Engineering Leadership: Balancing Experience & AI-Driven Productivity

As AI becomes more deeply embedded into engineering workflows, leaders are asking: how do we make it work for our people, not just our products?

This invite-only roundtable brought together senior Engineering Leaders to explore how AI is reshaping productivity, processes, and team structures. From embedding AI tools into day-to-day delivery, to balancing technical experience with AI fluency, this session will spark honest conversations about what’s really working, and what’s not.

Hosted by Grace Duffy, and moderated by Meng Muk (VP of Engineering), this session was part of Burns Sheehan’s ongoing Engineering Leadership Series, connecting forward-thinking tech leaders across the UK to share insights, challenges, and ideas that shape the future of engineering teams.

Community events create practical playbooks, leaders share what actually works, where AI helps, and how to avoid common traps. The outcome: better decisions, faster delivery, stronger teams.

Discussion Topics

💡 Exploring AI-driven productivity:
How are teams adapting to the productivity gains from AI tooling — and what practices are producing consistent, measurable wins?

💡 Balancing experience and AI fluency:
As juniors bring stronger AI skills and seniors bring deeper industry experience, how are leaders structuring teams to get the best of both?

Key themes

1) Purpose over polish: AI to solve real problems

• Shift from “perfect code” to measurable outcomes and faster learning loops.

• Adopt AI for explicit business goals not FOMO.

2) Measuring productivity (beyond “it feels faster”)

• Quant metrics are hard inside engineering; clearer in non-engineering workflows.

• Example shared: work that once took ~1 month now ~4 hours when re-framed with AI.

• Guard against confusing busy with effective.

3) Team design: blend experience with AI fluency

• Seniors/Staff: system design, architecture, governance, and mentoring.

• Juniors: stronger AI skills; need early systems thinking education.

• Mixed teams outperform—pair depth + curiosity; enable reverse mentoring.

4) Quality, safety nets & the “massive PR” problem

• AI can produce huge diffs and “AI smog” (plausible but wrong code).

• Invest in observability, testing, rollback and tight review practices.

• Define experiment vs. production standards.

5) Practical adoption patterns

• Widespread use of Copilot, Claude, Tabnine for code, docs, comms, diagrams.

• Strong wins in POCs, legacy system understanding, and cross-audience translation.

• Back-end deployment & governance remain the bottleneck—tooling must reach prod.

6) Skills & frameworks for an AI era

• Update competency frameworks to include AI literacy, prompt craft, and teaching others.

• Emerging roles: Knowledge Engineer / AI Fluency Lead.

• Promote systems design earlier in careers.

7) Cost, risk & responsibility

• Volatile AI pricing and regulatory/brand risks need proactive governance.

• Keep a human-centred approach; your outputs still represent your company.

Takeaways you can apply this quarter

• Write your AI “why”: where AI creates business value; how you’ll measure it.

• Ship safely at speed: observability, test strategy, guardrails, rollback.

• Design the team: pair senior context with junior AI fluency; enable reverse mentoring.

• Tighten reviews: cap PR size, require readable PR descriptions (AI can draft them, humans verify).

• Evolve skills: add AI competencies to progression frameworks; teach systems design earlier.

• Close the loop: ensure tooling supports deployment, not just code generation.