Talent development tends to be treated as a linear path, one governed by output metrics, performance reviews, and hiring efficiency. On such a promotion cycle that equates time in seat with growth, a junior engineer might spend their first year completing onboarding modules and closing small tickets.
Corey Coto advocates for a very different, more active approach. He treats talent development as a real‑time read of how someone creates leverage across a system rather than a time‑based progression through roles. That same junior engineer would instead be reviewed for taking real ownership, unblocking teammates, navigating ambiguity, and influencing the system around them—signals that offer a far more accurate read on their trajectory and potential. He calls this talent velocity.
“Talent velocity is how quickly someone goes from new to trusted owner and then how fast they step into a bigger arena,” says Coto, an Operating Advisor at Fauntleroy Partners. In Coto’s model, data is the mechanism that makes sought‑after behaviors (ownership, cross‑team influence, and sound judgment under pressure) visible. For him, data, when used correctly, reveals who is creating leverage across a system and who is ready for more responsibility. When misused, data becomes a decorative layer that complicates decision making and obscures potential. The right signals accelerate growth, while the wrong ones slow organizations down.
Data in Practice: What Talent Velocity Looks Like
For him, data, when used correctly, reveals who is creating leverage across a system and who is ready for more responsibility. The right data reveals who is truly creating impact long before traditional metrics notice, allowing leaders to reward real influence instead of surface-level activity.
At Pluralsight, while serving as SVP of Product Development, Coto encountered two senior engineers whose résumés and performance ratings looked nearly identical, yet their trajectories were drifting apart. Data revealed the nuance that traditional evaluation methods missed. “It wasn’t lines of code or story points that differentiated them. It was cycle time, review behavior, and cross‑team impact,” he says. These indicators showed one engineer steadily expanding their sphere of influence by unblocking peers, taking on ambiguous cross‑team work, and elevating the quality of difficult reviews. Once leaders understood the pattern beneath the surface, promotion decisions became clearer and more consistent, shifting focus away from “perceived busyness and toward meaningful, system‑level impact.”
A similar dynamic played out at Amazon where he worked on software development. Incident analytics offered a more textured view of engineering judgment, making it clear who stayed composed under pressure and who built systems sturdy enough for others to rely on. “If I wouldn’t trust you on the pager at 2am, I probably shouldn’t give you a bigger team or more responsibilities.” By examining on‑call behavior, incident patterns, and the quality of root cause analysis, leaders could distinguish routine execution from the kind of reliability and decision making that signal readiness for greater scope.
How Data Goes Wrong
Even with the right intentions, organizations often misuse data in ways that stall rather than accelerate talent growth. Coto sees three patterns that show up repeatedly, each creating the illusion of rigor while distancing leaders from the signals that actually matter.
The first is treating metrics as scorecards instead of stories.When leaders mandate activity targets or experiment quotas, teams optimize for motion rather than learning, creating what he calls “metrics theater”—a flurry of data points with little insight underneath.
The second is an overreliance on lagging indicators.Promotion rates, regretted attrition, and annual performance ratings diagnose yesterday’s events but offer almost no guidance on who is ready for more responsibility now. These “autopsy metrics” describe outcomes long after meaningful intervention was possible.
The third is context‑free analytics. “If your metric doesn’t change who you bet on, how you staff, or how you onboard, it’s not data, it’s decor.” For Coto, data is only valuable when it influences real decisions about ownership, opportunity, and growth.
Building High Velocity Talent Systems
If data can accelerate growth and mislead at the same time, the natural question becomes how leaders should use it with intention. The most effective systems rely on a blend of qualitative judgment and quantitative signals, allowing managers to see not just what people deliver but how they grow. “What makes someone ramp fast here? Who grew faster than the system expected, and why?”
From those conversations, he introduces a set of targeted experiments designed to help leaders create clarity around what progress looks like and create capacity for people to reach that progress more quickly.
Coto encourages leaders to baseline talent velocity across a few representative teams by measuring time to first meaningful impact and the point at which someone is trusted to own a domain. He then pairs those definitions with a more intentional, experiential onboarding model that replaces generic checklists with structured 30‑60‑90 day plans, named buddies, early ownership moments, and simple confidence check‑ins. This gives leaders a clearer view into how and why someone is ramping, not just how fast.
There is also a real need to remove friction from daily work. it’s hard for talent to grow in environments weighed down by excessive meetings, slow review cycles, or too much work in progress. By reducing PR queue times, limiting context switching, and introducing protected review blocks, leaders create the headroom people need to accelerate. Equally important is making internal opportunities visible. A lightweight stretch‑opportunity board can reveal hidden ambition and give emerging talent a clear path to raise their hand for meaningful responsibility. “If after 90 days you can’t name at least one person whose ramp or scope changed because of these experiments, you’re still in slideware land.”
AI and the Future of Talent
People grow faster when leaders can see the right signals early, things like real ownership, judgment under pressure, or cross-team influence. AI stands to enhance that because it can surface these signals continuously, giving leaders better information sooner. Because of this Coto predicts talent systems of the future will feel more personalized and adaptive, helping people ramp and grow more smoothly.
AI will offer what he calls an always on talent radar, connecting signals from code, incidents, documents, and conversations into a living map of influence. It will surface hidden talent, anticipate ramp times, and recommend staffing decisions grounded in real behavior. Growth paths will become dynamic, adjusting like a curated playlist as people demonstrate new capabilities.
“AI can detect patterns, but it can’t decide your values or your risk tolerance,” he says. The hardest calls about stretching someone, supporting them through failure, or realigning their path will remain squarely with managers. This is why talent velocity matters. “Most companies don’t have a talent problem, they have a signal problem,” Coto says. Organizations that act on those signals will promote faster, onboard better, and spot emerging talent before the market does.
To learn more about Corey Coto’s work, connect with him on LinkedIn or visit his website.
