We Not Me copertina

We Not Me

We Not Me

Di: Dan Hammond & Pia Lee
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A proposito di questo titolo
 Exploring how humans connect and get stuff done together, with Dan Hammond and Pia Lee from Squadify. We need groups of humans to help navigate the world of opportunities and challenges, but we don't always work together effectively. This podcast tackles questions such as "What makes a rockstar team?" "How can we work from anywhere?" "What part does connection play in today's world?" You'll also hear the thoughts and views of those who are running and leading teams across the world.© Squadify Economia Gestione e leadership Management
Episodi
  • Is AI making life TOO easy?

    Is AI making life TOO easy?
    Oct 30 2026
    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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    31 min
  • Why AI needs to be led as a member of your team

    Why AI needs to be led as a member of your team
    Oct 15 2026

    Episode Summary


    In this hosts-only episode, Dan Hammond and Pia Lee start with an uncomfortable question: what's your P(doom), the probability you'd put on AI destroying humanity? They quickly move past it. Their view is that the bigger near-term risk is not the technology but who governs it, and that most leaders can't control that anyway. The question that matters is the one inside your own sphere of influence: how do you bring AI into your team without hollowing it out? Organisations are spending heavily on AI adoption with little clarity about why, cutting roles and then rehiring for them. People increasingly ask AI rather than a colleague, and polished "slop" is moving around organisations with no human owning it.

    What changes the picture is treating AI as a team capability rather than an individual productivity hack. Research suggests teams using AI outperform both individuals and teams working without it, and AI can act as a neutral facilitator that helps groups get aligned faster. Pia maps a recent HBR piece on leadership mindset for AI adoption onto Squadify's Three Cs. The pairs are clarity over certainty, exploration over expertise, and emotional insight over emotional control. Together they make the case that AI adoption is a leadership job, not a tooling decision. Dan adds a reminder to look after the whole person, because individualised, always-on AI work can lead to brain fry and isolation.

    Key Themes & Takeaways:

    • The real AI risk most leaders can influence is not doom but fragmentation: AI can turn a team into a "company of one" where everyone works alone to prove their individual value.
    • Teams using AI are significantly more likely to reach high-quality solutions than teams without it, so design AI in as a team sport, not a private shortcut.
    • AI can act as a neutral teammate: teams report getting on the same page faster when AI clusters and wordsmiths their ideas, because nobody's ego owns the output.
    • Leaders can't offer certainty right now, but they can build clarity, and confusing the two leaves people chasing something that isn't available.
    • Exploration beats expertise in new territory; holding tight to past expertise out of fear is how teams go down with the ship.
    • You are accountable for your output: if you pass on AI-generated work you haven't owned, you're handing colleagues slop they'll just feed back into AI to summarise.
    • When people ask AI instead of a colleague, it is often to avoid wasting that colleague's time; leaders need to judge when a conversation is worth more than the efficiency.

    Three Reasons to Listen:

    1. Listen if your organisation is spending on AI without being able to say what it's for, or has cut roles it may have to rehire.
    2. Listen if you've received a polished document, suspected no human really wrote it, and been tempted to ask AI to summarise it for you.
    3. Listen if you think of AI adoption as a tech rollout. Dan and Pia argue it's a leadership challenge, and that the human skills of pausing, reading the room and exploring matter more now, not less.


    Notable Quotes

    "AI needs leading and the adoption of it needs leading."
    — Pia Lee
    "Being curious and exploring is not the same as saying, I'm curious, and then making your opinion very much felt."
    — Dan Hammond
    "We've got to let the technology actually connect us in a really conscious way."
    — Pia Lee
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    23 min
  • Why change fails and what leaders can do to get a different result - with Michael Lopez

    Why change fails and what leaders can do to get a different result - with Michael Lopez
    Oct 1 2026
    Episode SummaryMost transformation programmes are built around the hardware of an organisation: the systems, the ERP rollout, the AI licences switched on and handed out. Michael J. López, consultant, author of CHANGE, and the person behind a new national workforce study of 1,000 workers, has spent two decades delivering that traditional playbook, and the last five trying to change it. His starting point is simple: when his team asked employees for the top five things they get from their company during change and the top five things they actually need, the two lists had nothing in common. What people get is the stuff done to them. What they need is the stuff done by them.The conversation with Dan and Pia moves from diagnosis to what leaders can actually do differently. Michael names the "failure minimisation cycle," where everyone downplays how hard change will be to get budgets approved, which leaves people under-resourced and more cynical each time. His alternative is to treat change as a capability rather than a project: stress-tolerant cultures, executive teams that genuinely lead together, goals people feel they've contributed to, and progress measured through behavioural inputs and stories as well as hard outputs. It's an honest episode about why change takes 18–24 months, and why pretending otherwise costs more.Key Themes & TakeawaysIn Michael's study, the top five things employees receive during change (emails, communications, training) had zero overlap with the top five they need (experimentation, room for productive mistakes, coaching, leaders going first, the right environment).The "failure minimisation cycle" is well-intentioned but corrosive: underselling complexity to win approval leaves people under-resourced, so change feels harder than promised and trust drops with every programme.Treat change as an organisational capability, a muscle built over time, rather than a project with an end date, because the next change is always coming.65% of employees in the study felt their company's change plan asked them to move faster than was realistic; the systems can be installed quickly, but people need more time.The "what's in it for me" document rarely moves anyone; people commit to goals they feel they've contributed ideas and effort to, not goals handed down with a reward attached.The CEO's job includes preparing the executive team to lead change together; a committee without a committed champion is where programmes stall.Measure progress through "hard input signals" (new leadership and team behaviours) and "soft signals" (stories and cultural shifts), not only red-amber-green dashboards of outputs.Three Reasons to ListenListen if you've watched a transformation programme get approved on a promise everyone privately knew was too optimistic, and then watched people pay the price for it.Listen if your AI rollout amounted to "here are the licences, go and innovate" and you're wondering why so little has changed.Listen if you've ever been expected to show certainty in a planning meeting when the honest answer was "we'll learn as we go."Notable Quotes"What we tend to get from change is all the stuff that's done to people. And what employees really said is we need all the stuff that's done by people." — Michael J. López"I believe that change is about accuracy more than about precision." — Michael J. López"It does take boldness in a world where people mistake confidence for competence." — Dan HammondResources from MichaelWebsite: michaeljlopez.coachRethinking Change Management (the 2026 workforce study): Free Executive Summary · Full studyBook, CHANGE: Six Science-Backed Strategies to Transform Your Brain, Body, and Behavior: Amazon · Audiobook on Apple BooksTop Voice Podcast (live every Tuesday): YouTube · Apple Podcasts · SpotifyFollow Michael: LinkedIn · Instagram
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    37 min
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