It’s time to embrace your quirks: why radical transparency is the key to attracting and hiring the right talent
As the economy tightens, fear of recession looms, interest rates increase, and unemployment reaches record lows, organizations are forced to evaluate their budgets to determine where efficiencies can be realized. In other words, many talent teams are being asked to do more with less.
While some organizations are letting people go, and others are simply scaling back their hiring efforts, the truth is that organizations are still hiring, but specifically for critical roles. It’s more important now than ever to ensure that organizations place the right people in the right roles and equip them with the tools and resources needed to deliver maximum impact. These conditions demand a more transparent and immersive approach to sourcing candidates — one that gives them a true sense of their ability to thrive within the quirky nuances of your organizational world.
When layoffs happen, it’s not just low performers who lose their jobs.
For companies that are hiring during economic downturns, this means that there can be an abundance of good talent available. However, poor performers also lose their jobs, meaning that less-desirable talent is abundant, too. How can organizations make sure they are getting the best talent available from that diverse pool? It’s not easy. Candidates (and hiring organizations) paint themselves in the best possible light, making it hard to distinguish between a qualified candidate who might be good for the role and one who doesn’t have the skills you need. Anyone that has used a dating app is familiar with this dynamic, and has likely experienced the frustration that comes when an illusion obstructs someone’s true potential.
Reading between the lines of a job description
It is tempting to think, “There’s good talent in the job market, so it should be easy to identify people who have been successful in similar roles at other organizations. It’s like shooting fish in a barrel.” There are two problems with this logic, however. Firstly (as discussed), while there are plenty of good fish in the barrel, there are also a lot of not-so-good fish. Secondly, one’s success in one organization does not guarantee success in a very similar job at another organization: context matters. It’s not enough to simply describe the expectations of the role and then evaluate people against those expectations. While doing so correctly can help identify candidates with the right experiences for the job, such an approach ignores the more nuanced aspects of the job not included in the description.
Consider the following example:
A client recently partnered with BTS to help them evaluate candidates being considered for placement into the role of president for their largest business unit. There were two frontrunners being considered, both of whom were strong contenders with track records of great success. However, the key difference between the two candidates was that one sought independence from the executive leadership team, seeing them as stakeholders who should be brought in only at critical milestones for input and oversight. The other candidate sought to partner very closely with the executive leadership team, looking to them for detailed guidance on the future direction and strategy of the business unit.
Without knowing anything about the context of the situation, the reader may believe that the former candidate — the “independent” one — was better aligned with the role of president. The reality, however, was that the executive leadership team expected to play an active role in the business unit, and had wanted to be closely involved in major decisions impacting the business. Whether this was the right approach for them or not, it was the reality of the situation. Based on our assessment of both candidates, BTS painted two pictures for the executive leadership team, one of what the future would look like if each of the two candidates were selected for the role.
The decision for the executive leadership team was easy. Nowhere on the role description was the phrase “Must run all major decisions affecting the business past the executive leadership team for approval,” but this was critically important. The point is simple: The best person for a job in one context is sometimes very different from the best person for the same position in another context. Again, context matters.
Your organization’s culture, values, ways of working, systems and tools, and mission all demand something unique from your people. These organizational truths are just as deterministic of a candidate’s success in a role as the job description. With so much talent available in today’s talent pool, how can you find the few special people that will build upon the precious foundation you’ve built for your business?
Meeting time-to-productivity expectations
Let’s examine why your hiring practices might not be ready for today’s realities. If your hiring process was created during a period of unfettered growth, high demand, and mass hiring, today’s economic landscape may strain or even break that system. During periods of high growth and demand, organizations scale fearlessly: they hire extra people with the expectation that not everyone will work out. In this setting, new hires receive leniency and patience when figuring things out, and the impact of a bad hire is diluted by the near-constant onboarding of new employees.
