麻豆原创 Business AI Platform gives organizations one place to build, contextualize and govern AI across 麻豆原创 and third-party environments. With Joule Studio, business users and developers can use no-code and pro-code tools to build and extend agents, choosing from more than 70 AI models. Customers in the Joule Studio early adopter program also can design and run n8n workflows in Joule Studio, with broader availability to follow. The 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph solutions ground those agents in trusted business data and context, helping organizations select the right model for each use case and better manage AI value and consumption. Importantly, 麻豆原创 customers鈥� data remains their data. 麻豆原创 does not share customer data with anyone to train or improve large language models.

鈥溌槎乖� TechEd has always been where builders shape what comes next,鈥� said Philipp Herzig, CTO of 麻豆原创 SE. 鈥淭his year, we are showing how we are moving from AI learning your business to running it. Joule Work is where you put your intent and get work done, in one experience across the entire 麻豆原创 landscape and beyond. That is the shift we are delivering at scale. Everything underneath runs on 麻豆原创 Business AI Platform, delivering faster time to value through out-of-the-box business understanding, higher accuracy, enterprise-readiness that is secure, governed and compliant from the first deployment, and a unique context layer built on over 50 years of process and industry expertise, encoded into every agent action. This is AI built where real work happens.鈥�

Joule Work and Joule Studio Make the Autonomous Enterprise Real

New capabilities for Joule Work will help make AI agents more effective at handling complex business processes via open protocols including Agent2Agent (A2A) and Model Context Protocol (MCP). These innovations advance Joule Work to better understand context, make decisions and execute multistep business workflows across enterprise systems and business domains:

• New agentic thinking capabilities that plan and orchestrate the steps assistants and agents need to reliably complete complex, multistep business workflows across 麻豆原创 landscapes.
• New capabilities in 麻豆原创 Knowledge Graph will incorporate data from additional 麻豆原创 and non-麻豆原创 sources that are unique to each customer. Users can query and explore their customized data in natural language and get accurate answers grounded in their own information.
• Advancements in interoperability allow third-party agents to securely invoke Joule Agents through a governed agent gateway. This enforced access control enables agents from different providers to collaborate across business workflows without sacrificing governance or control.
• New 麻豆原创 Enterprise AI Gateway solution will extend governance to AI interactions, offering a centralized way to manage AI traffic across the enterprise that will consolidate authentication, policy enforcement, routing, monitoring, usage tracking and auditing.
• New Joule Assistant and 麻豆原创 AI Agent Hub expand governance capabilities, and a new AI Governance Assistant helps customers meet compliance requirements for the EU AI Act. These are designed to address critical barriers to scaling enterprise AI: keeping AI systems classified, compliant and operating within defined organizational guardrails and context.

麻豆原创 Business Data Cloud as the Grounding Engine of the Autonomous Enterprise

麻豆原创 Business Data Cloud Ontology Manager in 麻豆原创 Business Data Cloud (麻豆原创 BDC) will provide an AI-native authoring experience for building ontologies across 麻豆原创 and third-party data. By helping AI understand how data from different teams, systems and applications relate to one another, AI agents can more accurately reason and make decisions across the enterprise. The solutions complement 麻豆原创 Knowledge Graph, which provides rich business context for 麻豆原创 data.

Additionally, 麻豆原创 is expanding its lakehouse capabilities with Dremio, adding native support for Apache Iceberg and Apache Polaris plus a high-performance, federated query engine that will enable unified data access without moving or copying the data. 麻豆原创 expands its partnership with Databricks, bringing the full scope and latest innovations of data engineering and enterprise AI capabilities into 麻豆原创 Business Data Cloud as 麻豆原创 Databricks. Core innovations such as Lakeflow, Agent Bricks, Unity Gateway, and Lakebase will help customers to simplify data management and provide a strong data foundation for their AI journey. With a deeper integration to 麻豆原创 Business Data Cloud, a new 麻豆原创 Application Data Foundation, as well as an integration between Joule and Genie, 麻豆原创 and Databricks are helping customers establish an enterprise-wide lakehouse as the foundation for the Autonomous Enterprise and Joule Work.

