In 2012, 74% of SaaS account executives hit quota. Today, it's just 48%.

It's tempting to chalk that up to a software problem. But the pattern is broader. Alexander Group found that just 49% of core sellers across nine industries hit or exceeded quota in 2023.

But here's the kicker: the average seller in that study still reached 89% of target.

Across industries and benchmarks, attainment has been drifting down, but this isn't a story about a handful of great sellers carrying the number while everyone else is falling dramatically behind. The typical seller is getting close to quota.

Sellers aren't suddenly incapable of selling, the context around selling has changed.

Buyers still need sellers… they just need them differently

1. Seller's aren't guiding a single buyer anymore

B2B deals now involve six to 10 stakeholders, and enterprise purchases can involve 17 or more. About 77% of buyers describe their last purchase as complex or difficult.

The seller's job is increasingly to help a group reach a decision, not simply help one person make one.

2. The seller's information advantage is shrinking

The seller's information advantage is shrinking

Buyers now use about 10 interaction channels during a purchase, roughly twice as many as in 2016. AI is accelerating that shift, giving buyers more ways to research, compare and pressure-test options before they ever engage a seller.

The value of simply knowing more than the buyer is disappearing.

3. Buyers can avoid seller friction for longer

About two-thirds of B2B buyers prefer a rep-free buying experience. It's easier than ever to research a category, build a shortlist and develop a point of view without talking to sales.

That means sellers have to earn their way into the conversation by adding value, not just showing up.

4. Sellers are becoming sense-makers

Buyers aren't necessarily struggling to find information. They're struggling to determine what to trust, what matters and what to do with it. In fact, 69% say they turn to sales reps to validate AI-generated insights.

Which brings us to the kickoff

All of this has big implications for the one moment each year when we bring the entire commercial organization together to prepare for the year ahead.

The good news? You don't need to scrap the kickoff, you just need to redesign it.

That's work we do with clients all the time, across sales kickoffs and other high-stakes moments where strategy needs to become action.

And, in the spirit of not gatekeeping the good stuff, here are three things every kickoff designed to change behavior needs:

Before

Start with the change, not the agenda

This is where many sales kickoffs go wrong. They start with the event: strategy update, product roadmap, messaging, breakouts, keynote. The agenda becomes the organizing principle.

Flip it.

Start with the behavior you need to change, then design the kickoff around it.

That means thinking beyond the room itself. What needs to happen before people arrive? What do they need to experience and practice during the event? And what needs to happen after they get back to work?

Find the real constraint

Most revenue leaders have a hypothesis about what's holding their teams back. Few have enough data to know.

We ran a readiness diagnostic for a financial services client preparing for an AI leadership event. The leadership team came in expecting the biggest barrier to be skepticism: Would sellers actually believe AI could help them sell differently?

They were wrong.

Sellers and leaders already believed in the value of AI. The real gap was much more practical: confidence and ability. People understood the promise but weren't yet sure how to use it in their day-to-day work or bring it into a client conversation.

That changed the intervention completely. Instead of spending the event convincing people that AI mattered, we could spend the time helping them actually use it.

That's what a good diagnostic buys you: permission to change the plan.

A useful diagnostic doesn't need to be complicated. Ours can take 15 to 20 minutes per person and, when deployed across the full population, give leaders a ranked view of the constraints by region, function or level.

It also gives you a baseline for the behavior you're trying to change. Without a clear starting point, there's no way to know whether the kickoff actually moved the needle.

Get leaders ready before the room

Leader preparation shouldn't be a briefing the night before. If leaders aren't aligned on the change, the room won't be either.

Before the wider team arrives, leaders need to understand the data, the capability gap and, most importantly, what they need to do differently to close it.

The strongest version we've run gives leaders a dedicated session ahead of the main event. They arrive aligned on what needs to change, why it matters and how they'll reinforce it with their teams.

The test: Can every leader explain what their team needs to change, how kickoff will help and what they'll do differently afterward?

If not, you're asking the organization to change before its leaders are ready to lead the change.

During

Get people working, not watching

Most events fail for a pretty unglamorous reason: People sit and watch.

If you want behavior change, the room needs to feel more like the work and less like a conference.

A simple experiential architecture works:

1. Start with the real work. Put people into a live or simulated situation before explaining the framework.
2. Let them see the gap. Pause and reflect before introducing the solution. Let the insight come from the room.
3. Give them something to try. Introduce the model, tool or methodology when people have a reason to use it.
4. Practice before they leave. Don't assume understanding will translate into behavior. Let people try the new behavior while they're still in the room.

