Pierre du Plessis, strategist and founder of Be Brave, and the event’s guest speaker, explored how trust, culture and meaningful engagement can transform teams and communities, touching on the  of ‘tribes’ – small, connected groups where people feel seen, trusted and valued. 

“Profit and growth are lagging indicators of deep chemistry, not ,â€� he told attendees. “The board may set a strategy, but the tribe makes it real. Only the tribe builds the movement.â€�

Through storytelling and practical examples, Du Plessis illustrated why connection matters more than metrics alone, and why shared experiences are valued over shortcuts. 

“Trust is fundamental to any team,� he explained. “It enables conflict, commitment, accountability and ultimately meaningful outcomes.�

Research highlighted at the session reinforced these insights. Gallup studies show that, globally, only 21% of employees feel truly engaged at work, while actively disengaged employees can undermine entire teams. Loneliness and over- in organisations not only erode morale but also have measurable influences on productivity and wellbeing. 

“Humans are pack animals,� Du Plessis emphasised. “We thrive in communities, not in isolation.�

The session also explored the concept of meaning and transcendence at work. Drawing from examples ranging from Trappist monks to special needs teachers, Du Plessis argued that fulfilling work is about impact, not just a pay packet. “These Belgian monks produce some of the world’s best beer,â€� he said, “but their purpose isn’t profit, it’s sustaining the community. Meaning  performance; profit is a side effect.â€�

As organisations grapple with digital transformation and AI-driven workflows, this message resonates: human connection remains a competitive advantage. This philosophy is also shaping AFSUG’s own strategic direction.

“At AFSUG’s local Â鶹ԭ´´ user conference, Â鶹ԭ´´HILA, last year, we spoke about  our community and establishing our tribe, and this is exactly what we’re doing as an organisation,â€� explained Amanda Gibbs, AFSUG CEO. 

“AFSUG has reached an inflection point – one where we’ve looked back at why the organisation was originally founded: as a peer-to-peer customer networking platform that helps customers bridge the gap with Â鶹ԭ´´. We are now  a path towards a community that is purpose-led, operationally sound and designed for  impact across .

“The shift we are putting into place is to move away from being event-driven, reactive and purely informational, towards becoming outcomes-driven, influential and insight-led. The pivotal role that AFSUG must play is as the trusted voice of the African Â鶹ԭ´´ community and an independent advocate.â€�

AFSUG chairman Duke Mathebula echoed Gibbs’ statement, adding that the organisation is committed to action, structured advocacy and creating real, measurable value for the local Â鶹ԭ´´ community, as well as empowering transformation.

“True impact comes from creating tribes of engaged, courageous and committed people who can make a real difference, and this is exactly how AFSUG plans to move forward,� he said.

The post Why Trust and ‘Tribes’ Still Matter in an Age of Metrics appeared first on Â鶹ԭ´´ Africa News Center.

For much of the past decade, AI has lived in labs, pilots and PowerPoint decks. The next phase is different. AI is moving into the operational core of organisations, reshaping how decisions are made, work is executed and value is created.

AI is becoming the most significant technology shift enterprise leaders will face in this generation. Not because the algorithms are new, but because the operating model required to make AI work at scale is fundamentally different.

One of the clearest changes heading into 2026 is the move from AI that assists humans, to AI that acts on their behalf.

Early enterprise AI tools functioned as copilots: surfacing information, generating insights or suggesting next steps. Increasingly, organisations are deploying autonomous AI agents that recommend actions – and take them – executing multi-step business processes within defined roles and controls.

This transition matters because it forces leaders to confront new questions of trust, accountability and governance. Autonomous AI can deliver significant productivity gains, but only if organisations are prepared to define where machines can act independently, where human approval is required, and how exceptions are handled.

In practice, this means treating AI agents less like software features and more like a digital workforce: assigned roles, clear permissions, monitored performance and escalation paths when things go wrong. Without this discipline, autonomy becomes risk rather than advantage.

Intelligence must be built in, not bolted on

Another defining trend is the move toward AI-native systems. Many organisations still treat AI as an add-on: a layer of intelligence bolted onto processes designed decades ago. That approach is reaching its limits.

AI-native architecture embeds intelligence directly into core workflows, allowing systems to understand intent rather than simply execute transactions. Instead of navigating complex menus and dashboards, users express what they want to achieve, and systems orchestrate the necessary steps across functions.

For leadership teams, this is not a user-interface upgrade, but a shift in how work gets done. Ideally, decision-making accelerates, organisational friction reduces, and the boundary between analysis and execution begins to disappear.

However, this only works when underlying systems are clean, standardised and integrated. Which leads to a harder truth many organisations are discovering.

Data quality is the real AI constraint

The biggest barrier to AI success is not model sophistication, but data reality. AI systems amplify whatever foundations they are given. Clean, consistent data produces reliable outcomes, while fragmented, poorly governed data produces confident nonsense.

This is why data has become the strategic nervous system of the modern enterprise. AI depends on shared definitions of customers, products, suppliers and processes. It requires transactional integrity, accessible historical context and the ability to combine internal and external information in real time.

Organisations that have postponed data discipline are finding that AI exposes weaknesses instantly, often in ways that affect customers, regulators or financial performance. In the year ahead, leaders will increasingly be judged on whether they treated data as a strategic asset early enough, rather than as an IT hygiene issue.