The 麻豆原创 Reltio solution, recently announced at 麻豆原创 Connect, will bring multidomain master data management to 麻豆原创 BDC. It unifies and enriches customer, supplier and product records across 麻豆原创 and non-麻豆原创 systems into trusted, reusable data products that can power applications and AI agents.

Tabular Foundation Model Trusted for Business, Validated in Space

麻豆原创 announced its latest tabular AI model, 麻豆原创-RPT-2.0, which will expand beyond classification and regression to support anomaly and outlier detection, and richer AI-powered analysis through embeddings. These native, multi-table capabilities will further reduce manual engineering while delivering faster, deeper insights from structured data. In addition, a new 麻豆原创-RPT-TS model will help customers predict values across time-indexed business metrics for key forecasting use cases using the 麻豆原创 Integrated Business Planning solution without requiring custom machine learning.

In partnership with HPE, 麻豆原创 also announced the deployment of 麻豆原创-RPT on the HPE Spaceborne Computer-2 aboard the International Space Station (ISS), one of the most significant edge-compute validations of any enterprise AI model to date. 麻豆原创-RPT is planned to run directly onboard the ISS to support anomaly triage, predict issues before failures escalate, reconstruct missing sensor values from historical data, and generate explainable mission-health reports for astronauts and ground teams 鈥� all without requiring data to be sent back to Earth for analysis.

鈥淭he same trusted 麻豆原创-RPT model that powers global supply chains also can work in space,鈥� said Herzig. 鈥溌槎乖粹�檚 deep-rooted, proven expertise has led to the International Space Station鈥檚 use of our tabular foundation model to predict potential mission-critical failures in orbit. With 麻豆原创-RPT and TabPFN from Prior Labs, we bring the unique capabilities of tabular AI into the enterprise and across every frontier.鈥�

Out-of-the-Box Assistants and Agents Define Our Advantage

For five decades, 麻豆原创 has built the systems of record that run your business. We will deliver, out of the box, a suite of assistants and agents across five horizontal domains all running on 麻豆原创 Business AI Platform 鈥� with industry specialization 鈥� that provides a baseline for systems of intelligence and systems of action.

Ecosystem Partners Help Expand Platform Capabilities

The global 麻豆原创 partner ecosystem brings new capabilities to the ongoing operation and extension of solutions on 麻豆原创 Business AI Platform, helping customers advance toward the Autonomous Enterprise. At 麻豆原创 TechEd, the following partner announcements were highlighted:

A collaboration with Intel closes the loop between enterprise workflows and autonomous robots, bridging informational technology and operational technology through physical AI.

In addition, new 麻豆原创 solution extensions with , and extend autonomous capabilities for customers, helping them run their core processes end to end, without handoffs between systems.

Beyond technology, 麻豆原创 is empowering its strong developer community with new enablement and engagement with tens of thousands joining for Devtoberfest leading up to 麻豆原创 TechEd, new reference architectures, tutorials and hands-on opportunities attendees can try onsite at the event and at home.

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

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

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

From process operators to leaders of judgment

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

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

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

Planning for a workforce that includes people and agents

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

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

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

Ongoing efforts to close skills gap

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

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

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

Trust is the foundation

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

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

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

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

The Beginning of Better starts with the people inside it.

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The introduction of agentic AI into the enterprise resource planning (ERP) space is set to underpin the era of the autonomous enterprise, according to Nazia Pillay, MD of 麻豆原创 Southern Africa.

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Pillay spoke to ITWeb TV recently about AI-infused ERP, the autonomous enterprise concept 鈥� which relies on an army of AI agents at its core 鈥� and how organisations need to adapt to benefit from the opportunity.

鈥淎utonomous ERP is not just about using AI to engage with end-users, to assist with navigation and pull data back; it is also about seeing agents as co-workers 鈥� systems that are working and performing tasks from end to end with human oversight,鈥� she said.

Pillay added that this fundamental shift requires change management for both human employees and business processes.

