“We have one more shot” 7 reasons why CRM implementations fail and how to make yours a success

Today’s customers are more informed, more selective, and more time-poor. They need conversations that help them prioritize, decide, and move forward.

And yet, 58% of sales meetings fail to create real value.

Not because sellers lack capability, but because conversations are not designed to move decisions forward.

“Customers don’t act on every need they recognize.
They act when something becomes a priority.”

 In this short executive brief, you’ll discover

• Why most conversations inform… but don’t drive action
• What actually makes customers prioritize and move
• How to create urgency without damaging trust
• The shift from presenting solutions to enabling decisions
• What separates conversations that stall from those that accelerate momentum

If your teams are experiencing stalled deals, delayed decisions, or slow pipeline movement, this brief will help you understand why, and what to do differently.

In today's fast-changing business environment, excelling in Revenue Growth Management (RGM) is essential for Commercial Leaders aiming to boost revenue and profit, both now and in the future.

Unlike traditional methods that confine RGM to pricing actions, forward-looking Commercial Leaders recognize that activating a holistic, end-to-end RGM strategy that is consumer/shopper focused and customer-back, leads to more significant growth and allows leaders and teams to not only anticipate, but actively influence consumer demand and customer needs.

Historically, Revenue Growth Management (RGM) has been approached as a temporary and reactionary project, which was typically led by external experts in response to inflationary markets. This limited approach confined the benefits to a small part of the business and focused on short-term results, rather than embedding RGM as an ongoing, fundamental aspect of business strategy that could deliver sustained, long-term growth.

Today, mature RGM organizations treat RGM strategy and execution much differently, positioning the actions at the center of their strategic operations, embedding capabilities deeply within their organizational processes and ways of working. This transformation is not just procedural but is a shift that forces RGM strategy, tactics, and mindset into every action and function of the business.‍

Strategic integration of RGM at scale: A roadmap for success

1. Build strong in-house expertise: To see the scaled benefits of RGM, develop strong capabilities within your commercial teams and intermediate understanding of your cross-functional teams. When your leaders and teams fully grasp RGM tactics and mindsets, it creates scaled-impact that can be sustained without external reliance.
2. Encourage cross-functional collaboration: The effectiveness of RGM strategy and execution is only fully realized when it involves a fully cross-functional team. Promoting collaboration between sales, marketing, finance, R&D, and the supply chain enriches insights, strategy and execution feasibility, and organizational success.
3. Integrate RGM strategy into key business processes: By connecting RGM directly to critical operations such as budgeting and strategic planning, you ensure that RGM principles are woven into the fabric of annual planning instead of being treated as a one-time project. This integration influences everyday decisions and guides long-term business strategies.
4. Overcome implementation challenges with effective change management: Embracing a robust RGM approach involves substantial change and a shift in traditional revenue growth mindsets. Address these challenges through strong change management practices, aligning team incentives with new strategies and providing clear, successful examples of RGM in action to inspire and motivate your teams.

The competitive edge of building RGM capability across the organization:

1. Encouraging innovation from Consumer-Back: It’s no surprise that RGM should be activated starting with consumer and shopper insights. When truly building a strategy from the consumer-back, you build a mindset and process that is ripe for innovation. This helps your company stay competitive and lead industry trends and demands, instead of reacting.
2. Aligned decision-making for the short and long-term: A thorough RGM strategy speeds up and improves the day-to-day decision-making process of consumer and customer facing commercial teams. It helps ensure that decisions—like setting pricing strategies, choosing promotional activities, or allocating resources—are aligned with the market’s immediate needs and long-term goals for the category.
3. Boosting market responsiveness: In today’s volatile business climate, the ability to swiftly adapt to market changes is invaluable. Decentralizing RGM capabilities enables cross-functional local teams to be agile to market shifts in strategic ways, turning potential challenges into opportunities, while still staying aligned to the longer-term market objectives.
4. Cultivate a results-driven culture: Building RGM roles across the organization allows for greater ownership and accountability to improve revenue and ultimately grow market share. This means a greater population has a direct role to play in driving business performance and are responsible for keeping an external pulse on consumers, shoppers, and customers.

Implementing a cohesive RGM strategy, instilling the right mindsets, and providing the leaders and teams with the tools and processes needed to be successful, is no small feat. However, the revenue, profit, and market share impact can be substantial when an aligned RGM strategy is deployed at scale. This strategic commitment positions your company for enduring success and a powerful competitive advantage in today’s dynamic consumer, shopper, and customer landscape.

While small and medium-sized enterprises (SMEs) play a vital role in driving economic growth and innovation, they often face unique challenges when dealing with banks. This makes relationship banking crucial to their success. By strengthening their relationship banking models, banks can differentiate themselves from competitors by improving the support they provide to SMEs, helping these businesses overcome challenges and thrive in the marketplace. In discussions with owners of SMEs about their experiences with banks, four common concerns emerge:

1. Access to credit. Obtaining financing for purposes such as working capital, expansion, or equipment purchase is a significant challenge for SMEs. Banks often perceive them as riskier borrowers due to their limited credit history, lack of collateral, or volatile revenue streams, which can make it difficult to secure loans.
2. High interest rates and fees. SMEs may face higher interest rates and fees compared to larger, more established businesses due to banks' perception of greater default risk, as well as limited financial transaction volumes.
3. Complex application processes. SMEs often face time-consuming and complex loan application processes, requiring extensive documentation such as financial statements, tax returns, and business plans.
4. Inflexible lending terms. SMEs may struggle with inflexible lending terms, including strict collateral requirements, short repayment periods, or covenants that restrict operational flexibility. These terms can make it difficult for SMEs to manage cash flow and invest in growth.

By adopting the following approach, relationship managers can help businesses overcome these challenges:

• Advocate for clients within the bank, helping them secure financing for business expansion, capital investments, working capital growth, and asset accumulation.
• Offer guidance on optimizing cash resources within the constraint of limited capital resources.
• Provide advice on managing personal wealth accumulated through business ownership.

This approach requires a set of knowledge and capabilities:

• Business acumen—an understanding of SMEs’ unique needs and business challenges.
• Recognition of the essential role cash flow plays in small business success and an understanding of how to optimize it.
• Familiarity with the financial impact of bank products on SMEs' finances.
• Understanding of small business funding models, including the roles of owners, banks, and investors.
• Insight into the migration of SMEs to medium-sized enterprises.

Equipped with these capabilities and this knowledge, bankers can employ critical relationship management skills at four key stages:

• Planning. Gain local market knowledge and industry/sector expertise before engaging with clients.
• Discovery. Approach SMEs with a focus on their unique needs, recognizing the distinct characteristics of owner-managed and owner-financed businesses.
• Engagement. Position offerings from a client-impact perspective, rather than a bank- product perspective, addressing the specific needs and challenges of SMEs.
• Closing. Adopt a partnering approach and act as an advocate for SME clients within the bank, particularly when dealing with credit functions and decision-makers.

By focusing on these areas, banks can enhance their relationship banking model for SME customers, providing personalized support and tailored financial solutions to help small businesses succeed in a competitive landscape.

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.  

‍

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.