Why Your Enterprise Sales Team is Throwing Money Into a Leaky Bucket

A conversation with Patrick Monnot, founder of Pod, reveals how AI deal coaching is transforming complex B2B sales by focusing on effectiveness over automation. You can find the full transcript here.

Organizations spend millions on lead generation, intent data, and top-of-funnel activities. Marketing teams optimize campaigns, SDRs work the phones, and qualified leads flow into the pipeline. Then something curious happens: once that hard-earned lead reaches an account executive, the investment in guidance and optimization drops dramatically.

This is what Patrick Monnot, founder of Pod, calls "throwing money into a leaky bucket." After years in go-to-market operations and extensive conversations with enterprise account executives, he's built an AI deal intelligence platform that addresses a fundamental problem in B2B sales: effectiveness.

The Complex Sales Problem

Enterprise sales isn't about volume—it's about precision. When you're working million-dollar deals with long sales cycles and complex buying committees, the margin for error shrinks dramatically. Yet many AEs navigate these high-stakes situations with limited guidance.

"A lot of reps end up making mistakes and losing deals because they lack the guidance and the know-how on how to navigate these complex deals," Patrick explains. "They don't engage with the right stakeholders. They don't prioritize the right deal. They don't know how to engage with these stakeholders. They drop the ball on day-to-day deal execution."

The result? Missed opportunities and lower win rates, despite significant investment in generating those leads in the first place.

AI as Effectiveness Multiplier, Not Replacement

Pod's approach deliberately avoids the automation-first mentality that dominates much of sales tech. Instead, it functions as what Patrick calls "the equivalent of the virtual top sales performer" that guides reps on the best actions to take.

"It's not about automation. It's about effectiveness, helping them make better, faster and smarter decisions in their day-to-day," he emphasizes.

This distinction matters. In complex B2B sales, human relationships remain paramount. You're not signing a million dollar contract with an automated robot, you're talking to someone, you have to look at them in the eye, build trust, build rapport, build partnership.

The platform connects to existing data sources—CRM, email, calendar, call recordings, social media—to provide specific, actionable guidance: which deals to prioritize and exactly what actions to take to improve win probability and velocity.

Making Sales Frameworks Actually Work

Most sales organizations have heard of MEDDIC, SPIN, Challenger Sale, or other methodologies. They train their teams, add fields to Salesforce, and declare the framework "implemented." Yet when you talk to reps and managers on the ground, they'll tell you the data isn't trusted or used.

"Sales methodology frameworks have to be used in-game, right? Like in the moment to guide the rep," Patrick notes. "Before a call or after a call, or to diagnose the health or progress of the deal."

Pod operationalizes these frameworks by analyzing each deal against the chosen methodology and identifying gaps. For a MEDDIC framework, it might show how well each element has been covered and suggest specific next steps to improve understanding of economic buyers or decision criteria.

This transforms frameworks from static training concepts into dynamic coaching tools that actually influence daily activities.

The Buying Committee Obsession

One of Patrick's key insights centers on what differentiates top performers: their obsession with buying committees. While many organizations stop at identifying an ICP ("we sell to VPs of Marketing"), enterprise deals involve multiple stakeholders with varying levels of influence and different concerns.

"The best sales reps or account executives in B2B sales are ones that are very intentional and obsessed with their buying committee in every deal," Patrick observes. "They don't just walk into it and navigate willy-nilly, but they put a lot of thoughts into who should be on the buying committee."

This means understanding not just who's involved, but their sentiment, concerns, and what business case needs to be built for each department. The VP of Marketing might champion your solution, but IT needs security assurances, finance wants ROI justification, and procurement will scrutinize contract terms.

Pod's contact sentiment feature tracks this complexity, showing reps when a champion's enthusiasm is trending down or when a previously neutral stakeholder is becoming more engaged. This allows for proactive intervention rather than reactive damage control.

Learning from Historical Data

Rather than treating every deal as completely unique, Patrick advocates for pattern recognition based on historical performance. "If you look at a thousand deals that your company has closed in the past, identify pockets of organizations or deals that have similar characteristics."

This data-driven approach reveals trends: which buying committee compositions correlate with closed deals versus losses, when certain stakeholders typically get involved, and what engagement patterns predict success.

"Every organization is unique," Patrick acknowledges, "but if you look at a large enough data set with enough statistical relevance, you're able to draw very interesting insights."

The Problem-First Philosophy

Patrick's background spanning consulting, startup operations, and product management taught him a crucial lesson that applies beyond sales: spend more time defining the problem before jumping to solutions.

"Everyone should spend a lot more time than you think defining the problem that you're trying to solve, before jumping into the solution, because the solution, once you've really well understood and defined the problem, is going to be much easier to define."

This philosophy extends to how organizations should evaluate sales technology. Rather than starting with "we found this great tool," the conversation should begin with understanding specific challenges, quantifying their impact, and only then exploring solutions.

Human Skills in an AI World

The conversation reveals an important nuance about AI in sales. While technology can provide data, insights, and recommendations, the fundamentally human aspects of selling—building trust, active listening, reading between the lines—remain irreplaceable.

Patrick frames this as augmentation rather than automation: "AI is used to make that job easier so that the rep can focus on the truly human part of their work more often or a bigger percentage of their time."

The goal isn't to remove human judgment but to enhance it with better information and more systematic approaches to complex decisions.

The Effectiveness Gap

The broader implication of Patrick's work points to a systematic underinvestment in sales effectiveness. Organizations optimize extensively at the top of the funnel but provide limited support for converting qualified opportunities into closed deals.

This represents both a risk and an opportunity. The risk is that substantial lead generation investments get wasted through poor deal execution. The opportunity is that improving effectiveness at the bottom of the funnel can dramatically improve ROI on existing marketing spend.

Looking Forward

As AI capabilities continue advancing, the temptation will be to automate more of the sales process. Patrick's experience suggests a different path: using AI to make human sellers more effective rather than replacing human judgment entirely.

Until we have robots buying from robots, it has to be human to human. The winning approach combines technological insights with human relationship-building skills, systematic methodologies with adaptive execution, and data-driven guidance with intuitive understanding of buyer psychology.

For sales leaders, this means thinking beyond lead generation metrics to conversion effectiveness. It means implementing frameworks that actually guide daily activities rather than just populating CRM fields. And it means recognizing that in complex B2B sales, the human element isn't a weakness to be automated away—it's a competitive advantage to be amplified.

The leaky bucket can be fixed. But it requires acknowledging that effectiveness, not just efficiency, determines whether your sales investment pays off.

Patrick Monnot is the founder of Pod, an AI deal intelligence platform for B2B sales teams. Learn more about Pod and their approach to sales effectiveness at their website.

Learn More

• Get a free demo of Pod on their website

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