Letting Some Fires Burn: Prioritize the AI Projects That Matter to Your Business

In The Qualified Sales Leader, John McMahon talks about selling above the noise. Seeing the whole forest.

Understanding which fires are worth firefighting — and which you can afford to let burn — so you can align your product and motion accordingly.

For RevOps leaders, the same mindset applies to AI implementation.

AI can do almost anything: route leads, write content, handle approvals, onboard customers. But just because it can doesn’t mean it should…at least not right now.

Before diving in, ask yourself: What does the business need right now?

If your company is over 50 people, a unified motion matters more than ever. Implementing AI can only add value if it connects to your existing operating model and is leveraged as a part of your GTM strategy. This is unique to your business.

Without this connectivity, it can add more noise than momentum.

Use This Matrix to Prioritize What to Do Now

Try mapping your AI initiatives to these four quadrants to assess what’s worth doing now, what needs planning, and what can wait. We’ll break it down with a few examples (below).

These are just examples and the use cases might fall into different quadrants at your business. Remember, all are worth doing if they are aligned to your overall strategy and the ROI is there.

🟢 DO NOW

Use case: AI lead handler for inbound demo requests

• Impact: High – speeds time-to-contact and reduces lead leakage

• Ease: Easy – clear handoff logic and CRM integration is ready

• Priority = High: This project is simple, ties directly to revenue, and aligns with the customer journey. It’s a no-brainer.

🔵 PLAN & SCOPE

Use case: AI approval assistant for pricing exceptions

• Impact: High – reduce manual work and unified enforcement of pricing and revenue growth.

• Ease: Hard – complex approval logic, CPQ dependencies, unclear policies, a lot of change management

• Priority = Medium: Can be extremely valuable, but needs upfront work and time spent trust-building. It’s worth planning out, but not worth rushing to get done.

🟡 DEFER TO LATER

Use case: Auto-generated account plans.

• Impact: Low – reps aren’t consistently using account plans to drive outcomes today, so automating this won’t do anything unless used to execute actions.

• Ease: Easy – simple to generate templates from CRM, enrichment, and news data.

• Priority = Low: While the output is quick and clean, it doesn't move revenue unless account planning is already embedded in the sales process.

🔴 LET IT BURN (for now)

Use case: Custom predictive lead or deal scoring engine

• Impact: Low – You don’t have the volume or process maturity to justify this level of complexity. Reps are able to get to all leads with the scoring model in place today.

• Ease: Hard – requires clean data, heavy modeling, and monitoring

• Priority = None: High complexity and high cost for questionable value. Your team can get most of the benefit from strong rules-based scoring and clean GTM execution.

Sequence with Purpose: The Timing and the Why Matter.

Every one of these projects might get done someday. But the order of priority and focus matters.

Start with (1) The biggest pain (2) The clearest revenue opportunity and (3) The easiest to execute with your current team and systems.

A unified strategy along the customer journey should always be your goal.

If the change doesn’t reduce friction, plug into your customer journey, or bring your GTM teams closer together…it’s not urgent. Let that fire burn.

Operators have been doing this for years.

Strategic sequencing of projects, thorough scoping, and incremental improvements that compound over time.

This is how you transform your business…one high-leverage step at a time.

BONUS - Want a tool to help prioritize and scope AI projects?

Use this checklist to evaluate whether an AI project is worth prioritizing.

The goal is to focus first on high-impact, executable changes that align with your GTM strategy and deliver real value.

✅ Is this aligned to a known pain point?
✅ Will this reduce rework or manual effort?
✅ Does this touch a core part of our customer journey?
✅ Do we have the right data and systems to support it?
✅ What's the potential revenue impact?
✅ Do we have the team capacity to implement this now?
✅ Will this create more unification or more complexity?
✅ Is the outcome clear and measurable?
✅ Is this reactive, or does this build toward our long-term AI vision?
✅ Can we scope and execute it without taking teams offline too long?