Are you leaving employees better off or burned out?
Are you leaving employees better off or burned out? How do you lead in a world where roles are changing faster than people can adapt? In this episode, Rick Cheatham talks with Steph Peskett and Abi Scott about the second major trend from their recent research: human sustainability. The conversation covers everything from AI disruption to proactive talent strategies—and why the most forward-looking companies are rethinking how they grow, support, and retain their people.
Most of us want to lead in a way that matters; to lift others up and build something people want to be part of. But too often, we’re socialized (explicitly or not) to lead a certain way: play it safe, stick to what’s proven, and avoid the questions that really need asking.
This podcast is about the people and ideas changing that story. We call them fearless thinkers.
Our guests are boundary-pushers, system challengers, and curious minds who look at today’s challenges and ask, “What if there is a better way?” If that’s the energy you’re looking for, you’ve come to the right place.
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Rick: Welcome to Fearless Thinkers, the BTS podcast. I’m your host, Rick Cheatham, and this is the second in a two-parter on some research that we’ve been doing around trends in talent management, specifically Steph Peskett and Abi Scott. Have gone deep into what great talent management looks like today. In the first episode, we focused on trust and power, and here we’re gonna go deep into human sustainability.
I guess it probably is time for us to start talking about that second trend a little bit. So why don’t you start with an overview and then we dig a little deeper.
Steph: Okay, I’ll try and ground us then in this idea of human sustainability. You know, in recent years there’s been an increasing focus on wellbeing and COVID was definitely an accelerator of that.
And I think the thing that’s really fascinating right now is that human sustainability is much more about something broader and more long term. It’s really about autonomy, growth, and employability. I absolutely love this topic because for me, human sustainability is so fundamental to making sure that every time we have an experience with an employee, whether that lasts for a year or decade or even longer, that they’re feeling that they’re being left better all of the time now.
The thing that’s fascinating in the research that we did is that only 43% of employees feel their organization have left them better off than when they started. And I find that absolutely staggering ’cause I think it, it just goes to the heart of what we’re trying to solve for in human sustainability.
And it’s not good enough. In my opinion.
Rick: Yeah. And again, this is the second time in this conversation that you’ve just shocked me because how on earth could organizations that are designed to drive continuous improvement have people walking away from that experience going, yeah, I might be worse off than I came in.
That’s a tough one. Obviously, there’s not a focus on sustainability at all in that environment.
Steph: It’s roughly half of workers, right? So, it leads us to, I think, really examine that really deeply held value of, you know, here for good, here for making a difference in the lives of the people that we touch in our employees, you know, in our consumers, in our customers.
All of those things are just so important, but it’s similar to that previous point where the thing that’s also disrupting the space is of course AI and it’s another turn of the screw, uh, really because digital has been disrupting the, uh, experience of workers automation and now we’re in the age of AI.
That too is also creating a disrupt in terms of that sustainability, quite literally, of humans and people are nervous. People are definitely scared and wondering what it will mean for them.
Rick: Wow. So how do we begin, Abi to build human sustainability into our fabric so that we don’t end up in the type of situation that Steph was just describing?
Abi: Mm, yes. Yeah, no, and I guess the thing is we can’t predict the future, and we’re not used to this speed of pace. You know, with AI. I think it’s akin to, I guess when we got the internet, when we got emails, you know, in the workplace with when we got calculators. It’s technology helping our lives. There is that moment where you go, you know, when will the robot take my job?
The World Economic Forum reported some statistics just in January where they suspect that 85 million jobs could be displaced by the end of 2025 due to AI. But on the flip side of that, the good news at 97 million jobs might be created because of AI. So, I think it talks to the nature of work changing.
I think it’s about working out how can we work alongside the bots? How can we get the bots to help us?
Rick: Yeah, so help me think about what else our listeners, what actions they could be taken. Again, whether it’s enterprise wide or even within my team and who I work in day in and day out to help create a.
More focused organization when it comes to human sustainability.
