Alteryx, Inc., an AI-ready data and analytics company, today released its “2026 IT Leader Research: The State of AI Ownership, Agents, and ROI” report, revealing that organizations are entering a new phase of AI maturity where success is no longer defined by AI adoption alone, but by the ability to translate AI investment into measurable business outcomes.

Among 1,400 IT leaders surveyed globally, 80% expect AI spending to increase over the next two years, while 69% report moderate or significant ROI from their AI investments. Yet despite growing investment and early returns, more than half (53%) say their organization struggles to translate business context into AI systems and workflows. At the same time, 77% agree business context is critical to producing accurate and relevant AI outputs, underscoring a widening gap between AI ambition and operational readiness.

Key Findings at a Glance:

• 80% of organizations expect AI spending to increase over the next two years.
• 69% report moderate or significant ROI from their AI investments.
• 93% of IT leaders are confident agentic AI could deliver measurable ROI for their enterprise within the next two years.
• 77% agree business context is critical to accurate, relevant AI outputs.
• 53% say their organization struggles to translate business context into AI systems and workflows.
• Only 18% of organizations have achieved fully self-service access to cloud data for business users.

AI Investment Is Accelerating. Expectations Are Rising Even Faster.

AI investment continues to accelerate as organizations move beyond experimentation toward enterprise-scale deployment. Eighty percent of organizations expect AI spending to increase over the next two years across infrastructure, workflow automation, data platforms, and governance.

With that investment comes greater accountability. Technology leaders are increasingly measuring AI success through productivity improvements (53%), cost reduction (45%), and revenue growth or broader business impact (39%). More than one-third (35%) say the ability to measure AI ROI will be one of the capabilities that most distinguishes technology leaders from their peers.

The findings suggest AI has entered a new phase where organizations are no longer asking whether AI works. They are asking whether it consistently delivers measurable business value.

Business Context Is Emerging as a Barrier to Enterprise AI

As AI becomes embedded in everyday business processes, organizations are discovering that models alone are not enough. According to the research, 77% of IT leaders agree that business context, including the rules, definitions, and operational knowledge that shape how their organizations operate, is essential for producing accurate and relevant AI outputs. Yet more than half (53%) say their organization struggles to translate that business context into the systems and workflows AI depends on.

The challenge isn’t simply giving AI more data. It’s giving AI the business logic that tells it how the business actually works. While AI can analyze information and generate responses, it cannot consistently apply company-specific rules, policies, thresholds, and decision criteria unless that knowledge is built into the workflows it uses to make decisions.

Much of that business logic still lives in spreadsheets, macros, documentation, email threads, and the expertise of the people closest to the work. A financial forecast depends on assumptions. A tax process depends on rules and exceptions. A supply chain decision depends on inventory thresholds and timing. AI should be grounded in the rules and logic the business already trusts.

“Our research highlights a growing gap between AI ambition and enterprise-scale execution,” said Andy MacMillan, CEO of Alteryx. “Organizations have proven they’re willing to invest in AI, and many are already seeing returns. But scaling AI requires more than better models. It requires making the business knowledge people use every day available to the systems making decisions.”

Limited Data Access Continues to Slow AI Adoption

Despite years of investment in data democratization, only 18% of organizations report that business users have fully self-service access to cloud data. Most organizations continue to rely on IT or data teams for routine data access and analytics, with 38% describing a mixed model and 15% saying business users remain largely dependent on technical teams.

The findings suggest this dependency extends beyond productivity. The employees with the deepest understanding of how the business operates are often the same people waiting on IT to access the data needed to build, validate, and improve AI workflows. That disconnect makes it more difficult to embed business context into enterprise AI systems, limiting AI’s ability to generate meaningful business outcomes.

Enterprise AI Works Best When IT and the Business Work Together

The research also points to a growing consensus that AI performs best when technical expertise and business expertise work together. Two-thirds of technology leaders say AI and agent-based systems are most productive when managed within the line of business. Additionally, 71% believe AI initiatives are most successful when IT and business teams collaborate closely.

This sentiment correlates with past Alteryx research. Yet, strategy (37%) and delivery (38%) remain concentrated within IT, while business teams are most often responsible for defining requirements (30%). That disconnect between where business knowledge resides and where AI systems are built continues to slow enterprise AI adoption.

