Exclusive interview with Shadi Hatoum, Regional Director MEA at Tealium

The UAE is moving quickly from AI experimentation to deployment. What do you think will determine which organizations actually turn adoption into measurable business value?

The organizations that create measurable value will be those that connect AI to a clearly defined business decision, then give it the right data and operating model to support that decision.

The UAE has already built significant momentum. Boston Consulting Group found that 42% of UAE organizations qualify as AI Leaders, while 37% have reached the scaling stage of AI maturity. The next test is whether that maturity translates into sustained outcomes.

Deploying a model is not the same as changing how a business operates. AI needs trusted, consented, and real-time context around customer identity, behavior, intent, and journey stage. It also needs clear ownership, measurable objectives, and teams that understand how to use the output.

From my perspective, data readiness and organizational adoption will be the two biggest differentiators. The organizations that can put reliable context in front of AI at the moment of decision, and embed the resulting insight into daily workflows, will be best positioned to turn adoption into measurable value.

How much of the current gap between AI ambition and AI impact comes down to fragmented or outdated customer data?

A significant part of the gap comes down to data readiness, although technology alone is not the whole answer. Most organizations do not lack data. Their challenge is that the data is fragmented, difficult to interpret, or unavailable when a decision needs to be made.

Historical data provides useful depth, but AI also needs data in motion. It needs to understand what a customer is doing now, what has changed, what the customer has consented to, and whether the signal belongs to the correct individual or account.

Tealium’s Future of Customer Data research found that 88% of organizations consider real-time data important to achieving business objectives. That is important because an AI system working from yesterday’s profile may produce a technically valid recommendation that is no longer relevant.

The real opportunity is to create a trusted context layer that connects identity, behavior, consent, and intent, then makes that context available to models and decisioning systems while the customer interaction is still taking place.

Does agentic AI create a new governance problem for enterprises, particularly when agents are acting on customer data without constant human intervention?

Agentic AI does not make the principles of governance entirely new, but it raises the stakes and increases the speed at which controls need to operate. When an AI agent can move from recommendation to action, governance can no longer be treated only as a periodic policy or review exercise. It needs to operate within the data flow and decision process itself.

Enterprises need clear controls over which data an agent can access, the purpose for which it can use that data, how identity and consent are verified, which actions it is authorized to take, and when a person must review or approve the outcome.

The key question is not only whether an agent can access a piece of customer data. It is whether the agent should use that data for this customer, for this purpose, at this moment.

That requires trusted data, current consent, auditability, defined action limits, and clear escalation paths. As autonomy increases, accountability needs to become more precise, not less.

Which sectors in the Middle East do you think are furthest ahead in using real-time customer data and AI together effectively?

From my experience across the region, telecommunications and travel, hospitality, and tourism are among the most active and promising sectors because they generate frequent customer signals and have a clear need to respond in the moment.

In a recent discussion with the Chief Commercial Officer of a major telecom operator in the region, we talked about growth as filling a bucket while also stopping the leaks. Acquisition fills the top, but poor service, irrelevant engagement, and unresolved friction allow value to escape through churn. Sustainable growth requires both attracting new customers and protecting the loyal customer base already in the bucket.

This is where AI-supported decisioning can create real value. Trusted, consented, real-time behavioral data can help an operator recognize changes in usage, service issues, digital engagement, and signs of churn. The next best action may be to resolve a problem, recommend a more suitable plan, position a bespoke package at the right time, or avoid making an offer when the customer first needs support.

The objective is not to push more offers. It is to improve relevance and timing. That can support retention, strengthen loyalty, and create opportunities to grow average revenue per user, or ARPU, by responding to what the customer needs in that moment.

Travel, hospitality, and tourism have a similarly strong opportunity. The customer journey moves from inspiration and research to booking, arrival, the in-destination experience, and loyalty. Each stage creates different needs. A traveler facing disruption needs assistance, while a guest already at a destination may value a timely, personalized experience or service.

These sectors show why hyper-personalization at scale depends on trusted, consented behavioral data in motion. The value comes from giving AI initiatives the current context they need to choose the next best action for that specific moment.

At AI Everything Abu Dhabi 2026, what are the biggest changes you are seeing in the conversations enterprises are having compared with a year or two ago?

The biggest change is that enterprises are no longer asking whether they should adopt AI. They are asking how to move it into production, connect it to measurable outcomes, and govern it at scale.