In today’s reality, organizations are hiring fewer people, and those new hires are under tremendous pressure to be productive as soon as possible. There is less tolerance and patience from leadership for poor hiring decisions. We see this explicitly in the tech industry today, particularly within go-to-market teams.
For example, take the comments Salesforce CEO Marc Benioff recently made in an internal Slack message, as reported by Business Insider: “We don't have the same level of performance and productivity that we had in 2020 before the pandemic. We do not.”
Later, Benioff stated during a call that nearly all of Salesforce’s “annual contract value was being delivered by 50 percent of sales account executives.” In the face of more highly-scrutinized hiring decisions and raised expectations for time-to-productivity, talent teams must be more confident than ever that they are finding those who are already trained to succeed and thrive. This requires a new set of tools, or at least a new mindset.
So, what’s an organization to do? How do hiring systems, tools, and strategies need to shift in this new period of economic uncertainty and a world where we are asked to “do more with less”? Here are three considerations for talent teams to evaluate.
1. Firstly, seek a deeper understanding of what traits are needed from candidates for each role:
What does success look like in this new environment? What capabilities and behaviors will help your organization drive future success in an evolving world? How important are attributes like learning agility, being nimble and resourceful, etc., to success — not just today, but also in the future?
For example, in the context of today’s economic uncertainty, skills like empathetic listening, industry-specific business acumen, and articulating value in the language of a CFO are among the most critical capabilities for sales professionals. In the past, these strengths may have been de-prioritized in favor of skills such as executive presence, storytelling, and domain expertise.
2. Secondly, identify the pivotal moments in the daily life of a target role during which those capabilities and behaviors are most critical.
When is a skill like empathetic listening most critical? Is it when a sales professional conducts a discovery workshop, or when they encounter a hesitant buyer’s objections? Whatever these pivotal moments are, they provide clear context for your job candidates to respond the challenges they’ll be certain to face, and you can observe their behavior in such moments.
3. Finally, create the opportunity for observation and immersion into an environment that emulates your organization and the realities of the role.
Day-in-the-life assessments can give candidates insight into the nuances of the organization and role — letting candidates try the job on for size, so to speak. They also give the hiring organization insight into candidates’ capabilities and behaviors. Most importantly, these assessments let them see how candidates will respond to some of the more unique elements of the organization.
After all, both the candidate and the organization are making a very important decision, and it’s imperative they enter an employment relationship with eyes wide open. This is no different than what we expect from any long-term personal relationship, within which one accepts and value the entirety of another — their strengths, weaknesses, flaws, beauty, and quirks. This means that, at some point, we need to be transparent, vulnerable, and honest about what makes us unique. Why should our approach to hiring decisions be any different?
This radical transparency requires a mindset shift for many of us.
Prioritizing honesty and inviting immersion into the quirkiness of both parties is critical to ensuring candidates and hiring organizations make the best decision. In a world where we have to be more confident than ever in our hiring decisions, we can’t afford to gloss over the aspects of our organizations that make us who we are. Isn’t it better to understand the full picture of the individual on the other end of a dating app before you make a long-term commitment.
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There’s a specific kind of strategy meeting getting scheduled right now, in nice hotels with bad coffee: the AI reset off-site.
And for good reason. In a 2026 WRITER survey, 48% of leaders described their AI rollout as, in their own words, a "massive disappointment." That's nearly half the room.
What that number really measures is the distance between what these tools can do and what people are doing with them. In our experience, that distance is almost entirely human.
Which is why the off-site is the right instinct. Making the most of that time is the harder part.
What separates an AI reset that actually changes the game from an expensive two-day conversation? In our experience, it comes down to avoiding four common design mistakes.
Mistake 1
Blaming the bots
The gap between AI investment and real adoption is almost always about people, not technology. And when adoption stalls, we usually find it's one of four things.
- They don't think it will help them
- Nobody around them is using it
- They don't feel capable
- Or they don't have real access to the tools they were promised
Four different problems, and four completely different fixes.