Closely linked to data readiness is a simple but central principle: keeping core enterprise systems clean.

Years of excessive customisation have left many organisations with fragile ERP environments that are difficult to upgrade and harder to integrate with modern AI capabilities.

The shift toward standardised cores with extensions built outside the core system creates an environment where innovation doesn’t break operations.

For boards and executive teams, this requires a mindset shift. Standardisation is not a loss of competitive differentiation, but the price of adaptability. The differentiation moves to how organisations use data, design experiences and make decisions, not how many lines of custom code they maintain.

Technology alone will not deliver results

Perhaps the most underestimated factor in AI success is change management, which consistently accounts for a larger share of AI outcomes than technology itself.

AI changes roles, not just tools. Finance teams move from processing transactions to managing exceptions. HR shifts from administrative workflows to skills intelligence.

Operations leaders rely more on forecasts and simulations than static reports. These changes demand new skills, new incentives, and new ways of measuring performance.

This year, leaders must invest in adoption with the same commitment and focus as they invest in new capabilities. AI literacy should be a core leadership competency not just a specialist function.

As AI initiatives multiply, so does the risk of fragmentation. Different business units experimenting independently can create inconsistent standards, duplicated effort and unmanaged risk.

This is why many organisations are establishing AI centres of excellence that coordinate AI innovation. Effective governance frameworks address five questions: how AI systems are approved and retired, how decisions are logged and audited, how policies are enforced, where human oversight is required, and how performance is measured.

In 2026, AI governance will be viewed much like financial governance: a prerequisite for trust, not a brake on progress.

From pilots to production or paralysis

A final challenge looms large: scaling. Many organisations are stuck in what has become known as “pilot purgatory�, where successful experiments never reach enterprise impact.

The reasons are consistent: poor integration with core systems, unclear ownership, lack of user trust, weak data foundations and vague ROI metrics. Moving from pilot to production requires deliberate planning, phased rollout and visible executive sponsorship.

Leaders who expect AI to scale itself will be disappointed, while those who design for scale from day one will pull ahead quickly.

As we accelerate into 2026, AI is an operational reality. The real strategic question for leaders is whether their organisations are structurally ready for AI, with clean systems, trusted data, skilled people and disciplined governance. With these foundations, AI becomes a durable source of advantage.

In a volatile global environment, leadership is increasingly defined by the ability to move forward without perfect certainty. Business AI, deployed responsibly and at scale, is becoming one of the most powerful tools leaders have to do precisely that.

The post Business AI in 2026: Execution, not Experimentation, Will Define Success appeared first on Â鶹ԭ´´ Africa News Center.

From managing customer support workflows to orchestrating complex supply chains, agentic AI is redefining how businesses operate, respond, and grow. These intelligent digital co-workers act with autonomy, context, and speed, with growing capabilities for reasoning, making decisions, and working alongside humans to execute multi-step processes across departments.

 , agent-driven applications are rapidly becoming the standard for enterprise management. Global estimates suggest AI agents could contribute trillions to the world economy by 2030 through productivity gains, faster decisions, and cost reductions.

Transformative impact

Despite pervasive AI skills shortages, South African companies are moving quickly from experimentation to execution. Financial institutions are embedding AI agents into ERP systems to reroute inventory and manage disputes. Healthcare providers use AI meeting agents to generate follow-ups and automate patient admin. Legal firms use AI to prepare case files and speed up settlements.

This shift is being driven by a combination of pressure and potential. Faced with economic headwinds, skills shortages, and rising customer expectations, South African companies are looking to AI agents to unlock productivity, streamline operations, and free up human talent for higher-value work.

But deploying AI agents effectively requires more than buying the latest tool. The success of AI agents depends on deep integration of data, processes, and applications through a suite-first approach.

Leading with a suite

According to an IDC Spotlight Report, companies that adopt AI-powered suites like Â鶹ԭ´´â€™s see measurable gains:

• 37% report improved process productivity
• 39%Ìýachieve greater cost efficiency
• 36% boost workforce productivity
• 35%Ìýaccelerate speed to market

By leveraging an AI-powered suite integrated to a core business technology platform, companies can empower their AI agents to act with full business context. Unlike siloed tools, a suite-first approach supports real-time collaboration between agents, humans, and systems, making AI agents not just smarter, but more impactful on the overall performance of the business.

Â鶹ԭ´´â€™s Joule, an AI agent framework embedded into the Â鶹ԭ´´ Business Suite, offers companies a system of intelligent agents that collaborate across business functions, from finance and procurement to HR and supply chain, to execute complex workflows and drive better decisions at scale.

These agents leverage knowledge centres and data cloud to ground actions in real-time, contextual business data. Working alongside teams, the agents augment human decision-making, accelerate task completion and minimise manual errors. In finance functions, agents can optimise working capital by accelerating accounts receivable matching, while in procurement they can surface the most relevant suppliers based on business rules and past performance.

AI agent readiness check

Before companies deploy AI agents like Joule, they need the right digital foundation. Â鶹ԭ´´ recommends a four-part readiness framework:

1 Data quality and accessibility – Agents are only as good as the data they use. Clean, structured, and real-time data from across the enterprise is critical for effective agent decision-making. Silos, outdated data, or missing context will slow adoption and risk poor outcomes.

