global companies expecting AI initiatives to deliver positive ROI faster than other investments.

鈥淚t is critical that organisations align their AI projects with the key pillars of cost savings, decision time, risk and revenue. By prioritising embedded AI, companies can secure significant ROI while unlocking听 broader innovation benefits across the organisation.鈥�

He shares insights into how AI initiatives built on four key pillars can show provable, measurable return-on-investment for organisations:

听Pillar 1: Reduce manual effort and waste to drive cost savings

Cost reduction is often the first benefit that organisations receive from AI investments. 鈥淎I allows teams to redirect hours toward higher-value work by automating repetitive and rule-based tasks,鈥� says Moyo. 鈥溌槎乖� data shows that companies achieve up to 20% operational cost reduction in key functions like accounts payable and HR through effective AI deployments.鈥�

In procurement functions, organisations are cutting administrative workload and improving sourcing outcomes by automating statements of work (SOWs) and supplier research, while in finance, AI accelerates reconciliations, optimises cash collections, and automates journal entries, thereby reducing close cycles and improving liquidity.

鈥淎 good approach here is to quantify the time saved across manual processes and to calculate the cost of labour hours that have been freed up and reinvested into more strategic work thanks to AI,鈥� says Moyo.

听Pillar 2: Improve decision time for faster insight and action

AI tends to thrive when speed and accuracy matter most. By analysing large datasets, surfacing trends, and recommending next steps, AI shortens the gap between information and action.

鈥淒ata shows that embedded AI improves forecasting and demand planning in the supply chain, resulting in less downtime and a 25% productivity bump for planners,鈥� says Moyo.

Organisations are also using generative AI to accelerate their hiring processes and producing job descriptions and candidate rankings in real-time. Finance also benefits from AI-generated insights that help CFOs and analysts make faster, more confident decisions using cleaner data.

鈥淥rganisations should measure reductions in cycle times, from planning to resolution, and link them to improved responsiveness or reduced delays,鈥� says Moyo. 鈥淭his provides a clear path to ROI and its impact on core business performance.鈥�

听Pillar 3: Reduce risk through better controls and compliance

In a volatile business climate, organisations typically seek greater risk mitigation across their operations. According to Moyo, AI plays a growing role here.

鈥淎I embedded into core business processes can flag anomalies, detect fraud patterns, and monitor compliance thresholds in real time. AI agents are increasingly being used in risk reduction, for example by scanning procurement data for irregularities and highlighting non-compliant purchases.鈥�

AI agents such as 麻豆原创鈥檚 Joule can also bolster procurement processes by suggesting suppliers with stronger reputations, and support finance teams by reducing bad debt write-offs through improved collection strategies. In HR, AI-driven insights reduce legal risk by providing bias-aware hiring recommendations and fairer performance reviews.

鈥淭o quantify the benefits that AI brings to risk reduction, companies should track aspects such as risk events avoided, compliance costs reduced, or error rates minimised,鈥� says Moyo.

听Pillar 4: Grow revenue through personalisation and conversion

According to Moyo, organisations should look beyond back-office efficiency to maximise AI value. 鈥淎 smart AI strategy will consider front-end growth by prioritising customer experiences. For example, AI fuels personalisation by recommending products, predicting churn, and guiding service agents with next-best actions to enhance customer satisfaction and drive retention.鈥�

Organisations that embed AI into their core business processes can connect sales, service and marketing data to build richer customer profiles, increase upsell success and reduce attrition. Organisations also accelerate their campaign rollouts through AI-powered content generation, while smart selling tools shorten deal cycles and increase the average basket size.

鈥淭his results in higher revenue per customer interaction, as well as more predictable sales pipelines,鈥� says Moyo. 鈥淗ere, ROI is readily quantified through tracking lead conversion rates, customer churn, and average order value.鈥�

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Across the world, 95 percent of companies say growth is a priority for the year ahead. Their top focus areas鈥攊ncluding expanding market presence, broadening distribution through partners, and scaling operations鈥攕peak to leaders鈥� determination to create value in a climate of uncertainty and change.

In my engagements with customers, I see this reality every day. Companies everywhere want to grow, but they want to grow with confidence. They are looking for partners who understand their unique challenges, who support them with their long-term ambitions, and who can help them keep pace with rapid change.

Technology is central to this ambition. Nearly all respondents in the study rank simplifying work and improving processes alongside growth. Here, artificial intelligence stands out. Nine in 10 organizations have already made generative or agent-based AI a priority, and more than 70 percent have some form of AI in use. While concerns about data quality and talent remain, the message is clear: AI has moved beyond experimentation into the mainstream of how companies operate and unleash value.

From Frankfurt to Dubai to Singapore: How regional differences shape opportunities and risks

Regional differences tell a powerful story. In Europe, AI adoption comes with caution. Large enterprises put compliance, privacy, and transparency first, while many mid-market firms are still piloting solutions. In Asia-Pacific, the pace is different. Mid-market companies there already report strong AI use above global averages, and growth expectations run high. For them, AI is a way to seize advantage quickly in a fast-moving market.

These contrasts show why cultural intelligence matters so much for global leaders. Whether in Frankfurt, Singapore, or Dubai, I see how local realities, regulations, and expectations shape both risks and opportunities. In Europe, energy costs and geopolitical uncertainty drive supply chain strategies. In Asia-Pacific, digital adoption and market dynamism set a different pace.

