By Tejas Mehta, Senior Vice President & General Manager, Middle East & Africa at Qlik

For decades, organizations have worked to use data to make better decisions and drive better outcomes. Data has become the lifeblood of business, and AI now has the power to unlock it in new ways. With AI adoption across GCC organizations surging from 62% in 2023 to 84% in 2025, the paradigm is shifting from dashboards and visual interfaces to AI-driven experiences.

But too much data is still stuck in silos, incomplete, and inaccurate. Many analytics workflows remain manual, which slows time to value, limits insight quality, and raises costs. This challenge is visible across the GCC, where rapid digital transformation agendas are generating vast volumes of data, but organizations still struggle to unify and operationalize it effectively.

A common misstep among organizations is assuming that more AI or better models alone will solve this problem. In reality, the gap is not in intelligence, but in how data, context, and workflows are connected. Without that foundation, even the most advanced AI will fall short of delivering meaningful business impact.

But what if AI could do more of the heavy lifting, safely and reliably?

That’s the promise of agentic AI, and it’s quickly becoming reality. Agentic AI can reason through multi-step problems, adapt its approach, and engage the right capabilities to achieve a goal with minimal human involvement. Done right, it accelerates insight, lowers costs, and allows teams to focus more on running the business rather than managing manual processes.

Rethinking AI in Practice

Today, we are seeing the emergence of AI systems capable of handling structured analytics, unstructured knowledge, anomaly detection, and decision support, all within a unified experience. More importantly, these systems are becoming interoperable, allowing organizations to integrate AI into existing tools and workflows rather than replacing them entirely.

This flexibility is crucial in the GCC, where enterprises often operate across hybrid environments and must balance innovation with governance, compliance, and data sovereignty requirements.

Overall, there are effectively two entry points into this new AI paradigm:

First, embedded AI experiences within enterprise platforms are enabling faster, more contextual insights, grounded in trusted data and existing business logic.

Second, open integration layers are allowing organizations to connect AI capabilities into the assistants and environments they already use, ensuring flexibility while maintaining governance and control.

Making Data Work for AI

To move from fragmented data and isolated AI initiatives to true agentic systems, organizations need a clear operating model that connects data, insights, and action. This is where three practical priorities come into focus:

• Achieve AI: Organizations need trusted, explainable insights embedded directly into workflows, while maintaining governance and context.
• Accelerate AI: Many enterprises have already invested heavily in data models and business logic. The focus now is on building on that foundation to prove value quickly and scale efficiently.
• Adapt AI: The future will not belong to a single assistant, vendor, or ecosystem. Interoperability will define success, allowing organizations to evolve without starting over.

Across the GCC, this adaptability is especially important as governments and enterprises push for AI leadership while maintaining flexibility to adopt global innovations.

Lessons from Early Adoption

Early adopters of agentic AI are already demonstrating tangible value.

A commercial leader can ask what changed in renewals this quarter, and immediately see the drivers, segments, and recommended next steps in one place.

An operations team can move from identifying a spike in service issues to understanding where it is concentrated, what factors are correlated, and what actions to prioritize, without switching between multiple tools.

A finance team can reconcile narrative and numbers while maintaining traceability, ensuring every insight is backed by clear evidence.

These use cases are highly relevant in the GCC, where sectors such as banking, telecom, and government are under increasing pressure to deliver faster, data-driven decisions while maintaining transparency and accountability.

A Regional Perspective on What Comes Next

AI conversation is moving beyond models. The real challenge lies in making AI dependable, explainable, and useful within the flow of work.

If organizations cannot connect analytics with knowledge, they don’t have agentic AI. They simply have automation without accountability.

For the GCC, where trust, governance, and strategic national initiatives play a central role, this distinction is critical. AI must not only be powerful; it must be responsible, transparent, and aligned with long-term economic visions.

Ultimately, the opportunity is clear: organizations that can successfully unify their data, embed intelligence into everyday workflows, and enable AI to act with context and accountability will define the next era of digital leadership in the region.

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?