2 Process maturity – AI agents thrive on well-defined workflows. Before automation, companies must ensure their business processes are standardised, documented, and ready for orchestration. Automating chaos just creates faster chaos.

3 Organisational clarity – Who will use these agents? For what tasks? How will they hand off to human employees? Clear role definitions and communication are essential for adoption and trust.

4 Governance and guardrails – Just like human employees, AI agents need rules. Define permissions, escalation paths, ethical boundaries, and auditing practices. Agents should act autonomously but within the boundaries of the businesses in which they operate.

AI agents are more than just another layer of automation. They represent a new model of work, one that is collaborative, contextual, and continuous. The true value of AI agents is unlocked only when companies are ready. And the companies that unlock the greatest value the quickest are those deploying their AI agents through an AI-powered suite integrated to a core business technology platform.

The post The Suite Spot: A Practical Guide to Business AI Agents appeared first on Â鶹ԭ´´ Africa News Center.

Foundational capabilities such as cloud, business AI, and ERP running on clean-core data strategies will be measured not only by their adoption and use within the business, but how quickly and effectively they can be unleashed across the SME’s operations.

This vital sector accounts for nearly 95% of registered businesses in sub-Saharan Africa and generate roughly half of the region’s GDP, yet many remain under-digitised. A World Bank report found that  that have adopted digital technologies make intensive use of them to improve the running of their businesses.

The B20 South Africa 2025 Digital Transformation Task Force listed SME digitalisation and AI literacy as . A separate report projected that , creating 300 000 jobs and expanding access to essential services to millions.

However, this digitalisation is also introducing greater cyber risk.  revealed a continent-wide escalation of cybercrime, with about one in 15 organisations in Africa facing a ransomware attempt each week – significantly higher than the global average.

SMEs seeking to scale their digital capabilities for greater efficiency, innovation and growth this year must take heed of the trends and forces shaping Africa’s digital economy, including:

Trend 1: Cloud as the default operating model

Cloud computing has crossed a tipping point among African businesses, with adoption growing across the continent. For SMEs, the attraction is straightforward. Cloud replaces large upfront capital costs with predictable subscriptions, supports hybrid and mobile work, and allows businesses to scale systems as they grow. Just as importantly, it reduces the operational burden of maintaining infrastructure, patching systems and managing uptime.

Simplified cloud adoption through offerings such as GROW with Â鶹ԭ´´ for new ERP customers and RISE with Â鶹ԭ´´ for those moving from on-premise systems to the cloud ease the path to adoption. The emphasis is not on infrastructure alone, but on packaged best practices, faster implementations and built-in compliance and security.

With hybrid and remote work now an established reality for SMEs, demand for cloud-based human capital management systems is surging. These systems integrate payroll, performance, learning and workforce analytics, equipping even smaller firms with digital payslips, employee self-service, compliant payroll processing and basic people analytics.

Trend 2: Business AI moves from hype to habit

The most important AI trend for African SMEs is not experimentation with standalone tools, but the quiet embedding of AI into everyday business workflows. Finance, HR, supply chain and customer operations are increasingly augmented by AI that automates routine tasks, highlights risks, and supports better decisions.

The expected gains are practical rather than futuristic: faster invoice processing, improved cash-flow forecasting, better demand planning and more efficient HR administration. For example, Â鶹ԭ´´â€™s Joule AI copilot is being embedded across core business applications, enabling natural-language interaction with trusted enterprise data. Instead of building AI capabilities from scratch, SMEs consume intelligence directly through their ERP, HR and planning systems.

This matters in African contexts, where skills and budgets are constrained and trust in data is critical.  found that nine in ten African organisations were suffering from negative impacts due to a lack of AI-related skills, including delays in implementations, failed innovation initiatives and loss of clients.

Trend 3: ERP is the digital nerve centre

This year, cloud ERP will be less about “modernisation� and more about survival. SMEs that remain on fragmented, on-premise systems will find it harder to compete on cost, speed and trust.

Once seen as too complex or expensive for smaller firms, modern ERP is increasingly modular, cloud-native, mobile-friendly and AI-enabled. It integrates finance, operations, people and partners into a single source of truth. For SMEs, ERP is no longer just a back-office system but a digital nerve centre that enables AI, supports compliance, strengthens security and connects businesses to wider ecosystems.

In 2026, African SMEs that build capability stacks around cloud ERP, embedded AI, secure platforms and digital skills will be able to compete with far larger organisations. Those that delay risk being locked out of supply chains, talent pools and digital markets.

Trend 4: Cybersecurity becomes existential

Ransomware, business email compromise and data breaches are no longer rare events, and the financial impact can be devastating.  found that the global average cost of a data breach reached $4.4m in 2025. For many SMEs, such a breach represents an existential threat.

The volatile cyber threat landscape is shaping technology decisions. Cloud platforms can help reduce overall risk by consolidating security, patching and monitoring into professionally managed environments. For example, Â鶹ԭ´´â€™s cloud ERP strategy emphasises secure-by-design architectures and shared responsibility models that reduce the burden on small IT teams.

