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  • AI budgets are up 88%. ROI isn’t keeping pace.

    AI budgets are up 88%. ROI isn’t keeping pace.

    Nine in ten UK businesses raised their AI budgets this year. Fewer than a third can point to a return on it. That gap between rising spend and flat results is the real AI story for UK agencies right now, and it isn’t a technology problem.

    According to Spicy Advisory’s 2026 UK SMB AI adoption guide, 85-91% of UK organisations increased AI spend this year, yet only 31% report positive ROI. Seventy-seven percent saw no measurable revenue change at all. Where AI does pay off, it shows up as time saved rather than top-line growth, which would be fine, except most firms aren’t measuring time saved either, so the return is invisible even when it’s real.

    ONS figures from June 2026 point at why. The average AI-adopting UK business uses just 1.6 AI tools, and only 10% report extensive use of any of them. Adoption has tripled since 2023, but depth hasn’t moved much at all. Only 15% of businesses in the 0-9 employee bracket — where most agencies sit — use AI at all, against 68% of large firms. UK firms aren’t short of AI. They’re short of depth.

    There’s a skills story sitting underneath both numbers. Over 60% of firms name the skills gap as their primary barrier, and only 11% have trained more than half their workforce in AI skills. Buying a licence isn’t the same as building the muscle to use it well, and most firms are stopping at the purchase.

    More tools, same problem

    Tool fragmentation is Spicy Advisory’s second-named barrier to AI ROI, right behind the skills gap. It’s a pattern I run into constantly with clients: a Copilot licence bought for the whole team, a ChatGPT subscription three people actually open, a Claude account someone on the leadership team set up after a conference, and none of them wired into an actual workflow. Everyone in the building is “using AI.” Nobody’s using it for anything specific enough to measure.

    Success in 2026 isn’t about using every AI tool. It’s about choosing the right tool for the right task and going deep on it, as the Stop Chasing Every New Tool newsletter put it this year.

    That’s the uncomfortable part for a lot of firms: the fix isn’t procurement, it’s subtraction. Before evaluating a fourth or fifth tool, work out what the first three are actually doing for you, if anything.

    Try this: a 20-minute tool audit

    You don’t need a consultant for the first pass. Block 20 minutes and do this:

    1. List every AI tool with an active licence. Finance can usually pull this off the card statement faster than IT can list it from memory.
    2. Write down the one workflow each tool touches — not “content” or “admin”, but the actual task: “drafting first-pass client proposal decks”, “summarising onboarding calls”.
    3. Cut or pause anything without a named workflow attached. If nobody in the room can say what a tool is for, it isn’t producing ROI, measured or not, and it’s a licence you can reclaim this afternoon.
    4. Pick the tool touching your highest-volume repetitive task and go deep on it. Build the prompt library, the template, the checklist, whatever makes the fiftieth use faster than the first. That’s where the actual time saving lives, and it compounds; a tool used once a week never gets good enough to notice.

    Most firms find step 3 uncomfortable, because “we’re paying for it, so we must be using it” feels true even when the usage logs say otherwise. Check the logs. Most seat-based AI subscriptions show login frequency somewhere in the admin panel, and it’s usually a harder number than anyone expects.

    Then close the loop: whichever tool you keep going deep on, write down what you’re measuring before you start: hours saved on that one workflow, turnaround time, or output volume. Not “productivity”, a specific number tied to the specific task from step 2. That’s the difference between joining the 31% who can show ROI and staying in the 69% who are pretty sure it’s helping but can’t prove it.

    The standard stack, plus one

    Spicy Advisory’s guidance for UK SMBs is blunt and, in my experience, correct: standardise on Microsoft 365 + Copilot or Google Workspace + Gemini for the baseline, add one external assistant — Claude or ChatGPT — for the work your core suite doesn’t cover well, and stop there until you can show the first two are earning their keep.

    That’s a smaller stack than most firms are currently running, and that’s the point. Depth over breadth isn’t a slogan here: it’s the difference between the 31% seeing ROI and the 69% who aren’t. The 31% aren’t using more tools. They’re using fewer tools for longer, on narrower, higher-volume tasks, until the tool actually knows the job.

    If the audit above turns up five half-used subscriptions and nobody in the business owns the decision of what to cut or what to go deep on next, that’s exactly the kind of prioritisation call worth getting an outside, structured view on. That’s the whole point of an AI opportunity audit: not another tool recommendation, but a ranked list of where AI actually pays off in your specific workflows, and where it’s just spend.

    The opinion part

    AI budgets will keep rising into 2027 whether or not ROI follows: the EY and BCC data both point that way, and nobody wants to be the firm that “isn’t doing AI.” But the 69% gap between spend and return isn’t going to close by adding tool number four. It closes by someone in the business deciding which one tool gets used until it’s actually good, and which three get switched off. That decision is boring, it takes 20 minutes, and almost nobody is making it.

