Tech Features

Why AI Transformation is a Human Imperative, and the Role the CHRO Must Play

Published

2 months ago

May 8, 2026

A year after IBM’s Deep Blue defeated Garry Kasparov in 1997, Kasparov did something unexpected. Rather than retreat, he invented a new form of chess he called ‘advanced chess’, pairing human players with computers to see what they could produce together. The result was remarkable. Even moderately skilled players, armed with a standard machine, were capable of defeating both grandmasters playing alone and computers operating without human input. The combination was categorically superior to either element in isolation.

By: David Henderson , Group CHRO, Al-Futtaim

That experiment carries an important lesson for organisations navigating AI today. The instinct understandable, but mistaken, is to frame AI as a technology story. It is not. AI reshapes jobs, redistributes decision rights, resets operating models, and forces us to reconsider deeply embedded ways of working. It intersects directly with creativity, cognition, confidence, identity and employability. It produces as many human questions as it does technical ones.

This is why the organizations that are genuinely converting AI from experiment into competitive advantage are those that have understood it, first and foremost, as a large-scale human transformation, one that demands the business, the CHRO and the CIO working as genuine partners, each bringing what the other cannot.

The organisations winning with AI are not those with the most sophisticated technology. They are those that have most deliberately redesigned how humans and machines work together.

The Case for the CHRO

The most effective AI transformations are driven by a tight three-way partnership:

the business setting the agenda and owning outcomes,

the CIO providing the technology platforms,

data infrastructure and governance,

and the CHRO leading the human transformation that determines whether AI delivers value at scale or stalls in pilots.

Each is essential. None is sufficient alone.

What has changed is the recognition that the human dimension, the design of work and decision rights, the building of workforce capability, the management of trust and ethics, the orchestration of adoption across large and diverse employee populations, is not downstream of the technology. It is a primary enabler of it. That is the CHRO’s territory, and it demands the same strategic weight as the technology agenda itself.

In this paper, I propose a model for how CHROs can lead AI enablement through four interconnected roles: Design Architect, Capability Steward, Adoption Catalyst, and Transition Guardian. Each role addresses a distinct dimension of the human transformation that AI demands. Together, they represent a holistic operating mandate for CHROs who are serious about delivering sustained enterprise value from AI, not just deploying tools.

01) Design Architect: Redesigning work, roles and decision rights for the AI era

AI transformation fails far more often because of organisational design choices than because of technology limitations. When companies deploy AI tools without redesigning how work is done, decision rights blur, accountability erodes, adoption stalls, and productivity gains remain trapped in pilots. The technology is rarely the binding constraint. The organisation almost always is.

The CHRO’s role as Design Architect is to get ahead of that problem. This means providing overarching direction on how work should be redesigned so that human judgment and AI-generated insight are deliberately combined, not accidentally layered on top of each other. It means clarifying which decisions remain human-led, which are AI-supported, and where accountability ultimately sits. And it means building an operating model architecture that is dynamic enough to evolve as AI capabilities continue to develop rapidly.

In my own experience, incrementalism in this domain is almost always destined to fail. The organisations that are getting this right are making bold, decisive design choices, and in some cases, breaking up parts of the organisation that have long been treated as untouchable.

In Practice — Procter & Gamble
P&G redesigned decision models across forecasting, procurement and product innovation so that AI produces insights and options while humans retain final say on portfolio bets, supplier strategy and innovation priorities.

Critically, AI was embedded directly into logistics decision forums — rather than remaining siloed in group-level analytics teams, removing information-sharing barriers and enabling real-time decision-making at scale.

In Practice — Microsoft
Microsoft intentionally redesigned all knowledge-work roles so that AI copilots handle drafting, synthesis and retrieval, while employees retain judgment, prioritisation and accountability. The result was not simply cost reduction,it was the redeployment of released cognitive capacity into revenue-generating innovation and customer experience improvement.

Being intentional on organisational design means staying one step ahead of technological adoption, not one step behind it. The CHRO must proactively reimagine how AI reshapes the value chain and translate that vision into operating model decisions — rather than reactively course-correcting after tools have already been deployed.

02) Capability Steward: Building enterprise-wide, continuous learning systems that keep pace with AI

In the AI era, capability, not technology, is the primary constraint on value creation. The organisations that are scaling AI effectively are not those with the most sophisticated tools. They are those whose people know how to use them confidently, critically, and productively in the context of real work.