That's why diagnosis comes first. If you don't know which barrier you're dealing with, every intervention becomes an educated guess. And you cannot tell which one you have by staring at a dashboard. A belief gap and a skill gap look identical in a status report and need opposite interventions. Show up guessing, and you'll spend real money teaching people to use a tool they simply don't trust yet. Congratulations - you've just catered the wrong conversation.
Mistake 2
Letting leaders off the hook
One of the biggest predictors of whether change sticks is also one of the most overlooked: leadership.
If your executives show up as observers, nodding along and quietly answering email under the table, your people clock it in about four minutes.
That doesn't mean your CEO has to emcee the thing. It means they use the tools in front of everyone, participate in the conversation, and make it clear this isn't someone else's initiative.
Recently we’ve been working with a Fortune 200 global professional services firm who’s top 120 leaders were at very different points with AI. Some were redesigning entire processes. Others were using it to summarize emails, or not at all. Rather than focus on the technology, the four-hour session focused on what leaders could do with AI, applying it to a live strategic challenge and ending with a personal commitment to lead differently. The response was strong enough that the organization is now cascading the experience globally.
The lesson is simple: when leaders experience AI as a strategic capability, they're better equipped to model the behavior that makes adoption stick. Nothing you build during those two days survives without that entire chain of leadership doing its part.
Mistake 3
Chasing the wrong outcome
Without a behavioral baseline, you have no way to prove anything actually moved. No baseline, no ROI. You're just hoping the energy in the room was good, which is a wonderful feeling and a terrible metric to bring to your CFO.
But the baseline isn't just about proving the off-site worked. It's about understanding where you're starting in the first place. And you'll want that clarity, because the quiet resistance is real. In that same 2026 research, nearly a third of employees admitted to actively working around their company's AI strategy. If you don't win their belief in the room, some of them will keep politely ignoring the whole thing from their desks. You can't measure your way out of that. You have to earn your way out of it.
Which brings us to the biggest reframe of all.
Mistake 4
Leaving follow-through to chance
We've been working with a Fortune 100 medical device company on their AI strategy for three years. It started with their leadership team, a three-hour session built around what those leaders would do differently, and it landed. What became clear afterward was that the same experience needed to happen everywhere else. So, it expanded: 90-minute activations for 15,000 people, and this year intact teams redesigning their own workflows.
Three years in, that first session is the smallest part of the story.
Your event is where momentum gets created. What happens at 30, 60, and 90 days is where results get made.
If you're planning one of these and want to change what happens on Monday, not just how everyone feels on Friday, that the work we do.
We'd be glad to help you design it.
Candidates now arrive at interviews pre-coached by AI, with their resumes optimized to pass every checkpoint. Polish has stopped being a signal. The traditional hiring process was built to read exactly the cues that AI is now best at producing, and the signals hiring managers once relied on have weakened as a result. And for roles where the wrong hire carries real business consequences, losing the ability to tell who will actually perform is not a minor inconvenience. It is a material risk, and it exposes the business to unnecessary turnover, reduced performance, and heavier investment for talent growth and development.
So how do you observe the behaviors that matter most, before someone is in the role?
Not by asking better questions, but rather by putting candidates in situations designed to elicit that behavior.
The limits of predicting from paper
Credentials tell you what someone has done. Structured interviews tell you what someone says they would do. Neither lets you observe what they actually do in the moments that count.
This distinction matters most in client-facing, relationship-driven roles, where the performance gap between a strong hire and a weak one plays out in real business outcomes (revenue, retention, client growth). Organizations that hire at scale in these roles carry that gap across hundreds of decisions at a time.
The better approach is to watch candidates do the work before you hire them. Put them in simulated, role-relevant scenarios, and pair the simulation with a second, different kind of measure so no single method carries the whole decision. That combination is what lets you evaluate real performance before anyone is in the role. Organization-specific simulations provide a clear read on who is ready and capable of performing on day one. In a world of AI-supported candidate signals, the use of simulations makes the process harder to prep for. It is harder to fake. And, when designed well, it is substantially more predictive than other hiring methods.
