Leadership in an age of AI
Most boards now have an AI slide. It tends to list a handful of pilots, a licence count and a line about productivity. What it rarely shows is a plan: what the business is trying to change, what that will cost over three years rather than one, how the return will be measured, and who is accountable for delivering it. The gap between activity and ownership is where much of the money is going.
The research is consistent on this. Gartner's 2025 survey of CIOs found that 74% said the cost of AI currently equals or exceeds the value it has delivered. In January 2026 Gartner reported that at least half of generative AI projects had been abandoned after proof of concept by the end of 2025, citing poor data quality, weak risk controls, escalating costs and unclear business value. In October 2026 its latest CEO survey found that 44% of chief executives struggle to demonstrate financial returns from AI to their stakeholders. McKinsey's 2026 global survey tells a similar story from a different angle: eight in ten respondents say AI has improved their own productivity, yet only 37% can attribute any impact on EBIT to it, a figure essentially unchanged from the year before, and only about 6% report AI contributing 5% or more of EBIT.
None of this means AI does not work. It means that, for most organisations, individual productivity gains have not yet turned into anything a finance director would recognise on the P&L. Time saved is not money saved unless someone redesigns the work, the roles and the cost base around it.
Meanwhile the bill is changing. Having spent heavily to build these tools, the large providers are now turning them into revenue. Microsoft raised list prices on most Microsoft 365 commercial suites from July 2026, alongside expanded Copilot features, while the full Copilot product remains a separate per-user add-on and agents are charged by usage. Salesforce raised list prices on its main enterprise editions by an average of 6% in August 2025 and introduced per-user Agentforce add-ons and consumption-based credits. Google folded Gemini into Workspace in 2025 and raised base plan prices as it did so. AI is becoming a recurring operating cost rather than an experiment, and McKinsey found that around one in five organisations are already limiting their use of it because of operating costs.
For a large enterprise these are line items. For a business turning over £10m or £50m they are decisions, and the margin for error is much thinner. The gap is already visible. The Office for National Statistics reported in July 2026 that 28% of UK businesses with fewer than ten employees use at least one AI technology, against 49% of those with 250 or more, and that adoption is shallow across the board. McKinsey found that over half of organisations with revenues above $1 billion are scaling AI across the enterprise, compared with a third of smaller ones. Cost is not the only barrier; the ONS and the OECD both find lack of expertise and uncertainty about where AI fits matter more. But with costs rising and capability thin, smaller and mid-sized businesses face two poor outcomes: falling further behind, or paying steadily for tools that never earn their keep.
So why is it failing? In most organisations AI is still treated as a technology purchase, owned by one function, measured by licences deployed and pilots launched, and reported to the board as a project. The value does not sit in the technology. It sits in data that is fit to use, in roles and processes redesigned around the new capability, in a business case that finance can track, and in changes customers actually notice. Those belong to different leaders, and when each works in its own silo with its own priorities and its own vendors, the result is the pattern the research describes: plenty of activity, rising costs and little to show for it.
The evidence on what works points the same way. Gartner's own conclusion from its 2026 CEO survey is that the AI value problem "will not be solved by simply adding more AI to existing ways of working", and 63% of the chief executives it surveyed see radically redesigning the operating model as the way to accelerate financial returns. McKinsey's high performers are far more likely to have fundamentally redesigned workflows, and twice as likely to have senior leaders visibly committed to the work and defined processes for measuring its impact. Getting a return from AI is a leadership task that needs technology, data, people, finance and go-to-market working as one.
That is the gap Chalk & Salt is set up to close. We provide senior executive leadership across technology, operations, go-to-market, people, AI and finance, on an interim, fractional or advisory basis, for PE-backed and founder-led businesses and for larger multinationals. What matters is less the individual disciplines than the fact that they work as one team, with one point of contact for the board. The technology choices, the data work, the changes to roles, the business case and the commercial impact are designed together rather than negotiated between functions.
We start with The Operating Baseline, a short, fixed-scope look at systems, data and operating model that ends in a plain-English summary for the board and a costed roadmap. Its purpose is an honest picture of where the business actually stands: what it runs on, what state its data is in, what AI is already in use and at what cost, and where AI could realistically return money rather than simply add to the bill. It gives the board a shared starting point before any further spend is committed.
From there we work with the leadership team to choose a small number of priorities worth doing well, selected for value and feasibility rather than for how well they demonstrate. Each has an agreed measure of return expressed in money, not just hours saved, and a full view of cost including licences, usage, integration and people's time, so the board can see what is working and stop what is not. Because adoption decides whether any of this pays, bringing people with you is part of the work from the start: redesigning roles, building skills and making sure those affected understand why the change is happening.
None of this depends on a particular vendor or tool. It depends on senior leadership that owns the outcome and works across functions rather than within them. AI will reward the businesses that treat it as a change in how they are run rather than a line in the IT budget. The question for boards is no longer whether to act, but who is leading the work, and whether they are working as one team.
Sources
Gartner, IT Symposium/Xpo 2025 keynote takeaways, citing the 2025 Gartner AI Survey (CIO and technology leader view), 2025: https://emt.gartnerweb.com/ngw/globalassets/en/chief-information-officer/documents/2025-itsym-keynote-takeaways.pdf
Gartner, "Why 50% of GenAI Projects Fail", 26 January 2026: https://www.gartner.com/en/articles/genai-project-failure
Gartner, "Gartner Says 44% of CEOs Struggle to Demonstrate AI Financial Returns", 20 October 2026: https://www.gartner.com/en/newsroom/press-releases/2026-10-20-gartner-says-44-percent-of-ceos-struggle-to-demonstrate-ai-financial-returns-as-technology-becomes-central-to-growth
McKinsey, "The state of AI in 2026: On the road to ROI", 25 August 2026: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
Microsoft, "Microsoft 365 Pricing and Packaging Updates", effective 1 July 2026: https://www.microsoft.com/en-us/licensing/news/2026-m365-packaging-pricing-updates
Salesforce, "Updated Product Packaging and Pricing", 17 June 2025: https://www.salesforce.com/news/stories/pricing-update-2025/
Google Workspace, "The future of AI-powered work for every business", 15 January 2025: https://workspace.google.com/blog/product-announcements/empowering-businesses-with-ai
Office for National Statistics, "Artificial intelligence in UK businesses: 2023 to 2026", 20 July 2026: https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/articles/artificialintelligenceinukbusinesses/2023to2026
OECD, "Generative AI and the SME Workforce", 5 November 2025: https://www.oecd.org/en/publications/generative-ai-and-the-sme-workforce_2d08b99d-en.html