The CHRO’s role as Capability Steward is to build the learning infrastructure that makes this possible at scale. This means moving decisively away from episodic, one-size-fits-all training models, which are structurally unsuited to the pace of AI change, towards continuous, contextual learning systems that are embedded in daily workflows.

It means developing AI fluency across the workforce, not just in specialist teams. And it means maintaining ongoing insight into which capabilities are emerging, shifting or declining as the skills economy evolves.

In Practice — Amazon

Amazon treats AI capability as core workforce infrastructure rather than a specialist skill. It has built role-specific learning pathways combining foundational AI fluency with immediate, in-role application, particularly in operations, logistics and corporate functions.

The result has been faster adoption of AI tools across large frontline and corporate populations, with measurable productivity gains driven by applied capability rather than isolated expertise.

From My Experience — Zurich Insurance

During my time at Zurich, we built an enterprise-wide AI and digital capability ecosystem that combined broad AI literacy with deep domain-specific learning for underwriters, claims handlers and risk professionals. Learning was continuous and embedded in daily workflows.

Critically, we also focused on transferable skill identification, enabling us, for example, to rapidly retrain and redeploy claims handlers as customer service agents based on strong overlaps in their underlying skill profiles. That flexibility became a genuine competitive asset.

The CHRO must protect long-term capability health and resilience, not simply optimise for short-term productivity. Organisations that treat AI learning as a one-time training event will struggle to sustain adoption. Those that build continuous learning as an organisational capability will compound their advantage over time.

03) Adoption Catalyst: Empowering employees as co-creators of AI value, not passive recipients of it

Many CHROs of my generation were trained in a change management orthodoxy that starts at the top of the house, guiding coalition, executive sponsorship, structured project timelines. That model is not wrong, but it is increasingly insufficient for AI.

Top-down governance and strategy remain essential. But scalable AI value does not come from mandates. It comes from the bottom up, from employees who understand the work and are empowered to apply AI where insight is deepest and value most immediate.

The CHRO’s role as Adoption Catalyst is to create the conditions for this to happen: building cultures of experimentation and knowledge-sharing, aligning incentives and recognition to reward participation, and enabling employees to co-create AI use cases rather than simply receive them.

This is a fundamental shift from change management to what I would call change orchestration, leaders creating the environment in which adoption flourishes, rather than driving it through compliance.

In Practice — Al-Futtaim Blue Loyalty Platform

The clearest proof point I can offer comes from our own experience at Al-Futtaim. The group’s Blue Loyalty Platform uses AI to combine behavioural, transactional and partner data to deliver personalised offers and purchase recommendations across our retail and service channels.

What made this work was not central design — it was that the use cases were developed by multi-disciplinary frontline retail employees, working in agile action-learning teams, applying their direct customer insight to build the recommendations.

AI was embedded into frontline and digital workflows by the people who understood those workflows best. The result has been measurable revenue uplift driven by use cases rooted in real customer interactions — not boardroom hypotheses.

In Practice — Google

Google runs AI adoption through a culture of experimentation supported by internal communities, shared tooling and lightweight governance. Employees apply AI to improve workflows, products and services; successful use cases are productised and scaled through internal platforms. This produces rapid diffusion of best practices, strong employee ownership, and continuous improvement generated by those doing the work.

Employees need to define the tools they need , not simply learn the tools they are given. That distinction is everything when it comes to whether AI adoption takes root or stalls.

Bottom-up adoption is not a cultural nicety. It is the mechanism through which AI becomes embedded, differentiated and commercially meaningful at scale. Organisations that get this right do not deploy AI. They make AI part of how the organisation thinks.

04) Transition Guardian: Ensuring AI adoption is ethical, transparent, and in the long-term interest of employees

AI introduces legitimate concerns that the CHRO cannot afford to minimise: fairness, transparency, surveillance, bias, job security, long-term employability. If these concerns are not addressed proactively and honestly, trust erodes, and without trust, adoption stalls regardless of how good the technology is.

The CHRO’s role as Transition Guardian is to ensure that AI adoption is consistent with organisational values and strengthens, rather than undermines, the employee value proposition.

This means embedding ethical guardrails and human oversight into AI adoption from the outset, not retrofitting them under regulatory pressure. It means communicating honestly with employees about what AI will change, what it will not change, and what pathways exist for reskilling and redeployment.