Skip the practice and you have awareness. Practice it and you have a shot at behavior change.

Put your leaders to work too

Leaders should facilitate, participate, join teams at the tables and model the behaviors they're asking others to adopt.

If executives are on stage for 20 minutes and gone by lunch, you've designed a broadcast.

People notice the gap between what leaders say matters and what they actually spend their time doing.

The test: Did leaders do the same work they're asking their teams to do?

After

Build reinforcement into the work

This is where a lot of the investment quietly disappears.

Managers today are carrying more people and more work than ever before. Gallup reports that average team size rose from 10.9 direct reports in 2024 to 12.1 in 2025. Yet managers remain one of the biggest variables in whether new behaviors stick, with Gallup estimating they account for 70% of the variance in team engagement.

So don't give managers another program to administer.

Make the first 90 days part of the design

• Weeks 1–2: Translate the kickoff into 90-day commitments with clear owners and dates. Give leaders an executive synthesis they can use with their teams.
• Days 30–60: Bring managers together in peer groups of six to eight to compare what's working. Give them simple guides they can use in 1:1s and pipeline reviews.
• Day 90 and beyond: Connect the new behaviors to business-led check-ins and actual performance.

Make leaders accountable for what happens next

Leaders set the expectation, managers coach it, executives model it, and the organization measures it.

No platform substitutes for a manager asking about the behavior in a 1:1.

The test: 90 days out, can a participant remember the last time their manager asked about the behavior?

If they can't, kickoff probably didn't stick.

The opportunity is bigger

The best kickoffs don't just get everyone aligned on the year ahead. They create a shared understanding of what selling requires now, give people a chance to practice it, and make it easier to carry that behavior back into the work.

If you remember nothing else from this blog, I'll leave you with a simple but powerful question to take into the planning process for your next event:

When your sellers walk back into the field on Monday, what will they be able to do that they couldn't do before?

If you have a good answer, you're probably on the right track.

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.

1. They don't think it will help them
2. Nobody around them is using it
3. They don't feel capable
4. 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.  

‍

Leadership is the work of creating shared understanding, and language is the primary tool for doing it. Yet we spend remarkably little time examining our words. Every decision, expectation, priority, and piece of feedback reaches another person through words. If those words aren't doing the job, neither is the leadership.

The lighthouse

Some years ago, a large company hired a strategy consulting firm to rethink its leadership model. The firm came back with a beautifully produced framework built around a central metaphor: the lighthouse.

Leaders, the model declared, should be lighthouses.

The metaphor quickly found its way into playbooks, performance reviews, onboarding decks, and town halls. People repeated it with the confidence of those who had paid a lot of money for it.

There was just one problem: nobody could agree on what a lighthouse was supposed to do.

Was it warning people away from danger? Guiding them toward a destination? Standing firm while everything else changed?

Eventually, the company hired another team to translate the metaphor into specific, observable leadership behaviors.

It was an expensive way to discover that a word everyone confidently repeated wasn't creating nearly as much shared understanding asthey thought.

Why jargon prevails

• Parking lot that
• Double-click
• Close the loop

Business jargon survives because it’s largely designed to manage social risk. Using it signals, I know how this world works. It demonstrates membership, competence, and credibility.

Business is messy, and leaders don't always have complete information. Abstract language lets us project confidence while preserving flexibility.

Altitude without traction

Specific language creates accountability. The more specific you are, the easier it is for people to disagree, question your thinking, orhold you accountable. That's part of the appeal of jargon. It creates distance between the speaker and the detail. The more abstract and elevated your language, the more strategic you sound.

A 2020 study by Harvard Business School professors Laura Huang and Andy Wu, published in the Academy ofManagement Journal, analyzed over 1,000 early-stage startup pitches and found that founders who spoke in abstract, visionary terms were significantly more likely to advance in the funding process.

A separate study, published in Applied Cognitive Psychology in 2025, found the other side: jargon raises how credible a speaker appears and lowers how much the audience retains.

The very language that helps people see you as a leader can make you less effective once you're leading.

The most trusted leaders tend to be the ones who resist impressive-sounding language and say the plainest version of what they mean.

In fact, four words probably do more for a leader's standing than any carefully crafted message: I made a mistake. Not "we encountered some headwinds," not "there were learnings from this experience," but the plain version.

The cost of comfort

When a conversation gets uncomfortable at work, it almost always feels easier to soften your message than to say exactly what you mean. You hedge, add qualifiers, cushion the point with extra reassurance, or leave the hardest part unsaid. Most of the time, you mean well. You don't want to discourage someone, damage the relationship, or create unnecessary conflict. The conversation becomes less uncomfortable for a moment, but the work often becomes harder afterward.

