Would They Tell You?

00:00:13: Hello and welcome to this reflection episode.

00:00:16: Let me start with a couple of questions.

00:00:19: If you are being asked, give all your knowledge & insights To someone else And if you aren't planning on giving up the job What would you do?

00:00:28: You're seeing someone younger and less expensive Who can do what they want in half time.

00:00:33: How does it make them feel?

00:00:35: A colleague under your responsibility Produces low quality work Does something potentially harmful for their company?

00:00:43: Would you report it or try to fix and hide?

00:00:46: Protecting ourselves in our position is typical human behavior.

00:00:51: We don't like give up on a position, or status which we earned in any kind of way especially not if was through hard work.

00:00:59: Now I could have asked the same questions And made them about AI instead.

00:01:03: another person The dynamics driving all behaviour stay mostly the same.

00:01:13: Over the past years, there have been enough AI-related layoffs and headlines predicting a job apocalypse.

00:01:20: People are seeing the productivity gains —and know the numbers game most companies are playing—so securing their own position already drives internal competition in politics at many companies... ...and its undermining collaboration and collective

00:01:35: learning.".

00:01:36: When people hide work-slop, mistakes & unethical A.I behavior It becomes another sign of a lack of leadership driving one of the biggest disruptions to human work and life.

00:01:48: In the main episode on psychological safety, I argued that AI adoption doesn't just expose leadership weaknesses – it amplifies them!

00:01:58: When decisions about how build connections between humans are made by people who don't get to see their daily works we need show what really drives results from a company.

00:02:09: In many organizations, this doesn't happen.

00:02:12: The reporting is focused on metrics that cannot represent collaboration collective thinking and idea generation.

00:02:20: They don't show the human connection That can not be replaced by machines but holds significant value And what can't be measured isn't seen and not accounted for.

00:02:30: If people cannot talk about how they work with technology What's the value?

00:02:35: Where it falls short of producing any organizations will lose.

00:02:40: The lack of meaningful returns reported by many companies who heavily invested in fast AI adoption is just one cost.

00:02:49: Companies eventually loose trust and engagement from their human workforce, which brings productivity further down.

00:02:56: People who feel like a cog on the machine will either behave like one or leave to company that still values their work—and Motivation and new ideas goes right with them.

00:03:09: If performance is your key metric, you'd better know what drives it!

00:03:13: And the foundation to this knowledge IS psychological safety.

00:03:18: In the main episode I presented Google as a company that understood value of psychological safety.

00:03:24: They thrived on their people-centric open culture.

00:03:28: But they have changed.

00:03:29: When growth slowed down Their focus shifted towards bottom line.

00:03:33: They hired a Wall Street CFO and followed the financial market logic.

00:03:37: Sustainable long-term growth, as held up by founders Larry Page & Sergey Brin was replaced by shareholder Focus.

00:03:45: One of their first big layoffs was highly criticized for how it was executed.

00:03:50: People received their notices via email And were cut off from organization immediately.

00:03:55: That's opposite to an open and people centric culture.

00:03:59: It is kind signal that needs no further explanation.

00:04:02: The race is on and you have to fight to stay on it.

00:04:06: And this is the reality for many workers today, not knowing what will happen in their jobs in the near

00:04:11: future.".

00:04:12: The question is if that kind of uncertainty would allow anyone really leverage the value of AI technology and manage their business in a sustainable way?

00:04:23: Navigating these uncertainties is one important leadership responsibility right now!

00:04:28: Of course no-one can foresee the future and your work depends on the strategy of a company.

00:04:34: Your responsibility is to focus what's in your hands, how you team works together... The way they use technology.

00:04:41: it just as much under control & influence As openly talk about its limitations And real value.

00:04:49: So let me give two practices and one principle To build and protect psychological safety.

00:04:55: First is more general.

00:04:57: Second will be focused on AI adoption.

00:05:00: Consider the first practice foundational.

00:05:03: It's about your own and your team's reactions, The moment someone takes a risk.

00:05:08: Let me explain what I mean by a risk.

00:05:10: Someone admits a mistake Has a question that might sound stupid.

00:05:15: A team member disagrees with you or someone else in their team Someone brings bad news Or simply says...I don't know.

00:05:23: Every one of these is small risks someone has taken With you And others And in the half second after they take it, something happens in your face.

00:05:33: It shows in your tone and whatever you say next.

00:05:37: Catching reactions is a starting point.

00:05:39: The people infront of you will notice these signals consciously or subconsciously So becoming aware them knowing how to handle them is crucial.

00:05:49: You can hold talks about psychological safety as much as like If you and other team members show reactions that read like disapproval, blame or feel humiliating in any kind of way.

00:06:02: the message was sent before you said a word.

00:06:05: We all check what it costs us to speak up... ...and so does everyone else in the room.

00:06:10: People run these small tests all the time And they calibrate how much to risk next-time based on what comes back.

00:06:17: So the practice is to guard your reaction In the moment its being tested For the next weeks, treat every one of those moments as a small assessment.

00:06:26: When someone admits a mistake, disagrees raises question or concern notice first impulse check how you intend to act on it and stop if would make them regret doing that.

00:06:39: when somebody has questions notice whether your about making feel slow for even asking If they bring you bad news, notice whether your reaction is teaching them to come to use sooner next time or keep it longer or entirely from you.

