Cloverleaf fits knows how any two of your people will work together and coaches the relationship

No single assessment shows you the whole person, and none shows you how two people will work together. Cloverleaf synthesizes 13+ market leading behavioral assessments into one read of how each person is wired and where any two of them will align and where they might conflict. It is built on established assessments like DISC, CliftonStrengths®, and Enneagram, not a proprietary quizzes or assessments, so it is accurate from the first day, for the new hire and the team that just formed. Cloverleaf uses validated science and can be trusted from day one.

We have been building exactly that for eight years, before the category had a name, across 45,000 teams, with two approved patents and enterprise deployments at organizations including Google, Adobe, T-Mobile, and the U.S. Air Force. 86% of users report improved team performance within 30 days. We measure what changed between people, not what was completed.

We have also made a deliberate choice. We never gave the AI a name or a persona, and it does not train on your employees’ data. Every insight it surfaces points the person toward another person. AI is good at speed, accuracy, and scale. Humans are the ones who handle the friction, the repair, and the trust. The goal was never to put a coach in someone’s pocket. It was to help people walk into the relationships that make or break their work a little better prepared.

The recognition is real, and we are grateful for it. The skeptics who roll their eyes at “AI coaching” are also not wrong, and we have agreed with them in print. Both things can be true. The category is maturing, and its name still points at the wrong unit.

 Not one manager’s knowledge, but whether a specific manager and a specific report can have a hard conversation, give feedback that lands, and work through a disagreement. Then the same thing for the next relationship, and the next, across the whole organization. That is a relationship problem, repeated thousands of times. Coach those relationships, and performance follows.

See what coaching the relationship actually looks like. Request a Cloverleaf demo.

Five questions that reveal whether an AI coaching tool will actually change behavior

The failure modes above are not mysteries. They point directly at the design decisions that separate coaching that changes behavior from coaching that just generates usage metrics. You can use them as an evaluation checklist in your next vendor demo.

1. Does the coaching come to your people, or does it wait for them to come to it

The useful version shows up as a few sentences in the tools people already work in, like Slack, Teams, email, and calendar, timed to the moment they matter. The other 98.5 percent of someone’s interactions happen outside the programs HR runs. Coaching that cannot reach those moments stays contained to the calendar HR controls.

2. Is it triggered by what is actually happening in the organization?

A manager just inherited a new team. A review flagged adaptability. A new report joined a recurring meeting. Those are the moments coaching is genuinely useful, and detecting them requires a connection to the systems of record, like Workday and other HRIS, that know when those moments happen. A tool that waits to be asked only reaches the manager who already knows they need help.

3. Is it built on validated market leading behavioral data?

There is a real difference between coaching that knows a person’s name and coaching that understands how that person processes information, hears feedback, and responds to stress. That understanding comes from market leading behavioral assessments like DISC, CliftonStrengths®, and Insights Discover. If a platform makes you abandon the assessments you already adopted and adopt a new proprietary one, it is adding friction and discarding a shared language you already built.

4. Is it connected to your own frameworks?

Coaching grounded in your competency model and values closes the gap between a general insight and the specific situation a manager is actually in.

5. Does it measure behavior change, not logins?

“Two thousand managers logged in last quarter” is not evidence. “Manager feedback conversations are measurably more specific than they were six months ago” is. The first counts activity. The second names what changed.

What good AI coaching looks like in a manager’s day

Strip away the buzzword and the version worth building is concrete. And it centers on the manager, for good reason. Gallup’s analysis of 2.7 million employees found that managers account for roughly 70 percent of the variance in team engagement. Reach the manager in the moment, and you reach the team.

It looks like a manager getting three sentences in Slack before a 1:1 with a direct report who hears feedback differently than they do. Here is how this person prefers to receive it. Here is what they need from you right now. Here is what to watch for. Not a ten-page report. Three sentences, before the conversation, when it can still make a difference.

It looks like a prompt the week before performance reviews that ties to your organization’s actual leadership competencies. It looks like the goal from that review surfacing again in the weeks that follow, in the flow of daily work, instead of sitting in a system someone opens twice a year. Awareness alone does not change behavior. Repetition tied to a real moment does.

And it looks like the kind of just-in-time support that used to be reserved for executives with expensive coaches, available to every person in the organization, not only the top 10 percent.

