AI and GTM: An operator’s playbook for doing the hard things

AI is rapidly changing go-to-market (GTM) strategies, but the best operators know what can’t be automated. Here are three hard-won lessons AI won’t replace.

First-time caller, long-time listener.

I’m a former operator turned IC.

Yes – this is another AI article but no – it will not end with me asking you to type “AI GTM” in the comments for a playbook.

This article captures a few of my musings from my days building internally and with clients. I’m maniacal about building impactful things (eg – recurring revenue > recurring impact – shoutout to Jacco) & AI took that to another level.

But I’ve also learned one thing the hard way: AI can’t do the job you’re supposed to own.

Everyone feels the pressure. Boards, investors, peers; to “do something with AI.” That’s good. You need AI to win. But you can’t get lazy and hope it bails you out.

My Experience Building GTM Teams in Sales Environments

Before this role, I spent years in the weeds of GTM and tech adoption. Operationalized GTM plans. 0 to 1. “From nothing to something”. This is what energizes me.

• At a global industrial tech company, I helped stand up a GTM Center of Excellence and rebuild a broken forecast process. Had to untangle legacy systems and competing global business units just to get everyone on the same page.
• At a global financial services provider, I built a digital sales playbook, trained 75 sellers, tied activity to revenue. And did it in a highly regulated environment while stitching engagement tech into workflows for analysts, SDRs, and AEs.
• At a leading SaaS company, I worked with SDRs drowning in volume. We ripped out the “spray and pray” mindset, rebuilt outbound around fewer, better sequences, and fixed incentives.

Different industries. Same theme: tools only work if you do the hard work of wiring them into a strategy that makes sense.

How I Scaled Go-to-Market with AI

In another operating role, I set out to modernize how commercial teams worked by deploying AI across the go-to-market engine. I introduced Clay for one-to-one personalized outreach, replacing hours of manual prospect research.

We connected LLMs and APIs through n8n MCP servers to orchestrate workflows that could automatically generate webinars and content. We even began developing an ‘AI sales engineer’ capable of joining live calls and handling highly technical questions in real time.

But something that I noticed, not just from our best clients but from my own experience implementing and using AI daily, was this: the teams who got the most out of AI all had the same three things in common.”

Three Hard Things AI Won’t Replace in Sales Leadership

1. Define and calibrate what “good” looks like.

Don’t guess — dig into closed-won deals and understand the cadence and behaviors that actually got them across the line. From how many “licks it takes to get to the center of a Tootsie Pop” (how many activities it takes to book a meeting), to what reliably moves deals from stage to stage: how many calls, how many discovery meetings, how many texts, what kinds of questions, and how many of them.

And it’s not just about counting touches; it’s about recognizing patterns. Did a deal close because a rep asked three budget questions, or because they pulled in the CFO at the right time? Did velocity increase because of more emails and texts, or because of a well-timed multithread to IT? If you can’t answer those questions, you’re flying blind.

The best operators establish a common language so reps, managers, and execs all know what “good” looks like. Without it, you can’t model capacity, you can’t calibrate CAC, and you can’t prioritize enablement. AI can help — it can scan thousands of calls, highlight activity patterns, and show you where stage conversions accelerate or stall. But it won’t tell you what “good” should be for your team. That part is on you. Only you can define the standard; AI simply makes it faster to analyze, validate, and scale once you’ve set the bar.

2. Obsess over coaching, precisely in a time when you’re tempted to delegate it to AI.

Yes, we’re talking about practice. I live in the City of Brotherly Love, so AI here means Allen Iverson before it means artificial intelligence. Practice means being in the gym shooting free throws. Steph Curry didn’t wake up one morning and all of a sudden start draining threes (sure, some of that is natural-born talent) he was in the gym, reps on reps.

The best leaders treat coaching that way. Feedback is constant, practice is routine, reinforcement is non-negotiable. It’s slow, it’s not sexy, but it’s the biggest predictor of sustained sales success.

I get it. Handing off coaching to AI was my instinct as well. But that’s missing the point. I’m not talking about silver-bullet feedback that blows everybody’s mind; I’m talking about consistent reinforcement: gathering data points on how your sellers show up, spotting themes and trends, and giving feedback you’re uniquely positioned to provide. Feedback you know will resonate; the kind that sticks and makes the next call better.

This is the type of coaching that matters. And it’s not about outsourcing it to AI; it’s about letting AI do the tactical work. From sifting recordings, surfacing patterns, drafting analyses, so you can focus on the moments only you can deliver. That’s how you supercharge your ability to coach and make a lasting impact.

How many times has your calendar block for call listening been chewed up by five ad-hoc fires, two reporting meetings, or a last-minute request? By the time you get to your one-on-one, you’re scrambling. Small talk, generic check-ins, maybe even rambling because you didn’t come prepared.

Now imagine the opposite: instead of one out of four one-on-ones in a month where you deliver real, executable feedback, it’s four out of four. Not massive revelations, but small, consistent adjustments. What would that do for your rep? What would that do for their quota attainment? What would that do for their wallet?

3. Own your pipeline and forecast.

The best Sales Leaders and RevOps leaders don’t just stare at dashboards; they roll up their sleeves and inspect deals. They ask the tough questions and know buyer dynamics cold. They know who’s really in the room, where the budget actually sits, and what objections are coming. That’s why they forecast within ±5%. It’s not magic; it’s discipline.

Owning the forecast isn’t about interpreting fields in SFDC or calculating weighted percentages; it’s about digging into deals and knowing instinctively: this buyer isn’t sold, this champion is weak, this deal is real. That discipline pays off because it lets leaders allocate resources. Marketing dollars, hiring plans, exec attention before the quarter blows up.

AI can help here too. It can automatically flag missing champions, highlight when deal velocity slows, and predict risks earlier. It can synthesize hours of calls into a digestible pattern in minutes. But AI won’t own the call reviews for you, and it won’t have the conversation with a rep where you say, “This is real, this is fluff, here’s what you need to do.” That part is yours.

AI accelerates the inspection; you still own the judgment. And the judgment of knowing your pipeline dynamics & buyer/seller dynamics better than anyone else is what separates leaders who miss by 20% from the ones who land within ±5%.

AI Saves Time, But You Still Have to Do the Work

AI isn’t the enemy of fundamentals; it’s an accelerant. It saves time, gives leverage, and opens up new plays. But it’s not a substitute for doing the hard things.

The teams winning with AI define what good looks like, they coach like crazy, they own their pipeline – then they let AI scale their strategy & execution.

Real transformation happens when you don’t outsource your responsibility & engineer AI to work for you.

AI is wildly impactful, but you still have to do the hard things.

This edition was curated by Chase Schardt, Marketing Manager at Revenue.io, and written by Guirae Jang, Director of Partnerships at Revenue.io.

You can find Guirae Jang on LinkedIn here!

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