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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

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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.

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