There is a metaphor we keep coming back to. Imagine a basketball coach who sits in the corner and waits for a player to come ask for help. That is not a coach. A real coach is watching the whole time, bringing context, going to the player before they know they need it, and seeing how each player works with the others. Not just on game day. Every day. Most AI coaching tools sit in the corner. The standard Hebba describes is the coach who shows up.

How Cloverleaf is built to meet that standard

We will say this part plainly, because the rest of the piece does not depend on it. Cloverleaf was built around this idea for eight years, before the category had a name. It synthesizes 13+ behavioral assessments into a single view of each person, signals how specific people are likely to work together, connects to your HRIS and your competency frameworks, and surfaces coaching in Slack, Teams, email, and Workday. No new login. Proven across 45,000 teams, with 65 million coaching moments and two approved patents behind it.

We have also made a deliberate choice that matters here. We have never given the AI a name or a persona, because it is not human and is not meant to feel like it is. Every output points the person toward another person. AI is good at speed, accuracy, and scale. Humans are the ones who handle the friction, the repair, and the trust. The goal is not to replace the relationships that make work meaningful. It is to help people walk into them better prepared.

What to do next: bring the standard to your next vendor demo

Hebba is right that the category has an image problem, and she is right not to soften her critique. The instinct that something feels off in most demos is worth trusting. But the thing underneath the bad name is real, and it is worth getting right.

So keep the standard she set. Bring it to your next demo and ask the vendor to show you, in the product, what the employee actually has to do to receive the coaching. The platforms that clear all of it are a short list. That is the version worth being excited about, cringe name and all.

Our thanks to Hebba Youssef for taking an honest look at where this category is headed. You can read her full piece, “The Term ‘AI Coaching’ Is Cringe, But Thankfully the Concept Isn’t,” in I Hate It Here.

Why building privacy in early made our system simpler, not more complex

The honest answer is that building privacy in early has made things simpler, not harder. That might sound counterintuitive. The common objection is that privacy-first means slower development, you’re building for requirements you don’t have yet, adding complexity that isn’t justified. We heard that internally. And for any single decision in isolation, it can look true.

When you’re adding a new data store and you also need to think about PII segmentation, that’s one more thing in an already complex conversation. It’s one more item to defend when someone is asking why we’re not building the more visible feature instead.

But when you step back and look at the system as a whole, what you find is that building privacy in at the architectural level eliminates entire categories of ongoing complexity.

You stop writing workarounds throughout the codebase to handle special cases. You stop maintaining manual scrubbing processes as a layer on top of the database.

You stop catching the bugs that appear when someone builds a new feature without realizing it has implications for a legacy carveout.

The system works one way. Everything gets simpler, and simpler systems ship faster and break less.

We saw this play out clearly when we overhauled our partner channel architecture. We had a model where partners and their customers were handled through a set of special cases wired throughout the product. It was hard to maintain, limited what partners could offer their customers, and was a constant source of bugs when someone built a new feature without knowing about the edge cases.

When we rebuilt that into a clean federation model, where every account works the same way with a defined relationship between them, we eliminated the workarounds. Everything just worked. The partner channel, which had been stagnant for five years, doubled the following year. Architectural simplicity had a direct business impact.

Privacy built into architecture is the same category of decision. The upfront investment produces compounding returns as the system grows, because every new capability inherits the correct properties automatically rather than requiring a special audit to verify it.

Questions to ask any AI coaching vendor about data privacy

Here’s what I’ve noticed in enterprise sales: when we get into the detailed compliance conversations with customers, something shifts. For a new customer, we’re initially just another SaaS vendor. But then we’re sitting with their compliance team, talking about how we’ve approached PII handling at the data model level, how we’ve thought about data minimization in the context of AI processing, how we’ve worked through the IAPP frameworks and what they mean in practice for behavioral data.

The conversation changes. Instead of the usual back-and-forth, the litigious haggling over a specific point in the contract, they realize they’re talking to someone who actually understands what they’re trying to protect their organization from. We have the same objectives. We’re not adversaries. We’re partners.

63% of HR leaders cite data security as their top concern when implementing AI tools. And yet most vendor evaluations stop at certifications. Certifications tell you the vendor passed an audit. How they explain their architectural decisions tells you how they actually think about your data.

If you want to understand how Cloverleaf’s AI coaching platform actually handles behavioral data, a few questions cut through the marketing language quickly.

Ask about data minimization.