Sustainability is another area where nuance matters. European companies place it near the top of their priorities, tracking or slightly exceeding global benchmarks. Asia-Pacific firms value sustainability but often rank it lower than growth and speed to market. Each is weighing trade-offs in its own context, creating exciting opportunities for 麻豆原创 to bring the most relevant technology, data, and practices to each region to help organizations achieve both economic and environmental goals.

The through-line in all of this is agility. Supply chain fragility, geopolitical conflict, inflation, and regulation continue to test even the best-run organizations. Technology can enable agility, but only if leaders embrace change themselves, rethinking processes, investing in skills, and building cultures of continuous learning and exploration. Security and ethical standards must also be the cornerstones of every AI conversation.

Turning AI potential into outcomes by centering value creation and integration

I believe this is a time for grounded optimism. The appetite for growth is real and the technology to achieve it is more advanced than ever. Innovation is accelerating at an extraordinary pace, with daily breakthroughs showcasing the expanding potential of AI.

There is a recent example that demonstrates AI鈥檚 ability to process multi-step tasks for over 30 hours. This achievement highlights not only the rapid evolution of AI, but also how increasingly accessible and capable these technologies are becoming.

However, as AI systems grow more autonomous and context-aware, organizations must recognize that true value doesn鈥檛 come from raw capability alone. To harness AI effectively, especially in enterprise environments, a consistent semantic layer is essential. It ensures alignment among data, tasks, and outcomes, enabling AI to reason reliably across systems and scale impact without losing coherence.

Companies must also move beyond simply adopting AI to actively testing and refining applications to gain a significant advantage. Equally important is a deliberate approach to managing the human element of a transformation, rooted in structured and human-centric change management.

Realizing AI鈥檚 true promise requires a fundamental shift in how people, applications, and data connect. Success relies on deeply connecting every part of an organization鈥檚 business, delivering end-to-end transformational value. A seamless, integrated suite provides insight and agility, whether responding to a problem or ensuring readiness when opportunity knocks.

This is where 麻豆原创 Business Suite is a game changer, integrating applications, data, and AI in a virtuous cycle that delivers tangible business outcomes. At our inaugural听麻豆原创 Connect听event earlier in October, we showcased new applications, strategic data partnerships with Google Cloud and Databricks, and a new network of role-based AI assistants in Joule across every line of business.

Altogether, our听听marks the beginning of a new era powered by self-reinforcing AI, data, and applications. By keeping customer needs and value realization at the center and leading with innovation, businesses can not only navigate uncertainty, but build a more resilient, intelligent, and sustainable future.

Manos Raptopoulos is chief revenue officer of APAC, EMEA, and MEE, and a member of the Extended Board of 麻豆原创 SE.

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According to of 麻豆原创, you need 鈥渙n the fly鈥� access to insights via seamless data streamlining. 鈥淥ur vision is the only way to create value in a business is by connecting applications, data, and AI, withthe data available with the right context鈥� he stated at the 麻豆原创 business innovation summit in Johannesburg today. 鈥淭hat means that AI systems will be able to understand what the data is doing, how it鈥檚 implemented. That is why we believe that starting from the application phase, we explore the data with the right context, with the right security, with the right governance鈥� quips Mulder.

Businesses using Enterprise systems today have various applications that extract data, however Mulder points out that the moment you extract your data, you lose the context connection. 鈥淵ou have the highways, the timings, the massive data, the security, you lose that context鈥� he states.

Some of the challenges highlighted at the event today are: limited reach, as well as business data positioned in separate silos. The solution according to 麻豆原创 is 鈥� 鈥淒ata as as Service鈥� 鈥� DaaS. This adds data products to business logic and intelligent applications that will analyse and prepare automated reports or dashboards.

Flywheel of Data Concept

Mulder presented the concept of a continuous loop of generating data, then adding AI to ultimately, generate even more data, and using that to convey the information within a contextual framework. He introduced the 麻豆原创 concept of a that works as follows: The system retrieves various data from various data points, and then will apply business logic, to bring you intelligent applications. this then provides you your data as a service. 麻豆原创 systems does the extraction of the data, ensuring data points are available to the business, with the right business context.

麻豆原创 has partnered with multiple data platform providers, and so is data agnostic, enabling data from almost any platform to be incorporated into its Business Data Cloud platform that then becomes the channel for their AI driven software able to extract and analyse within a wider context.

This system according to Mulder is saving up to 67% in time for some of their European clients utilising the Business Data Cloud. 鈥淚t鈥檚 about creating a principle that BDC is actually the one source of quality security data, curated data with the right properties, and making that available to all other technologies鈥� comments Mulder.

鈥淢ake sure you get the right answers. You need the right answers. That鈥檚 why the context gap is there between AI and data.Without context, there is no AI鈥� Concludes Mulder.

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What happens when machines and algorithms can complete knowledge work faster and more effective than even the most high-performing teams?

Thanks to the accelerating power, speed and accuracy of artificial intelligence (AI) over the past few years, the arrival of artificial general intelligence (AGI) is no longer a matter of what-if, but of when.

Artificial general intelligence refers to machine intelligence that can perform any intellectual task a human can.

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