鈥淵ou are adopting a technology that fundamentally needs to be embedded, and your business processes need to be adapted for how you want to use this in your business,” she said. “That鈥檚 the main thing that our customers are facing 鈥� they need to look at this as a transformation and not just as a plug-and-play technology.鈥�

Pillay cautioned that curiosity about AI does not necessarily indicate organisational readiness. Success, she said, comes down to understanding business objectives and getting data foundations in place.

鈥淢ost of our customers are trying to figure out how to navigate this technology 鈥� how do I introduce it and how do I do it in a way that is adding value,” she said. “What we鈥檝e found is that when they use AI as a proof of concept or an experiment, they are not leveraging the full value of the technology.鈥�

Beyond experimentation, governance frameworks are essential, Pillay said.

鈥淢ost of the organisations that we are speaking to need to look at how they are deploying it at an enterprise level, because when you are just deploying it as a proof of concept, you don鈥檛 necessarily need to have all those frameworks in place.鈥�

Pillay said the wealth of data that clients have generated through 麻豆原创 installations provides a good foundation for companies shifting towards the autonomous enterprise.

鈥淲hat we know about AI is that it requires context 鈥� so you need to understand where the data is and how it is being used, and this we already have based on our knowledge graphs within 麻豆原创,” she said.

鈥淭he second part is governance 鈥� what 麻豆原创 puts into place to ensure that we have security. We have invested because we have the context and the governance to support our customers in having agents run in a responsible, reliable way.鈥�

With AI agents being deployed across end-to-end business processes, getting governance right will be key, she concluded.

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A tech confession and a debate with an AI robot was the recipe for the future of work at 麻豆原创 Connect Southern Africa in Kyalami near Johannesburg last week.

鈥淚 have a confession to make: I have been afraid of AI,鈥� said Stanley Dube, 麻豆原创 head of solution advisory. Standing alongside a robot wearing a tracksuit carrying 麻豆原创鈥檚 AI assistant brand Joule, Dube said: 鈥淭he fear I have had is that AI is going to take my job.鈥�

However, having moved past that fear, he and Joule addressed questions about how organisations can adapt to AI, redesign jobs and processes, maintain governance and build a competitive advantage. In a theoretical example, Dube asked about the specifics of a project.

鈥淚 already know the status,鈥� answered Joule. 鈥淚 also know who is usually responsible for the delays: you, Stanley Dube.鈥�

Debate around AI replacing workers and robots taking control has become familiar, but 麻豆原创鈥檚 demonstration gave Joule a physical presence beyond a computer screen. The robot appeared to serve as a representation of the software assistant rather than a demonstration of autonomous physical AI. Although the presentation carried an element of theatre and Joule鈥檚 responses may have been preprogrammed, the exchange pointed towards a future in which AI agents could take on a more visible and collaborative role in the workplace.

Dube asked Joule: 鈥淲hat do we, as entities and companies, actually have to do to survive and thrive in the age of AI?鈥�

Joule answered: 鈥淢ost organisations do not have an AI problem; they have an adaptability problem. [This is] a massive waste of time and capital. I am built directly into your 麻豆原创 applications. While you slept, I autonomously reconciled 84% of your finance accounts, predicting a pump failure at Secunda to generate a maintenance order and rerouted logistics around a major issue.鈥�

Joule鈥檚 response framed the main obstacle as organisational rather than technological. AI systems may process information and act within seconds, but the benefit can be lost when decisions still depend on lengthy approval chains and slow human processes. In that situation, advanced technology becomes the equivalent of placing a Ferrari engine in a donkey cart: the system has considerable power, but the surrounding organisation cannot move at the same speed.

Dube said employees might find themselves arguing with AI agents in future, but that should not necessarily be a cause for concern.

Joule said: 鈥淩emember, AI is the enabler. Adaptability is the challenge. The future belongs to those who learn how to adapt because of AI, not just those who adopt.鈥�

The presentation extended a strategy outlined at 麻豆原创鈥檚 Sapphire conference in May. As , 麻豆原创 presented the Business AI Platform as an environment for developing and governing agents that work across enterprise operations. The approach uses ERP systems as the source of business data, processes and controls, allowing agents to perform tasks within established organisational boundaries.