Specifically, how they handle the tension between giving AI enough context to produce useful coaching and not sending more data than necessary. A vendor who has thought about this will give you a specific answer about how they process data before it reaches AI consumers. A vendor who hasn’t will tell you about their encryption.

Ask what happens when an employee leaves the organization and their data needs to be removed.

A vendor with privacy built into the architecture will describe how the data structure handles it automatically. A vendor with privacy bolted on will tell you about their offboarding process.

Ask whether their approach to privacy has required them to make tradeoffs in other areas of the product.

A vendor who has actually built to a high standard will be able to name specific decisions where privacy won. A vendor doing minimum compliance will tell you there were no tradeoffs.

For a broader view of what to look for, Cloverleaf’s talent leader’s guide to vetting AI coaching covers the five features that separate real coaching systems from rebranded chatbots. And for the enterprise procurement process, this guide on asking the right questions in AI coaching evaluations walks through what to ask about coaching methodology, integration depth, and data practices in a way that actually surfaces meaningful vendor differences.

The regulatory environment is not going to get simpler. Every year, AI-specific requirements get more specific, jurisdictional requirements compound, and enterprise compliance teams get more sophisticated in what they’re asking. The vendors who built for the minimum are going to keep having fire drills. The vendors who built above suspicion are going to keep having partnership conversations.

That’s the choice we made in 2022. Not because it was easy to defend at the time, it wasn’t. But because we understood what we were handling and what our customers were trying to protect. Thirteen licensed assessment partnerships. Two approved patents. 45,000 teams. Sixty-five million coaching moments. None of that works if people don’t trust us with their data.

See how Cloverleaf handles your data

Cloverleaf is the only AI coaching platform offering a free trial, so your team can start getting coaching built on validated behavioral science today. To explore our security and integration architecture, visit our integrations and security overview, or book a demo to talk through how we approach behavioral data with your team’s specific requirements.

A coaching-trained AI and an assessment-trained AI answer different questions

This is where the terminology confusion creates real organizational friction. “AI coaching” has become a catch-all label covering tools with fundamentally different designs. Understanding the distinction doesn’t mean choosing one over the other — it means knowing what each is actually built to do, so you can use both for what they’re good at.

A coaching-trained AI is trained on coaching methodology. It’s designed to help someone examine their own thinking, surface assumptions, process an experience. When a manager is preparing for a difficult conversation and asks for guidance, a coaching-trained AI responds the way a skilled coach would — with questions that help the manager find their own answer. There’s real value in that. Reflection and self-examination are meaningful parts of how leaders develop.

An assessment-trained AI is trained on validated behavioral science. When a manager asks how their direct report is likely to receive critical feedback, it responds with a specific answer — drawn from that person’s actual DISC profile, Enneagram type, CliftonStrengths® themes. It can tell you how a High S typically communicates under pressure, what an Enneagram Type 1 tends to avoid in conflict, how someone whose top strength is Responsibility tends to respond when they believe they’ve fallen short. It coaches — but the coaching is grounded in behavioral data the organization already built.

The distinction isn’t coaching versus not coaching. It’s what informs the coaching — a methodology framework or a scientific understanding of the specific people involved.

An enterprise talent leader working through this recently put it plainly. Her organization had been using its AI coaching tool for reflection-based leadership development and found genuine value there. But when she needed to help a manager understand how to approach a specific direct report — someone with a known CliftonStrengths® profile and a Hogan debrief on file — the coaching tool couldn’t help. “It’s not trained on debriefing my assessment report,” she noted. “I’d have to share not just my report but additional context. And even then it’s working from what I upload, not from the assessment science itself.”

That’s the gap. She doesn’t need to give up her coaching tool. She needs an AI layer that actually knows her people — one where the coaching draws from the behavioral data the organization has spent years building, not from a generic methodology that treats every manager and every direct report the same way.

But the coaching is grounded in who you’re actually talking to.

Ten minutes before a standup, a manager gets a Slack message. Not a reminder to “engage her team.” A specific note: Scott, Alex, and Shelby all tend to need predictable structure in meetings, especially during transitions — and her natural comfort with ambiguity is likely reading as withholding rather than patience. That’s the behavioral gap between her profile and her specific team’s, surfaced at the moment it’s actionable.

When she needs to practice a difficult conversation — giving a senior direct report feedback about taking more initiative — she doesn’t role-play with a generic AI avatar. She practices with an AI that’s loaded with her direct report’s actual behavioral profile: detail-oriented, process-driven, cautious about new initiatives, likely to press for specific boundaries before acting independently. The AI responds the way that profile suggests that person actually would. The manager practices, gets evaluated on where she was clear and where she was vague, and walks into the real conversation having already navigated it once.