What counts as evidence
Claims about predictive power are easy to make. Evidence for them is rarer than you would expect.
A predictive validity study, the kind that links pre-hire assessment scores to how someone actually performs once hired, is some of the hardest evidence to produce and the rarest to see. Many assessments are validated against proxies: another test, or a theoretical model of the role, rather than real results on the job. Connecting scores to concrete business outcomes and doing the statistical work to show the link holds, takes years of shared data and a level of commitment from both the assessment provider and the client that most partnerships never reach. That is precisely why it is worth asking for. A provider who can show how assessment scores track to training completion, retention, and first-year output is offering something categorically different from one who can only show a correlation with another test.
Why simulation holds up where other methods do not
When a candidate sits across from a trained assessor (someone playing the client or prospect on the other side of the conversation) and has to work through a real situation, they cannot rely on a rehearsed answer. The scenario is specific. The stakes feel real. What you see is close to what you would get on the job.
That is the value of simulation-based assessment: it does not test what candidates know about the role.
It shows how they use what they know when a real person is on the other side of the conversation, before the stakes are real.
For roles that carry significant business responsibility, this distinction is the whole game. The cost of the wrong hire in a high-stakes client-facing role is not just a missed quota for a quarter - It plays out in relationships that do not develop, clients who leave, and productivity losses that compound over time. Getting those hiring decisions right, at scale, with consistency, requires methods that are built for predictive accuracy, not just candidate experience or hiring speed.
What this means for how organizations think about hiring
Most organizations are still optimizing the wrong things in their hiring process. They invest heavily in employer branding, application flow, and interview structure, all of which matter, but less in the core question: does our hiring process actually predict who will succeed in this role?
AI has sharpened the stakes here. If every candidate can present as polished and prepared, screening based on presentation becomes less useful. What holds up is direct observation of the behaviors that the job requires.
A few principles worth building from:
- Measure what the job requires, not what is easy to measure. Cognitive tests and personality questionnaires have their place, but they do not look much like the job. The closer the assessment is to the actual work, the better it predicts performance in it.
- Ask what your assessment predicts. Training completion? Retention? First-year output? Most organizations cannot answer that question today, largely because providers have rarely been asked to prove it. It is a fair thing to ask for.
- Take the human element seriously. In a simulation, a candidate is having a real conversation, responding in real time, navigating a situation that requires judgment. Even with the help of AI, that is hard to game. And it remains one of the strongest predictors of on-the-job performance available.
The data exists to make hiring decisions more accurate, fairer, and more directly tied to business outcomes. For organizations operating in high-stakes roles at scale, there is too much on the line to rely on methods that cannot hold up to that standard.
You may be interested in BTS’ thought leadership in the five talent shifts AI is forcing now.
La IA ya forma parte del día a día de las ventas. Hoy cualquier asesor puede llegar a una reunión con datos, tendencias e insights generados en segundos. Sin embargo, disponer de más información no garantiza conversaciones de mayor valor.
A través de una experiencia real con un consultor comercial, este artículo explica por qué la inteligencia artificial funciona como un GPS: ayuda a interpretar el entorno, pero no conduce la conversación ni entiende las prioridades del cliente.
En este artículo descubrirás:
- Por qué el acceso a la información ya no supone una ventaja competitiva.
- La importancia del business acumen para interpretar los datos con criterio.
- Cómo hablar el lenguaje del cliente genera credibilidad y diferenciación.
- Por qué las relaciones B2B evolucionan hacia relaciones P2P basadas en la confianza.
- Qué capacidades consultivas seguirán siendo exclusivamente humanas incluso en la era de la IA.
La tecnología seguirá evolucionando, pero la ventaja competitiva estará en quienes sean capaces de combinar inteligencia artificial con conversaciones centradas en el cliente, pensamiento estratégico y relaciones de largo plazo.