FEATURE enough to use. But this is no longer the case. Tools are improving rapidly and some of the world’ s largest organisations are deploying them at scale – Gartner predicts enterprises will have an average of 150,000 agents in deployment by 2028.
At the same time, nearly US $ 3 trillion is projected to be invested into AI infrastructure in the next three years. This means that AI is becoming faster and more efficient and is increasingly embedded in enterprise platforms rather than experimental tools.
New agentic systems mark a step up in capability. They do not just produce new data in response to questions or queries, they can act, decide and coordinate independently. They are capable of orchestrating data and processes at multiple levels of an organisation with unparalleled speed and efficiency, reflected in this year’ s HFS Horizons: Agentic Services research that uncovered productivity as the top priority for AI amongst 74 % of enterprises. There is a lot of AI hype and comparison but the change underway is genuinely as pivotal as the introduction of electricity into factories in the 19th century, which did not just make elements of the manufacturing process more efficient but powered the entire enterprise and reshaped how production was organised.
Leadership mistakes
Too often, CIOs are deploying and testing generative tools as part of fragmented roll-outs that offer measurable but comparatively limited gains instead of embedding agentic AI systems that run automatically across an end-to-end process.
Consider the difference. A helpdesk AI chatbot saves minutes; an agentic system embedded in core operations impacts profit and loss.
AI adoption must be a board-level priority that requires a fundamental reassessment of technical systems at every level of an organisation.
If not, workers often find themselves producing‘ AI workslop’ – that is, low-value or erroneous www. intelligentcio. com
INTELLIGENT CIO NORTH AMERICA
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