In conversation with Alexander Khanin, Founder, Polynome Group, as he shares insights on their participation at Machines Can Think 2026 and AI adoption across UAE.
Can you talk about your presence at Machines Can Think 2026, why is this event important for global AI leaders right now?
Machines Can Think 2026 matters because it is a strong platform for collaboration between global AI leaders and the UAE’s professional and research community. Most real breakthroughs in AI do not happen in isolation; they happen at the intersection of different disciplines, cultures, and operating environments. This summit is built around that idea.
Polynome invests in this event to contribute and support UAE’s growing AI ecosystem, creating a space where international expertise meets local ambition and execution capability. The caliber of support reflects that intent, with backing from organisations such as Mohamed bin Zayed University of Artificial Intelligence, NVIDIA, Meta, Mubadala, G42, and strong public sector partners.
What makes the event distinctive is the balance it creates. Roughly half of the contributors come from global technology and research organisations, and half from the UAE and the region. That mix enables practical exchange rather than abstract discussion. For global AI leaders, this is an opportunity to collaborate in a market where AI is already moving from strategy into real deployment.
With AI moving to real-world deployments & execution, what key mindset shift do leaders need to make in 2026?
The first mindset shift leaders need to make in 2026 is to look beyond Generative AI and large language models and ask what comes next. GenAI was an important step, but it is not the end state. The real challenge now is how intelligence is embedded into organisations in a sustainable way.
The second shift is organizational, not technical. Many AI initiatives fail not because the technology is insufficient, but because companies try to deploy AI while keeping the same operating models, incentives, and ways of working. Businesses cannot introduce AI-driven processes without changing how people are motivated, how decisions are made, and how accountability is structured. For instance, PwC’s Middle East workforce survey shows that 82% of respondents report productivity gains from AI, yet those gains only compound when organisations align operating models and accountability structures around them.
AI deployment has to happen in parallel with organizational change. That includes redefining roles, adjusting performance metrics, and building trust in automated and assisted decision-making. This is why learning from peers matters. At the summit, leaders from industries and corporations that have lived through these transitions will share what worked, what did not, and what they would do differently.
How is Polynome driving real AI adoption. What are the biggest barriers preventing organizations from scaling AI today?
At Polynome, we focus on adoption that works in real operating environments, not controlled pilots. The barriers to scaling AI are well understood, but they are often underestimated.
Scaling AI requires sufficient computational capacity and deployment-ready infrastructure. Without the right computing foundation, even well-designed systems cannot move beyond experimentation.
However, access to data alone is not enough. Poorly structured, inconsistent, or weakly governed data leads to unreliable AI outputs, constraining how effectively intelligent systems can learn and adapt. High-quality results depend directly on high-quality inputs.
AI deployment is not a one-click process. Organisations need skilled people to design, deploy, monitor, and continuously improve systems. The UAE’s demand for tech specialists in the AI boom is expected to grow by 54% by 2030, highlighting the scale of this challenge. Real adoption requires all three elements to mature together and that’s where Polynome comes in.
How can business leaders move beyond hype and ensure AI delivers measurable business impact?
Leaders move beyond hype by being disciplined about outcomes. AI should be treated like any other strategic investment. If the success criteria are not clearly defined, it becomes impossible to judge whether a deployment is working.
Many organisations experiment with multiple AI approaches at once, hoping value will emerge organically. Without defined KPIs, this often leads to confusion rather than impact. This pattern is reflected in an MIT study showing that 95% of companies generated no meaningful return from broad, unfocused AI deployments, despite significant investment. Leaders need to be clear on what problem AI is meant to solve, which metrics will indicate success, and how results will be measured over time.
This does not mean limiting experimentation. It means anchoring experimentation to business objectives. Whether the goal is cost reduction, service quality, risk management, or speed of decision-making, those targets must be explicit. AI output should be reviewed against operational performance, not technical benchmarks alone.
When success criteria, processes, and measurement frameworks are established early, AI becomes easier to scale and govern. The organisations seeing real returns are those that treat AI as a managed capability, not an open-ended innovation exercise.
What outcomes do you hope participants will take away from Machines Can Think 2026?
Participants should leave Machines Can Think 2026 with a stronger sense of connection to the global AI community and a clearer understanding of where they personally fit within it. The conversations are designed to build relationships that extend beyond the event, because meaningful progress in AI depends on sustained collaboration rather than isolated effort.
Polynome also wants people to walk away with greater confidence in where AI truly stands today. That confidence comes from understanding both what current solutions can deliver and where their limits are, rather than relying on hype or assumptions. Clarity is far more valuable than optimism when organisations are making long-term decisions.
Equally important is that participants gain concrete answers to their own business challenges. This is critical as AI becomes a core economic driver and the UAE’s AI economy is expected to reach AED170 billion by 2030. The speakers and contributors have hands-on experience deploying AI at scale, and that practical insight is what helps move conversations from theory into action.
The summit creates space for honest reflection on what has not worked in the past. Learning which approaches failed, and why, is often the fastest way for organisations to avoid costly missteps. The intended outcome is that participants leave better connected, better informed, and better equipped to make sound decisions about AI in their own organisations.
