AI, Data and Analytics

In 2026, the conversation shifted from whether organisations adopt AI to how they build around it, with the pace of investment and ambition across the Middle East outpacing many global markets and creating entirely new intersections of talent.

Software engineering is becoming central to that shift. AI factories and dedicated AI functions are increasingly being built on software foundations, with greater emphasis on engineering principles, product thinking and building for production and true business utilisation. At the same time, AI-assisted development is changing software engineering itself - bringing Software, Data and AI closer together than ever before.

The lines are blurring elsewhere too. Quantitative modelling is converging with Data Science and Machine Learning, AI is becoming embedded within specialist domains, and Software Engineers are moving deeper into AI. Some of the most interesting roles we are seeing no longer fit neatly into one traditional job family.

There is also greater pragmatism around how AI value is created. Organisations are combining available models, enterprise platforms and off-the-shelf capabilities with proprietary technology where it creates genuine differentiation – while simultaneously building the scalable and governed data foundations AI ultimately depends on.

This is changing what “good” talent looks like. Technical depth still matters, but increasingly so does versatility – people who understand how software, data, models, infrastructure and the business connect. As these disciplines converge, we expect some of the strongest demand in 2027 to emerge in the spaces between them.