'If You Wait 18 Months, You're Dead': Why AI Success Demands Leading Indicators

As organizations acquire sophisticated tool stacks, teams continue to retreat to legacy habits. The technology promises scale and speed, but it's effectively dead-on arrival as teams refuse to adopt.

I recently spoke with Jason Hochman, who scaled Anaconda's revenue team from 10 to 100 and revenue from $50M to $160M. We bypassed model parameters to dissect the operational bottlenecks: change management, tool fatigue, and workflow friction.

"Change management is the most important thing of any implementation. No matter what your software is... if you don't actually get them adopting it and using it, it's a complete waste of time."

This article provides the 3 lessons for navigating the operational side of the AI transition, based on my chat with Jason.

1 – The Adoption Paradox

What struck Hochman most about AI in sales? How many people refuse to use it—even when it works.

"We were tracking actual AI usage," he explains. "People who use AI the most were actually the most successful salespeople, but other people just still won't adopt it."

The resistance to such adoption sounds paradoxical: Free & demonstrable ROI for individual contributors. Yet adoption stalls.

Jason identifies 3 culprits:

• Change aversion: "People don't like change, even if it's change that seems good, seems easy."
• Complacency: The "I'm good enough" mindset blocks improvement.
• Bandwidth: "People don't like being told what to do. When you tell them to go use AI, they're like, well, you told me I got to do this thing."

Mandates, essentially, trigger resistance. So leadership, here, could push for tools reduce friction and internal proof:

• Target friction first: Does the tool reduce clicks? Eliminate data entry?

• Leverage internal proof: Jason's observation was that top performers became the heaviest AI users.

2 – Continuous Training & Coaching

Jason notes that annual training events fail to sustain performance.

Human managers cannot physically provide the reinforcement required across fast-growing teams. The "Sales Kickoff" model creates a knowledge spike that decays in weeks after.

"We've learned that you've got to do a lot of reinforcement. Just like you've got to do reinforcement learning with AI, you've got to do it with people, too."

AI alters the unit economics of training, so deploying AI for role-play, practice, and virtual coaching. This shifts training from periodic training to continuous, thereby enforcing messaging and technical accuracy without increasing enablement headcount.

"Rolling out products that allow you to just do it over and over and over again and let them practice until they've got it killed," Hochman says, "is a big, easy win."

3 – Tool Adoption: Specialization vs. General Purpose

Procurement of software faces a dichotomy between superior specialized solutions versus generalist 'do it all' platforms.

To Jason, context switching erodes productivity. If a rep must navigate Salesforce, Gong, and a knowledge base to answer one query, the friction destroys the value of each individual tool. Call it the "Toggle Tax"—every additional click costs attention.

"I'm a believer in giving me a general solution that does almost everything... I'll be happier than having the best thing in the world, but I've got to go to 6 different places to use it."

His target state? Embedded intelligence that surfaces contextually, without forcing users to leave their primary workspace.

"My ideal world is I go into Salesforce, and everything is in Salesforce. Every other tool I use shows up in Salesforce at the right time and the right place."

Taking Strategic Ownership: 3 Priorities

Based on Jason's experience scaling technical teams, CCOs and VPs must shift from passive procurement to active strategy:

1. Audit the Toggle Tax

Map the team's workflow. Count the tabs required to resolve a complex query. If the number exceeds three, prioritize integration over new capabilities. Consolidate or embed AI into the primary view.

2. Monitor Leading Indicators

Revenue is a lagging metric. Jason is blunt: "If you wait 18 months [on a new hire or tech], you're dead."

Measure velocity and quality instead:

• Are discovery notes more detailed?
• Is time-to-competency for new hires shrinking?
• Is time-in-stage decreasing?

3. Mandate Technical Empathy

Jason's most effective team members have technical backgrounds and understand how to build and use the products they're selling. In an AI era, "slick sales people" are obsolete and teams must function as trusted advisors.

"Speak the language of your customer," he says. Don't "just pretend, but actually go and do it... so that they see you as a peer and a trusted advisor, not as some slick salesperson."

An actionable tip here for Sales leaders is to require non-technical staff to build something with the product. If you sell data tools, then get them to build a dashboard. They cannot support what they do not understand.

About the Interview

This post draws on a conversation with Jason Hochman, a revenue leader who scaled technical organizations including Anaconda. He specializes in bridging complex technical products with go-to-market strategy.