This year, cybersecurity will be firmly established as a board-level issue for African SMEs, on par with cash flow and regulatory compliance.

Enterprise technology is heading toward cloud, business AI and end-to-end solutions that improve planning, efficiency, execution and innovation capabilities. For African SMEs, the opportunity lies in adopting these capabilities pragmatically and early, turning global platforms into local competitive advantage.

The post The Essential Tech Trends for African SMEs appeared first on Â鶹ԭ´´ Africa News Center.

That message landed at a pivotal moment. Renewable power capacity reached , with solar, wind and hydropower accounting for about 40% of global electricity generation.  of new power capacity additions now come from renewables. Yet deployment rates still fall short of the COP28 goal to triple installed capacity to around 11 TW by 2030.

Behind every gigawatt of clean power sits a minerals story. Lithium demand is expected to . Cobalt demand is projected to rise by 50–60%, copper by around 30%, while graphite and nickel are set to at least double. Securing reliable, diversified supply chains for these materials has become a strategic priority for governments and corporates alike – and the geopolitical competition to secure them is intensifying.

Africa indispensable to energy transition

Africa sits at the heart of this equation. The continent holds . The Democratic Republic of Congo supplies over 70% of global cobalt. Zambia remains one of the world’s major copper producers. Zimbabwe has emerged as Africa’s leading lithium producer, while South Africa .

Little wonder, then, that the continent has become a focal point of geopolitical competition. The EU’s Critical Raw Materials Act, which entered into force in 2024, sets binding targets to diversify supply away from single-country dependence. The United States has committed over $4-billion to the Lobito Corridor connecting the DRC and Zambia to Angola’s Atlantic port, with total global investment now exceeding $6-billion. China, meanwhile, has stakes in fifteen of the DRC’s nineteen cobalt mines and continues to expand its Belt and Road footprint across the continent. For African producers, this convergence of competing interests creates both leverage and risk.

Yet this enormous potential is . Regulatory uncertainty, infrastructure deficits in power and transport, skills shortages, environmental pressures and illegal mining all heighten risk. Despite its resource base, Africa attracts less than 10% of global exploration spending.

Without policy coherence, infrastructure investment and transparent governance, Africa faces what might be called a “green resource curse�: exporting raw ore while importing finished technology and foregoing industrialisation. Consider that the DRC exports cobalt at a fraction of the price battery-grade cobalt hydroxide commands on world markets. Zimbabwe ships raw spodumene while lithium hydroxide, the processed product automakers actually need, is refined almost entirely in China. If the energy transition simply replicates old extractive patterns under a green label, Africa’s mineral wealth will once again benefit others more than its own citizens.

Coordination holds the key

Modern mining is no longer a purely extractive business. It is a data-driven, multi-stakeholder ecosystem that must integrate geological modelling, capital allocation, environmental performance, community engagement, logistics and global supply-chain compliance. Technology is the coordination fabric that links these moving parts.

Integrated digital platforms now give stakeholders a shared view of reserves, project timelines, ESG metrics and logistics flows. By reducing information asymmetry, they  between miners, governments, development finance institutions and private investors. In an era where climate finance and transition-minerals funding depend on transparency, digital traceability is foundational.

Advanced analytics and AI are reshaping core mining processes. Digital twins allow operators to simulate mine design, production scenarios and environmental impacts before committing capital. Predictive maintenance reduces unplanned downtime and extends asset life. Across major operations, these tools are compressing exploration timelines and lifting productivity measurably.

Equally important for African producers seeking to move beyond raw exports is supply-chain integration. Digital commodity platforms that connect contracts, logistics, pricing and ESG attributes can help African refiners and processors demonstrate responsible sourcing at scale. This matters because the EU’s due-diligence requirements and the US Inflation Reduction Act increasingly reward traceable, locally processed minerals with green premiums and preferential market access. Technology thus becomes an enabler not just of efficiency, but of beneficiation and in-country value addition.

Meeting operational and ESG demands

Enterprise technology platforms such as those offered by Â鶹ԭ´´ play a strategic role by providing the operational backbone across planning, asset management, procurement, logistics and ESG reporting. When a mid-tier miner can compress its sustainability reporting cycle from weeks to days, or a junior explorer can present investors with independently verified ESG data alongside geological assays, the conversation with capital markets shifts. Cloud ERP systems that integrate production data with financials in real time, embedded Business AI that flags maintenance risks before they become failures, and human capital management tools that track scarce skills and certifications all translate directly into bankability and investor confidence.

Crucially, the same platforms that manage production can also measure environmental and social impact. By combining operational and ESG data, mining companies can model emissions, water use and community outcomes and share evidence-based reporting with regulators and investors. In a capital-intensive sector where financing increasingly depends on sustainability credentials, this digital transparency becomes a competitive advantage.

Africa’s mining sector is therefore not just a supplier of transition minerals but a test case for how the energy transition can be aligned with industrial development. With the right policies, infrastructure corridors, skills programmes and digital coordination frameworks, critical minerals can underpin new refining capacity, regional value chains and millions of jobs.  – “Stronger together: Progress through partnerships� – was well chosen. If Africa’s mineral potential remains constrained by fragmentation and mistrust, global decarbonisation will slow. If, however, stakeholders act in concert, using technology as the connective tissue and beneficiation as the organising principle, Africa can move from raw-materials exporter to central architect of the net-zero economy.

