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AI adoption is measured by what changes, not what ships
Good morning. The announcements that landed today keep circling the same distinction: the gap between adopting an AI tool and actually changing how work gets done. A licence bought is not a task finished, and the studies and awards below all measure value at the point where work is completed and data is joined up, not at the point where a tool is switched on.
That distinction matters more than it sounds, because it changes how you should read any vendor's claims when you are choosing software. "We rolled out AI" tells you almost nothing. What you want to know is whether a task that used to take a person an afternoon now takes them ten minutes, whether the second draft is closer to final, and whether the data the tool needs is actually reachable. Everything below is a way of testing that question against the day's news.
What we're tracking
- The last mile between a draft and finished work The most useful frame today came from a Dropbox-sponsored study of professionals who already use AI at work [1]. The headline finding is about a gap: AI is now often where work starts, but getting from that start to something you can send, publish, or file still takes manual effort. That final stretch — the checking, the corrections, the missing context, the judgement about what is good enough — is the last mile.
- Why "we rolled it out" is not evidence Zapier published its 2026 Zappy Award winners today, and the framing is a good antidote to rollout theatre [2]. Most organisations, the piece argues, still count AI by inputs — seats bought, licences assigned, pilots launched. Those numbers prove a company has started. They do not prove anything about whether the work changed.
- Start from the work, not the tool A companion Zapier profile makes the same point from the inside of one company. Writing about an AI adoption lead at Jobber, the piece observes that organisations "often start with what they rolled out: the model, the license, the training program, or the pilot" [3] — and that the more productive starting point is the work itself.
- When software starts absorbing services The funding news of the day sharpens the build-versus-buy question. Ema, an enterprise AI company, raised a fresh round and now reports having "raised $140 million to date" with "more than 50 enterprise customers, including Google and Microsoft" [4]. The framing around the raise is that AI is beginning to absorb work that used to be sold as software licences and as human services.
- Joined-up data is the precondition, not the feature The last item is a reminder that most AI value depends on something unglamorous. Salesforce described how two universities are using AI agents, noting that "By using AI to connect fragmented data and handle routine tasks, colleges can rethink how they support students in their educational journeys" [5]. The interesting word is fragmented.
From the blog
When a spreadsheet stops being enough
Spreadsheets fail in predictable ways once several people depend on them. Here are the specific signs that a shared system is overdue, and what each one is really telling you.
How to choose software you will still use in a year
How to judge a tool by the work you actually repeat every week, instead of by the length of its feature list — and the questions to ask before you commit for a year.
What to measure in a small business
Most dashboards are full of numbers that feel productive and change nothing. Here is how to tell the handful that should move a decision from the ones that only look busy.
Sources
- AI is the new starting point for work, but finishing it still requires a manual "last mile" Dropbox
- Meet the 2026 Zappy Award winners: the builders who put AI to work Zapier
- How Ethan Schwandt helped Jobber turn AI adoption into a building culture Zapier
- Ema raises $77M as AI starts eating into enterprise software and services TechCrunch
- How Higher Ed Is Putting AI Agents to Work Salesforce
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