And it means treating strategic workforce planning not as an HR administrative function, but as a core enabler of organisational resilience.

Today’s employees need to focus less on specific target jobs and more on building transferable skill profiles that will serve them across a career that is certain to be turbulent. They need to feel that their organisation has their back. The CHRO must make that commitment credible, not through reassurance, but through concrete pathways.

In Practice — Salesforce

Salesforce has embedded ethical and responsible AI as a prerequisite for scale rather than a control imposed after deployment. The company requires mandatory Responsible AI training, applies humanin-the-loop oversight for AI-enabled decisions, and maintains clear disclosure standards when AI influences employee or customer outcomes.

The trust this generates has driven faster adoption, stronger employee engagement, and meaningfully reduced legal, regulatory and reputational risk.

In Practice — Unilever

Unilever explicitly links AI adoption to employability and internal mobility. As AI reshapes roles, the company invests heavily in reskilling and redeployment pathways, reframing AI as augmentation rather than displacement.

Workforce planning, learning and ethics are intentionally connected rather than siloed , and employees can see a credible future for themselves within the transformation.

Trust is not a soft outcome of AI transformation. It is the hard prerequisite for scaling it. The CHRO who treats it as such will find that ethical, transparent AI adoption does not slow the transformation down — it is the thing that makes it durable.

The CHRO Skill set for AI Enablement

Having defined the four roles the CHRO must play, it is worth being specific about the skills and attributes required to execute each one. In an environment where AI success is increasingly determined by organisational design, capability building, adoption dynamics and trust, not technology, these capabilities define whether the CHRO is shaping the transformation or reacting to it.

Design Architect	Capability Steward	Adoption Catalyst	Transition Guardian
Operating Model Design	Learning at Scale	Change Orchestration	Ethical Judgement
Work & Role Deconstruction	AI Fluency Translation	Employee Empowerment Mindset	Trust Stewardship
Decision Rights Clarity	Skills Architecture & Workforce Sensing	Incentive & Recognition Design	Strategic Workforce Planning
Systems Thinking	Action Learning Systems	Business Experimentation Literacy	Risk Anticipation
Enterprise Co-Creation	Future Capability Stewardship	Cultural Signal Awareness	Clear, Honest Communication

A few points of emphasis.

As Design Architect, the most underrated skill is enterprise co-creation — the confidence and credibility to act as a genuine co-owner of AI strategy with the CIO and business leaders, not merely as a supporting function.

As Capability Steward, future capability stewardship is distinct from short-term productivity optimization; CHROs must protect long-term organisational resilience, not just near-term performance.

As Adoption Catalyst, cultural signal awareness is often more powerful than formal programmes, leadership language and behaviour either accelerate or silently undermine adoption at scale. And as Transition Guardian, clear and honest communication, including on uncertainty and difficult tradeoffs, is the foundation on which all of the other skills rest.

Without it, none of the others land.

Conclusion: The Human Transformation Imperative

Organisations that are genuinely winning with AI are not those with the most sophisticated technology stacks. They are those that have most deliberately and thoughtfully redesigned how humans and machines work together, rethinking operating models, building capability at scale, empowering employees as co-creators, and managing the transition with ethics and transparency.

The CHRO who grasps this, who acts as Design Architect, Capability Steward, Adoption Catalyst and Transition Guardian simultaneously, becomes one of the most important executives in the organisation. Not because HR has staked a claim to a technology agenda, but because the most important levers for AI value creation are organisational and human, and those are precisely the levers that CHROs are equipped to pull.

Kasparov’s advanced chess experiment showed us, a quarter of a century ago, that the most powerful outcomes emerge not from humans or machines working alone, but from their deliberate, skillful combination. The CHRO’s mandate is to make that combination work, at enterprise scale, at pace, and without losing the trust of the people it depends on.

That is not a supporting role. It is a defining one.

_______________________________________________________

David Henderson is Group CHRO of Al-Futtaim Group, one of the Middle East's largest diversified conglomerates. He has previously served as CHRO of Zurich Insurance Group, MetLife and PepsiCo.