This article first appeared in magazine.

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, HR Director at Â鶹ԭ´´ Africa, says there is a noticeable shift away from purely qualification-led hiring toward skills-based thinking, but cautions against overstating how far this has actually progressed.

“We like the idea of a skills-first approach,� says Koolen. “In practice, many organisations are still deeply attached to traditional credentials, even while saying they can’t find the talent they need. There’s a tension between what the market says it wants and what it still screens for.�

That tension is becoming more visible as demand grows in areas such as AI, cybersecurity, cloud computing and data analytics. Organisations are increasingly defining roles in terms of specific technical capabilities, yet the pipelines producing those skills remain slow, uneven and often disconnected from real work.

Recent research into African enterprises shows that companies are increasingly defining roles by specific skills in areas such as AI, cybersecurity, cloud computing and data analytics. In a study commissioned by Â鶹ԭ´´, 85% of organisations identified AI development skills as a priority, while 86% ranked cybersecurity capabilities as critical.

Systemic overhaul needed

“There’s a lot of talk about AI skills as the new currency,� Koolen adds. “But currencies only work if there’s a functioning system behind them. In many African contexts, we’re asking for advanced capabilities while under-investing in the basics such as access, foundational training, mentorship and realistic on-the-job exposure.�

This gap is partly driving interest in short, intensive learning formats such as micro-learning and micro-credentials. Designed to build focused skills over weeks rather than years, these programmes are often positioned as a solution to Africa’s tech skills shortage. Koolen urges caution.

“Micro-learning can be powerful when it’s well designed and tightly linked to actual roles,� she says. “But it’s not a silver bullet. A six-week course doesn’t replace experience, judgment or systems thinking. The risk is that we oversell speed and underplay depth.�

For many professionals, however, short-form learning is simply more realistic than stepping away from work to pursue long, expensive qualifications. “Most people can’t afford to pause their livelihoods,� Koolen notes. “Bite-sized learning allows movement, but only if employers are willing to support learning on the job, not just tick a training box.�

°Â³ó¾±±ô±ðÌý that many African organisations now offer regular training, Koolen is clear that frequency does not equal effectiveness. “Offering monthly learning is not the same as building capability. Too often, training exists in isolation from workforce planning, role design and actual delivery pressure.â€�

Call for cross-sectoral collaboration

Closing the skills gap, she argues, requires more honesty and collaboration across sectors. “Education institutions, business and the public sector all have a role, but alignment is still weak. We’re not short of initiatives; we’re short of coherence.�

Within Â鶹ԭ´´â€™s ecosystem, targeted programmes such as graduate bootcamps and early-career development initiatives aim to bridge some of these gaps by combining technical training with real project exposure. Koolen sees these as useful — but again, not sufficient on their own.

“They work because they’re intensive, contextual and tied to real demand,� she says. “But they don’t scale easily, and they don’t solve the broader systemic issues around employability, access and long-term career progression.�

Universities across Africa are experimenting with edtech platforms and stackable credentials to stay relevant, yet Koolen believes higher education is still wrestling with its purpose in a rapidly changing labour market.

“The question isn’t whether degrees still matter,� she says. “They do. The question is whether we’re honest about what they prepare people for, and what they don’t.�

Traditional MBAs and long-form qualifications continue to offer strategic breadth and critical thinking, but on their own they no longer meet the immediate needs of organisations grappling with fast-moving technologies.

“The future isn’t either-or,� Koolen concludes. “It’s layered. Foundational education, practical experience, short-form learning and vendor-specific skills all matter. The danger is pretending that one quick fix will solve a problem that’s structural, uneven and deeply human.�

The post Degrees Vs Skills: Africa’s Tech Debate appeared first on Â鶹ԭ´´ Africa News Center.

The comments were made by Sunil Geness, director of global government affairs and CSR at Â鶹ԭ´´ Africa, and Sergio Maccotta, senior VP and GM for Â鶹ԭ´´ Middle East and Africa – South.

Geness and Maccotta said businesses that can sense change, make decisions and act with minimal human intervention are reshaping how companies operate and compete. According to Â鶹ԭ´´, autonomous operations – where nearly half of all business processes run independently and most operational work is automated or AI-augmented – are being deployed across finance, supply chain and human resources in industries including energy, retail and manufacturing.

“This is made possible through AI embedded within enterprise resource planning processes, from finance and procurement to supply chain and HR systems, that understand context and act autonomously,� said Maccotta. “Self-optimising systems that learn, improve and adapt in real-time are matched to clean-core ERP architecture to give organisations simplified, cloud-based systems that drive profitability, reduce risk and enhance decision-making.�

Â鶹ԭ´´ cited data from  indicating that the global autonomous enterprise market is projected to grow from about $49 billion in 2024 to more than $118 billion by 2030, with adoption accelerating across Europe, the Middle East and Africa.

The company said while few surveys use “autonomous enterprise� as a formal category, data on AI, ERP and AI agents reflects technologies underpinning autonomous models.

Recent data for the Middle East and Africa region indicates a market size set to grow at a compound annual growth rate of 8.7%, reaching $10.2 billion by 2032.