MacMillan concluded, “The organizations creating lasting value from AI will be the ones that operationalize their business logic so it becomes visible, governed, repeatable, and ready for AI.”

To learn more and explore the full findings, download the “2026 IT Leader Research: The State of AI Ownership, Agents, and ROI” report.

By Jürgen Salenbacher, Creative Leadership & Personal Brand Strategist, Founder of CPB-Lab. 

Consider a family-owned retail group in Dubai, third generation, four hundred staff, twenty-two stores. Its marketing director resigns. The instinct built over fifty years is to replace her: post the role, run a six-month search, pay a full package. Instead the board hires a chief marketing officer for nine days a month, who also works with a logistics scale-up in Riyadh and a hospitality brand in Doha. Twenty years ago that would have signalled a business in trouble. Today it signals a business paying attention.

Fractional leadership, meaning chief marketing, financial and technology officers holding part-time mandates across several companies at once, has moved from the start-up margins into the mainstream of the Gulf economy. Interim and fractional C-suite engagements have risen sharply worldwide since 2021. The UAE now counts more than 1.4 million registered companies, a quarter of a million added last year alone, and nine in ten GCC organisations reported a skills gap in 2025. The model is what happens when demand for judgement outruns the supply of executives who have done the job before.

Artificial intelligence is the accelerant. There is an old cartoon about the company of the future: a man, a machine and a dog, where the man feeds the dog and the dog makes sure the man doesn’t touch the machine. That is not what has happened. AI has not deleted the marketing department. It has collapsed the execution layer between a decision and its consequence.

Take that retail group. A full-year media plan across six markets in Arabic and English used to occupy four people for three weeks. A competent strategist now produces a defensible first version in an afternoon, with scenario models at three budget levels attached. The scarce thing is no longer the work. It is knowing that the real question was never the media plan, but whether the group should be defending its hypermarket position at all. That judgement takes twenty years to acquire and about four hours a week to apply. A region that appointed the world’s first minister of state for artificial intelligence in 2017 is feeling this shift faster than most boards have adjusted for.

The case in favour is strong. Cost is the obvious argument: senior expertise without the salary, bonus, visa and gratuity of a full package. Speed is the better one. A mid-market logistics company facing a funding round and a tax filing in the same quarter does not need a permanent CFO. It needs someone who has closed eleven rounds, embedded within three weeks for ninety days, who leaves behind a data room and a finance manager able to maintain it. Breadth matters too, since an executive advising four companies across three sectors carries pattern recognition no single-employer colleague can match. And the mandate is honest. Reid Hoffman described careers as a series of tours of duty, time-bound alliances built on ethics rather than the fiction of permanence. Both sides know the brief, and both know when it ends.

The case against deserves equal weight, and it matters more here than in most markets. Attention is divided by design. When a distribution partner walks away on a Tuesday, or a product recall lands, the fractional leader is on a call with another client. Accountability blurs, since an executive with three other mandates absorbs only a fraction of the consequence when a strategy fails. And knowledge leaves on the last day. The most common failure is not a bad strategy but an excellent one: a brilliant repositioning handed to three people who were never taught to run it, quietly abandoned by the following spring.

Then there is the deeper problem. Culture is the bridge between strategy and implementation, and culture is biological, growing at the pace of a tree rather than a quarter. Entropy is real: an ordered system left without energy drifts towards disorder. Trust cannot be installed part-time and left to hold while the installer is elsewhere. The word “company” comes from the Latin companio, one who eats bread with you. The majlis makes the same point without the etymology. In a family business here, an executive who appears for nine days and never sits at the table will find his recommendations politely received and quietly ignored, whatever his record elsewhere.

So the model works only under conditions. The first is that the fractional leader arrives to facilitate rather than instruct. Consulting is not the way forward, facilitating collective learning is. A CFO who instructs leaves a slide deck and a hole. One who facilitates spends the ninety days turning the finance manager into someone who no longer needs him. Instead of authority, inspiration. Instead of hierarchy, collaboration. Instead of delegation, participation.

The second condition is character, in four parts. Substance: genuine expertise, not a LinkedIn headline. Style: clarity in how a leader communicates and shows up. Conviction: a world view worth being held to. Grace: the elegance to enter someone else’s culture as a guest rather than an occupier.