Related Topics:AI Al-futtaim CHRO Co-creation CoverStory Feature Featured Goverance HumanElement

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THE REALITY OF AI DEPLOYMENT ACROSS THE WORKFORCE IN THE REGION

Published

1 day ago

July 6, 2026

Integrator Web-Admin

By Alfred Manasseh, COO & Co-Founder of Shaffra

Across the GCC, AI is becoming more operational. The conversation has moved beyond whether organisations are testing AI and toward how deeply these systems are being embedded into daily work. McKinsey’s finding that 84% of GCC organisations have adopted AI in at least one business function shows the region’s strong momentum, but the more important shift is where this technology is now creating measurable value.

AI is beginning to operate inside real enterprise workflows, where productivity, cost, speed, service quality, and governance can be measured. This practical shift means AI is being judged less by novelty and more by whether it can reduce manual work, improve response times, and support better execution across organisations.

Where AI is being deployed

AI deployment is gaining traction in structured, high-volume functions where it can remove this coordination burden and give employees more capacity for skilled output. Asana’s research has found that around 60% of time is spent on “work about work,” such as chasing updates, attending unnecessary meetings, and switching between tools.

Customer service teams are using AI for automated query handling, routing, escalation management, and multilingual support. Operations teams are applying AI to order processing, workflow coordination, and SLA monitoring.

In HR, AI is supporting CV screening, interview scheduling, and onboarding orchestration. In finance, it is being used for invoice processing, reconciliation, and anomaly detection. Sales teams are also applying AI to lead qualification, follow-ups, CRM hygiene, and pipeline updates.

Regional governments are also preparing the workforce for this reality. Digital Dubai recently launched the AI Workforce Transformation Program, known as AI+, to help train 50,000 government employees for an AI-ready workforce.

Three phases of AI workforce evolution

AI use across the workforce can be understood in three phases. First, AI acts as an assistant through copilots, chat interfaces, summarisation, drafting, search, and advisory tools that improve individual productivity. Second, AI becomes an operator, completing defined tasks across CRM, HR, finance, customer service, and operations systems within controlled boundaries. Third, AI develops into a workforce layer, where systems are assigned roles, KPIs, access rights, escalation pathways, and governance controls. At this stage, Autonomous AI Teams operate as governed digital employees, helping structure, assign, monitor, and improve work.

How mature AI deployments operate

AI is not replacing entire jobs. It is restructuring work by taking over repetitive tasks within roles. Human teams are shifting toward oversight, exception handling, decision-making, escalation management, and quality control.

Autonomous AI Teams operate as coordinated systems rather than standalone models. They support humans through role-based actions with defined responsibilities, structured access to enterprise systems, clear decision boundaries, controlled autonomy levels, human escalation pathways, performance metrics, auditability, and governance.

From tools to workforce infrastructure

Before scaling autonomous AI systems, executives need clear visibility into decision-making, accountability, risk controls, and human intervention points. Trust grows when productivity gains are measurable and governance is visible. IBM research shows that 77% of UAE senior leaders have already seen significant productivity gains from AI, which reflects growing confidence in its operational value.

Across Shaffra deployments, Autonomous AI Teams have contributed to more than 2 million manual work hours saved monthly across operational workflows. Organisations have reported up to 80% reductions in operational costs, customer service teams can manage up to five times more queries, and HR recruitment cycles that previously took weeks can be reduced to hours.

The future workforce layer

The GCC has a strong appetite for AI adoption, but many organisations still need to redesign workflows and overcome fragmented legacy systems before AI teams can function as part of daily operations. Research showing that 94% of UAE data leaders lack complete visibility into AI decision-making processes reinforces why explainability, governance, and workflow design must develop alongside deployment.

The next phase of AI is about building a governed workforce layer where humans and Autonomous AI Teams execute together with clarity, accountability, and valuable impact.

Tech Features

FROM CODING TO INTENT: HOW GENERATIVE AI IS REWRITING THE RULES OF PROFESSIONAL CREATIVITY

Published

1 day ago

July 6, 2026

Integrator Web-Admin

Contributed by Jeff Jacob, Regional Business Team Lead – ISBG at ASUS Middle East & Africa

AI Creative Ecosystems Are Transforming Professional Workflows from Technical Execution to Intent-Driven Innovation

For decades, professional creativity was defined by a precise, hard-earned technical mastery. To be a digital creator involved understanding the underlying mechanics of software: knowing which shortcut keys to press, how to modify complicated codes, and how to adjust render engines frame by frame manually. Designers studied sophisticated software interfaces. Editors memorised keyboard shortcuts. Architects explored multiple layers of modelling systems. Filmmakers designed workflows around rendering pipelines. But the limits of the digital interface restricted creativity. The creator’s thoughts generated an idea, but their hands spent hours, days, or weeks converting that vision into a language that the computer was able to understand.

