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.

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.

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.