A year or two ago, many conversations centered on experimentation and individual use cases. Today, the questions are more operational. Leaders want to understand how AI will work with their existing data, how agents will receive current customer context, how decisions will be controlled, and how value will be measured beyond a successful pilot.

I am also hearing much more emphasis on adoption. Organizations recognize that a model does not create value by itself. Teams need clear use cases, practical training, shared measures of success, and confidence in the data behind the recommendation.

The conversation has therefore moved from AI capability to AI readiness. That includes the quality of the data foundation, the governance around it, the ability to make decisions in real time, and the people and processes required to turn those decisions into action. For me, that is a sign that the market is becoming more mature and more focused on sustainable business impact.

Exclusive interview with Bilal Baig, Vice President, Solutions Engineering, Trend Micro

What is Trend Micro showcasing at GISEC Global 2026, and how does it reflect the shift towards proactive, AI-powered cyber risk management?

We are highlighting our unified cybersecurity platform, Trend Vision One, which is designed as a proactive security platform.

AI has shifted how organisations, governments and agencies respond to threats. It is no longer enough to take a reactive approach. Security needs to become proactive, particularly given the speed at which AI is developing.

We are using AI in two ways. First, we are using AI internally within the platform to identify vulnerabilities. Second, we are using AI to protect our customers.

At GISEC, we are showcasing agentic SIEM, agentic SOAR, XDR capabilities and our full-stack AI security platform. We are also highlighting new developments in AI security, including how AI can help protect against vulnerabilities and zero-day attacks.

How has Trend Micro evolved in both using AI for cybersecurity and securing AI systems themselves?

AI has increased the speed at which organisations can move into production. At the same time, both defenders and attackers now have access to AI. The key question is how organisations manage that risk and how quickly they can protect customers and predict an attack before it becomes a breach.

We created Cybertron, an industry-first cybersecurity LLM, and we also work with frontier AI providers including Anthropic, OpenAI and Microsoft. We use frontier AI to consume vulnerability information, while our customers have access to our broader AI security capabilities.

We are now moving into the agentic AI era, where AI agents can make decisions on behalf of humans. These agents can access systems, emulate human behaviour and perform tasks independently.

For us, agentic AI security comes down to four key areas: visibility, observability, governance and response.

Visibility means understanding what is happening. Observability goes a step further by understanding what an action performed by an AI agent could cause. Governance determines how those agents should be controlled, while response is about deciding what action to take.

What new security and governance challenges arise as agentic AI moves from experimentation to enterprise deployment?

One of the biggest questions is whether an agentic AI system should be treated like a human identity or like software.

A software system needs updates, patches and maintenance. A human has an identity, a job and defined responsibilities. Agentic AI combines elements of both.

Organisations therefore need to give AI agents an identity, establish guardrails around what they can do and ensure that someone within the governance structure is responsible for their actions.

If an agent is given additional responsibilities, organisations need to understand how those permissions are managed and eventually removed when they are no longer required.

In an agentic AI environment, every communication and action needs to be considered within a governance framework. Organisations need to look at every interaction, understand its potential outcome and decide whether an action should be allowed to proceed or stopped.

What does the rise of autonomous or rogue AI agents mean for cybersecurity?

We are entering a world where rogue AI agents can become highly sophisticated systems. This means security cannot focus only on whether the underlying AI model is secure. Organisations also need to examine the actions those models are performing and whether those actions could create a cybersecurity problem.

The attack surface is now changing in terms of both scale and sophistication. Attackers have AI capabilities that can help them launch sophisticated attacks much faster.

This means organisations need AI on the defensive side as well. Security solutions need to match that speed and sophistication while ensuring that governance frameworks prevent malicious outcomes.

How should organisations manage the growing number of vulnerabilities identified by AI?

AI and frontier models can identify vulnerabilities that may not have been visible previously. An organisation that once had to manage 30 or 40 patches could suddenly face thousands.

It is not realistic to address every vulnerability in the same way. Organisations need to prioritise based on the risk and importance of their environment.

They need to identify which vulnerabilities are most important for their particular environment rather than simply looking at a vulnerability’s CVE score.

This is where cyber risk exposure management becomes important. Organisations need to understand the risk, the asset and the identity involved, and then decide which security gaps are most important to close.

How is the UAE’s cyber threat landscape changing as AI adoption and digital transformation accelerate?

