Develop a staying, growing, thriving culture
HR Daily Advisor editorial...In a rapidly changing work environment, organisations grapple with retaining quality talent amidst an exhausted workforce, shrinking budgets, and an uncertain economy. The desire to stick around is decreasing among employees.According to Gartner, employee attrition averaged around 20% in 2022. The evolving landscape of employment—characterised by a surge in remote working, the loss of critical knowledge due to an aging workforce, and a demand for purpose-driven roles—underscores the need for a fresh approach.In fact, purpose has arisen as an important driver for employees because it connects individuals to their companies.Traditional development systems no longer suffice. Instead, companies must democratise and personalise learning at scale, fostering a culture that aligns with both individual and organizational purposes. While 83% of business leaders agree that development is important at every level, only 5% of businesses have implemented development initiatives at all levels.As jobs and careers undergo transformative shifts and the lines between global and local blur, businesses must adapt. The significance of a purposeful learning culture, its impact on retention, and practical strategies for its implementation become paramount.It’s not just about equipping employees with skills. It’s about giving them a reason to stay, grow, and thrive.Here are three steps for implementing a learning culture throughout an organisation...
Step 1: Aim For Personalisation in Corporate Learning
The world of work progresses at a breakneck speed. To keep up with change, employees and teams need to consistently reskill. Though investments in learning and leadership development are at an all-time high, 70% of employees surveyed by Workplace Intelligence feel unprepared for the future of work.One size doesn’t fit all when it comes to learning. Personalisation enables learners to focus on areas where they need the most improvement, allowing for targeted skill-building and efficient use of time and resources. This requires a blended and modular approach to give all learners access to training and materials at the right moments to unleash their potential.Sadly, many learning programs prioritise ease of implementation and compliance, employing a rigid design suited for traditional learning academies and generic perspectives. Modern learners demand flexibility, including full access to quality materials, opportunities for exploration, and learning from others.Democratising materials and personalising learning at scale across verticals can be challenging. However, adopting a more self-directed, human-centered approach is vital for the future of learning. Embracing technology to make learning tools and experiences accessible and relevant to everyone empowers workers to cultivate their skills and foster a stronger connection to their companies, reducing turnover.
Step 2: Create Ownership of Learning and Reward Curiosity
With a learning culture, every member of an organization must lead by example. It’s not enough to 'sell and tell' a learning strategy. People—including the executive team members—need to know the 'why?' behind learning.All must feel a deeper commitment to the outcomes and impact of knowledge improvement. Individuals must perceive and experience the rewards of investing time and energy in learning.For instance, when everyone develops their business and technology acumen, the path to digital transformation becomes smoother. Corresponding productivity gains benefit both employees and the business.The cause and effect of learning on business results must be highlighted and rewarded.Rewarding curiosity goes beyond praising and promoting those who show eagerness to learn. It also involves cultivating an environment that nurtures critical thinking, where debates and voicing opinions are encouraged, even if it leads to disagreement. As an added advantage, transparency in learning and development fosters psychological safety. Employees understand that they are encouraged to enhance their skills and won’t face penalties for applying newly acquired knowledge, even if the outcomes are unexpected or undesirable.Rather than fearing excessive innovation, employees will be motivated to present novel strategies. Ultimately, this strengthens their connection with their work and the organisation’s culture.This doesn’t mean that there shouldn’t be a balanced perspective between learning, testing, and relearning. An organization’s strategy and culture need to be mutually reinforced by means such as finding an equilibrium between accountability for progress while allowing the experience to be rich in positive, authentic coaching and feedback.
Step 3: Design Learning Portfolio Offerings Rich in Community, Experiences, and Content
More than 50% of employees who work remotely at least some of the time say they feel disconnected from their colleagues. Compounding this feeling, many learning strategies actively scale out human connections through technology. A well-balanced learning and development strategy will stimulate a learning culture when it optimises for the right mix of three key things: community, experience, and content.
- Communities are the best way to deliver lasting change because they create a connection between people and accountability
- Experiences are one of the most effective ways to disrupt mindsets and create the capacity to change
- Content is the foundation for guiding and reinforcing perspectives and ways of working
Getting this balance is critical and should be the top priority for any learning organisation.One method of determining if participants are finding meaning from a learning portfolio is by measuring the impact through employee engagement surveys and similar vehicles. Together, the vehicles should measure three categories: the head, the heart, and the hands. In terms of the head, measurements should identify if the learning unlocks people’s intelligence so they can contribute to the company’s mission of outstripping the competition.When it comes to the heart, the measurement should reveal whether employees are happy. As for the hands, the measuring device needs to indicate whether training has prompted productivity and performance.Most companies accept that training their people is essential. However, far too many leaders haven’t changed their learning and development focus in years. That’s a liability in a modern labour market where talented individuals are quick to switch jobs.The better way to ensure more retention and higher engagement is to invest in purpose-rich training that benefits all parties and creates a dynamic learning culture.
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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.