Today, that equation is fundamentally changing. Generative AI is ushering in a new era in which the focus shifts from execution to intention. It is changing the laws of professional creativity, propelling us from manual digital workflows to the era of intent-driven innovation.

When an efficient AI model can create complex codes, display hyper-realistic settings from a text prompt, or isolate audio frequencies in seconds, technical project execution becomes commoditised. The fundamental value of the human creator centres on intent, the ability to direct, curate, refine, and orchestrate complicated visions. The world is transitioning from one in which creators are valued for how they code or compile to one in which they are appreciated for what they aim to build and why it is important.

This shift represents a significant challenge for conventional hardware philosophy. For years, the computing industry saw professional machines through a strictly quantitative lens. Traditional parameters for evaluating creative laptops and workstations included processing power, graphics performance, display accuracy, storage capacity, and the most aggressive thermal cooling. These factors remain important, but in an intent-driven environment, passive hardware is no longer enough. If the creative process is to become an ongoing, fluid interaction between human intent and artificial intelligence, the technology must evolve. It must grow into an intelligent partner rather than a mere productivity tool.

This is precisely where the concept of technological design must pivot, a shift that many brands anticipated with the expansion of their AI art ecosystems. Rather than seeing AI integration as a superficial software tool, when it is developed as an intelligent, creative collaborator, it bridges the gap between raw computing capacity and human intuition.

A single campaign today may involve long-form video, short-form social assets, AI-generated photography, interactive experiences, 3D content, spatial design, and linguistic adaptations all at the same time. This requires a whole new level of physical and digital collaboration. The modern hardware anticipates the creator’s next action by using dedicated Neural Processing Units, tailored AI workflows, and fully connected software ecosystems. It optimises system resources based not only on raw CPU load, but also on the cognitive needs of an AI-powered pipeline. Physical control interfaces are no longer just shortcuts for legacy software sliders; they are physical extensions of intent, allowing creators to dynamically scrub through AI-generated iterations, manipulate parameters in real time, and maintain a tactile connection to an increasingly non-linear process.

Furthermore, this evolution alters the perspective on the mobility of professional talent. Intent-driven creativity thrives on cross-disciplinary exploration. A filmmaker may need to create architectural backgrounds on set, or a designer may need to run localised, big language models during a client pitch to iterate on branding concepts in real time. By compressing massive AI computing capabilities into extremely sophisticated, colour-accurate, and portable forms, the modern ecosystem assures that the studio is no longer confined to a single desk.

Yet, despite the excitement around AI, a major misconception must also be addressed. Generative AI does not replace creativity. It reframes where human value fits into the creative process. Historically, technical expertise has been a barrier to entrance. Having the ability to master complex structures determined who could participate in creative industries. AI lowers those barriers, but it also emphasises the importance of distinctively human skills such as judgment, taste, narrative, emotional intelligence, cultural understanding, and strategic thinking.

This is why the discussion on AI-powered creativity must extend beyond software. Infrastructure matters. Devices matter. Ecosystems matter. Professionals driving the future of creative industries will require technology that can enable sophisticated AI-native tasks while maintaining reliability, portability, security, and precision. The brands that recognise creativity as a human experience enhanced by intelligent technology will be the ones to succeed in the next phase. Every technology leader must now face the same question: in a future where AI can generate practically anything, how can we empower humans to create something meaningful?

The change of professional creativity is a story of structural emancipation rather than human replacement. As generative AI continues to demystify the technical aspects of execution, the primary focus returns to where it always belonged: the depth of human insight and the precision of artistic vision. The future of professional creation belongs to those who can master the art of intent.

Tech Features

THE UAE’S NEXT AI CHALLENGE ISN’T INFRASTRUCTURE, IT’S ENABLEMENT.