That’s coaching. Just coaching that knows who it’s talking about.

Individual AI coaching can’t see team dynamics because it’s only looking at one person

There’s a second gap the AI coaching wave hasn’t touched, and it’s harder to name because the category barely exists yet.

Individual coaching — whether from a human coach or an AI — develops one person. It builds self-awareness, strengthens specific competencies, helps someone think through a situation more clearly. That matters. But most of the friction that slows organizations down doesn’t live inside individuals. It lives between them.

A team where the two most vocal members share the same behavioral style and consistently steamroll the quieter ones. A manager who gives feedback in a way that’s effective for her own communication preference but lands poorly with most of her reports. A cross-functional project that keeps hitting the same wall, which looks like a disagreement about priorities but is actually a collision between how different people process ambiguity. These are team dynamics problems. Individual coaching doesn’t see them.

Talent leaders who have spent years building assessment programs often feel this gap most acutely — because they’ve already given people the frameworks and the shared language. What they haven’t been able to give them is a way to apply those frameworks in actual team context. To see how a team’s behavioral composition shows up in how they communicate, make decisions, and handle conflict at scale.

Cloverleaf’s research shows that organizations with more than 1,000 employees average 20 different assessment tools. Companies above 5,000 employees average 35. That’s not a data gap. That’s a data activation gap — assessment infrastructure that exists but has no system to put it in front of the right person at the moment it would actually change something.

What’s been missing isn’t more individual coaching. It’s coaching that accounts for the full picture — not just who you are, but who you’re working with and how that specific combination tends to play out.

A manager who just went through a reorg can tell Cloverleaf her situation — she’s inherited a new team, people are anxious, and she doesn’t yet have clear direction to give them. Cloverleaf asks clarifying questions, then sends a coaching nudge in Slack: “You likely tolerate not knowing far better than most of your new team does. Scott, Alex, Shelby, and Peggy all prefer clear structure and predictable steps. Your silence about uncertainty probably feels like withholding rather than patience.” That’s not a reminder to communicate more clearly — a coaching-trained AI could generate that generic advice. That’s a read of the behavioral gap between how she processes ambiguity and how the specific people on her team experience it. The coaching doesn’t just develop her. It maps her to her team.

A coaching nudge ten minutes before a 1:1 isn’t just about the manager’s development in the abstract. It’s about this manager, this direct report, this relationship, today.

You’re organization is probably not underinvested in assessments. You’re under-activating them.

Here’s the practical argument for organizations navigating this: assessment-integrated AI coaching isn’t competing for new budget. It’s making the case for existing spend.

Enterprise organizations with certified internal debriefers are paying workshop costs and ongoing time investment to maintain that capability. When a platform can answer the same questions those debriefers are trained to answer — and deliver those answers proactively in Slack or Teams before the moment passes — the organization faces a legitimate resource question. Not “should we add this?” but “does this change how many internal subject matter experts we need to maintain the same quality of assessment support at scale?”

The same logic applies to assessment licensing. Organizations carrying 20+ assessment tools are paying multiple vendors for data that lives in multiple portals with no connective tissue. An assessment-integrated AI coaching platform pulls that data into a single activation layer. The licenses already paid for start doing something.

As Brandon Hall Group has noted in their analysis of the AI coaching landscape, this creates a genuine cost rationalization story: “Organizations leverage existing assessment investments and language, turning what competitors see as net-new budget into an extension of current spending.”

This is a different kind of business case than most AI coaching pitches make. It’s not “here’s the ROI of better coaching.” It’s “here’s the ROI of the investment you’ve already made, finally working.”

The question for any talent leader carrying both an AI coaching tool and an active assessment program is straightforward: does your AI coach know who your people are? Can it tell a manager, before they walk into a difficult conversation, how the person across the table processes feedback, what typically motivates them, and where they’re most likely to disengage? Does it see the team, or just the individual?

If the answer is no, the assessments are still stranded. The coaching is less effective. And the investment isn’t compounding.

Five places to make Enneagram insight available for leaders

1. Before the 1:1, when the manager is figuring out how to open the meeting.

A Type 2 direct report whose recent work has been criticized in front of the team often needs the conversation to start with what they’re contributing — before the manager raises the gap. A Type 5 typically needs space to process, not a rapid-fire check-in. A Type 8 usually wants the issue named directly, and gets disengaged when their manager dances around it.