Amy Edmondson, Novartis Professor of Leadership and Management at Harvard Business School and author of The Fearless Organization, found that the fear of making a negative impression pushes people to stay silent exactly when clarity is most needed. According to her research, silence is one of the most consistent predictors of teams that miss problems early and never learn from them. The friction avoided in the meeting resurfaces later, at greater cost.

KimScott, former executive at Google and Apple and author of Radical Candor, calls this ruinous empathy: softening your message to protect someone's short-term feelings comes at the cost of the clarity they need. Her argument is simple and uncomfortable: clear, direct communication, even when it is hard, is an act of care.

The quiet power of saying what you mean

None of this is an argument for bland, colorless language.Vivid, precise writing does the opposite of jargon: it sharpens meaning instead of hiding it. Before reaching for a word, pause on two questions:

• What do I mean by it? 
• What will my audience hear? 

A surprising amount of corporate language wouldn't survive those two questions.

Leaders have more influence over language than they often realize. Whatever tone, vocabulary, and level of directness they model becomes the standard everyone else copies. Word choice is one of the quietest ways leaders shape culture.

This matters even more as we hand our language to AI. These tools can already learn to write in our voice. The question now is whether we've been deliberate enough about that voice in the first place to like what we see.

You can't predict the future. You can be disciplined about how you face it.

That's where Future Storming comes in. Future Storming is a process for looking at the trends and signals already visible in the market, understanding how those forces connect, and thinking more clearly about where they may lead.

Recently, we've been applying that lens to talent strategy, running Future Storming sessions with talent leaders across industries to understand which forces are already reshaping how organizations find, develop, and retain the people they need. When you look across those conversations, one thing is hard to miss: AI runs through almost all of the most significant trends, and not as a future scenario. It's already reworking the talent systems most organizations have leaned on for years, often quietly, and often faster than leadership teams have had time to respond.

From these sessions, five high-likelihood, high-impact shifts have emerged as the ones every talent leader needs to be watching right now. What follows is what each of them may mean for your organization.

1. The frameworks most organizations use to define great leadership were built for a different era

Skills and competency models describe work that no longer exists in many roles or that AI now performs alongside, or instead of, humans. The gap between what organizations say they're selecting and developing for, and what the work actually requires, is widening quietly.

This creates a real problem. Organizations that don't redefine what great looks like now will be developing the wrong people for the wrong future optimizing for capabilities that are becoming less predictive while under-investing in the ones that matter most.

• Rebuild leadership profiles from a future-back perspective, starting with where the business is heading, not where it has been.
• Focus on the distinctly human capabilities AI cannot replicate judgment in ambiguous conditions, relational intelligence, ethical reasoning, the ability to set direction when there is no precedent.
• Increase the use of behavioral observation in selection and development. It's the only methodology that shows how someone actually thinks and decides under real pressure.

The signal worth chasing isn't on a resume, it's in the room in how someone handles a real situation, under genuine pressure. It's the only place where someone can't prepare their way out of being themselves.

2. Human differentiators are the last mile AI cannot close

Judgment. Empathy. Creativity. The ability to navigate genuine ambiguity. These are increasingly what separates human contribution from AI output and they're precisely the things most talent systems have always found hardest to measure.

For a long time, organizations could afford to treat these as qualities that would emerge naturally with experience. That's no longer an option. The human differentiators are becoming the job. And most organizations still aren't measuring them well.

The methods exist behavioral assessment, simulation, structured observation. And AI is now making them accessible at scale in ways that simply weren't possible before. The question isn't whether to use them. It's how to deploy them thoughtfully, with the governance and transparency that -stakes talent decisions require.

• AI-powered behavioral observation that surfaces how people actually perform in the flow of work, (i.e. judgement, decision-making, adaptability) not self-report
• Assessment that evaluated how people work with AI, not just without it because that's increasingly what the role looks like
• Simulation-based approaches that reveal thinking in action - the kind of evidence no credential or output can provide

3. The talent pipeline is broken

AI is displacing the early-career work that has traditionally served as the on-ramp into organizational life. Those tasks once gave emerging employees something more valuable than work product. They gave them foundational experiences, relationships, and judgment. The kind of judgment that eventually grows into leadership.

The impact won't show up immediately. That's exactly what makes it worth paying attention to now. Within three to six years, benches will thin and succession pipelines will require far more intentional investment. Organizations will find themselves asking why their internal talent isn't developing the way it used to.

