Is the pursuit of purpose the latest management fad? Nope. But it is getting more personal…

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.  

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