Keep Moving Forward

Trent Cotton jokes that he used to hire anyone with a pulse and a prayer, back when the hard part was finding enough candidates. Now nearly a third of candidate drop-off happens after the interview, and slow hiring teams lose people to whoever makes the first offer.

Trent, Head of Talent Insights and Analyst Relations at ICIMS and author of Sprint Recruiting, joins Gemma Versace on Keep Moving Forward to talk about where candidates drop off and how leaders can tell real AI ability from tool familiarity.

They cover:

- Why 32% of hiring drop-off happens after the interview stage

- How two-week recruiting sprints keep a stretched talent team focused on the roles that matter most

- The screen-share exercise Trent uses to see how candidates think with AI

- Why AI training and apprenticeships can win over candidates that a bigger paycheck can’t

Sunita Verma has been building AI since before anyone called it AI, and her warning to engineering leaders today is about what happens after the code gets written. Speeding up one step of development, she says, just hands the bottleneck to the next one.

Sunita, CTO of Ironclad, spent years at Google and Character AI before leading AI strategy in legal tech, a heavily regulated field. On Keep Moving Forward, she connects AI burnout to deployment choices and makes the case for spelling out what a model shouldn't do before it does anything. She also shares how Ironclad keeps its engineers moving in the same direction while the tools change underneath them.

They cover:

- When to hand a task to AI outright, and when staying in the back-and-forth creates more work

- What agentic coding does to code review when nobody redesigns the review process

- How Ironclad decides when a human stays in the loop, using a risk-based framework that lets standard work like NDAs run autonomously

- Why Sunita ran a 20-day AI program across every location to give a distributed team a shared vocabulary

- What Ironclad saw after pairing its most AI-forward engineers with teammates further behind the curve

When an automated system answers instead of a person, Ryan Estis has one reflex: ask for a human, immediately. It's the same instinct he says most AI adoption plans forget to plan for.

Ryan Estis, keynote speaker and co-author of Prepare for Impact, calls that instinct authentic intelligence. In this episode of Keep Moving Forward, he talks about what it actually takes to lead a team well as AI changes the shape of the work, and why coaching still depends on a person who can build trust rather than just track performance. He also gets into why the return on AI investment comes down to leadership judgment more than the technology itself.

They cover:

- Why the same AI tools available to everyone stopped being anyone's advantage

- What Ryan means by authentic intelligence, and what automate the tangible, humanize the intangible actually rules out

- Why retention still comes down to the relationship between a manager and their team, not the technology layered on top

- Ryan's idea of building a "leadership family tree," and why he believes good leaders prepare people to eventually move on

- How Ryan manages a mix of human employees and AI agents inside his own business

Ee Lyn Khoo runs weekly AI experiments across every function of the business. Danny Fields builds the guardrails that make that safe to do. Neither one thinks the other is wrong.

Danny, CTO, and Ee Lyn, Chief People Officer at Avalara, join Keep Moving Forward to break down how they arrived at the same AI accountability principle from opposite sides of the business, and why a no-blame engineering review can quietly become a no-accountability one. They also get into what actually separates useful experimentation from reckless improvising.

They cover:

- Why Ee Lyn's weekly cross-functional AI experiments only work because of the guardrails Danny builds around them

- Why a tool is something people opt into, but infrastructure is something everybody depends on

- What changes about hiring, onboarding, and training once "good" AI use gets a shared definition

- Why every AI-prepared tax return at Avalara still needs a human to click approve before it's filed

- How a no-blame engineering review keeps resilience from becoming an excuse

An employee spent two years waiting for a promotion he never once asked for. His manager had no idea he wanted it.

Ben Brooks, founder and CEO of PILOT, has watched that gap play out from both sides of the desk more times than he can count. In this episode of Keep Moving Forward, he breaks down why assuming someone already knows what you want almost never works, why he built his company around a list of problems instead of one big idea, and where AI coaching quietly trades accountability for approval.

They cover:

- Why an employee waited two years to ask for a promotion he thought he'd already earned

- Why most managers assume silence means satisfaction, and most employees assume effort speaks for itself

- Why fear of rejection, not laziness, is what keeps people from asking for what they want

- Why Ben built PILOT around a list of problems instead of a single idea worth chasing

- What separates a coach who builds real accountability from an AI tool that's built to flatter

Your AI implementation strategy is only as good as the talent you have to execute it.

This episode of Keep Moving Forward is adapted from a live webinar hosted by Gemma Versace of X-Team in partnership with SmartBrief featuring Craig Stewart, senior vice president of product and engineering at Stord, Paola Jacobs, vice president of human resources at Stord, and Christine Park, chief people and AI transformation officer at Branch.

They talk about why executives report far more confidence in their organization's AI talent than the people doing the day-to-day work actually feel, what AI talent really means once you get past the headlines about scarcity and costs, and how three different leaders are closing that fluency gap from their own perspectives. They also cover why organizations using embedded AI teams, rather than internal staff alone or short-term contractors, are seeing meaningfully stronger outcomes.

Many organizations manage enterprise AI adoption through watching login counts. Track usage, watch the dashboard climb, call it done. Porter Orr watches for something different: a language shift, the moment a team stops calling the tool "it" and starts giving it a name.

In this episode of Keep Moving Forward, Porter explains why measuring AI success only in cost savings is a mistake, how his team built adoption in stages, and the exact language shift that tells him a tool has been fully embraced in organizational workflows.

They cover:

- Why focusing only on cost savings with AI is thinking too small

- How The Standard built adoption in stages, starting with a low-stakes internal chatbot

- The language shift that signals when a tool has become a trusted teammate

- Why building governance in from day one speeds up deployment instead of slowing it down

Most organizations treat AI governance like a gate. Something legal wants before anyone can move. It’s actually not a gate. It’s an on-ramp. That’s why Rob Stone built it first and says that's exactly what let SS&C deploy at scale.

In this episode of Keep Moving Forward, Gemma Versace sits down with Rob Stone, who runs the $550 million AI and automation business at SS&C Technologies, to talk about why AI governance is the fastest path to production and what it actually takes to move from pilots to agents to agentic AI.

They cover:

• Why unsanctioned AI use is already inside your organization

• How SS&C built an AI governance framework and then gave it away

• The difference between AI agents and agentic AI — and why it matters for orchestration

• How to use the risk spectrum to decide where to deploy AI first

The governance conversation usually starts as a question of risk tolerance. Rob Stone says the real question is what happens if you do nothing.

Giving employees formal feedback once a year is prehistoric. Once a quarter is medieval. And most organizations are still doing both.

In this episode of Keep Moving Forward, Gemma Versace sits down with Arnaud Grunwald, chief product officer at ClearCo, to talk about why the philosophy behind the annual performance review no longer holds up and what AI for managers is about to replace it with.

They cover:

- Why the scheduled feedback model is already running out of road

- What real AI adoption looks like beyond the product layer

- How to build leadership alignment that holds under pressure

- Why the organizations that come through the AI transition well won't be the fastest

The review isn't getting better. The model is being replaced. Here's what comes next.

Most organizations think self-assessment tells them what their people are capable of. It doesn’t. It tells them how confident their people are. Those aren’t the same thing — and the gap between them is quietly distorting how teams get staffed, developed, and led.

Dustin Clinard, CEO of Ignis AI, has spent close to two decades watching this play out. In this episode of the Keep Moving Forward podcast, I sit down with Dustin to dig into why self-assessment is so unreliable when it comes to skills, why L&D investment has been nearly impossible to measure, and why the most important shift an engineering leader can make is treating skill as a live variable, not a fixed input.