From AI ambition to real-world impact: What the UAE needs next

As artificial intelligence moves rapidly from experimentation to real-world deployment, AI Everything is set to bring together technology leaders, policymakers, and enterprises to explore the next phase of AI development and adoption.

Ahead of the event, the MENA TECH team spoke with Vikraman Poduval, CEO and founding member of Saal.ai, about the challenges enterprises face as they move from AI ambition to practical deployment, the importance of organizational readiness, and the growing need to develop stronger local AI capabilities.

The conversation is increasingly moving beyond whether businesses should adopt AI. The bigger question is how organizations can turn it into measurable value while building the infrastructure, talent, and organizational readiness required to support it.

Competing in a global AI market

One of the biggest changes in the current AI landscape is that access to advanced technologies and frontier models is no longer limited to a small number of markets.

According to Poduval, this creates an opportunity for companies emerging from regions such as the Middle East to develop technologies locally while competing against international players. He says: “With all the latest technology and frontier models, the arbitrage of innovation is gone. Now we can innovate from any part of the globe.”

He pointed to regional technology companies that have expanded beyond their home markets as evidence that geographical location is becoming less of a limitation for innovation.

The challenge, however, is ensuring that companies use that access to build products, intellectual property and expertise rather than focusing solely on adopting technologies developed elsewhere.

Moving beyond AI experimentation

For many enterprises, AI pilots are relatively easy to launch. Turning those projects into systems that create sustained business value is considerably more difficult. Poduval highlighted three areas that organizations need to address.

The first is use-case selection. Instead of introducing AI simply because it is available, organizations need to identify business problems where it can create clear and measurable value.

The second is organizational change. AI adoption can significantly alter workflows and responsibilities, making employee engagement and change management an important part of implementation.

The third is technology and data readiness. Companies need reliable infrastructure and properly connected, high-quality data before advanced AI systems can be effectively integrated into core operations.

Together, these factors illustrate why enterprise AI adoption is increasingly becoming an organizational challenge rather than simply a technology deployment.

The question of local AI ownership

As AI adoption expands across the region, another issue is emerging alongside it: who develops, owns, and builds expertise around the technology. Poduval says: “There is a major push for the adoption of AI, but at the same time, not many are talking about the ownership of AI. We have a responsibility to take ownership of AI at a local level.”

That ownership can take several forms: from developing intellectual property and locally relevant applications to investing in technical skills and creating stronger links between universities and industry.

For the UAE and the wider region, building those capabilities could become increasingly important as AI moves deeper into critical business processes and public-sector services.

Saal.ai, for example, works with universities through initiatives including curriculum development, laboratory access, internships, and industry forums. More broadly, such collaboration reflects the growing need to reduce the gap between academic AI education and the skills companies require in the workplace.

Building an ecosystem around deployment

Platforms such as AI Everything can play an important role in bringing together technology companies, enterprises, policymakers, and researchers to exchange perspectives on where AI adoption is heading and what is needed to move projects from experimentation to scale.

The discussions are also increasingly extending beyond new models and technical capabilities to questions around deployment, regulation, infrastructure, workforce development, and commercial applications.

This kind of collaboration can help identify where adoption is succeeding, where obstacles remain, and what organizations need to address as AI becomes more deeply integrated into their operations.

As organizations move into the next phase of AI adoption, the differentiator may ultimately be less about access to the technology itself and more about how effectively businesses integrate it into their operations, prepare their people and data, and develop the local capabilities needed to sustain it.