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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鈥淭here is a near-universal need for AI-related skills among African companies this year,鈥� says Genevieve Koolen, HR Director at 麻豆原创 Africa. 鈥淎nd since traditional IT skills such as cloud- and cybersecurity-related competencies remain in high demand, companies now face the dual challenge of attracting and retaining traditional tech talent while also building greater AI competencies within their businesses. It is unsurprising then that most African organisations provide career development opportunities for employees with AI specialisations.鈥�

麻豆原创 recently released its 鈥樷�� report, which revealed that all companies surveyed expect the demand for AI skills to increase in 2025. Nearly half said they expect a 鈥榮ignificant鈥� increase.

Koolen says that while there is an urgent need for policymakers and education institutions to fast-track AI skills development initiatives among Africa鈥檚 swelling youth population, companies also face pressure to equip existing workers with future-ready skills.

Impacts of skills shortage widespread

听that AI could contribute $1.5-trillion to Africa鈥檚 economy by 2030, provided the continent can capture 10% of the global AI market. African organisations are alive to the possibilities presented by AI-related innovation, with companies citing perceived value in improved decision-making (64%), marketing capabilities (51%) and innovation (47%) enabled by AI.

However, poor access to AI-ready skills is already causing negative impacts among the same companies, including failed innovation initiatives, delays completing projects, greater pressure on teams, and an inability to take on new client projects.

鈥淥rganisations are rising to this challenge by increasing the frequency of training offered to employees, with 94% saying they offer training at least monthly,鈥� says Koolen.

However, the latest data indicates a drop in the allocated budget for skills development. In a previous survey conducted in 2022, a quarter of organisations said they spend more than 15% of their HR or IT budgets on skills development and training. This year, not a single organisation that formed part of the research spent more than 10%.

Practical steps to more AI-capable workforce

While the full impact of AI and other emerging technologies on Africa鈥檚 workforce remains to be seen, Koolen says there are practical measures companies can implement to ensure they cultivate the correct skills mix.

1 Be prepared

With universal demand for tech and AI-related skills and an ongoing skills scarcity, African organisations must prepare for a shortfall in critical AI-related skills this year.

鈥淭he moment calls for a pragmatic approach that combines longer-term skills development 鈥� including reskilling and upskilling 鈥� with short-term measures that alleviate some of the immediate pressures and creates space for more robust skills development initiatives. Organisations also need to take care to support employees through this uncertain period, for example by using human capital management technologies that help HR teams identify concerns.鈥�

2 Prioritise training

Koolen says it is surprising that budget allocations for training and skills development appear to be shrinking. 鈥淭oo many digital transformation and innovation initiatives fail to deliver the expected business value due to a lack of appropriate skills. In light of the rapid pace of technological advancement, any organisation that fails to invest in skills will likely find they are unprepared and unable to leverage new innovations. In time, this will erode their competitiveness and lead to significant impacts to the bottom line.鈥�

Instead, organisations should place skills development at the core of their business strategies to ensure a steady stream of work-ready talent and invest sufficient budget to guarantee high-quality outcomes for employees and the business.

3 Partner well

While Africa has the fastest-growing youth population of any continent, there are still significant systemic challenges with equipping youth with adequate work-ready skills. 鈥淎frica鈥檚 ability to reap the benefits of AI-related innovation rests on broader public-private sector efforts at cultivating the correct skills mix,鈥� says Koolen. 鈥淧artnering with educational institutions and other industry skills development initiatives can accelerate the rate at which skills become available to companies.鈥�

She adds that technology vendors can also play a valuable role. 鈥淟arge technology companies often have large global workforces and strong employer brands, allowing them to attract top talent. Partnering with tech venters can augment organisations鈥� skills base and provide valuable support to AI-led initiatives.鈥�

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鈥淪targate鈥�, a US initiative to build the largest AI data centers the world has ever seen, aims to invest $500-billion over the next four years. Saudi-Arabia and the United Arab Emirates just announced plans to buy hundreds of thousands of advanced AI chips.

And at France鈥檚 鈥淎I Action Summit鈥� earlier this year, public and private actors pledged investments surpassing 鈧�300 billion to advance AI in Europe.

It is very clear that expectations about the economic benefits of this technology are sky high. Yet, a recent McKinsey survey found that more than 80% of organisations worldwide are not yet seeing any tangible impact on their profits.

So where does this mismatch come from?

The key to the answer is that AI is not a stand-alone technology. For its benefits to materialise, AI has to be deeply embedded in business processes. And for that, companies have to put three pillars in place: modern cloud software, modern data management, and a consistent stack of AI technologies linking with them.

Let鈥檚 start with software: All successful companies use software to organize and optimise their business operations 鈥� from order intake and procurement to production, delivery and customer service. Yet, many enterprises still rely on legacy on-premise software 鈥� that is, a wide range of programs installed on the company鈥檚 local IT servers.

This 鈥渟oftware landscape鈥� often consists of disparate applications plugged together 鈥� heavily modified over the years and frequently not up to date with the latest innovations. These complex systems are costly to maintain, and they make it difficult for companies and their leaders to respond to challenges and opportunities with agility and speed.

AI applications, too, face major obstacles in legacy systems: They have a hard time grasping the company鈥檚 inner workings, making sense of fragmented and widely distributed datasets, and may not be able to find certain key information.

The first step towards powerful business AI, therefore, is the move from legacy on-premise software to modern cloud software 鈥� that is applications that are centrally managed and maintained in professional data centers, constantly updated with new innovations, and tightly linked so information can flow freely between the different parts of the company.

For companies today, this so-called cloud migration is faster, smoother and more transparent than ever before 鈥� thanks to the proven methods and advanced digital tools now available. And the prize is larger than ever before, too: integrated cloud applications work together out of the box and cover the company鈥檚 software needs end-to-end across departments.

This integration allows a car maker, for example, to reduce time and cost 鈥� say, from receiving an order through the vehicle鈥檚 production to its final delivery.

Similar benefits extend to all other industries and workflows.

A cloud migration, consequently, is more than an IT project: it is the digital foundation for a thorough modernisation of the entire enterprise, for moving from 鈥済ood鈥� to 鈥済reat.鈥�

Once in the cloud, companies can add advanced data management solutions with little effort. Think of advanced data management as a magic filing cabinet: it automatically stores and organises all documents, all information, all data automatically in the right place and in perfect order 鈥� always up to date, perfectly searchable, without duplicates and errors, smartly annotated, and everything in the right context.

In their combination, integrated cloud applications and advanced data management allow company leaders a holistic view of their enterprise. At the same time, they enable AI technologies to access, understand, and facilitate transactions across the company 鈥� assisting human users with repetitive tasks as well as with deep analyses and insights.

And the next evolution is already at hand: Based on integrated cloud applications and data management, digital coworkers 鈥� also known as 鈥淎I agents鈥� 鈥� are now able to carry out complex work assignments. For example: find overdue invoices, identify what went wrong, resolve the issue, and make sure payment targets are met.

Realising the tremendous benefits of AI is thus about going on a journey: from on-premise software to cloud applications, then onwards to modern data management and the use of AI agents throughout the enterprise. It is this journey that unlocks the tremendous potential so many see in AI 鈥� and enables us to completely reimagine how our businesses and economies are run.

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