Is Your Team's AI Chaos Quietly Draining Productivity?

Hello,

This is Simon with the latest edition of The Weekly. In these updates, I share key AI related stories from this week's news, list upcoming events, and share any longer form articles posted on the website.

I was talking with a customer this week, who mentioned they'd yet to settle on a single AI tool to use across the business. He said you could walk past 10 people's desks and see 10 different AI tools being used. Right now, tool selection is purely down to individual preference. He asked if we'd seen benefits from selecting just one AI tool on an Enterprise plan, and outside of challenges like "shadow IT", governance and security, I said we had.

There are a number of day-to-day benefits from everyone using the same system, and a lot of it comes down to collaboration. Take Claude, for example, which lets you create and install Skills. It's one thing creating something for yourself, but the value multiplies when you share it with the rest of your team, or even the whole organisation. If AI is supposed to save time, it doesn't make sense for everyone to duplicate effort building their own individual skills. As an example, a colleague of mine in the US built an "Account Handover" skill that's perfectly designed to pull specific information together, helping an incoming account manager get up to speed on an account. Our marketing team has also built a skill for use with Claude Design that reflects our corporate brand guidelines for presentation slides — useful for anyone in the business who creates presentations. That saves far more time than having individuals try to build this themselves.

There are other benefits too, like being able to share a chat conversation with a colleague facing the same challenge, so they can see all the previous answers but take the conversation in a different direction suited to their own requirements.

It's when all these small elements add up that the real benefits start to appear. Using AI at an enterprise level, rather than an individual one, is how leading organisations are extracting the biggest advantage. These organisations also tend to have an overarching strategy for how AI should be used in the business, which helps not just with operational gains, but also governance and security.

Has your company settled on a single AI solution for the business, or do people still use whatever tools they personally prefer? If not, what’s stopping you?

Monday 7am. Three messages in #growth.

Stripe revenue by channel, Meta and Google spend reconciled against GA4, Klaviyo flow performance, Shopify AOV by source. Posted by Viktor at 6am.

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"Viktor is like the most capable all-round colleague you can imagine." Sam, CEO, Givr.

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For this week’s use case, we take a look at how the world of golf uses AI for content production.

The Bank for International Settlements published its annual report on June 28, warning that a potential AI bust ranks alongside inflation and sovereign debt as one of the biggest threats to the global financial system. The five largest tech "hyperscalers" — Google, Microsoft, Amazon, Meta, and Apple — are on track to commit more than $1 trillion to AI infrastructure across 2025 and 2026, a pace the BIS notes is outstripping their earnings and free cash flow, pushing some to borrow heavily to keep up. The report explicitly draws parallels to canal mania in the 1830s, railway mania in the 1840s, and the dotcom bubble — each kicked off by a genuine technology breakthrough that attracted more capital than returns could justify. "Disappointment in returns could trigger a sudden pullback in financing and turn the capex boom into a protracted investment bust," the BIS warned.

Why it matters: The BIS isn't known for crying wolf. If the world's central bank umbrella body is treating AI infrastructure as a systemic financial risk, board-level conversations about AI investment strategy need to account for the possibility that the market is pricing in outcomes that won't materialise — and that the correction, when it comes, could be sharp.

Bain & Company published analysis this month showing a widening gap between what companies are spending on AI and what they're getting back. Global enterprise AI spending reached $2.59 trillion in 2026 — up 47% on 2025 — yet fewer than 1% of organisations report significant ROI of 20% or more, with most seeing 1–5% returns, typically measured as productivity improvements rather than financial impact. Around 30% of generative AI projects are abandoned after proof of concept, with costs, poor data quality, and unclear business value cited as the main culprits. One healthcare enterprise consumed 1 trillion tokens over six months, racking up more than $6 million in unplanned costs before the finance team understood what was driving it.

Why it matters: The ROI gap is becoming a governance problem. Organisations that don't build cost visibility, usage controls, and clear success criteria into their AI programmes are finding that "positive AI ROI" often means "we saved some time somewhere" — which doesn't translate into the P&L impact that justified the spend. Finance directors and CFOs are starting to ask harder questions.

The Bank for International Settlements published its annual report on June 28, warning that a potential AI bust ranks alongside inflation and sovereign debt as one of the biggest threats to the global financial system. The five largest tech "hyperscalers" — Google, Microsoft, Amazon, Meta, and Apple — are on track to commit more than $1 trillion to AI infrastructure across 2025 and 2026, a pace the BIS notes is outstripping their earnings and free cash flow, pushing some to borrow heavily to keep up. The report explicitly draws parallels to canal mania in the 1830s, railway mania in the 1840s, and the dotcom bubble — each kicked off by a genuine technology breakthrough that attracted more capital than returns could justify. "Disappointment in returns could trigger a sudden pullback in financing and turn the capex boom into a protracted investment bust," the BIS warned.

Why it matters: The BIS isn't known for crying wolf. If the world's central bank umbrella body is treating AI infrastructure as a systemic financial risk, board-level conversations about AI investment strategy need to account for the possibility that the market is pricing in outcomes that won't materialise — and that the correction, when it comes, could be sharp.

Thanks for reading, and see you next Thursday.

Simon,

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