Ignite Your GTM With AI, Chapter 9: The End of "Filing Cabinet" Enablement and the Rise of Readiness Architecture

Welcome back to our deep dive into "Ignite Your GTM With AI." We are moving through the gears of the modern Go-To-Market engine, and today we arrive at a function that is currently undergoing an existential crisis—and a radical rebirth.

We are talking about Chapter 9: Coaching & Enablement: AI-Driven Skills Development.

If you work in revenue leadership or enablement, you likely feel a specific kind of tension. You have more content than ever. You have more tools than ever. Yet, you have a nagging suspicion that your teams are less prepared than they were five years ago.

To tackle this paradox, we brought in the heavy hitters for this chapter: Toby Carrington (CBO at Seismic), Sheevaun Thatcher (VP of Enablement at Demandbase), Christina Brady (CEO at Luster), and Amanda Whiteside (Managing Director at Uber).

They didn't mince words. In fact, the chapter opens with a declaration that might make some executives squirm, but will make most practitioners nod in exhausted agreement.

Traditional sales enablement is dead.

But what rises from its ashes is something far more powerful. It is no longer about managing files; it is about architecting readiness.

The "Filing Cabinet" Problem and the Diagnostic Crisis

Sheevaun Thatcher rips the band-aid off immediately. She argues that enablement has died because it morphed into a "massive filing cabinet of stuff."

Over the last decade, organizations optimized for creation and storage. We built libraries of PDFs, decks, and battle cards. We tagged them poorly. We ignored version control. We buried our sellers under an avalanche of information and then wondered why they couldn't find the one slide that mattered during a negotiation.

This created a Reactionary Trap.

Enablement teams, originally designed to be strategic partners, were forced into the role of emergency response units. Sales leaders, panicked by a bad quarter, would demand "more training" or "more content." Enablement would scramble to produce it. The result? A frantic cycle of activity that looked like work but didn't produce revenue.

But the content chaos is just a symptom. Christina Brady identifies a much more dangerous disease: The Diagnostic Crisis.

She compares the current state of coaching to a visit to a terrifyingly incompetent doctor. Imagine walking into a clinic, telling the doctor your symptoms, and having them look at you and ask, "So, what do you think you have?"

You might guess, "I don't know... maybe the flu?"

And the doctor replies, "Great. I'm going to ask all my patients today what they think they have. If most people say 'the flu,' we will treat everyone for the flu."

It sounds absurd, but this is exactly how most GTM organizations run skills development. We rely on subjective self-assessments or broad-brush manager intuition. We treat everyone for "negotiation issues" because three loud reps complained about pricing pushback, while ignoring the silent majority who are failing because of poor discovery skills.

We are guessing at the cure because we never properly diagnosed the disease.

The Readiness Gap: Why Completion ≠ Competency

While enablement teams are fighting the content fires, a silent killer is eroding revenue. Toby Carrington calls it the Readiness Gap.

For years, we have conflated two very different metrics: Training Completion and True Readiness.

We assign a course in the LMS. The rep watches the videos (at 2x speed), answers the quiz, and gets a green checkmark. The system says they are "enabled." The dashboard looks green.

But are they ready?

Can they handle a buying committee of 13 stakeholders (the current Gartner average)? Can they pivot when a competitor is mentioned in a live call? Can they articulate value when the CFO enters the Zoom room unexpectedly?

The LMS doesn't know.

This gap between completing a task and possessing a capability is where revenue goes to die. As long as enablement is measured by "butts in seats" or "modules completed," we are optimizing for compliance, not performance.

The Shift: From Reactive to Predictive Enablement

The core thesis of Chapter 9 is that AI allows us to flip this model on its head. We are moving from Reactive Enablement to Predictive Enablement.

In the old world (Reactive), the cycle looked like this:

1. A mistake happens (deal lost).
2. A post-mortem is conducted.
3. Retraining is assigned.

You are always cleaning up a mess that has already cost you money.

In the new world (Predictive), the cycle looks like this:

1. A signal is detected (behavioral pattern).
2. An intervention is triggered automatically.
3. The mistake is avoided.

Amanda Whiteside describes this as the realization of the "Just-in-Time" learning promise we've been hearing about for years. It’s not about drowning reps in content during onboarding and hoping they remember it six months later. It’s about pushing the exact insight they need, exactly when they need it.

But how do you actually build this? The chapter outlines four dimensions of the new Intelligence Architecture.

1. Context Engineering: Training on Your "Good"

Generic sales advice is useless. Your reps don't need to know how to "sell better"; they need to know how to sell your product to your market against your competitors.

Context Engineering is the process of training your AI models on your specific definition of excellence. This means feeding the system your best sales calls, your product marketing materials, your HR competencies, and your winning deal autopsies.