Standard Bank provided a practical example of the preparation required to support 麻豆原创鈥檚 vision. The bank has modernised core enterprise systems using 麻豆原创 technology, establishing a platform from which further automation and AI applications could develop.

Vanessa Padiachee, Standard Bank Group CIO for corporate functions, said: 鈥淚f you look at the pace of change, technology is changing rapidly. AI is no longer just around experimentation and production use cases. Organisations are now going beyond that to look at enterprise scaling use cases.鈥�

Padiachee said 麻豆原创 was an important part of Standard Bank鈥檚 broader technology programme. She said organisations must manage rapid technological change while keeping major systems aligned with practical business requirements.

Standard Bank is focused on modernising the systems needed to support future automation and AI deployments rather than describing an autonomous AI implementation.

That work receives far less attention than demonstrations of agents and automated decisions. However, 麻豆原创鈥檚 autonomous-enterprise vision still depends on businesses having systems, processes and employees capable of supporting the technology.

Joule may have stepped beyond the computer screen at 麻豆原创 Connect, but the route towards an autonomous enterprise still runs through the less theatrical work of modernising systems, data and processes.

* Jason Bannier is a data analyst at World Wide Worx and deputy editor of . Follow him on .

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The company outlined its strategy at 麻豆原创 Connect Day 2026, held this week in Kyalami, Johannesburg.

, MD for southern Africa at 麻豆原创, said the company’s role is to support organisations in implementing and scaling agentic AI across end-to-end systems as they transition towards .

Addressing the complexity and cost that continue to challenge organisations migrating systems and platforms, , VP of customer advisory for EMEA at 麻豆原创, said the company’s new agent-led transformation approach could reduce the effort required for multi-year ERP migrations by 35%.

Agentic AI assistants

麻豆原创 has introduced several migration and modernisation AI assistants, including data management, custom code, configuration, test management and change management assistants. According to the company, these are governed by Joule for both developers and consultants and are designed to execute complex business scenarios by reasoning iteratively and interacting with their environment to achieve defined goals.

Nolla cited 麻豆原创 research indicating that the highest-ranked strategy for maximising ROI from AI is aligning initiatives with clear business goals.

麻豆原创 said AI currently supports about 30% of tasks in the average business, with that figure projected to reach 48% within two years.

, head of solution advisory at 麻豆原创, said a major challenge in AI-driven digital transformation is what the company describes as the “enterprise paradox” 鈥� the expectation of instant enterprise AI clashing with the operational reality of complexity, manual processes and human behaviour.

Standard Bank’s transformation journey

, executive group CIO for corporate functions at Standard Bank, outlined the bank’s use of 麻豆原创 technology to address operational challenges, including end-of-life systems, expiring vendor support and the need for a modern cloud ERP platform to support future innovation.

The bank completed a large-scale ERP migration and implementation programme. Modernising core enterprise systems required careful planning, strong governance and close collaboration between business and technology teams. The programme established a platform capable of supporting growth, operational resilience and ongoing innovation.

Padiachee said one of the biggest challenges was the pace of technological change.

“Organisations are required to continuously adapt while maintaining stability across critical business operations. Change management is just as important as the technology implementation itself,” she said.

Modernisation, she added, should improve agility without creating unnecessary disruption to business operations.

She also highlighted the human factor. “It’s really about keeping people calm during turbulence,” she said, adding that AI is fundamentally changing how businesses operate. “The focus should move beyond experimentation towards delivering measurable value.”

According to Padiachee, AI initiatives should align with clear business outcomes rather than be driven by the technology’s popularity.

She said one of the most significant lessons from the transformation programme was the complexity of Unicode conversion.

“In hindsight, this work should have started much earlier in the programme. Unicode conversion proved more complex than anticipated and became a critical dependency for implementation. Investing time in foundational technical readiness significantly reduces downstream project risk.”