“AI is enhancing efficiency and fostering innovation across industries, from automating routine tasks to enabling complex data analysis and providing predictive insights, while also improving decision-making and optimising business operations,� Geness said.

Â鶹ԭ´´ added that some analysts estimate AI could contribute $1.5 trillion to Africa’s economy if the continent captures 10% of the global AI market by 2030.

Data sovereignty

Â鶹ԭ´´ executives said Africa’s digital transformation continues, with data sovereignty emerging as a key factor.

“For multinational organisations and technology companies, success depends on the ability to localise without losing global efficiency, to build trust while maintaining innovation, and to invest strategically in infrastructure and partnerships that align with both regulatory demands and customer expectations,� Maccotta added.

The company said Africa’s data centre market is projected to exceed $9 billion by 2029, although it remains a small share of the global total.

Executives said autonomy does not eliminate human roles but changes them, shifting employees from managing repetitive processes to supervising systems and making strategic decisions.

Maccotta advises business leaders seeking to build autonomous enterprises to start by modernising the core and fixing the data foundation for AI-driven innovation.

“Remember that technology is only part of the story, and that the strategy, people and partners that businesses choose are as important to building a truly connected autonomous enterprise. Focus then on automating processes that protect revenue, improve cashflow or unlock capacity first, and take care to prepare people by prioritising reskilling and upskilling.

“Finally, collaborate with technology partners that act more as advisors than vendors, and can guide the process of redesigning core business processes for the AI era. True transformation happens when businesses combine intelligent systems with visionary leadership, skilled people and trusted partnerships.â€�

This article first appeared in .

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The real issue is not whether countries should pursue digital sovereignty in critical sectors, but how best to achieve it, says Christian Klein, CEO of Â鶹ԭ´´ SE.

Digital sovereignty is now a fundamental national security capability worldwide. In Europe, secure data, resilient operations and legal control form the backbone of stability and crisis preparedness.

From Klein’s perspective, digital sovereignty is understood across four dimensions: data, operational, technical and legal, supported by verifiable governance and transparency over critical operations. However, a lack of agreement on how to define sovereignty is impeding Europe’s ability to deploy secure digital systems and is slowing digital transformation across public services and regulated industries.

“Across Europe, and increasingly across the global technology landscape, digital sovereignty is interpreted in different and sometimes conflicting ways. This lack of alignment creates uncertainty for public authorities, regulated industries and technology providers. It directly undermines the adoption of cloud, AI and mission-critical digital services.�

Regulatory complexity and lack of harmonisation risk turning sovereignty into a barrier instead of an enabler of innovation, particularly when each country defines its own implementation model. AI adoption depends on trust, and if governments and regulated industries cannot use AI securely, they risk falling behind in an increasingly competitive global environment.

Klein emphasises that integrating global technologies ― within European legal and operational frameworks ― can boost AI adoption and strengthen Europe’s position in responsible innovation that is both competitive and compliant.

“From my perspective, digital sovereignty means the ability to exercise choice, control and governance across data, operational, technical and legal layers,� he says.

“This is not about technological isolation or excluding global partners. Europe must remain open to innovation and international cooperation.

“However, openness must be anchored in enforceable European law, clear accountability and auditable operational control. In practical terms, credible sovereignty means public authorities can verify where data is processed, who operates critical systems, which legal framework applies and who is accountable at every level.

“Without this clarity, sovereignty risks remaining a political concept rather than a reliable basis for trust, compliance and innovation, especially for AI-driven solutions.�

When we talk about digital sovereignty, he says too often the focus is on where the infrastructure is located. The reality is that digital sovereignty goes far beyond that. Infrastructure alone does not address the full risk profile.

Security and resilience depend equally on governance: who controls the software stack, who operates the platforms, and who bears legal and operational responsibility. If these questions are not clearly resolved, risks persist even when the infrastructure is physically located in a given jurisdiction.

“Sovereignty washing� ― where compliance claims are not supported by verifiable controls ― creates false assurance and weakens public trust. Sovereignty must be demonstrated in practice, through auditability, effective supervision and clearly assigned responsibilities across the entire digital value chain from data generation to AI inference.

From a governance perspective, the priority is to shift the focus from infrastructure alone to transparency, accountability and enforceable oversight.

Klein says European innovation needs a supportive environment to thrive, with legislation that encourages progress rather than holding it back. Such an environment includes one unified definition of technological sovereignty in the European Single Market, ensuring harmonized and practical implementation of EU law, such as the AI Act and Data Act, and building out clear, sector-specific requirements that industry and public authorities can implement at scale.

Europe has established strong regulatory foundations, notably through GDPR and the AI Act. The central challenge now lies in consistent implementation and deployment at scale. Public administrations and regulated sectors require sovereign cloud and AI solutions that are operational, compliant and ready for use.

Experience from public services and critical industries shows that AI can be deployed at scale while remaining fully aligned with EU law, including data protection, operational control and security requirements.

This can be achieved through partnerships that provide sovereign cloud environments where customers retain control over data access, operations and legal jurisdiction. AI is essential to improving efficiency, quality and responsiveness in public services, but adoption depends on trust.

If public authorities cannot deploy AI in a secure and lawful manner, innovation will stall. Sovereign cloud and AI are therefore essential enablers for translating Europe’s regulatory leadership into tangible economic and societal outcomes.

