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Governments Must Move Beyond AI Pilots to Unlock Full Potential: McKinsey

Governments must move beyond isolated AI pilots and redesign public service delivery to fully harness the technology's potential, according to a McKinsey report. The public sector lags behind other industries with an AI Quotient score of 28 versus the global average of 33, and the report recommends a four-step approach focusing on mission outcomes, workflow redesign, AI-centric operating models, and human oversight.

read2 min views1 publishedJul 24, 2026
Governments Must Move Beyond AI Pilots to Unlock Full Potential: McKinsey
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Governments need to move beyond isolated artificial intelligence (AI) pilot projects and redesign the way public services are delivered to fully harness the technology's potential, according to a McKinsey report.

The report said AI can improve public sector efficiency, enhance service delivery and help governments make better use of public resources.

However, it noted that many AI initiatives fail to move beyond the pilot stage due to challenges such as workflow integration, data access, model risks and operating costs.

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Read Now The Grand Design "Achieving real impact, however, requires more than isolated pilots. Fully realizing AI's potential demands organizations rewire by redesigning processes, ways of working, and operating models to make the technology part of everyday service delivery," the report said.

According to the report, governments should adopt a four-step approach to AI implementation by focusing on mission outcomes instead of technology, redesigning end-to-end workflows, building AI-centric operating models and ensuring human oversight in critical decisions.

Why is AI adoption in the public sector lagging behind other industries? #

The report said the public sector remains behind other industries in AI adoption, with an AI Quotient score of 28 compared with the global average of 33.

It attributed this gap to fragmented data, legacy technology systems, workforce constraints and stricter transparency and accountability requirements.

McKinsey said public-sector AI programmes are more successful when they redesign complete service journeys instead of introducing standalone use cases. Around 70 per cent of domain-based AI programmes reach production, compared with 30 per cent of programmes built around individual use cases, it added.

The report also said governments should begin implementing AI alongside efforts to improve data systems rather than waiting for complete data modernisation.

It further recommended that governments focus on measurable outcomes such as reducing waiting times, speeding up public services, improving accuracy and lowering fraud.

The report added that for every $1 invested in technology, around $5 should be allocated to adoption, training and capability building to successfully scale AI across public services.

(With inputs from ANI)

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