As artificial intelligence shifts from experimentation to operational necessity across the Middle East, leaders are being forced to rethink how machine intelligence is understood, governed, and deployed. In this exclusive interaction with The Catalyst, Alexander Khanin, Founder and CEO of Machines Can See and Polynome, shares his perspective on the most persistent misconceptions surrounding AI today, the real-world value of applied intelligence, and how responsible, locally aligned systems are shaping the next phase of enterprise and government adoption across the region.
From your perspective, what is the biggest misconception leaders still have about machine intelligence today?
There are two recurring misconceptions, and both slow progress. Some leaders underestimate AI and worry it will replace jobs or take control of decision-making. That fear leads to hesitation, delayed pilots, and missed opportunities at a time when competitors are moving quickly.
Another misconception is that some decision-makers apply AI everywhere, even when the problem is simple and deterministic. In those cases, AI becomes expensive infrastructure rather than a solution, increasing the total cost of ownership without delivering proportional value. With 69% of organisations in the Middle East planning to increase AI investment in 2026, this understanding becomes more consequential.
There is no universal formula for where AI belongs. The key is that AI should never be deployed for its own sake. Every application must be driven by a clear business or service outcome, with a realistic understanding of cost, complexity, and impact. Leaders who approach AI this way move faster, spend smarter, and build trust internally rather than resistance.
Polynome Group is showcasing a multi-channel AI concierge for corporate and government organisations. What real-world problems does this solution solve that traditional digital interfaces cannot?
Traditional digital interfaces place the burden on users to understand how systems are structured, where information sits, and which path to follow. In complex environments such as government services or large enterprises, that friction quickly turns into frustration.
Our multi-channel AI Concierge removes that burden by allowing people to interact in natural language at touchpoints they already use across voice, text, websites, mobile apps, kiosks, and call centres. Users are supported in their native languages, which is critical in diverse societies like the UAE and directly improves accessibility and customer satisfaction without adding complexity for the organisation.
Because the solution is hosted locally, responses are delivered with extremely low latency while remaining fully aligned with local regulatory requirements. This combination of natural interaction, language inclusivity, and fast response times changes how services feel in practice.
The outcome is not novelty, but dependable service at scale, where speed, consistency, and user confidence are built into every interaction rather than managed through workarounds.
Machine Vision often raises concerns around privacy and ethics. How do you address these concerns while deploying AI at scale?
Privacy and ethics must be addressed by design, not added later. Our approach is built around full compliance with local regulations and responsible AI principles from the outset. All processing happens locally, eliminating the risk of data leakage.
At Polynome, we focus on behaviour and flow, working only with depersonalised data. We analyse digitised patterns such as engagement timing, interaction duration, and service bottlenecks using existing camera infrastructure, without facial recognition or personal identification. This ensures compliance with both internal governance standards and government regulations and removes many of the ethical risks commonly associated with machine vision.
We also apply the latest practices in safe and responsible AI to ensure systems are robust, auditable, and purpose-bound. Machine Vision should deliver operational insight, not surveillance. When organisations are clear about what is measured, how data is handled, and why insights are used, trust follows naturally. At scale, responsible AI is what allows these systems to deliver long-term value without compromising public confidence.
How important are platforms like Machines Can Think in accelerating real-world AI deployment versus theoretical innovation?
Platforms like Machines Can Think matter because they connect research to reality. Too often, breakthroughs stay isolated from deployment, or policy discussions remain detached from technical feasibility. The summit addresses both sides together, structured around how AI behaves inside live environments, including public services, critical infrastructure, and regulated industries.
Machines Can Think showcases cutting-edge research while placing equal emphasis on applied use cases. More importantly, it brings researchers, government leaders, and enterprise decision-makers onto the same stage, creating a shared language that accelerates translation from theory into working systems.
With AI expected to contribute $320 billion to the Middle East’s GDP by 2030, regions that succeed will be those that align ambition with delivery. When people responsible for policy, funding, and operations hear directly from those building the technology, conversations become practical. Constraints are surfaced early, assumptions are challenged, and implementation paths become clearer.
This kind of environment shortens the distance between innovation and economic impact. AI delivers value only when it operates inside real organisations, under real rules. Platforms that reflect that reality help regions move faster and more confidently from experimentation to adoption.
With backing from global players like NVIDIA and regional leaders like Mubadala and Abu Dhabi Police, how do partnerships shape Polynome’s innovation roadmap?
Partnerships are central to how our roadmap evolves. It is a privilege for Polynome to contribute as an enabler within the UAE’s AI ecosystem. Global technology partners bring access to frontier compute, architectures, and research depth while regional partners bring operational reality, regulatory context, and scale.
Beyond individual collaborations, we also see value in the partnerships that form around the ecosystem itself. As an organiser, we are encouraged when sponsors, exhibitors, and participants build new relationships through the platform. Those connections strengthen the entire market.
This collective momentum benefits everyone involved. When stakeholders trust the environment, innovation becomes shared rather than isolated, and deployment becomes faster, more disciplined, and more sustainable.
Looking ahead to 2026 and beyond, which AI capabilities will move from “nice-to-have” to “mission-critical” for enterprises?
AI will become mission-critical simply because competitiveness depends on it. Across banking, retail, logistics, and public services, organisations will need AI to optimise operations and improve customer experience at scale. Those that do not adopt will fall behind on cost, speed, and relevance.
Beyond efficiency, decision support will define the next phase. Enterprises will rely on AI to surface insights, adapt to changing conditions, and improve service quality in real time. These capabilities will no longer sit at the edge of the organisation and will be embedded into core workflows.
At the same time, governance and understanding will matter more than raw capability. Leaders will need visibility into how AI influences outcomes and confidence in when to intervene.
AI fluency will become a baseline leadership skill. Organisations that combine intelligent systems with informed human oversight will move faster and take fewer risks than those treating AI as a standalone technical function.
As AI moves decisively from experimentation to execution, its true value is being defined by clarity of purpose, responsible design, and real-world impact. Organisations that align intelligent systems with concrete business and public-service outcomes, supported by strong governance and informed leadership, will be best positioned to scale with confidence. In the Middle East’s fast-evolving digital landscape, success will belong to those who move beyond hype, embed AI into core operations, and use it as a trusted enabler of efficiency, resilience, and long-term competitiveness.
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