Better forecasting accuracy is achievable – Here’s how
This article was originally published in Sales & Marketing Management.
The ability to accurately forecast results and behaviors has always been fundamental to business success. If a company can predict future outcomes, it will be able to leverage its skills and opportunities to increase revenue and make progress against its goals.
Greater predictability leads to more effective budgeting and planning so that leaders and teams can make decisions with greater confidence. For instance, if a company can accurately forecast the response of different demographics to its ongoing sales campaign, it can dynamically allocate resources to capitalize on these insights. This adaptive approach allows the business to optimize the performance of current campaigns and fine-tune the services offered, potentially opening pathways to introduce other products.
From a weekly to a quarterly to an annual scale, from market trends to supply chain visibility, predictions can help teams plan for success. This is always the case in business, but it is even more important today, as greater agility is needed due to changing circumstances.
What stands in the way of accurate sales forecasting?
Improving the accuracy of sales forecasts and building predictable revenue are top-of-mind challenges for chief revenue officers, and this pressure filters through the organization. So, what stands in the way? Why can’t teams find the predictability they need?Often, the problem comes from inaccurate assessments of deals. Sellers often do not correctly assess the size and the timing of the deals they have entered into their CRMs. They may not have adequately understood their clients’ needs or budgets.Another problem is misunderstanding signing authority. Often, a seller will misjudge the level of authority they need to close a deal. They’ve been talking to a mid-level leader who seems eager, not realizing that they will need stakeholder alignment and signoff from a senior executive in order to close. They may also need to get multiple peer executives to sign off on the purchase if the budget is not clearly owned by one buyer.A third issue that stands in the way of accurate forecasting is “super seller syndrome.”
How super seller syndrome hinders predictability
Super seller syndrome happens when sales leaders are promoted into their new roles not because they are great coaches but because they are themselves high achievers as sellers. The company needs sales, but the super seller may not have learned how to motivate, engage, and cultivate the introspection needed to effectively drive the team’s success.A team that prioritizes super sellers for promotions tends to suffer further down the road. Talent development stalls because leaders focus on selling rather than coaching, leading to a diminishment of other crucial skills like account planning, customer experience, and relationship management. Average sellers become disconnected, and trust disappears.
Make more accurate sales forecasts
People need strategies to create more predictability in their processes. This is especially true for sales teams and for the super sellers attempting to guide them.
Leverage AI for real-time coaching
AI is here for you now. It is a tool with broad applications and is getting safer and more advanced all the time. Sales teams can use AI in coaching and training situations to give sellers personalized, data-driven advice in a way that feels nonjudgmental and actionable. The advantages of real-time feedback are many. It can help leaders make their coaching more nuanced, help sellers gain control over deals, and turn customer insights into continuous learning opportunities.
Turn super sellers into super coaches
Selling skills don’t always translate into sales leadership skills. You can create greater forecasting accuracy by empowering leaders to develop the sales talent in their teams. This will include sales coaching, but it will also extend to communication skills, relationship management, the ability to use AI tools and integrate them into a CRM routine, and more.
Implement an opportunity summary sheet
A custom opportunity summary sheet can help sellers review and assess large and complex deals more accurately. Include technical, architectural, and service elements of the deal in the summary, and run this by the pre-sales team. Every deal at the proposal stage must then be examined by leadership to validate size, timing, negotiating points, and stakeholder signoff.
Build a culture of practice
Doing something more often creates a greater level of accuracy and predictability. A golfer who practices putting every day will develop an accurate sense of where the pin is and how hard she’s hitting the ball toward it. Practice the different sales stages — discovery, qualifying, negotiating — to give sellers opportunities to hone their skills in an accessible, low-pressure way.
Reward early, active CRM use
In some organizations, sellers wait until very late in the process to put deals into their CRMs. This may be due to an expectation of harsh feedback from leaders or an attempt to avoid the appearance of failure. A better way is to examine and encourage pipeline health at all sales stages. Sellers should learn lessons from losses, and the company needs to accept that if it is not losing, sellers are not taking necessary risks to grow the business.By addressing the factors hindering predictability, leaders can guide their teams toward success with visibility, clarity and purpose. The world is shifting as we speak. However, if teams can develop a culture of adaptability and togetherness, they will be able to spot the way forward.
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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.