What if the real value of AI isn’t in replacing workers, but in accelerating the development of junior talent?

This counterintuitive insight emerged from our recent conversation with Stephen Whitworth, co-founder and CEO of incident.io. The company provides an incident management platform to help engineering and operations teams coordinate outages, reduce downtime, and serve as an “emergency response layer for the internet.”

Stephen isn’t just using AI to summarise meetings; he is deploying autonomous agents to write code, handle support, and reshape his organization.

While most leaders we speak to raise concerns around the scarcity of senior talent and limited resources, incident.io is finding that learning agility is beginning to outperform decades of rigid experience. Simultaneously, they are discovering that they aren’t constrained by talent or headcount, but by flow—bogged down by the drudge of nondifferentiated work, dependencies and waiting, all of which AI agents can now eliminate.

Stephen, who is actively operationalizing these counterintuitive insights, is using AI to redefine the unit of labour and remove the friction that kills organizational speed.

Here is how he is rewriting the playbook.

The Talent Inversion: Betting on Learning Agility over Experience

Many organisations obsess over credentials and years of experience on résumés. Stephen challenges this, experimenting with an unconventional talent strategy: investing heavily in junior talent paired with AI agents rather than competing for expensive senior hires. He believes that in an AI-native world, the value of “30 years of Kubernetes experience” (which hasn’t even existed that long!) is diminishing. Deep experience in specific technologies may matter less than learning agility.

“The push now with AI is… an amplifier for junior folks,” Stephen notes. “This allows senior leaders to focus on strategy and coaching rather than just execution.”

When junior staff are paired with AI agents on tasks such as coding, they can produce output that previously required mid-level experience.

Practical takeaway: Are you overpaying for experience in areas where AI agents can bridge the gap? Shift your hiring criteria to prioritise learning agility over résumé length. Consider piloting this approach in specific teams or projects. Track whether AI-augmented junior staff can deliver comparable results while building skills faster than traditional development paths.

Killing Drudge Work so Talent Is Free to Learn

Stephen views AI’s role differently from many executives. Rather than focusing on efficiency or headcount reduction, he uses AI to eliminate routine tasks that interrupt deep work. Every dollar of impact AI creates is a dollar that can be reinvested to go faster.

Here, Stephen sees the value of agents in eliminating drudge work dependencies. When teams have to wait on other humans for simple tasks—answering product questions, writing boilerplate code, or filling out sales templates—flow is destroyed.

incident.io uses internal agents (built with tools like Credal) that connect to its codebase, help docs and CRMs. Anyone can ask a question and get an instant, accurate answer without distracting an engineer.

“AI is ensuring the stuff at the bottom of the pyramid is getting done,” says Stephen. “This is changing the nature of work that people are actually doing.”

When you remove the mindless drudgery and delays that consume so much time in organizations, you free your people to do the hard work of deep research and complex problem-solving and learning.

Practical takeaway: Audit your organisation for drudge work—repetitive tasks that require intelligence but not creativity. Pilot AI agents for these tasks. If your sales reps are just filling in blanks on templates, you are wasting human potential.

Creating Space and Tools for Curiosity

Innovation does not happen by mandate; it happens by giving people space to experiment and learn with new tools. In many organisations, adopting a new AI tool requires running a gauntlet of procurement and security reviews. This gatekeeping slows teams down and stifles the very curiosity leaders say they want.

Stephen takes a calculated risk with a different approach. The message he sends is simple but powerful: “In a world where it is extremely hard to get funding and approvals to buy software… having your boss stand up saying, ‘You can go spend essentially unlimited dollars on these things’ gives people oxygen.”

By lowering barriers to experimentation, incident.io encourages good ideas to emerge organically and lets people learn on the job. For example, an AI note-taking tool called Granola spread virally through the sales team, not because of a memo, but because people saw their colleagues getting a “rocket booster” to their productivity.

Practical takeaway: Stop trying to pick the winning AI tool in a boardroom. Create a “curiosity budget” and lower the procurement hurdles for low-risk tools. Let the best solutions win through adoption, not mandate.

Authenticity Is the New Premium

As AI becomes capable of generating strategy docs, emails, and updates, what is left for the leader? Stephen argues that because large language models (LLMs) predict the next likely token, they pull toward the average—shifting leadership value to uniqueness.

“I think one of my personal strengths is authenticity, transparency, and clarity… Does that get less valuable? No—more valuable…Anywhere where you have uniqueness…distinctiveness…is more powerful,” says Stephen.

If a leader speaks like a jargon-filled corporate press release, AI can replace them today. Leaders in an adaptable organization who guide junior talent must provide the commander’s intent (the why) and let the teams (and agents) figure out the how.

Stephen uses AI to handle routine tasks such as meeting notes, action items, and status updates while preserving human judgment for strategic decisions and authentic communication that builds trust and alignment.

Practical takeaway: Use AI to critique your thinking rather than to create communications. Retain your own voice. Assess what has changed in the last five years for your role. As Stephen suggests, something is wrong if the answer is “nothing”. Experiment with using AI to handle tasks such as meeting notes and action items and focus the time saved on human connection and strategic clarity that machines cannot replicate.

The New Hierarchy of Competence

At incident.io, AI dissolves organizational friction at every layer—from procurement gatekeeping to the invisible drag of drudge work and internal dependencies. In doing so, the company breaks the traditional link between headcount and productivity.

The strategy focuses on velocity rather than reduction. When you clear the drudge work from a junior employee’s desk and give them the freedom to choose their own tools, you create space for deep work and rapid skill acquisition. In this environment, the traditional premium placed on tenure begins to erode. If a curious junior, equipped with the right agents and guided by a leader who provides clear commander’s intent, can outperform a rigid veteran, the hierarchy of competence changes.

The lesson isn’t that automation replaces humans. It’s that in a frictionless environment, learning agility becomes the primary source of value. Organizations gain a competitive edge not through lengthy résumés, but by leading with authenticity and clearing drudgery from the path to growth.