The organizations that get ahead of this have a real opportunity to build something more deliberate, more equitable, and better suited to the capabilities the future actually requires.

• Invest in real, simulation-based experiences, putting emerging leaders into the decisions and pressures that build genuine organizational judgment, not just task exposure.
• Redefine what early-career development is, building toward the capabilities the future requires, not the ones the old job description described.
• Build feedback into the flow of work. AI behavioral observation and practice AI role plays make continuous development possible at scale. The experience that used to happen informally has to be designed now.

4. People need to re-skill faster than any development model was built to support

People need to reskill faster than any development model was built to support.  Most organizational development infrastructure was built around a longer, more stable arc of skill acquisition. AI is compressing that arc significantly.

The implication isn't just that training needs to be faster. It's that the whole architecture of how organizations identify, develop, and deploy talent needs to be built for continuous recalibration not periodic refresh.

• Prioritize adaptability and learning agility over static expertise. The ability to acquire new capabilities quickly matters more than the specific capabilities someone holds today.
• Treat reskilling as a continuous organizational process, not an episodic program.

5. AI is absorbing leadership work and culture is losing it's anchor

This is the shift that's easiest to underestimate, and hardest to recover from once it arrives.

Culture is what people see leaders do. The behaviors leaders model how they make decisions, how they show up in hard moments, what they choose to reward and what they let go are how organizational culture gets transmitted. It doesn't travel through stated values. It travels through visible human behavior.

AI is absorbing the work that used to make leaders visible as humans making choices. Performance reviews written by AI. Communications drafted by AI. Coaching conversations mediated by AI. When the distinctly human work disappears, so does the signal. People don't know what to watch anymore. And culture which depends on that watching starts to fray.

The organizations that navigate this well won't be the ones that use less AI, they'll be the ones most intentional about which leadership behaviors remain visibly human, and why.

The behaviors that held culture together need to be rebuilt around what humans uniquely contribute now and that starts with getting the success profile right. That's exactly what the Future Ready Profile is built for.

Strengthen empathy-centered leadership capabilities. The human dimensions of leadership matter more, not less, as AI takes on more of the technical work.

• Strengthen empathy-centered leadership capabilities. The human dimensions of leadership matter more, not less, as AI takes on more of the technical work.
• Reinforce organizational purpose and human-centered culture as anchors.
• Treat culture as something you design, not something you inherit.

What this means

The organizations that navigate this well won't be the ones that adopted AI fastest, they'll be the ones that invested just as deliberately in the human systems around it.

These five shifts aren't warnings. They're design problems, and design problems have answers. The talent systems that come out of this moment can be more intentional, more equitable, and more fit for purpose than anything we've built before.

At BTS, this is the work we're doing every day. If you'd like to think through what any of it means for your organization, we’d love to talk.

The thinking in this article was shapped by Future Storming sessions, including a SIOP 2026 workshop, and by ongoing conversations with talent leaders navigating these shifts in real time.

In Part 1, I told you about the three decisions we made two years ago and the simulation flywheel that produced our first Applied AI diamond.

Here’s the field-notes version.

Over 80% of our global business have now adopted a new Applied AI approach for doing simulations in the first eight weeks, across 24 countries and every practice.

‍The flywheel didn’t stop with simulations. It moved into finance, sales enablement, legal, operations, and client delivery. Teams started building agents and bringing them onto their own org charts. We didn’t plan for any of that. We built the conditions for people to find their own breakthroughs.

What it felt like inside the flywheel.

When the simulation team went live with their first clients on the new way of working, the lead person hit a wall. Their words:

“You’re asking too much. You’re making me be a full-stack developer. Up until this point I did a small part, and I sent it to the team, and they built off the back end, and they brought it back. And now I have to end-to-end soup to nuts, basically alone.”

There was graphic UI work nobody had been trained for, the fear of delivering quality below what BTS expects of itself, and the weight of not having a playbook. This was not the joyful adoption story most consultancies tell.

Then something shifted. Six members showed up for product testing, where the usual was two or three. The work created teamwork I hadn’t seen at BTS in years. The breakthrough was not an instantaneous change from skepticism to celebration. It was a breakdown in confidence, then rally, then bonding. If we didn’t make room for the breakdown, we would have lost the rally.

The other breakthrough was global teamwork; not yet a BTS core strength. Our culture is beautiful: high-freedom and entrepreneurial. But people’s first identities are to their countries. Almost every prior attempt we’ve made at a global initiative has failed. The one exception was Covid. So, when I say what happened next surprised me, I mean it.