Every Enneagram practitioner knows this in the abstract. What changes manager behavior is a calendar-aware prompt ten minutes before the meeting that names the specific direct report, surfaces their type, and suggests an opening line.

That’s what in-the-flow-of-work coaching actually means. Not when someone remembers to log in. In the flow of work.

2. Before written feedback, when the wording helps influence whether it lands or backfires

A manager who has been told that Type 4s are “sensitive to authenticity” will sometimes pad the feedback with so much qualification that the substance gets lost. Or second-guess sending it at all.

The fix isn’t more abstract knowledge of types. It’s a coaching layer that sits in the Workday review form the manager is already writing in — and offers two or three concrete adjustments to wording at the moment of writing.

3. During team conflict, when triad imbalance could be what’s actually driving the argument.

Team conflict on a leadership team usually shows up as a content disagreement — about strategy, scope, or hiring.

Underneath, it can be a triad imbalance. Three Gut types and one Head type can steamroll a strategic question that needs a slower, more analytic conversation. Three Heart types and one Gut type can spend too long on whether everyone feels heard before naming what actually has to change.

Most leadership teams never see their own triad map. When they do, the conversation about what’s happening in the room often shifts in five minutes — and that data has to be in the room, not in a binder somewhere.

4. Between talent reviews, when type-aware readiness signals can show up before the missed promotion.

A high-performing Type 3 director may be objectively ready by every output metric and still six months from being ready for a VP role — because their default mode under stress can be to win the conversation rather than build consensus. A Type 9 senior manager may have everyone’s trust and still be passed over because the readiness gap is decision velocity.

These signals are often visible in the type pattern long before they’re visible in the 360. Companies that get behavior change pull them into the talent review, where they become a development plan instead of a post-mortem.

5. In the daily flow of work, where the insight has to live or it doesn’t live at all.

For most leadership teams now, that means Microsoft Teams or Slack, Outlook or Google Calendar, the performance-review tool, and the HRIS — and very specifically not the LMS.

Where Cloverleaf’s view differs from most Enneagram-only approaches

Type alone is a starting point. The Enneagram tells you that your Type 8 director is motivated by autonomy. That’s useful. It doesn’t tell you, on a Tuesday morning, that this particular Type 8 director communicates best in writing and is three weeks into a high-stakes project that’s running over.

Cloverleaf’s view, refined across customer deployments, is that the Enneagram does its real work for leadership development when it’s paired with the rest of a leader’s behavioral profile — DISC, 16 Types, CliftonStrengths®, Insights Discovery.

→ Type tells you motivation. → DISC tells you communication preference under pressure. → Strengths tells you what energizes. → The combination tells you, for a specific person on a specific day, what to do.

Most enterprise organizations have already invested in multiple validated assessments. The question is whether the data is sitting in PDFs in people’s inboxes — or whether it’s being put back in front of managers when they actually need it.

Buying another proprietary assessment from an AI coaching vendor doesn’t solve this problem. Activating the assessment data the company already owns does.

Two specifics that decide whether an Enneagram program holds up

A misuse safeguard, because the framework can get weaponized. “I’m a Type 8, I’m just direct.” “She’s such a 9, she’ll never push back.” In our experience, this is the second-biggest reason Enneagram leadership programs lose traction, next to the forgetting curve. Companies that get behavior change actively coach against type-as-identity and toward type-as-pattern. The arrows matter — every type integrates and disintegrates. The framework is about movement, not classification.

Behavior measurement, because attendance isn’t a metric. Most Enneagram-program measurement, when it exists, is workshop attendance and post-event self-reported confidence. Neither tells you whether anything changed. The behaviors worth measuring are visible in the systems leaders already use — frequency and quality of 1:1s, manager-effectiveness scores in 360 feedback, retention of direct reports under each manager, engagement with daily coaching prompts as a leading indicator.

The companies I’ve watched change leadership behavior with the Enneagram aren’t the ones with the deepest workshop. They’re the ones whose managers see the insight on Tuesday morning, before the 1:1 they’re already running late for. The Enneagram gives them the framework. The flow-of-work delivery gives them the behavior change. This is why we built Cloverleaf.

Why I tell my team I can’t be trusted with pricing

There’s a category of decision I do not get to make at Cloverleaf.