Europe’s competitive advantage lies in applied innovation, not in replicating global hyperscale infrastructure. Attempting to rebuild complete technology stacks domestically would be costly, inefficient and would slow down digital transformation. A more effective policy approach is to integrate global technologies within European legal, operational and accountability frameworks.

This enables Europe to benefit from global innovation while ensuring control under EU law. This model combines global technology partnerships with European governance, compliance and operational transparency. Europe should support and scale such integration models, rather than encourage technological isolation. When properly designed, this kind of openness and control would reinforce Europe’s long-term competitiveness and resilience.

How can Europe integrate global technology while retaining control?

Public institutions and regulated organisations across Europe are operating in a context of geopolitical uncertainty, increasing cyber risk and rapid technological change. They need trusted partners that combine technological scale with regulatory expertise and long-term stability.

Â鶹ԭ´´ contributes to this shift by delivering sovereignty across data, operational, technical and legal dimensions. We support public administrations and regulated industries in modernizing core systems, migrating securely to the cloud and deploying AI responsibly.

This includes clear audit trails, operational transparency, and contractual safeguards that give customers confidence in who controls their systems and data.

Because Â鶹ԭ´´ underpins many of Europe’s most critical public- and private-sector processes, we recognize that sovereignty cannot be added retrospectively. It must be designed into digital systems from the outset.

This is where Â鶹ԭ´´â€™s experience and long-standing engagement with European institutions provide value.

At Â鶹ԭ´´, we support interoperable, partnership-based approaches governed by clear rules and accountability. These models enable faster adoption, reduce fragmentation and enhance resilience. For policymakers, fostering such ecosystems is more effective than prescribing specific technologies.

Digital sovereignty cannot be achieved by individual actors alone. It depends on ecosystems, cooperation and shared responsibility.

Sovereignty and innovation are complementary objectives. Without secure and accountable frameworks, AI adoption will remain constrained. But if Europe gets digital sovereignty right, it strengthens resilience, competitiveness and public trust simultaneously.

It accelerates the responsible use of AI across health care, government, manufacturing, and financial services.

Achieving this balance positions Europe as a credible model and leader for applied, responsible innovation. It demonstrates that advanced technologies can scale without compromising legal certainty or societal trust.

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Many companies sit with years of technical debt, complex hybrid environments, disconnected systems and inconsistent metadata that make it difficult to build a reliable view of the business. Businesses still grapple with ageing applications that cannot integrate with cloud platforms, while siloed departmental systems prevent teams from accessing the full picture needed for AI-driven decision-making.

The result is predictable: stalled AI projects, unreliable outputs, and limited return on investment.

The need for a unified data foundation

The typical South African enterprise runs a mix of cloud services, on-premises applications and bespoke systems that were built years ago to solve specific operational needs. While these systems may still work, they often lack the interoperability required to support modern AI initiatives. Data is stored in inconsistent formats, lack proper metadata, and often depend on manual extraction processes that strip away the business logic that AI models need to understand the context of the data and what it really represents.

This fragmentation affects everything from financial reporting to customer experience. Without a single, trusted view of data, predictive models become unreliable, automated processes fail, and teams lose confidence in machine-generated insights. For AI to scale, data must be complete, consistent, governed and accessible across the organisation.

An IDC report commissioned by Seagate previously found that up to 68% of available enterprise data goes unused.

Modern data platforms address this by connecting all enterprise systems, preserving business context, enabling real-time data access and allowing organisations to integrate with other providers. The outcome is a unified data fabric that supports analytics, applications and AI at scale.

Preparing business data for AI

Building an AI-ready data foundation doesn’t happen by accident. It requires a deliberate, structured approach, following these five steps:

1 Assess the current data landscape

The first step is understanding what exists today. This means cataloguing data sources, identifying owners, documenting quality issues, and assessing integration gaps. It also involves mapping AI use cases to data requirements so that data preparation can be prioritised and aligned to real business needs.

For South African organisations with complex legacy environments, this assessment is essential to uncover hidden dependencies and address high-risk limitations early in the process.

2 Establish clear data governance and quality standards

Reliable data requires strong governance. Organisations should define roles, responsibilities and policies governing data access, security, metadata and quality. This includes setting measurable standards for completeness, accuracy and consistency, supported by automated profiling tools. Metadata management is particularly critical: without clear definitions and lineage, teams cannot trust or effectively use the data.

Governance should also reflect South Africa’s regulatory environment, including POPIA requirements around privacy and security.

3 Integrate and unify disconnected data sources

The next step is breaking down silos. Modern integration tools such as those built into Â鶹ԭ´´Â Business Data Cloud allow organisations to connect Â鶹ԭ´´ and non-Â鶹ԭ´´Â systems, unify data across cloud and on-premises environments, and maintain the business meaning of data as it moves.

This unified layer eliminates duplication, reduces manual extraction processes, and ensures teams work from a consistent, shared version of the truth. Real-time integration capabilities are especially important for AI models that need up-to-date information to make accurate predictions.

4 Clean, enrich and transform data

Raw data is rarely ready for AI. It must be cleaned, enriched and transformed, including correcting errors, removing duplicates, filling missing values and standardising formats. Organisations should also create new features that allow AI models to identify patterns more effectively and incorporate additional context from internal or external sources.