I asked to join the simulation team’s Slack channel rather than pulling them into status meetings. What I got to watch in the mornings was someone in South Africa waking up, posting “I tried this and got stuck,” then London adding on, then San Francisco weighing in, then a surprise breakthrough overnight from Tokyo. We didn’t engineer that. Curious and determined BTS’ers did. The problem was interesting enough that the org chart didn’t matter. It was amazing to see and a glimpse into the next evolution of the BTS culture.

The pattern: Explore, expand, institutionalize, renew.

What we’ve now seen play out, both inside BTS and with clients, follows the same four-step pattern. Each step asks a specific decision of the leader.

Explore.

Stay stubborn on the aspiration and fluid on the path. Our breakthrough wasn’t the path we originally took. We changed tools and approaches. Nobody could have foreseen that. And if the team had taken the first six months of learnings from AI as their definitive “this is the detailed path we will follow,” we never would have gotten the disruption. Five different tool combinations were tried before we found the one that worked. Companies that lock into a single path or tool too early are betting against compounding capability that doubles roughly every seven months. That is not a bet I’d take.

Expand.

Run the old way and the new way side by side. When the simulation team’s breakthroughs got real, the instinct was to retreat into more internal testing. We did the opposite. They ran old way and new way in parallel on 6 or 8 live client projects across all three geographies. Every single one ended up going live the new way. The backup was always there. They didn’t need it.

Institutionalize.

Burn the boats. The simulation team committed that no new client work would be done the old way after January 1. The other practice leads then committed to dates within Q1, even though most of them had not yet experienced the new way themselves. They had to trust their colleagues. If you can do it for the most complex thing, you could probably do it for the less complex ones. By February 15, we had approaching 90% global adoption across 24 countries, across all practices. I was shocked and proud. We had spent years failing at exactly this kind of global rollout.

Renew.

Treat your agents as contractors. People on our diamond teams are now managing 30+ agents they built themselves. Our teams give agents performance feedback. We terminate their contracts when they don’t deliver. We expand the responsibility of agents when they outperform. The frontier question we’re wrestling with now is token budgeting. Two friends of mine running engineering-heavy companies believe that within 6 - 9 months, their token cost per engineer will exceed the cost of the engineer. Whether that’s the right framing is open. The question is real, and every CEO will be asked some version of it within the year.

What had to be true for this to scale.

Once we achieved this amazing global innovation, the leadership sat down to figure out what made it work. We named five things. None of them were about the technology.

Real pain points as the starting point. We had so many people frustrated from those ways of working, all the back and forth and all the wasted time, that this was gold for them. The old way was already painful. The new way wasn’t a forced disruption; it was relief. Find the workflow where the pain is loudest and start there.

The diamond unlocked creativity, it didn’t constrain it. This was the most differentiated insight, and the one most leaders miss. It wasn't "here's the new tasks and rules." It was, "once you learn how to do this, the sky's the limit. You can be even more creative." If your rollout feels like a new set of rules constraining your people, you’ve built the wrong thing.

Pair deep expertise with fresh eyes. The disproportionate share of our breakthroughs came from a tenured tinkerer with total command of the work, paired with someone new to the role who hadn’t yet built the muscle memory of how it had always been done. Without that pairing, you get incremental improvements to the work you already know how to do, instead of a reinvention.

Refuse the “people are too busy” reflex. When I brought the rollout to the global leadership team, the excuses came fast. “Our people are too busy. They’re burnt out. Q1 is going to be busy. No one’s going to have time.” My response: “This is a chance to eliminate the tasks you dread and expand what you love. I know it is a short push of extra work, and I think after the fact you and your team will feel joy and pride and say it was the best time we ever spent.” This is the moment most AI rollouts die.

Senior leaders must lead by example and do the work themselves. This is not middle manager’s job. This is not something you delegate. Even though you don’t build simulations anymore, you must know what this is. One of our partners proactively put time on senior leaders’ calendars and forced them to do the work. Once they started building, the excitement grew, and they could advocate for the rollout because they understood it. If your executives haven’t put their hands on the keyboard, you don’t have a rollout. You have a memo.

What we’re seeing across clients.

We’re now running this play with client organizations across industries and geographies. The companies whose flywheels are accelerating paired their A-players with their early-career talent, pulled IT and legal into the working sessions, refused the “too busy” reflex, and put their senior leaders’ hands on the keyboard. The companies whose flywheels are stuck almost always have a leadership pattern at the center of the stall. Not a tooling pattern. Not a governance pattern. A leadership pattern.