Pricing.

I am the worst at pricing. From very early on, I told the team: do not put me in a pricing conversation. I love to give things away. I cannot be trusted with that.

I’m telling you this not because it’s a confession, but because I think it’s a leadership development case study. I’m not describing a skill gap. I’m describing a fear-vulnerable area — a category of decision where my discomfort with conflict and my desire to be liked will, predictably, override what’s good for the business. And I’m doing the thing most leaders never do: I’m naming it publicly so the people around me can compensate.

This is the identity work that makes fear-driven behavior visible before it becomes a decision. It isn’t a competency framework. It’s a personal map of where you, specifically, are likely to flinch.

The leader who knows they avoid pricing conversations can put a CFO in the room.

The leader who knows they soften feedback when the receiver looks upset can ask their head of people to debrief them after every performance conversation for a quarter.

The leader who knows they over-rotate on the loudest stakeholder can require written input before any major decision.

None of these responses are skills. They’re structural concessions to fear. And they only work when the leader has done enough self-examination to know which concessions to make. This is why I think behavioral assessments — DISC, CliftonStrengths, Enneagram, Insights, the 14 frameworks we support on Cloverleaf — are most useful as fear maps, not personality labels. The point isn’t to know that you’re a “high D” or a “responsibility” theme. The point is to know what categories of decision your wiring will quietly bend in a fearful direction, and to design around that.

The four-day workshop isn’t the unit of behavior change. The Tuesday morning Slack message is.

The structural problem with most leadership development is that the moments where fear actually shows up aren’t in a workshop. They’re in the ten minutes before a hard one-on-one. They’re in the email drafted at 9pm and sent at 7am. They’re in the decision the manager made three days ago because they didn’t want to be the one who said no.

This is why training that doesn’t reach into those moments doesn’t move the lid. We wrote about a related dynamic in the leadership coaching priority paradox: managers say coaching is a priority, but it doesn’t happen, because the systems around them don’t make it happen.

The same is true for fear-aware leadership. It can’t be a quarterly initiative. It has to be a Tuesday morning prompt that says, “You have a one-on-one with Maya in twenty minutes. Last time, you held back the feedback. Here’s how to deliver it in a way she can use.” Or a reminder that says, “Your team has not had a written disagreement in 47 days. That’s not alignment. That’s avoidance.”

The unit of behavior change is small, repeated, contextual, and tied to a specific person and moment. The reason we send 65 million coaching moments a year isn’t because volume is the point. It’s because the only thing that breaks a fear pattern is being met inside the moment when the pattern is forming.

Three things L&D can build into existing programs without rebuilding them

You don’t need a new curriculum to develop fear-aware leaders. Here are three additions I’d ask you to layer into programs you already run.

First, change what you ask managers to commit to after a workshop. The standard ask — pick three things to work on — produces vocabulary, not change. The better ask is: “Name one category of decision where you predictably flinch, and tell your manager and one peer what it is.” That single sentence does more than a behavior change plan, because it converts a private fear into a public commitment with witnesses.

Second, train the team, not just the manager. Most psychological safety programs aim at the leader. But the work I’m describing — being called out by a teammate when you’re behaving from fear — requires that the team has the skill, the language, and the standing to do it. Build a 30-minute team module into manager training that teaches the team how to flag fear-driven behavior in the moment, kindly and specifically. (Our work on DISC profiles and team performance is a useful starting point for the language.)

Third, measure what’s not happening. Most leadership development tracks completion, satisfaction, and self-reported skill gain. None of those measure whether the leader is making the same fear-driven decision they made last quarter. Build a six-month follow-up that asks the leader’s direct reports a single question: “Is there a category of decision where your manager has visibly changed their pattern in the last six months?” That’s the only signal that matters.

The leader’s job isn’t to raise the lid. It’s to dissolve it.

The most useful reframe of Maxwell’s law isn’t that leaders need to grow taller. It’s that the lid is mostly made of fear, and fear gets thinner the more it’s named. The day I told my team “I am bad at pricing decisions,” I wasn’t lowering myself. I was removing one of the bricks the lid was made of.

L&D leaders have spent a decade making managers more skilled. The next decade will be about making them less afraid — not by telling them to be brave, but by giving them the maps, the language, and the in-the-moment support to see fear when it’s driving, and the team conditions to act on what they see.

That’s the development work that actually lifts the ceiling. And it’s the work most existing programs aren’t yet built to do.