Is AI making life TOO easy? copertina

Is AI making life TOO easy?

Is AI making life TOO easy?

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 Susi O’Neill runs a consultancy that helps organisations build trust in technology, which right now mostly means AI. She learned the craft the hard way, doing communications for a Russian cybersecurity provider whose technology wasn’t automatically seen as trustworthy. Her starting point is that AI adoption is a people problem, not a technology one. Employees are getting mixed signals: first they’re told to adopt AI or fall behind, then CFOs start capping spend. Middle managers are stuck in a “pain sandwich”, pushing adoption down to sceptical teams while being asked to prove returns they can’t yet measure. The gaps aren’t evenly spread either. Susi’s annual research suggests women are adopting AI at a markedly lower rate than men in comparable roles, and that their AI-assisted work is trusted less.The conversation turns on an idea that sounds backwards: some friction is good for teams. When AI smooths everything out, people stop turning to colleagues for expertise and relationships weaken, which researchers are calling “relation slipping.” Susi compares generative AI to ultra-processed food: tasty but low in nutrients. Pia draws a parallel with how softer diets changed the way humans chew and breathe. The practical advice is to put people first. Susi suggests flipping the usual 70/20/10 split between technology, data and people, giving people proper training time, and deliberately keeping the friction that builds trust, knowledge and better ideas.Key Themes & TakeawaysTelling people to “adopt AI” and then capping their usage damages trust, because employees hear two contradictory messages about what’s expected of them.Middle managers carry the heaviest load in AI rollouts: they absorb resistance from teams and pressure from leadership before anyone can quantify the return.Individual productivity gains feel good but rarely show up in the numbers, so the real prize, and the harder work, is redesigning workflows at the team and organisation level.You can’t put AI agents into processes you haven’t mapped, and mapping messy, undocumented processes is valuable in its own right.When AI becomes everyone’s go-to source of answers, colleagues stop asking each other, so leaders need to protect the “positive friction” that keeps relationships and shared knowledge alive.Handing AI the actual writing in creative and communications work tends to flatten language into sameness; it adds more value in the research, analysis and challenge that come before the writing.Susi argues for flipping the traditional 70% technology / 20% data and process / 10% people split, with dedicated training time for everyone so they build real fluency rather than anxiety.Three Reasons to Listen:1. Listen if you’ve been told to drive AI adoption and to cut AI costs, both in the same quarter.2. Listen if your team relies on AI more and more, but you’ve noticed people talking to each other less.3. Listen if you suspect “make everything frictionless” is the wrong goal for a team that’s meant to think together.Notable Quotes“…generative AI can be like ultra processed food because it looks tasty, but it lacks nutrients.” — Susi O’Neill“There is something glorious about how inefficient organizations can be.” — Susi O’NeillResearch and articles“Team Dynamics Weaken When Employees Turn to AI Instead of Colleagues” (Harvard Business Review, October 2026). This is the “relationslipping” study Susi described. The survey of over 1,000 US workers found that people who turn to AI instead of colleagues trust each other less and coordinate worse. The research comes from BetterUp Labs and the Stanford Social Media Lab. https://hbr.org/2026/10/team-dynamics-weaken-when-employees-turn-to-ai-instead-of-colleagues“AI-Generated ‘Workslop’ Is Destroying Productivity” (Harvard Business Review, September 2025). The original workslop study, by the same research team. https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity“Your AI Marketing Transformation Will Get Harder Before It Gets Easier” (Harvard Business Review, October 2026). This is the J-curve article: work gets more involved and investment rises before efficiency gains appear. https://hbr.org/2026/10/your-ai-marketing-transformation-will-get-harder-before-it-gets-easierThe MIT “GenAI Divide” report (2025), the source of the “only 5% of AI pilots deliver returns” figure. MIT put the report behind a sign-up form, so here’s a balanced write-up instead: https://www.thestack.technology/what-cios-can-learn-from-mits-ai-report-beyond-the-doomsday-overhype/Harvard Business School research on the gender gap in generative AI use (2026). Across roughly 319,000 respondents, adoption was 47.8% for men and 39.3% for women, a relative gap of 22%. https://poetsandquants.com/2026/06/30/harvard-research-a-22-gender-gap-in-ai-use-that-is-barely-budging/APA 2026 Work in America survey. 48% of workers worry that AI...
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