If this resonates, let’s talk.

If you read Part 1 and asked yourself whether your flywheel was turning, the question I’d add now is sharper: do you have the conditions in place for a diamond to appear? If yes, you’re already moving. If no, the technology will not save you.

Here's where we're starting with clients: a working session, half day to a full day, with a small group that owns one of your highest-friction processes. Together we map where your first diamond is most likely to land, how to set up the side-by-side trial, and what your version of "burn the boats" should look like.

The destination, if we do this right, is a self-reliant culture of applied AI inside your company. 5, 10, 15 diamonds compounding into a fundamentally different way of operating. From what I have experienced this is a once in a career opportunity for dramatic shareholder value creation if you get that muscle going. I say that because I'm watching it happen, in real time, inside our own company and across our client base.

If you want to get your flywheels spinning and map your first diamond, start here. Bring your hardest workflow. We'll bring the playbook.

‍Three decisions that changed everything.

Two years ago, we made three deliberate decisions about how BTS would move with Applied AI.

We would become our own Customer Zero.

While others were building strategies, defining governance, and waiting for clarity, we made a different call: we decided not to wait. Not because the stakes were low, but because they were high. And because in a space evolving this quickly, clarity wouldn’t come from planning. It would come from movement.

So instead of starting with a roadmap, we started with three principles:

1. No top-down mandate. The people closest to the work figure it out.
2. IT must evolve from gatekeeper to enabler - leading AI trials and fast experimentation.
3. Don’t wait for certainty.

We set the organization in motion, and once we did, things started to move quickly.

What if we started this company today?

Waiting for certainty is itself a choice, and it’s costing companies more than they realize.

We started where we knew the work best: our simulations. No perfect plan, just teams moving, trying, and iterating.

Simulations are core to who we are at BTS. Companies that simulate don’t just make better decisions; they execute faster and build more engaged cultures.

The team asked a simple question:

"What if we were to start our company today?”

That question started the flywheel.

They asked IT for a few licenses and started building - vibe-coding, writing agents, and testing tools - moving at a pace that would make any VC-backed start-up smile.

The messy middle.

At first, the team was underwhelmed._‍

The early reports were blunt:

“Not good with math.”

“Poor graph capabilities.”

The team wasn't discouraged. They kept tinkering - jumping between tools, staying on top of new releases, experimenting constantly.

This was a small team, across 24 countries, building off each other’s ideas. Laughing at crazy creations. Breaking things. Iterating in a sandbox alongside real clientwork.

Each cycle produced something:

• A sharper scenario
• A faster build
• A more powerful simulation

The flywheel was turning, and it was generating something real.

When the diamond appeared.

Then something shifted.

The team moved into client trials across five countries. They figured out ISO compliance and built the architecture to handle the complexity, the “spaghetti.”

And what emerged wasn’t incremental:

• What used to take weeks started happening in days.
• Limited creativity started to feel like unlimited innovation.
• Clients became self-serving.
• Agentic simulations were built directly into client systems for real-time updates and preparation.

This was our first AI diamond - a high-impact outcome created by many cycles of experimentation compounding into real value.

It only appeared because we kept the flywheel turning, each cycle increasing the odds that something would break through.

95% adoption in eight weeks.

Then it was time to take the AI diamond global.

BTS is decentralized and highly entrepreneurial. We operate across 24 countries and 38 offices, where local teams have real autonomy.

And historically? That’s meant a low appetite for adopting something built somewhere else and pushed from the center.

So we expected resistance.

Instead, something surprising happened.

In the first eight weeks, we saw 95% adoption across our global footprint.

It felt completely different from our own digital initiatives, ERP implementations, top-down rollouts of the past.

This moved on its own. Why? 

We realized it didn’t start with a framework or a model, it started with a feeling.

The feeling of being at the leading edge of one’s craft and profession.

• Joy
• Excitement
• Pride

As we watched this play out across teams it stopped feeling like isolated wins.

There was a pattern to it. A repeatable, organic, innovation motion.

And the flywheel didn’t stop with simulations.

It spread across finance, sales enablement, legal, operations, and client delivery. Some cycles led to small improvements, and others revealed new diamonds.

Not becausewe planned for them, but because we built the conditions for people to find them.

The question I'd ask any CEO right now: Is your flywheel turning, or are you still waiting for the perfect plan?

In part 2, I’ll share the key success factors behind the breakthrough, and what we’re now seeing across more than 120 global clients.