When you do this, AI stops giving generic advice like "be more empathetic" and starts giving role-based feedback: "Your tone became aggressive when the customer asked about security compliance. In successful deals at this stage, top performers pivot to the 'Trust Center' document rather than arguing the point."

2. Human-AI Orchestration

There is a fear that AI will replace the manager. Sheevaun Thatcher argues the opposite: AI saves the manager.

Frontline managers are currently overwhelmed. They don't have time to listen to every call or role-play every scenario. Consequently, they don't coach.

The new orchestration model splits the labor.

• AI takes the "Drills": AI is perfect for identifying gaps, running diagnostic simulations, and helping reps practice scripts or objection handling in a safe, private environment. It never gets tired, and it's available at 2 AM.
• Humans take the "Nuance": Once the AI identifies the gap and handles the basic reps, the manager steps in for the high-value coaching. They work on the human connection, the strategy, and the complex soft skills that machines can't replicate.

3. Signals Mapping

This is the technical heartbeat of the new architecture.

Christina Brady explains that you can prioritize coaching based on risk. "You may have critical skill gaps for the $20,000 deal... but we can't uplevel you on everything all the time. So we're going to prioritize the $200,000 deal... where we know there's going to be erosion."

Signals Mapping involves connecting the dots between disparate data points. It’s knowing that Rep A has a struggle with "Technical Discovery" (Signal from Call Intelligence) and they have a meeting tomorrow with a CTO (Signal from Calendar) on a Stage 4 deal (Signal from CRM).

The system sees the collision course before it happens and intervenes.

4. Transparent Reasoning

Finally, we move from "gut feel" to data.

Toby Carrington emphasizes that we must stop assuming causation where there is only correlation. AI allows us to look at the highest-performing reps and work backward. What activities do they actually do?

This moves performance reviews from a subjective debate ("I think I'm doing well") to an objective analysis ("The data shows your discovery win-rate is in the top 10%, but your negotiation velocity is in the bottom 5%").

The Pitfalls of the New Era

The chapter warns against the "AI as Magic Solution" fallacy.

You cannot simply buy an AI coaching tool and expect miracles if your foundation is rotten. As Christina notes, large organizations are often shocked to realize they don't even have a "skills matrix" or a clear definition of what good looks like.

If you automate a broken process, you just break things faster.

Successful implementation requires a "crawl, walk, run" approach. Start by defining your competencies. Then, ensure your data is clean. Only then can you layer on the intelligence that drives predictive coaching.

Building the Nervous System for Readiness

This brings us to the "How."

Chapter 9 paints a beautiful picture of Signals Mapping and Just-in-Time intervention. But for most companies, this remains a fantasy because their data is locked in silos.

Your LMS knows the rep took a course. Your Calendar knows the rep has a meeting. Your CRM knows the deal value. Your Call Recorder knows the rep’s skill gap.

But none of these systems talk to each other.

This is the problem Momentum solves. We provide the Intelligence Architecture that turns these isolated signals into a cohesive, predictive nervous system.

You don't need a new LMS; you need an orchestration layer.

How Momentum Operationalizes Chapter 9:

• We Solve "Disconnected Signals": Momentum listens to your Calendar, Salesforce, Slack, and Call Intelligence simultaneously. We detect the pattern that no single tool can see.
• We Automate "Just-in-Time" Delivery: When Momentum detects that a rep with a known "Competitor X" weakness has a meeting with a prospect using "Competitor X," we don't wait for a manager to notice. We trigger an automated workflow that delivers the "Competitor X Battle Card" and a 2-minute micro-learning video directly to the rep via Slack, 30 minutes before the call.
• We Close the "Diagnostic Loop": Post-call, Momentum can ingest the summary and outcome. If the deal stalls, we update the risk profile. If it advances, we capture the successful behavior and feed it back into the "Context Engineering" loop for the rest of the team.
• We Power "Human-AI Orchestration": Instead of asking managers to "log into the dashboard," Momentum pushes a digest to the manager: "Sarah has a critical negotiation tomorrow. She has struggled with pricing objections recently. Here are three specific coaching points to cover in your 1:1 today."

We turn the passive "filing cabinet" of enablement into an active, predictive engine that pushes the right support to the right person at the exact moment of impact.

Stop Guessing. Start Architecting.

The days of "spray and pray" training are over. The days of guessing at skill gaps are over.

Chapter 9 proves that the technology now exists to ensure your team is measurably, verifiably ready for every interaction. But this future requires more than just buying tools; it requires building an architecture that connects your intelligence to your execution.

Don't let your revenue leak through the cracks of a disconnected tech stack.

To fully understand the four dimensions of AI coaching and see the blueprints for Readiness Architecture, you need to read the full chapter.

Get your copy of "Ignite Your GTM With AI" today.

And if you are ready to stop talking about predictive enablement and start building the infrastructure that makes it possible, let's talk.