Steph: Yeah, well, I mean, this is like my favorite topic, so I should get onto this. Like, I’m just so passionate about technological advances like this, and I love the age that we’re living in, and I think it’s really about how do we enable this?
To be one of the most spectacular periods in human growth that we could possibly imagine. And that really excites me. So I think, for example, if I think about, you know, picture yourself as a leader operating in a business today. Great news is that you have content help now, right? So that’s helpful. And that should take some things away from the burden of leading people and the challenges of that and instead focus on the wonderful opportunity.
And that means, you know, creating community in the workplace so people feel they belong, helping individuals grow and having more time to apprentice them. Creating experiences in the work that. So memorable and special that you become the place that people wanna be. I think there’s some amazing things that can be done in terms of your personal leadership and refocusing your attention.
And it does require conscious refocus because like most things, it’s a change in routine and discipline. I think the other thing that really excites me about this is the ability for the organizations who are brave to start to have really proactive talent conversations. How is our workforce and the shape of our workforce changing as a result of the advent of AI and HR really need to be leading on that conversation.
I know we’re a little scared and we might get disrupted too, but we have the ability to be really transparent with people and to start to look at re-skilling people in proactive ways. In the banking industry. It happened probably about 10 years ago. Branches were shutting everywhere and we were moving to digital and there was this real question of whose responsibility is it to re-skill?
Well, that’s not really coming out as loudly now, but. The beauty is that AI will disrupt us, but with fantastic tools like conversation tools for AI, we will be able to re-skill people in the flow of work. We’re just transparent about how is this changing? How will you be impacted? How do we keep you growing?
How do we keep you employed? How do we bring forward your beautiful human skills to sit alongside these incredible technology capabilities we now have.
Rick: Ah. So now my worlds are colliding. I’ve got trend one, I’ve got trend two. The thing that I often say about AI is we have to focus on our leadership and culture ’cause tools are gonna change faster than we can deploy them. And I think you’re saying something relatively similar. And so how am I transparent? How am I building trust when I don’t know? So, you know, as we talk about the sustainability and building trust kind of in the same breath, how does that work in this AI specific case?
Steph: I guess, you know, like most change, we have to start with ourselves, right? What’s my readiness for it? How do I feel about it? Am I looking to ride this thing out or am I gonna get out there in the front of it and get curious and start experimenting? Okay, so none of us are perfect in this and none of us.
Know all the answers, but I think it does start with experimentation and I think it starts with partnering up and, you know, challenging into our people functions to help us to get there. You know, this could sit in no person’s land in an organization unless, you know, it’s really claimed by the leaders and by our executives and our HR functions and everyone, you know, the beauty of AI is at everyone’s fingertips to, to get going, start experimenting.
Call a friend. What do you think, Abi? What do you reckon?
Abi: I totally agree. At BTS, we talk about, you know, executing strategy is about alignment, mindset and capability, and that relates to AI, you know, like how aligned are you as an organization towards. AI and new technologies, I guess, because it’s not just AI that will have an impact.
What mindsets do you as a leader have? What do your team members have? What’s the organizational mindset and then what skills do people have around AI? Internally at VTF, we’ve got stories of partners who are being apprenticed and mentored by very young talent who have extreme expertise in AI. Also as well, major upskilling programs so that we know how to better utilize AI in our work.
So, I think leadership does become more of a partnership model overall. So it’s okay for the leaders not to have all of the answers. Um, you know, you problem solve, and you work through it as a team and, and as an organization. So, I wonder that’s a little bit of a shift perhaps in how we might work going forward.
Rick: That’s great. That’s great advice. So again, I kind of want to continue down this road of tying these two trends together, so to speak. I also sit here and think, alright, most leaders, most organizations, hopefully all, but I can’t say all are going, you know what? We don’t really care if our employees trust us.
We don’t really care if they feel like talent development’s a black box. What we really want to do is build a completely unsustainable organization where people feel like they’re being run into the dirt. If no one wants to do that, I’m always curious as to, you know, what your research might say of the well-intentioned folks that are doing anyway.