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Organizations have long wanted to scale coaching, but have been limited by cost and capacity. With AI, that's beginning to change as new platforms make coaching more accessible, flexible, and available on demand, extending support beyond a select group of leaders to entire populations.

For talent leaders, this shift creates both opportunity and complexity. With greater reach comes a new set of trade-offs: how to balance access with depth, flexibility with accountability, and efficiency with meaningful development.

The limits of unlimited (coaching).

Unlimited coaching sounds like the obvious answer. Remove the barriers, give everyone access, let people engage on their own terms. What's not to like?

In practice, quite a bit.

When coaching has no defined structure or cadence, engagement tends to become episodic - people show up when something feels urgent and step back when it doesn't. The coaching relationship never quite deepens. Conversations cover ground but don't build on it. And the development that was supposed to happen keeps getting pushed to the next session, and the next.

Three patterns emerge:

1.  Sporadic engagement over sustained development. Without a rhythm to anchor the work, coaching becomes reactive. Clients bring whatever is most pressing that week rather than working toward something larger. Progress happens in bursts, if at all.
2. ‍Insights that don't compound. Great coaching reveals patterns over time - things a client can't see in one session but can't unsee after several. Without continuity, and without a consistent coaching relationship to hold the thread, each conversation starts close to zero.‍
3. Outcomes that are hard to measure. No milestones. No defined endpoint. No clear way for the organization, or the client, to know whether it's working. Activity fills the gap where impact should be.

The result is a model that's easy to scale and hard to defend. Which is exactly the problem talent leaders are navigating right now.

The relationship is the lever.

Decades of research into what makes coaching work keeps arriving at the same answer: it's the relationship. Not the platform, not the methodology. The relationship.

When a coach and client build trust over time, developing shared language, and returning to the same themes with increasing depth, something shifts. Conversations get more honest. Insights stick. The client starts doing the work between sessions, not just during them. That's when coaching becomes genuinely transformative, and it can't be rushed or replicated in a one-off session.

The ICF and EMCC are clear on this: continuity is what dives outcomes. The coaching engagements that produce lasting change are the ones where each session builds on the last, not the ones that simply offer more access.

Three principles make that possible: Consistency, Continuity, and Completion.

1. Consistency

The foundation everything else is built on.

The temptation when designing a coaching program is to treat flexibility as a feature - let people book when they want, swap coaches freely, engage on their own schedule. But frequent coach changes reset the clock. Every new coach has to earn trust, learn context, and find their footing with the client. That's time spent getting started, not getting somewhere.

A stable coaching relationship works differently:

• The coach starts to see around corners, uncovering patterns the client can't see on their own
• The client stops performing and starts being honest
• The relationship itself becomes a source of accountability, not just the sessions

Consistency doesn't constrain the work. It's what makes the deeper work possible.

2. Continuity

What turns a series of sessions into genuine development.

Without continuity, coaching tends to be additive at best- each session offers something useful, but nothing compounds. With it, the work builds on itself in ways that can't happen in isolated conversations.

What continuity makes possible:

• A limiting belief surfaced in session three becomes a thread that runs through the rest of the engagement
• A behavioral pattern the client couldn't see at the start becomes impossible to ignore by the end
• Space opens up for the harder work - the kind that requires sitting with discomfort across multiple sessions, not resolving it quickly and moving on

That slower, deeper work is where lasting change actually happens. It doesn't come from more sessions. It comes from the right sessions, in the right order, with the same person.

3. Completion

The most underrated principle of the three.

In a world of unlimited access, there's no finish line, and without one, it's surprisingly hard to know what you're working toward, or whether you've gotten there. A defined endpoint changes the entire shape of an engagement.‍

A clear endpoint creates urgency and focuses every session on what matters most.

• Shifts the question from "what should we talk about this week?" to "what do we need to accomplish before we're done?"
• Gives both coach and client a body of work to look back on, not just a log of conversations

For talent leaders, this is also what makes coaching legible as an investment. Sessions logged is an activity metric. A cohort of leaders who completed a structured engagement and can articulate what changed, that's a result.

Don't just scale it, design it (here’s how)‍ 

The opportunity in front of talent leaders right now is significant. The organizations that will get the most from this moment are the ones that treat coaching design as seriously as coaching delivery.

