Artificial intelligence has crossed a threshold. What was, only a few years ago, a research curiosity or a single flashy demo is now woven into the day-to-day operations of serious organisations. The question facing most leaders is no longer whether AI is relevant to their business, but how quickly — and how responsibly — they can put it to work.
The numbers bear this out. In McKinsey's 2025 global survey on the state of AI, 88% of organisations reported using AI in at least one business function — up from 78% only a year earlier. Adoption, in other words, is no longer the story. What organisations actually do with it is.
The first wave of AI adoption was defined by pilots: isolated proofs of concept that rarely left the lab. The current wave is different. Organisations are embedding AI directly into the workflows that generate value — the way they serve customers, process information, detect risk, and make decisions. AI is shifting from a project to part of the operating model. McKinsey estimates that generative AI alone could add the equivalent of $2.6 trillion to $4.4 trillion to the global economy each year, with roughly three-quarters of that value concentrated in four areas: customer operations, marketing and sales, software engineering, and research and development.
In financial services, AI is reshaping fraud detection, credit risk, and customer engagement. In healthcare, it is accelerating diagnostics and removing administrative drag. Manufacturers are using it for predictive maintenance and quality control; retailers for demand forecasting and personalisation. And in government and the public sector, it is streamlining citizen services, document processing, and the detection of fraud and error at scale.
What unites these examples is not a single technology but a pattern: tasks that were once slow, manual, or judgement-heavy are being augmented — and the organisations that move first are compounding the advantage.
Here is the uncomfortable counterpoint to near-universal adoption: most organisations are not yet seeing the returns. In the same McKinsey research, only around 39% of organisations reported any measurable impact on enterprise earnings from AI — and among those, the effect was usually modest. Just 6% qualified as high performers. The pattern is consistent across studies: the bottleneck is no longer access to models, but the discipline to redesign workflows, govern the technology, and carry it all the way through to production. Newer “agentic” systems — AI that can plan and execute multi-step tasks — show the same split: a majority of firms are experimenting, but far fewer have scaled them.
AI capability compounds. Teams that adopt early build the data foundations, the skills, and the institutional confidence that make the next initiative faster and cheaper. Those that wait don't simply stand still; they fall behind a moving frontier, and the gap is widening. Leveraging AI is becoming less a source of differentiation and more a baseline expectation. The lesson from the data is not to “adopt faster” for its own sake — it is to adopt deliberately, with the foundations in place to turn experiments into outcomes.
AI only creates value when it is matched to the right problem, built on sound data, and integrated into how your organisation actually works. SRR FTS helps you identify where AI will move the needle, build the data pipelines and models behind it, and put it into production securely and responsibly — end to end.
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