How are organizations breaking trust? How are organizations potentially making choices that. Aren’t promoting human sustainability but instead detracting from it. So, what’s the shadow side, so to speak, of what we’ve been talking about right now?
Steph: Look, I think the thing that organizations are doing that breaks trust, that I think we need to get really honest about is where we, as the owners, founders, operators, executives, whatever, in an organization, are not doing the hard yards to really define what we mean.
In a transparent and explainable way. So, it’s my opinion that any policy, any approach to talent, any approach to, you know, things like the working from home and all that stuff, it needs to be simple, clear, and transparent and explainable and. That is a responsibility we all have to employees. So how is it we can go from highly productive workforces during COVID when they worked from home to now an assumption that you are more productive if you’re in the office.
You know, we need to be evidence-based. We need to be clear and transparent about why we know that is better. After COVID, there was a lot of, uh, studies and questions done about how do you define productivity and white collar workers, right? Knowledge workers, easy and blue collar, but not so much in white.
Well, that’s never really been answered very clearly. And still we persist with policy changes that are confirmed on a hunch. Right. Same with hiring, same with succession, same with high potential, same with promotions. And I think we’ve all gotta do the hard yards to make sure it’s clear, transparent, explainable, and if it is great, go for it.
Abi: Yeah. So, if a leader can understand. Each member on their team, if they know what motivates that individual, if they know their preferences are ways of working, then you know, you obviously gotta make sure that the processes are, are fair and equal for all, but being able to tailor communication, tailor ways of working to that individual to get the best out of each individual.
There’s something in that and I wonder whether during COVID. We were more focused on individual differences and the needs of each person in our team. And now we’ve just gone back to this sort of whole of organization cohort level approach.
Rick: And that’s actually very interesting observation that we were, so again, everything was so wellbeing focused and so individualized.
We possibly, like I was saying earlier, possibly have swung too far back the other way. Well, I want to thank you so much for spending this time with me today, and I also want to thank you for doing this great research and narrowing it down to two things that we go do. So many times, everything’s over complicated.
So very, very much appreciated both your time and your thinking, and I’m sure we’ll have you back again soon.
Abi: Thank you for having us, Rick.
Steph: Appreciate it.
Rick: Thanks for joining me today. It’s always a pleasure to bring to you are fearless. Thinkers. If you’d like to stay up to date, please subscribe. Bios for our guests and links to relevant content are always listed in the show notes.
If you’d like to get in touch, please visit us at bts.com and thanks so much for listening.
Related Content
There’s a specific kind of strategy meeting getting scheduled right now, in nice hotels with bad coffee: the AI reset off-site.
And for good reason. In a 2026 WRITER survey, 48% of leaders described their AI rollout as, in their own words, a "massive disappointment." That's nearly half the room.
What that number really measures is the distance between what these tools can do and what people are doing with them. In our experience, that distance is almost entirely human.
Which is why the off-site is the right instinct. Making the most of that time is the harder part.
What separates an AI reset that actually changes the game from an expensive two-day conversation? In our experience, it comes down to avoiding four common design mistakes.
Mistake 1
Blaming the bots
The gap between AI investment and real adoption is almost always about people, not technology. And when adoption stalls, we usually find it's one of four things.
- They don't think it will help them
- Nobody around them is using it
- They don't feel capable
- Or they don't have real access to the tools they were promised
Four different problems, and four completely different fixes.
That's why diagnosis comes first. If you don't know which barrier you're dealing with, every intervention becomes an educated guess. And you cannot tell which one you have by staring at a dashboard. A belief gap and a skill gap look identical in a status report and need opposite interventions. Show up guessing, and you'll spend real money teaching people to use a tool they simply don't trust yet. Congratulations - you've just catered the wrong conversation.
Mistake 2
Letting leaders off the hook
One of the biggest predictors of whether change sticks is also one of the most overlooked: leadership.
