Building Your Leadership Pipeline: A Practical Framework for Identifying Future Technology Leaders

It’s a typical Tuesday morning. You’ve just finished the daily tag-up with your team, and everything is operating like a well-oiled machine. Then you see a new meeting notice titled “Interview” from your VP. And it’s in fifteen minutes. You didn’t apply for a new job. What on Earth is going on here?

This scenario captures a pivotal moment in every technology leader’s journey—that moment when someone sees leadership potential in you before you see it in yourself. But here’s the critical question: How did your VP know? What did they see that prompted them to tap you for the next level?

The Hidden Cost of Getting It Wrong

Many organizations believe they’re already evaluating and promoting talent effectively. Yet we’ve all encountered first-line technology leaders who weren’t truly equipped to lead. The consequences are costly: frustrated individual contributors, suboptimal team performance, micromanagement, and lack of strategic direction.

According to the recent paper “A Nose for Elevating Technology Leaders,” the same principle that applies to software defects applies here: identifying issues early costs far less than fixing them later. Early assessment of leadership talent requires less time and effort than dealing with a poorly equipped leader.

Why AI Can’t Do This for You

AI has infiltrated every corner of the hiring and promotion process—writing resumes, reviewing candidates, monitoring performance. While it may seem efficient to base leadership decisions on AI-generated metrics, this critical juncture demands deeper human judgment.

Why? Because the qualitative attributes key to becoming a leader—commitment, curiosity, outcome orientation, people skills, and technical credibility—reveal themselves through human interaction and observation, not automated dashboards. While GenAI might augment talent scouting, humans remain essential to qualifying potential and coaching for the next level.

Defining Technology Leadership

The authors define a technology leader as an individual who has moved beyond executing their own tasks to actively influencing and coordinating the delivery of software or product outcomes across a broader scope. This includes both people managers and senior ICs—principal engineers, staff engineers, and architects.

These leaders shape how work gets done within and across teams. Critically, their leadership is measured not by what they ship personally, but by the outcomes they help others achieve. This recognizes a fundamental truth: Leading is influence. A leader is someone who makes everyone around them more successful.

The Five Key Characteristics

The authors identify five key characteristics for successful first-line leaders:

1. Commitment: Receptive to feedback, takes on stretch assignments, does what needs to be done even without a title, and mentors others.
2. Curiosity: Seeks continuous learning, has ambition for team advancement, avoids cynicism, and engages with conflict constructively.
3. Outcome Orientation: Connects work to value, embraces change, knows what matters, and delivers results.
4. People Leadership: Values relationships, inspires others, and demonstrates emotional intelligence and tact.
5. Technology Leadership: Maintains technical credibility, influences without authority, simplifies processes, and tailors communication.

Crucially, these characteristics differ from those of a strong IC. Expert-level detailed knowledge and personal drive for task completion are valuable for ICs but can lead to burnout and micromanagement at the leadership level.

The Power of “Sniff Tests”

The heart of the framework lies in practical “sniff tests”—real-world assessments that reveal leadership potential through on-the-job observations and stretch assignments.

Testing Commitment: When one leader needed to staff a GenAI R&D team in a secure environment, they gave interested candidates simple homework: download, configure, and run an open-source LLM, then report observations. Over half didn’t complete it. But one person rebuilt their PC with an upgraded graphics card just to experiment. That person now leads the team. The lesson: Real work reveals real commitment.

Testing Curiosity: With a modest $250 learning budget, only 30% of one team used it. One junior engineer stood out—after his manager mentioned The Phoenix Project, he requested a 1:1 to discuss parallels in their workflows. He’d noticed bottlenecks and proposed lightweight changes to the code review process. Within weeks, throughput improved. His drive to learn and apply learning signaled leadership potential.

Testing Outcome Orientation: Given an open-ended task to “make a monthly meeting happen,” one IC didn’t just send calendar invites. She partnered with stakeholders to understand goals, built governance structures, and made the meeting self-sustaining. When she went on parental leave, the process held without her. This ability to turn vague tasks into durable, high-impact processes marked outcome orientation.

Testing Technology Leadership: After California’s Gender Recognition Act passed, a junior PM proposed an elaborate system supporting multiple gender concepts. After probing questions revealed no customer demand and limited downstream provider support, they held off, documented gaps, and logged it for future investment. Technology leadership isn’t just building elegant solutions—it’s knowing when not to build.

Red Flags to Avoid

Watch for anti-patterns: breeding cynicism, exercising authority through volume, condescending behavior, territoriality, taking credit for others’ work, and “not my job” mentality. These behaviors often appear more clearly than positive indicators.

From Identification to Development

Leaders are grown, not born. No one makes themselves a leader—someone already leading identifies potential and nurtures it through:

• Stretch assignments beyond comfort zones.
• Regular growth-focused 1:1s.
• Timely, specific, actionable feedback.
• Space for reflection and learning from mistakes.

The time invested upfront—administering tests, validating potential, coaching—always saves more than dealing with a poorly equipped leader later.

Your Path Forward

Start by asking: How do we currently identify and promote leaders? What’s working? What isn’t?

Then create your own sniff tests—stretch assignments, learning opportunities, cross-functional challenges—and watch for both positive indicators and red flags during daily work. Invest in coaching those who demonstrate high potential.

The jump from IC to leader requires significant changes in responsibilities and daily activities. This critical transition requires human judgment, human mentorship, and human relationship-building that no algorithm can replicate.

Your greatest contribution won’t be the systems you build or the code you ship. It will be the leaders you identify, develop, and empower to create a future you can’t imagine today.

The next time you see that flash of potential—the engineer exploring new technologies on their own time, the developer turning open-ended assignments into self-sustaining processes, the IC inspiring others without formal authority—don’t wait for an algorithm to tell you what you already know.

Trust your nose. Build your leadership pipeline intentionally, one conversation and one stretch assignment at a time. Because game knows game, and the future of your organization depends on spotting it.

This blog post is based on “A Nose for Elevating Technology Leaders” by Kent Beck, JD Black, Alana Henley, Clare Hawthorne, John Paul Herold, and Max Reele, published in the Enterprise Technology Leadership Journal Fall 2025.