Published

4 days ago

July 3, 2026

Integrator Web-Admin

By: Bindesh Vijayan, Chief Technology Officer at Myndlab

There is a line that gets repeated at every tech conference in Dubai, in every government briefing, and across most pitch decks: the UAE is building the future. Artificial intelligence is projected to contribute $96 billion to the UAE’s GDP by 2031, according to PwC and corroborated by the UAE’s own National AI Strategy. The country has invested AED 543 billion in AI since 2024 alone, as confirmed by Omar Sultan Al Olama, the UAE’s Minister of State for Artificial Intelligence. And according to Microsoft’s AI Diffusion Report for Q1 2026, the UAE has become the first country in the world to cross the 70 percent threshold for AI tool adoption among its working-age population.

These are not vanity metrics. They reflect a deliberate national strategy that has positioned the UAE as one of the world’s most ambitious AI markets and laid the foundations for long-term technological leadership. Yet despite that progress, a disconnect is emerging between the country’s AI ambitions and the day-to-day reality of the people building products within the ecosystem.

The Gap Between AI Infrastructure and AI Adoption

Much of the discussion around AI in the UAE has focused on infrastructure, whether that is sovereign AI models, data center investments, national strategies, or the capital required to support them. These are all essential components of a successful AI ecosystem. However, infrastructure alone does not create products. Founders, developers, and businesses still need the tooling layer that sits between AI capability and real-world execution.

This is precisely the challenge a new generation of AI-native development platforms is trying to solve: embedding software engineering best practices directly into the building process so that users can focus on the product rather than mastering prompt engineering.

One of the clearest examples of this challenge is language. Arabic is spoken by more than 400 million people across 22 countries. Yet developers across the region still rely heavily on tools that were primarily designed for English-speaking users. Researchers at Nature Middle East have previously highlighted how the relative lack of robust Arabic language models continues to create limitations around linguistic nuance, dialects, and cultural context.

At the same time, the developer tools, AI coding assistants, and product-building platforms that define the modern software stack were largely built around Western markets and workflows. They assume a particular type of user, a particular language, and a particular development environment. For many builders in the GCC, those assumptions become a source of friction that compounds throughout the product development lifecycle.

A founder in Dubai building a fintech product for Emirati consumers has to work through documentation written in English, prompts that perform better in English, and interfaces that treat right-to-left text as an afterthought.

The challenge is not that these tools fail outright. Rather, they introduce small points of friction throughout the development process that compound over time, affecting productivity, iteration cycles, and ultimately product delivery. Over time, that friction compounds across teams, product cycles, and entire businesses, becoming the difference between shipping and not shipping.

We’ve Seen This Before

This pattern plays out clearly in payments, an industry where many founders across the region have spent much of their careers. The UAE has built a sophisticated financial infrastructure, but for years, the tooling that sat on top of that infrastructure, the APIs, developer documentation, and integration frameworks, was largely oriented toward Western payment methods, Western card schemes, and Western compliance frameworks. Local founders had to build workarounds. Some of those workarounds were innovative, but workarounds are not a strategy. More often than not, they are a sign that the underlying stack was never designed for the people using it.

The same lesson applies to AI. Infrastructure creates possibilities, but it does not automatically create innovation. Innovation happens when builders can move quickly, efficiently, and confidently on top of that infrastructure. If the tools developers use every day are not designed for the realities of this market, then the UAE’s AI ambitions risk being partially realized by people working around their environment rather than with it.

What Comes Next

There is a real opportunity here to address the gap between the infrastructure the UAE has built and the tools its founders, developers, and businesses actually need.

The UAE has already demonstrated that it can build AI infrastructure at scale. It has invested heavily in research, talent, adoption, and national AI initiatives, creating one of the most ambitious AI ecosystems anywhere in the world.

The next phase of that strategy is not simply building larger models or attracting more capital. It is ensuring that the people responsible for creating products, launching companies, and deploying AI solutions have the tools they need to succeed. It also means reducing dependence on a small number of external AI providers. As AI becomes embedded in critical business and government workflows, questions around privacy, data governance, and long-term resilience become increasingly important. Building capable regional AI ecosystems is not simply about innovation; it is about ensuring that organisations can deploy AI with greater control, confidence, and sovereignty.

The countries that win the next decade of technology are not necessarily the ones that spend the most money. They are the ones where the people doing the building have the right tools for the job.

Infrastructure creates possibility. Tooling turns possibility into innovation. The next phase of the UAE’s AI story will be defined by how effectively it enables the people doing the building.