If your executives show up as observers, nodding along and quietly answering email under the table, your people clock it in about four minutes.
That doesn't mean your CEO has to emcee the thing. It means they use the tools in front of everyone, participate in the conversation, and make it clear this isn't someone else's initiative.
Recently we’ve been working with a Fortune 200 global professional services firm who’s top 120 leaders were at very different points with AI. Some were redesigning entire processes. Others were using it to summarize emails, or not at all. Rather than focus on the technology, the four-hour session focused on what leaders could do with AI, applying it to a live strategic challenge and ending with a personal commitment to lead differently. The response was strong enough that the organization is now cascading the experience globally.
The lesson is simple: when leaders experience AI as a strategic capability, they're better equipped to model the behavior that makes adoption stick. Nothing you build during those two days survives without that entire chain of leadership doing its part.
Mistake 3
Chasing the wrong outcome
Without a behavioral baseline, you have no way to prove anything actually moved. No baseline, no ROI. You're just hoping the energy in the room was good, which is a wonderful feeling and a terrible metric to bring to your CFO.
But the baseline isn't just about proving the off-site worked. It's about understanding where you're starting in the first place. And you'll want that clarity, because the quiet resistance is real. In that same 2026 research, nearly a third of employees admitted to actively working around their company's AI strategy. If you don't win their belief in the room, some of them will keep politely ignoring the whole thing from their desks. You can't measure your way out of that. You have to earn your way out of it.
Which brings us to the biggest reframe of all.
Mistake 4
Leaving follow-through to chance
We've been working with a Fortune 100 medical device company on their AI strategy for three years. It started with their leadership team, a three-hour session built around what those leaders would do differently, and it landed. What became clear afterward was that the same experience needed to happen everywhere else. So, it expanded: 90-minute activations for 15,000 people, and this year intact teams redesigning their own workflows.
Three years in, that first session is the smallest part of the story.
Your event is where momentum gets created. What happens at 30, 60, and 90 days is where results get made.
If you're planning one of these and want to change what happens on Monday, not just how everyone feels on Friday, that the work we do.
We'd be glad to help you design it.
Candidates now arrive at interviews pre-coached by AI, with their resumes optimized to pass every checkpoint. Polish has stopped being a signal. The traditional hiring process was built to read exactly the cues that AI is now best at producing, and the signals hiring managers once relied on have weakened as a result. And for roles where the wrong hire carries real business consequences, losing the ability to tell who will actually perform is not a minor inconvenience. It is a material risk, and it exposes the business to unnecessary turnover, reduced performance, and heavier investment for talent growth and development.
So how do you observe the behaviors that matter most, before someone is in the role?
Not by asking better questions, but rather by putting candidates in situations designed to elicit that behavior.
The limits of predicting from paper
Credentials tell you what someone has done. Structured interviews tell you what someone says they would do. Neither lets you observe what they actually do in the moments that count.
This distinction matters most in client-facing, relationship-driven roles, where the performance gap between a strong hire and a weak one plays out in real business outcomes (revenue, retention, client growth). Organizations that hire at scale in these roles carry that gap across hundreds of decisions at a time.
The better approach is to watch candidates do the work before you hire them. Put them in simulated, role-relevant scenarios, and pair the simulation with a second, different kind of measure so no single method carries the whole decision. That combination is what lets you evaluate real performance before anyone is in the role. Organization-specific simulations provide a clear read on who is ready and capable of performing on day one. In a world of AI-supported candidate signals, the use of simulations makes the process harder to prep for. It is harder to fake. And, when designed well, it is substantially more predictive than other hiring methods.
What counts as evidence
Claims about predictive power are easy to make. Evidence for them is rarer than you would expect.