The UAE is at the forefront of AI transformation. We are seeing multiple initiatives from the UAE Government, including AI training for government employees, government-focused AI initiatives and the introduction of AI education in schools.

There are already AI systems operating within government, so the digital transformation of AI in the UAE is moving forward rapidly.

Our focus is on helping secure that transformation. As AI systems become more interconnected and increasingly make decisions, the attack surface becomes more complicated.

A layered security approach is therefore important, from the large language model and API access through to the decision-making processes of AI agents, while monitoring for malicious activity.

What should organisations consider around security controls and data sovereignty as they expand their cloud and AI environments?

There is sometimes a misconception that moving to the cloud automatically means an organisation is secure. When cloud computing emerged, we often talked about security as a shared responsibility.

The exposure changes as organisations move from on-premises environments to the cloud and then into AI. The same threat can look very different across these environments.

Organisations need to consider where their assets and identities are located and how they will manage security across these different layers.

For highly sensitive environments, including air-gapped networks and systems involving critical data sovereignty, security may need to remain on-premises. In some national security environments, data cannot be processed outside the country.

Trend Micro has Vision One Sovereign and Private Cloud, which extends our AI cybersecurity unified platform to air-gapped and sovereign environments, with a focus on data sovereignty, localisation and air-gapped deployments.

What role does government-industry collaboration play in strengthening national cybersecurity preparedness and resilience?

Government-industry collaboration is extremely important. Working with organisations such as the Cybersecurity Council, national CERTs and government security services brings together different perspectives.

As governments move towards greater use of AI, industry can help secure that journey while governments provide the regulations and governance frameworks needed to manage these systems.

Without close collaboration, it becomes difficult to create policies that reflect what is actually happening in the private sector.

The objective should be to support innovation without overlooking cybersecurity. Technology is developing extremely quickly, particularly AI, so cybersecurity needs to be considered alongside that innovation.

Government and the private sector need to work together to make sure that while organisations remain at the forefront of technological development, they do not overlook the cybersecurity implications.

Dave Philp, Chief Value Officer at Bentley Systems, discusses how AI, digital twins, clean energy and integrated infrastructure planning are shaping the next generation of data centres across the UAE and wider Middle East.

The UAE and Saudi Arabia are investing heavily in AI and digital infrastructure. From your perspective, what makes the Middle East such an exciting market for the next generation of data centers?

One of the most substantial transformations we are witnessing is the transition of the UAE to a programmatic mindset in terms of data centre development, which makes it a desirable market for the next generation of data centres. We usually consider countries such as UK in terms of building or developing a singular project, but it is impressive that the UAE has been prioritising data centre corridors and digital infrastructure instead of focusing on individual hyperscale data centres.

While speaking to colleagues across the UAE and the Middle East region, I found it interesting as the UAE is constantly investing in the infrastructure needed to achieve its ambitious AI goal. This further includes larger digital ecosystems, sovereign cloud capabilities, and energy infrastructure. I believe it is not just about creating more data centres; it is also about establishing an infrastructure that can help achieve the ambitious economic vision of the UAE. It is important to understand that establishing a robust digital infrastructure is key to power AI, smart city initiatives and industrial diversification. This strategy is part of a much larger national economic plan. By combining digital aspirations with investments in energy and physical infrastructure, the UAE is successfully laying the groundwork for an AI-powered economy. We can also witness that the UK is making efforts to move towards a future economic model, which can motivate other countries to follow suit.

• Power is a growing challenge for AI data centers. How can the Middle East balance rising AI demand with its clean energy goals?

I believe that the UAE has secured a unique position in this regard as any capital investment in AI infrastructure needs to be strategized according to clean energy goals. As the Minister of Energy and Infrastructure highlighted, AI-enabled optimisation can coordinate data-centre demand with grid capacity, renewable generation, storage and shared cooling infrastructure. It can also identify opportunities to recover and reuse waste heat where local conditions make that technically and commercially viable.

This, in my opinion, will define the UAE’s next generation data centres. Integrated planning, which takes into account how data centres interact with renewable energy, solar opportunities, district cooling, battery storage, and demand management, will be prioritised over isolated engineering decisions. These factors need to considered by the UAE, not just in terms of separate workflows.

As I mentioned, these will be common across the region, but we are also witnessing unique models in the UAE. We are seeing a shift from isolated projects towards phased campuses and data-centre corridors supported by shared power, water, cooling and connectivity infrastructure.