00:06:55: You won't get it right every time – I still catch myself reacting before I've thought especially when i'm under pressure and think that already know the answer.

00:07:05: The practice requires continuous training.

00:07:07: It's to notice a moment for what it is And to repair it while getting it wrong.

00:07:13: So how should you handle it when your reaction or that of someone in the team wasn't helpful?

00:07:19: Address it, don't hide it.

00:07:21: You can use the following recovery moves When the bad reaction was yours The simplest is to name it and reopen the door.

00:07:30: I'm sorry this came across wrong.

00:07:33: if the moment has passed And you only realize it later go back to the person.

00:07:39: i've been thinking about our meeting.

00:07:41: I brush past what you raised, and i shouldn't have.

00:07:44: Can we pick it up again?

00:07:46: Going back is a strong move because it tells people that are still thinking about it.

00:07:51: That's the signal their contribution matters.

00:07:54: And when your not sure whether your reaction landed badly But you suspected did Just ask!

00:08:00: I think might've cut off on your concern Did come across this way.

00:08:05: It gives people an opening without forcing anything.

00:08:09: Now When the bad reaction came from someone in a team, The fastest repair is to put visible value back on what this person dismissed.

00:08:34: If the dismissal was rude or is continuous, address the behavior but do it on the behaviour and not the person.

00:08:42: During a meeting you can keep it lightly by saying let's keep this place where any question is fine to ask.

00:08:49: then take the sharper conversation two one-to-one.

00:08:52: Also check on the person who was brushed off afterwards.

00:08:56: Say something like You raised something good And got rough response.

00:09:01: I'm glad that said.

00:09:02: please continue.

00:09:04: Give them the security that what they said or asked is a safe thing to do.

00:09:10: It's your responsibility... ...to secure this standard across the team.

00:09:14: You can name it whenever the team needs a reminder Especially now with AI, you will have people who pick up on using it faster than others.

00:09:23: Don't let them create a gap by diminishing other peoples' requests for clarification and need for shared and continuous learning.

00:09:31: That was practice one!

00:09:32: Its The Foundation Openly sharing mistakes that happen when people are working with AI is the second practice.

00:09:39: People need to feel safe to admit mistakes, everyone makes them and a leader who doesn't talk about their own cannot ask others do it.

00:09:48: We learn from mistakes.

00:09:50: Teams needs collective learning in an environment where they're saved for address failures.

00:09:55: Similar to following claims of highly confident people.

00:09:59: we easily follow the ultra-confident sounding AI output even when either can be confidently wrong.

00:10:07: And how companies reward AI usage influences, How much people trust AI output without questioning it?

00:10:14: When they reward the fastest adopters and highest usage The dashboard numbers go up and everyone looks more productive.

00:10:21: but mistakes will Be part of the outcome.

00:10:24: High usage isn't the same as good usage.

00:10:27: As I mentioned in the main episode a lot Of that usage is fearful People protecting themselves using the tool because they're scared not to, and accepting whatever it gives them to deliver more results faster.

00:10:40: But we all know that quality and quantity aren't the same.

00:10:44: so don't reward someone who uses AI the most but those who catch where goes wrong.

00:10:50: What helps you in your team?

00:10:51: stay critical about AI output is run short weekly sessions which showcase AI mistakes.

00:10:58: Call them what ever you like.

00:11:00: One of the teams I worked with named it AI Confession Session.

00:11:04: Invite everyone to bring their case and give them a structure so they bring value for entire team.

00:11:10: This can be equally good learning, fun.

00:11:14: Make sure you bring your own cases And start sessions until every one has gotten used.

00:11:19: Then you can rotate.

00:11:21: The structure i would recommend is following Name the Case Give short description about goal.

00:11:29: Briefly present the prompt and which other resources an input you used.

00:11:33: Then show result, what caught your attention to.

00:11:37: question it.

00:11:38: Next, present how you resolved a mistake And what learned from use case.

00:11:43: If turn this into routine It not only invites everyone share their experience.

00:11:48: You also learn how team is using AI over time.

00:11:53: Start see whether these use cases bring actual value.

00:11:57: The team also feels engaged to stay critical and catch mistakes instead of blindly accepting output.

00:12:03: Everyone will notice patterns in repeated mistakes which raises collective awareness, And you leading.

00:12:10: this shows people that it's safe to share mistakes That the tool is a tool not replacement for critical thinking.

00:12:17: So those are two practices Watching your reaction.

00:12:21: questions concerns mistakes problems and catching mistakes.

00:12:26: working with AI openly.

00:12:28: Underneath both of them sits one principle, what you do is in service to two key responsibilities making sure the reality of work reaches people who make decisions about it And protecting the effectiveness your team.

00:12:42: That's what psychological safety for It.

00:12:45: not about agreement comfort or everyone feeling good at work.

00:12:49: It is about the work that needs to be done, To build and maintain environments That allow for openness, critical thinking learning And collaboration.

00:12:59: If you protect this environment For your team it will reach further than You can see.

00:13:04: The decisions About AI are mostly being made By people furthest from their Work And the only way that the truth reaches them is if people closest to their work feel safe enough to address what works and doesn't.

00:13:17: If it can travel up from there?

00:13:20: So let me leave you with two more questions... Speak with people, listen to the critical voices and remember that not everything That accounts for the value of human work gets measured.

00:14:07: Stay tuned.

00:14:09: Thanks for tuning into the New Work Playbook, where work gets better because people matter!