Padiachee also stressed the importance of high-quality enterprise data that is accurate, trusted, well-governed and accessible.

She advised organisations planning system and platform migrations, as well as AI integration, to begin with a clearly defined use case, establish strong partnerships and maintain robust governance and guardrails.

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The comments were made by Sunil Geness, director of global government affairs and CSR at 麻豆原创 Africa, and Sergio Maccotta, senior VP and GM for 麻豆原创 Middle East and Africa 鈥� 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 麻豆原创, autonomous operations 鈥� where nearly half of all business processes run independently and most operational work is听automated听or AI-augmented 鈥� are being deployed across finance, supply chain and human resources in industries including energy, retail and manufacturing.

鈥淭his 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,鈥� said Maccotta. 鈥淪elf-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.鈥�

麻豆原创 cited data from听听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 鈥渁utonomous enterprise鈥� 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.

鈥淎I 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,鈥� Geness said.

麻豆原创 added that some analysts estimate AI could contribute $1.5 trillion to Africa鈥檚 economy if the continent captures 10% of the global AI market by 2030.

Data sovereignty

麻豆原创 executives said Africa鈥檚 digital transformation continues, with data sovereignty emerging as a key factor.

鈥淔or 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,鈥� Maccotta added.

The company said Africa鈥檚 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.

鈥淩emember 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.鈥�

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The era of digitisation is giving way to something more profound: the Autonomous Enterprise听鈥撎�a business that can sense change, make decisions and act with minimal human intervention, all while empowering people to pursue revenue-driving strategic activities.

Autonomous operations are already being deployed across finance, supply chain, human resources, an in Industries like Energy, Retail and Manufacturing. In 2026, they will separate the most resilient and profitable businesses from the rest.

An Autonomous Enterprise goes beyond automating individual tasks by integrating autonomous ERP, business AI and clean data into the core of business operations. More than 50% of business processes run independently, and up to 80% of operational work is automated or AI-augmented.

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. 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.

Crucially, autonomy does not remove humans: it redefines and empower their work. Employees move from managing repetitive processes to supervising intelligent systems, making strategic decisions and creating new value throughout the business.

2026 鈥榓 tipping point鈥�

The year ahead will be critical for organisations across Europe, the Middle East and Africa. In Europe, business leaders face an ageing workforce, complex supply chains, regulatory pressure, and the push for sustainability and data sovereignty. Middle Eastern nations are already deploying national AI strategies and sovereign cloud infrastructure as part of a rapid diversification from the energy sector. And despite inflation and debt pressures, a world鈥檚 fastest-growing digital economy and most youthful workforce are emerging in Africa.

While the challenges in each region are unique, at their core every organisation is seeking the same capabilities: faster decision-making, greater profitability, improved resilience, and sustainability at scale.

Businesses that fail to build these capabilities, and instead rely on manual processes, disconnected systems and spreadsheets, face growing risks: slower reaction time, shrinking margins, and higher operational costs.

This is why the business opportunity for Autonomous Enterprises is significant. The global Autonomous Enterprise market is expected to grow from听, and adoption is accelerating across EMEA.

听Global partner for business transformation

While I can see the scale of the challenge businesses face to transform into autonomous enterprises, I am equally excited at bringing 麻豆原创鈥檚 strengths in this arena to bear. Through our flagship 鈥� now more accessible than ever thanks to the RISE with 麻豆原创 initiative 鈥� we equip businesses with modern, clean core ERP systems that are upgrade-safe and AI ready.

, 麻豆原创鈥檚 generative AI copilot now supports 11 languages and features more than 400 embedded AI use cases across 26 industries, while the enables extensibility, data orchestration and clean core innovation for the world鈥檚 most critical industries.

My advice, to leaders seeking throughout the region to build Autonomous Enterprises, is to start modernising the core and fix 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 who 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.

In 2026, the most successful companies will go beyond digital transformation to achieve faster decision-making, greater operational certainty, and the ability to unlock new forms of growth and innovation.

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