The reality of tomorrow is not fewer leaders. It is leaders held differently, by invitation rather than org chart, by contribution rather than title. The movement runs from dependency, through independency, into an age of interdependency, and the fractional C-suite is an early expression of it.

Organisations want to work with the machines, not for them. The ones that remember the difference will attract the people worth having.

By Afroz Nawaf, Founder of point a.cademy, Middlesex University Dubai

The typical career no longer follows a straight line. Alongside the traditional ‘study, then work’ pathway, something more fluid has emerged: learning, work, learning again. New skills and adapted roles. Back to learning.

By 2030, 39 per cent of workers’ core skills will change. It tells us something that the industry already feels: the pace of work has outrun the pace of learning. Students, skilled practitioners and hiring managers are asking one fundamental question: how do you move at the speed of change?

Three groups are already showing us what it can look like.

 Young people finishing secondary school can test their interests before committing to a pathway, building real work alongside practitioners and making far more informed decisions about what and where they want to study.

For students already at university, capability can be built in parallel with their degree: an engineering student learns to use AI for rapid prototyping, a business student applies AI to research and forecasting, a design student adds content creation or UX certification, while a film student develops AI-enabled workflows alongside their craft.

Mid-career professionals learn in compressed bursts. Someone pivoting industries takes a short course while maintaining their job. Micro-credential enrolments are up nearly 50 per cent year-on-year in 2026. People want capability built in layers, at their own pace, while maintaining work and life.

All three groups point to the same reframe. It’s not just about moving at the speed of change but doing so without abandoning depth. The answer emerging in the market is a fundamental shift in how learning is structured, shaped around people’s time, resources and ambitions.

When point a.cademy opened in early 2026, as an enterprise within Middlesex University Dubai, the market responded decisively. Our capability-building academy offers short, intensive courses in Film, Content, Design and AI, taught over one to five days, at industry standard. Within the first month, 500+ learners signed up, with multiple pathways booking out completely. 240 courses have been completed, with 37.5% of eligible learners continuing into further courses. This continuation rate matters. Learners aren’t stopping after one certificate, they are stacking capability and moving to the next course.

What we validated from these first cohorts is that different people move through compressed learning at fundamentally different rhythms. Some absorb rapidly through immersion, then need time to process. Others build gradually, testing each step. Some need tangible output, a project or a prototype, before concepts land, while others need conceptual grounding before they can engage. In a compressed learning environment, personalisation becomes particularly important, giving us the room to build on the different ways people engage with and apply knowledge. This is why we design courses around eight distinct learning personas, from the tentative newbie who needs confidence-building and the hands-on maker who learns through doing, to the serial pivoter, the purpose-seeker, the sponge who learns through rapid immersion, the chaos creative, the conceptual thinker, and late bloomer who takes their time. Each reflects a different way of engaging with learning.

When a three-day intensive respects the person, their rhythm, motivation and way of thinking, moving at speed does not mean losing the individual; it means creating learning experiences that respond to how different people engage, process and apply knowledge. Research supports this. In a review of personalised adaptive learning research, 59 per cent of studies reported improved performance.

The proof is in the applied work. More than 100 Middlesex University Dubai staff completed certifications through point a.cademy. These are not certificates simply hanging on walls; one staff member redesigned key internal processes using the Design for Storytelling frameworks they learned, creating more compelling messaging for prospective students. Another improved digital services with AI tools. A third redesigned administrative processes, cutting student ID card processing time by 74%. This is what moving at the speed of change looks like in practice: learn, apply, deliver, iterate. Not learn and apply later.

The human element matters more, not less, as AI reshapes every role. The people who move at market pace are not those who simply use AI. They bring human judgement, creativity, ethical thinking and specialist knowledge to it. That capability requires continuous, applied learning in parallel with work.

Education institutions that recognise this are expanding their role into lifelong learning ecosystems, creating end-to-end learning loops that allow people to enter, return and continue building capability at different stages of their lives. Short courses, studios and industry experiences can sit alongside rigorous degree education, extending a university’s reach beyond traditional cohorts and creating a broader community of lifelong learners. Institutions such as Middlesex University Dubai are already exploring this model, connecting academic foundations with applied, continuous learning experiences that allow their communities to keep evolving long after a single programme ends.

The market is moving. The question is no longer whether learning will change. It has. The real question is how education systems will evolve to meet it: how do learners move at the speed of change without losing the individual in the rush?