Practical design decisions:

• Define the arc before you launch: set the number of sessions, the cadence, and the goals upfront, not after people have already started booking
• Protect the coaching relationship: Make coach switching the exception, not the default, and design your program to discourage unnecessary re-matches
• Build in milestones: create structured check-ins at the midpoint and end of each engagement so progress is visible to both the coach and the organization ‍
• Separate on-demand support from developmental coaching: Use AI-enabled tools for in-the-moment guidance, and reserve structured engagements for the deeper work ‍
• Measure completion, not just activation: Track how many people finish an engagement, not just how many start one

Questions to pressure-test your design:

• Does every participant know what they're working toward before their first session?
• Can your coaches see enough context about a client's journey to pick up where they left off?
• Would you be able to show, at the end of a cohort, what changed, and for whom?

Access opened the door. Intention is what makes it worth walking through.

Across industries, safety is undergoing a structural shift. What was once managed primarily as a compliance function or performance metrics increasingly understood as a reflection of how organizations are designed, led and continuously improved.

In complex and high-risk environments, safety is not achieved through stronger enforcement or additional programs alone. It emerges from the interaction between leadership behavior, operational design, decision environments and the organization’s capacity to learn and adapt.

Drawing on global safety science, Human & Organizational Performance (HOP), research on psychological safety, and our cross-industry transformation experience, eight key shifts are shaping the next evolution of safety culture.

1. Safety as a Core Organizational Value

Safety is moving beyond being treated as a shifting priority. Priorities compete. Values guide.

When safety becomes a core organizational value, it shapes decision-making, trade-offs under pressure, operational planning and resource allocation. Safety becomes the natural consequence of how the system operates,rather than a campaign layered on top of production.

This shift also redefines the role of safety functions, from compliance policing to enabling safe and sustainable performance.

2. Learning as an Operating Discipline

Organizations are embedding continuous learning into everyday operations. Rather than focusing only on what failed, they exploreweak signals, near misses, operational friction and successful adaptations.

Learning becomes a core capability, accelerating insight, strengthening resilience and improving decision quality.

3. Leadership Ownership at All Levels

Safety culture is increasingly recognized as a leadership capability, not solely an HSE responsibility.

Executives define direction and tone.
Middle managers translate expectations into operational decisions.
Supervisors shape the daily decision environment.

Successful organizations translate safety expectations into concrete leadership behaviors and daily routines, creating clarity and alignment across levels.

4. Psychological Safety as Infrastructure

A strong safety culture depends on speaking-up environments.

When employees feel psychologically safe, weak signals surface earlier, risk trade-offs are openly discussed and learning accelerates.

Psychological safety is operational infrastructure , not a soft topic.

5. Amplifying What Works

There is growing recognition that most work is completed safely, often under variable conditions.

Studying success reveals adaptive capacity and strengthens resilience. This complements traditional incident analysis by reinforcing expertise and confidence.

6. Aligning Work-as-Imagined and Work-as-Done

Procedures and plans rarely capture operational complexity perfectly.

Leading organizations reduce the gap between policies and operational reality by inviting front line input and empowering stop-work authority.

The goal is better alignment between design and execution.

7. Designing for Human Decision-Making

Incidents often stem from predictable cognitive biases such as normalization of deviance, production bias, overconfidence and hindsight bias.

Recognizing these decision traps shifts focus from blaming individuals to strengthening decision environments.

8. Cultural Evolution as a Long-Term Capability

Sustainable safety culture requires integration rather than reinvention, structured capability journeys rather than one-off programs, and measurable behavioral impact rather than activity metrics.

Organizations that succeed:

• Integrate safety into existing leadership and operational systems

• Design earning journeys that support day-to-day application

• Measure behavioral change and operational outcomes

• Reinforce progress consistently over time

Cultural evolution is a sustained commitment to system alignment and capability building.

Conclusion

The evolution of safety culture is less about adding controls and more about strengthening systems.

Safety is something organizations produce — through leadership clarity, operational design, psychological safety and continuous learning.

Those who embed these capabilities consistently will not only reduce risk. They will build more resilient, sustainable and high-performing organizations.

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Sources & references:

• WorldSteel Association. Safety Culture & Leadership Fundamentals.
• Norsk Industri (2025). Safety Leadership and Learning: A Practical Guide to HOP.
• D. Parker et al. / Safety Science 44 (2006). Development of Organisational Safety Culture

• Hollnagel, E. (2014). Safety-I and Safety-II: The Past and Future of Safety Management.
• Hollnagel, E. (2018). Safety-II in Practice: Developing the Resilience Potentials.
• Conklin, T. (2012). Pre-Accident Investigations: An Introduction to Organizational Safety.
• Edmondson, A. (2018). The Fearless Organizations
• Reason, J. (1997). Managing the Risks of Organizational Accidents.

• Resilience Engineering research (Hollnagel,Woods, Leveson and others).

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