{"id":64731,"date":"2026-04-13T08:32:45","date_gmt":"2026-04-13T08:32:45","guid":{"rendered":"https:\/\/www.elixirr.com\/en-gb\/?p=64731"},"modified":"2026-04-13T08:45:50","modified_gmt":"2026-04-13T08:45:50","slug":"ai-is-not-a-software-upgrade","status":"publish","type":"post","link":"https:\/\/www.elixirr.com\/en-gb\/ai-is-not-a-software-upgrade\/","title":{"rendered":"AI is not a software upgrade"},"content":{"rendered":"\n
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This is the first in a two-part series on how\u00a0organisations\u00a0can successfully embrace AI.\u00a0In this piece, we set out why so many AI\u00a0programmes\u00a0fail to\u00a0deliver meaningful value, and why the problem is not the technology itself, but how\u00a0organisations\u00a0are set up to use it.<\/span>\u00a0<\/span><\/p>\n

Artificial intelligence is often discussed as if it were simply the next wave of enterprise technology: another platform to deploy, another set of tools to integrate, another capability to be absorbed into the existing stack. That framing is inadequate. AI is not only changing the technology landscape. It is changing how work is designed, how decisions are made, how\u00a0expertise\u00a0is\u00a0applied\u00a0and how value is created across the\u00a0organisation.<\/span>\u00a0<\/span><\/p>\n

That is why so many AI\u00a0programmes\u00a0disappoint. In most large\u00a0organisations, the issue is not whether the technology is powerful enough\u00a0\u2013\u00a0it is. The issue is that AI does not sit\u00a0neatly inside\u00a0one function. It cuts across operations, customer engagement, product, risk, compliance, data,\u00a0technology\u00a0and\u00a0leadership itself. The challenge, then, is not simply how to introduce AI tools. It is how to build the operating model,\u00a0authority\u00a0and governance\u00a0required\u00a0to use AI at scale in a way that is commercially valuable, operationally\u00a0sustainable\u00a0and trusted.<\/span>\u00a0<\/span><\/p>\n

This is now the central dividing line. The question is no longer whether\u00a0organisations\u00a0are engaging with AI. By 2025, McKinsey found that 88% of\u00a0organisations\u00a0reported regular AI use in at least one business function, and the pace of adoption has only accelerated since then. The more consequential distinction now is not between\u00a0organisations\u00a0using AI and those that are not, but between those generating AI activity and those redesigning themselves to capture AI value at scale.<\/span>\u00a0<\/span><\/p>\n

Technology alone has never been the point<\/span><\/span>\u00a0<\/span><\/h2>\n

History suggests this should not surprise us. General-purpose technologies rarely create lasting\u00a0advantage\u00a0simply because they are adopted. They create\u00a0advantage\u00a0when\u00a0organisations\u00a0redesign around what\u00a0the technology\u00a0makes possible.<\/span>\u00a0<\/span><\/p>\n

When manufacturing electrified, some firms simply replaced steam engines with electric motors and saw modest gains. Others redesigned the factory around electricity and transformed productivity. When retail\u00a0digitised, some incumbents treated the internet as another channel; others rebuilt the business around digital infrastructure and changed the economics of the industry. AI is producing the same split now. One group of\u00a0organisations\u00a0is layering copilots and isolated use cases onto existing workflows. Another is rethinking workflows,\u00a0roles\u00a0and\u00a0service models from first principles. The former will\u00a0gain efficiency. The latter will change their competitive position.<\/span>\u00a0<\/span><\/p>\n

That distinction matters because AI is not just another tool to bolt onto yesterday\u2019s operating model. It is a capability that forces\u00a0organisations\u00a0to ask what should be redesigned, what should be automated, what should remain human-led\u00a0and\u00a0how work should flow when intelligence is no longer scarce. The\u00a0organisations\u00a0that answer those questions well will move beyond incremental productivity and begin to reshape cost structures, service\u00a0models\u00a0and competitive advantage.<\/span>\u00a0<\/span><\/p>\n

Where most organisations are getting stuck<\/h2>\n

The current pattern across enterprise AI is not one of\u00a0inaction. It is one of fragmentation. Most\u00a0organisations\u00a0have pilots. Many have promising use cases. Some have whole portfolios of experiments spread across functions. But\u00a0relatively few\u00a0have turned that activity into repeatable enterprise value. The result is what might be called PoC purgatory: a state in which the\u00a0organisation\u00a0can point to dozens, sometimes hundreds, of proofs of concept and pilot initiatives, yet very little ever makes it into production in a form that changes cost, productivity, control, or customer outcomes.<\/span>\u00a0<\/span><\/p>\n

That is what makes PoC purgatory so deceptive. It creates the appearance of momentum. There are demos, working sessions, vendor conversations, internal\u00a0showcases\u00a0and a steady flow of experimentation. From the outside, it can look like the\u00a0organisation\u00a0is moving quickly.\u00a0In reality, it\u00a0is often spinning its wheels. Initiatives remain trapped in pilot mode because the harder decisions are never made: which use cases truly matter, which should be scaled, who owns them, how they will be governed, how workflows\u00a0must change around them, and what trade-offs the\u00a0organisation\u00a0is willing to make to bring them into production.<\/span>\u00a0<\/span><\/p>\n

This is why PoC purgatory is not simply a technology problem. It is a failure of operating\u00a0model\u00a0and\u00a0organisational\u00a0resolve. The\u00a0organisation\u00a0keeps proving that AI can do interesting things, but never builds the conditions required for AI to do consequential things. Proofs of concept multiply because experimentation is easier than\u00a0prioritisation, easier than redesigning workflows, easier than assigning\u00a0accountability\u00a0and\u00a0easier than forcing adoption. The result is AI theatre rather than AI transformation:\u00a0visible activity, limited\u00a0production\u00a0and\u00a0very little\u00a0measurable value.<\/span>\u00a0<\/span><\/p>\n

That is why AI should not be treated as a software problem, or even as a technology problem with some change-management implications attached. It is an\u00a0operating-model\u00a0problem. It touches strategy, decision rights, role design, governance, performance\u00a0management\u00a0and leadership\u00a0behaviour. The real gap is not between technical capability and technical implementation. It is between technical capability and\u00a0organisational\u00a0readiness.<\/span>\u00a0<\/span><\/p>\n <\/div>\n <\/div>\n<\/section>\n\n\n\n

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