South African businesses with extensive unstructured data, such as PDF reports, invoices or call centre notes, should prioritise converting this content into structured formats for easy ingestion into AI models.

5 Validate, monitor and maintain data pipelines

Even the cleanest dataset will deteriorate if not continuously monitored. Organisations should validate data before it feeds AI models, track data quality in real time, and monitor for drift or anomalies that can degrade model performance.

Automated governance tools help maintain data integrity, while clear documentation ensures teams understand how data is sourced, processed and used. Regular monitoring is essential in environments where systems, processes and regulations frequently change.

Getting the data foundation right is a technical requirement and a strategic imperative. The organisations that prioritise data quality, integration and governance today will be the ones that scale AI confidently tomorrow, reducing risk, improving performance and unlocking new opportunities for innovation in an increasingly competitive market.

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The good news, says , Managing Director: Southern Africa at Â鶹ԭ´´, is that real-world AI value is already becoming clear. “Across critical business functions like finance, HR, procurement, sales and service, and marketing, embedded AI is driving efficiency, accuracy, and smarter decisions at scale. From automating repetitive processes to delivering real-time insights and predictive recommendations, South African companies are unlocking unprecedented gains in productivity and efficiency.â€�

Analysts suggest African countries could unlock as much as $100-billion in economic value per year , while a PwC study estimates  if Africa can claim a 10% share of the global AI market.

“To unlock this vast economic potential, organisations must integrate AI into core business processes and achieve measurable outcomes for clear use cases. A clean core strategy further allows businesses to respond faster to market changes and adopt new technologies like AI and advanced analytics more easily, creating clear pathways to significant ROI and business impact.�

Of critical importance too is defining and implementing a comprehensive AI framework that prioritises governance at the outset. “An effective framework should include a clear AI vision, business-aligned goals, an operating model, and, critically, establishment of AI governance to ensure ethical, secure, and compliant scaling of AI across the organisation.�

Pillay highlights high-impact use cases across five key business functions that South African organisations can implement to boost productivity and drive growth.

1 Smarter financial insights & automation 

Finance departments are under increasing pressure to speed up reporting cycles, reduce operational costs, and strengthen cash flow visibility. “In South Africa, financial institutions are leveraging AI for everything from fraud detection to multilingual support, but practical automation is where the biggest gains are currently being made,� says Pillay.

Automated receivables matching is one such win. By applying machine learning to past payment behaviours, companies can automatically match and clear bank statement items,  and accelerating payment cycles.

Another high-impact tool is AI-assisted cost centre analysis. “Instead of manually wading through complex reports, finance teams can use generative AI to instantly summarise data, highlight KPIs, and recommend next steps,  and freeing up analysts to focus on strategic insights,� says Pillay.

2 Smarter hiring and better talent fit

AI is transforming HR from a reactive cost centre into a strategic function. Nearly 70% of South African HR teams  to streamline recruitment, performance reviews, and workforce planning, delivering up to 35% process efficiency gains.

“One practical use case gaining widespread adoption is AI-powered applicant screening, where machine learning scans CVs to match applicant skills with job requirements. This  and speeds up hiring for hard-to-fill roles.�

Pillay points to AI-generated job descriptions as another time-saving tool. “HR professionals input a few keywords and receive polished, bias-reducing job listings in seconds, helping improve candidate fit and .�

3 Scaling service and sales excellence

South African telecoms, ecommerce platforms and financial institutions are seeing measurable gains from AI-powered customer service.

“Tools like chatbots and virtual assistants now handle routine queries with 24/7 availability, cutting service costs and improving customer satisfaction,� explains Pillay. “Where a human touch is still needed, AI is helping agents generate case summaries, allowing them to respond faster by compiling email threads and communications into a single, easy-to-read brief. This can  and improve first-contact resolution by 10%.�

In field service, AI-driven equipment insights give technicians a clear picture of past service activity, parts used, and common failure patterns, resulting in 65% higher productivity and a 5% bump in first-time fix rates, .

4 Streamlined sourcing and planning

Procurement teams face mounting complexity as they juggle supply risk, regulatory compliance, and shifting market dynamics. According to Pillay, AI is easing this burden by automating strategic sourcing tasks and offering real-time decision support.

“AI tools have become indispensable to category planning efforts, . This is helping managers move 90% faster and make more informed and proactive decisions.�

Another valuable application is automated statement-of-work (SOW) generation. With minimal inputs, , slashing processing time by 71% and improving project alignment by providing clearer briefs to suppliers.

5 Improved customer engagement and inventory management

AI’s value in marketing is growing fast, especially in segmenting audiences and triggering personalised campaigns. South African firms are tapping into predictive tools to boost engagement and pre-empt churn.

“With AI-powered audience segmentation, marketers can automatically group customers based on predicted behaviours such as likelihood to convert or churn, driving higher rates of engagement and more relevant, timely outreach,� says Pillay.

In back-office support, AI-generated sales orders now , cutting manual effort, speeding up processing, and reducing errors by 25%.

“As embedded AI matures and business systems become more intelligent, these use cases will continue to expand, driving a new era of productivity, insight, and competitive advantage across every function,� says Pillay.

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