A predictive validity study, the kind that links pre-hire assessment scores to how someone actually performs once hired, is some of the hardest evidence to produce and the rarest to see. Many assessments are validated against proxies: another test, or a theoretical model of the role, rather than real results on the job. Connecting scores to concrete business outcomes and doing the statistical work to show the link holds, takes years of shared data and a level of commitment from both the assessment provider and the client that most partnerships never reach. That is precisely why it is worth asking for. A provider who can show how assessment scores track to training completion, retention, and first-year output is offering something categorically different from one who can only show a correlation with another test.
Why simulation holds up where other methods do not
When a candidate sits across from a trained assessor (someone playing the client or prospect on the other side of the conversation) and has to work through a real situation, they cannot rely on a rehearsed answer. The scenario is specific. The stakes feel real. What you see is close to what you would get on the job.
That is the value of simulation-based assessment: it does not test what candidates know about the role.
It shows how they use what they know when a real person is on the other side of the conversation, before the stakes are real.
For roles that carry significant business responsibility, this distinction is the whole game. The cost of the wrong hire in a high-stakes client-facing role is not just a missed quota for a quarter - It plays out in relationships that do not develop, clients who leave, and productivity losses that compound over time. Getting those hiring decisions right, at scale, with consistency, requires methods that are built for predictive accuracy, not just candidate experience or hiring speed.
What this means for how organizations think about hiring
Most organizations are still optimizing the wrong things in their hiring process. They invest heavily in employer branding, application flow, and interview structure, all of which matter, but less in the core question: does our hiring process actually predict who will succeed in this role?
AI has sharpened the stakes here. If every candidate can present as polished and prepared, screening based on presentation becomes less useful. What holds up is direct observation of the behaviors that the job requires.
A few principles worth building from:
- Measure what the job requires, not what is easy to measure. Cognitive tests and personality questionnaires have their place, but they do not look much like the job. The closer the assessment is to the actual work, the better it predicts performance in it.
- Ask what your assessment predicts. Training completion? Retention? First-year output? Most organizations cannot answer that question today, largely because providers have rarely been asked to prove it. It is a fair thing to ask for.
- Take the human element seriously. In a simulation, a candidate is having a real conversation, responding in real time, navigating a situation that requires judgment. Even with the help of AI, that is hard to game. And it remains one of the strongest predictors of on-the-job performance available.
The data exists to make hiring decisions more accurate, fairer, and more directly tied to business outcomes. For organizations operating in high-stakes roles at scale, there is too much on the line to rely on methods that cannot hold up to that standard.
You may be interested in BTS’ thought leadership in the five talent shifts AI is forcing now.
La IA ya forma parte del día a día de las ventas. Hoy cualquier asesor puede llegar a una reunión con datos, tendencias e insights generados en segundos. Sin embargo, disponer de más información no garantiza conversaciones de mayor valor.
A través de una experiencia real con un consultor comercial, este artículo explica por qué la inteligencia artificial funciona como un GPS: ayuda a interpretar el entorno, pero no conduce la conversación ni entiende las prioridades del cliente.
En este artículo descubrirás:
- Por qué el acceso a la información ya no supone una ventaja competitiva.
- La importancia del business acumen para interpretar los datos con criterio.
- Cómo hablar el lenguaje del cliente genera credibilidad y diferenciación.
- Por qué las relaciones B2B evolucionan hacia relaciones P2P basadas en la confianza.
- Qué capacidades consultivas seguirán siendo exclusivamente humanas incluso en la era de la IA.
La tecnología seguirá evolucionando, pero la ventaja competitiva estará en quienes sean capaces de combinar inteligencia artificial con conversaciones centradas en el cliente, pensamiento estratégico y relaciones de largo plazo.
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Organizations need a future-back approach to identifying potential, including simulations and key assessment methods, that goes beyond standard performance reviews.
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How moments-based simulations and AI role-play help leaders practice high-stakes conversations, build real behavior change, and give L&D predictive insight into performance.
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La adopción de inteligencia artificial crece entre las compañías, pero convertir esta tecnología en resultados tangibles sigue siendo uno de los grandes retos empresariales.
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