Why is AI adoption slowing down? Recent reports show slowing AI tool use in Q4 2025, and industry leaders are asking for AI to "do something useful" or risk losing social capital.
The trend: Growth in AI tool adoption stalled after surging up to Q4 2025.
The details: Gallup's 'AI in the Workplace' report shows that 60% of tech workers use AI frequently, but overall growth plateaued in Q4 2025. 49% of US workers report they “never” use AI in their role, with "lack of clear utility" cited as the main reason. Microsoft CEO Satya Nadella, believes that it is time for AI to "do something useful" or risk losing public support, especially amongst non C-suite workers.
Why it matters: If AI tools can't clearly improve everyday work, adoption will struggle to grow in 2026.
The trend: AI tools are mostly being built for individuals, not teams.
The details: A new Forrester study finds most AI tools focus on individual productivity, which limits business impact. 90% of leaders say collaboration is critical, and 75% believe current AI tools aren’t built for team-based work.
Why it matters: Shared workflows and digital canvases where teams already collaborate may be the best place for AI to deliver real value, rather than as a bolted on solo tool.
While OpenAI and Jony Ive work on their AI earbuds (and pen), Apple is making sure they're not left behind, as they're rumored to be developing a wearable AI pin with multiple cameras, a speaker, and microphones. AI-first devices haven't done well so far (see Humane's Ai pin and the Rabbit R1) wearables look to be on the rise - although mass adoption may be a few years away.
BCG report that companies expect to spend 1.7% of their revenue on AI this year, more than doubling the 0.8% average in 2025. Interestingly, leading companies are focus on depth over breadth, prioritizing 3.5 use cases, compared with 6.1 for industry on average. Placing larger bets on fewer initiatives appears to be the winning AI strategy in 2026.
Last time we asked what's holding data platforms back, and the answers were surprisingly consistent.
Scaling AI & predictive use cases was the #1 blocker
Engineering dependency followed next (an issue for just over 60% of teams).
Cost and unpredictable usage pricing came in 3rd place, holding back 33% of data platforms.
This time, we want to see how teams are actually using AI today. Take our 30 second survey and tell us how AI fits into your team, we'll share the results in the next edition.
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