AI Team of the Year: How high-performing AI teams are driving business innovation

AI adoption depends heavily on the people responsible for putting the technology to work. As companies expand their use of artificial intelligence (AI), the demands on these professionals are changing. Developing models and applications remains important, but businesses also need people who can connect technical capabilities with operational priorities and take responsibility for how systems perform.

The quality of an AI initiative is increasingly tied to the people behind its execution. Their contribution extends from identifying suitable applications to integrating systems into business processes and addressing problems that emerge after deployment.

The work behind implementation

Taking an AI application from development to regular business use involves decisions that extend beyond the technology itself. Teams need to understand the process they are trying to improve, assess whether AI is suitable for it, and establish how the application will fit into existing operations.


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For instance, an AI-based invoice processing system needs to interpret documents accurately and work with a company's existing financial software. It must also account for exceptions that require human attention. Technical professionals may develop the application, but finance specialists understand the processes it needs to support. The implementation depends on how effectively these different perspectives come together.

Similar demands arise across customer service, manufacturing and cybersecurity. Each function has its own requirements, and an application that performs well in a controlled environment may need considerable refinement before it becomes useful in daily operations.

What makes an AI team effective?

Technical expertise is essential, but it does not guarantee successful implementation. Teams also need people who understand business priorities and can judge whether a proposed application addresses a genuine need.

The work involves difficult decisions around data quality, system performance and human oversight. Teams must assess the limitations of an application and respond when its results fall short of expectations. Their responsibility continues after deployment, as systems need to be monitored and adjusted to changing requirements.

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Organisational support also influences how far these initiatives progress. Clear priorities, access to resources and collaboration between technology and business functions give teams the conditions to develop and improve their work. Without these, promising projects can remain confined to pilot stages.

The contribution of an AI team should ultimately be visible in the problems it solves and the results its work delivers. The number of projects undertaken offers little indication of their value if those applications fail to make a meaningful difference to business operations.

Recognising the people behind AI

As businesses invest in AI capabilities, the people developing and implementing these applications deserve greater recognition. Their work involves translating technical expertise into outcomes that organisations can use and build upon.

Also Read: ET AI Awards 2027: 40+ categories across Innovation, Functional Excellence, Leadership and Emerging AI

The AI Team of the Year category at the ET AI Awards 2027 recognises teams contributing to AI innovation and its application across business. Organisations can use their nominations to demonstrate the work behind their initiatives, including technical contributions, execution and business impact.

The category is part of the Leadership Track, which also recognises:

• AI Entrepreneur of the Year
• AI Technology Leader of the Year – CTO/CDO
• AI Woman Leader of the Year
• Business Leader of the Year – AI
• AI for India Leader

The awards feature 40+ categories across 4 tracks, recognising AI innovation and leadership across startups, SMEs and enterprises.

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