  • UK firms tripled their AI use. Few changed how they work.

    UK firms tripled their AI use. Few changed how they work.

    The Office for National Statistics published figures this month that should give pause to anyone selling AI to British businesses, and anyone buying it. AI use among UK firms with ten or more employees has roughly tripled since late 2023, climbing from about 12% to 35%. On the face of it, that reads like a technology winning.

    Then you look at the depth. The average number of AI tools per adopting firm moved from 1.4 to 1.6. Only 10% of adopters call their own usage “extensive”. Just 15% say more than half their staff use AI daily. And only 11% report that more than half their workforce has had any AI training at all.

    Three years of enthusiasm. Two-tenths of a tool.

    Adoption is not the same as change

    The gap gets wider the closer you look. The British Chambers of Commerce, working with Atos, put UK SME AI adoption at 54%. The government’s own DSIT adoption research finds roughly 16% of firms have made a strategic AI deployment. Both numbers can be true, because they are measuring different things. One counts firms where somebody, somewhere, has opened ChatGPT. The other counts firms that have actually rebuilt a process around it.

    The ONS data makes the same point in a different way. Nearly 60% of adopting firms use AI to make existing operations more efficient. Fewer than one in five use it to build new products, reach new markets, or do something they could not do before. Around half report no change in headcount, which is a polite way of saying nothing structural happened.

    Most firms have bought a subscription. Very few have changed a workflow.

    I see this constantly. A recruitment agency tells me they are “using AI” and what they mean is two consultants paste job descriptions into a chatbot and tidy the output. That is not nothing. It saves them twenty minutes a day each. But it is not the thing that changes the shape of the business, and it will not survive either of them leaving.

    The barrier is not what people assume

    Here is the finding I keep coming back to. When the ONS asked firms what was holding them back, 41% said nothing at all. No regulatory problem, no budget problem, no skills crisis. Nothing.

    Among firms that did name a barrier, the top answer was lack of expertise, cited by up to 18% of businesses with 100 to 249 staff.

    Put those together and you get an uncomfortable picture. A large share of British firms are not blocked. They simply do not know what to do next. They have the budget, the permission and the appetite, and they are stuck at the point of asking which problem is worth solving. That is a very different constraint from the one most AI vendors are selling against.

    It also explains the tool count. If you do not know where the value is, buying one more tool feels like progress. It is measurable, it is quick, and it produces something you can mention in a board meeting. Redesigning how quotes get produced does not.

    What the productivity numbers actually promise

    The upside is real, which is what makes the shallowness frustrating. Public First’s analysis for Google estimates AI tools could lift UK SME productivity by around 20%, roughly an extra working day a week, worth up to £198 billion across the economy. A 2025 government study found 56% of businesses using AI reported higher employee productivity.

    Notice the framing though. An extra day a week is not what you get from a chatbot subscription. It is what you get when a process that took four hours takes forty minutes, permanently, whether or not the person who set it up is in the office.

    Try this: the two-week audit you can run yourself

    You do not need a consultant to find your first real candidate. You need a week of honest observation and a spreadsheet. Here is the version I use before quoting anyone.

    1. Log the repeats, not the annoyances. For five working days, have your team note any task they did more than three times that week that followed roughly the same steps each time. Not what irritated them, what repeated. The two lists overlap less than you would think, and the repeats are where automation pays.

    2. Score each one on three columns. Hours per month. How much judgement it needs, high, medium or low. And whether the inputs live somewhere a computer can reach, an inbox or a CRM counts, someone’s memory does not. Anything with high hours, low judgement and reachable inputs is your shortlist. That is usually three or four things, and they are rarely the ones people guessed.

    3. Kill your favourite idea first. Whatever you were excited about before you started, check it against the three columns. If it needs high judgement or the data lives in someone’s head, it is a bad first project regardless of how good it sounds. Starting with the hard one is the most common way these efforts die.

    Do that honestly and you will end up with a ranked list of two or three processes with hours attached. That list is worth more than most AI strategy documents, because it is about your business rather than the category. If you would rather not run it yourself, that is more or less what an AI opportunity audit produces, but the method is not a secret and you are welcome to it.

    The uncomfortable conclusion

    The ONS numbers will be read two ways over the next few months. Vendors will use the 35% to argue that everyone is doing this and you are behind. Sceptics will use the 1.6 tools and the flat headcount to argue it is all hype.

    Both readings miss it. The interesting group is the 41% who report no barriers at all and still have not moved. They are not waiting for the technology to improve or the regulation to settle. They are waiting for someone to tell them which of their problems is worth pointing this at.

    My honest opinion, having built these systems for agencies and service firms: the firms that pull ahead over the next two years will not be the ones with the most AI tools. They will be the ones who picked two processes, rebuilt them properly, and left the rest alone. Breadth is what the statistics measure. Depth is what pays.