• As AI data centers require more cooling, how can operators build facilities that are both efficient and responsible with water use?

Yes, it is interesting. It is similar to not having a favourite child, like you do not want operators to choose between energy and water, but how to optimise both to create a resilient thermal management system. However, it is my belief that it should start the very beginning of the project, investment level, thinking about it as a water ecosystem. It is not just about water on site, but about the sourcing within it.

Additionally, I believe that there is a lot of innovation within data centres as well. Now, when it comes to water-stressed environments, usage of reclaimed water should be considered. On the other hand for high-density AI workloads, direct-to-chip and other liquid-cooling approaches can remove heat closer to the source. Closed-loop systems recirculate coolant rather than continually consuming it, although the overall water and energy performance still depends on how the facility rejects heat to the external environment. Also, we must understand how it will integrate within the local recycling infrastructure within there as well.

As a result, we can now model data centres, which will reduce pressure on potable supplies and improve operational resilience. Effective energy and water management are beneficial for businesses as well. They can monitor performance and optimise water usage, which benefits both the community and operators.

The most significant factor, in my opinion, is that we can move from reactive to predictive water and cooling management with digital twinning and AI. When connected to trustworthy operational data and engineering models, infrastructure digital twins can help operators move from static reporting towards predictive management, testing changes in workload, climate, water availability and equipment performance before those conditions affect operations.

And that can offer significant potential for resilience as well as sustainable data centres with robust governance. This requires a comprehensive and holistic approach to both energy and water, rather than individual components.

Ultimately, the objective is not simply to minimise water consumption in isolation. It is to optimise the complete thermal system, because some lower-water cooling configurations may use more electricity. The right solution depends on climate, workload density, water stress, grid carbon intensity and resilience requirements.

• As the Middle East invests in smart cities like NEOM, what role will data centers play in enabling these developments?

I believe that smart cities should communicate with each other as they are powered by AI. This, showcases proper planning works exceptionally when we consider master planning and data centre planning from urban systems that will be integrated into it.

Additionally, integrated value chains are necessary for communities within the UAE to move forward with smart cities. Now, when we discuss value chains, we mean the energy, water, cooling, mobility, and data services.

A significant portion of our work is conducted on city or municipality level, where we use digital twins to enable planners understand interdependencies and future scenarios that can optimise assets within the framework of smart cities. This is where Bentley Systems’ approach to infrastructure digital twins Infrastructure can provide planners with a shared environment in which to understand dependencies, test future scenarios and make more confident investment and operational decisions.

We frequently consider the data centre to be something we would prefer to keep hidden. But, in reality it is a strategic enabler of better, more resilient urban growth. If we do it correctly and consider, let us call it a digital built UAE, which is plausible, becoming an engine room for smart cities.

In fact, this achievement has to be celebrated. If we do it correctly, it becomes a positive contributor behind them. Therefore, I think that smart cities require smart infrastructure, and data centres are becoming a part of what we now refer to as civic backbone. It must be present, accountable, and advantageous to the communities it serves. Moreover,  it all comes down systems, smart dependencies, and data exchange between various departments. Because city-scale infrastructure spans many owners and systems, the digital-twin environment must be capable of federating trusted information through open standards, interoperable interfaces and appropriate governance.

• Beyond speed to market, what do you think will define the next generation of successful data centers in the Middle East?

Speed to market is still a very significant factor, but it must transcend that. I believe that if I was an investor considering data centres, I would clearly want to increase revenue at an expedited rate, but I would also want to ensure that it is resilient for a substantial time, which obviously includes water and energy.

 It can understand it is sustainable, but I also want certainty. There are certain longer-term concerns, such as whether there will be droughts in the future. My opinion is that it must evolve. Compute hardware and thermal requirements will continue to evolve over the life of the facility, often much faster than the supporting civil, power and utility infrastructure.

Additionally, I also believe it must be flexible and adaptable as cooling technology and workloads evolve. Therefore, we need to consider how we may apply digital twinning once again, not just for capital building, but also for operational excellence.

As AI campuses move into operation, investors will increasingly look beyond capital cost and installed megawatts towards the productivity of the infrastructure: how reliably and efficiently it converts energy and compute capacity into useful AI output. Measures such as cost per token and tokens per watt will sit alongside PUE, WUE, carbon intensity and availability, providing a fuller view of operational and commercial performance.