At Burns Sheehan, we’ve been working closely with our clients to help them get AI-ready, bringing the right skill sets into teams to kick off meaningful AI projects, hiring leadership to add structure and focus, and sharing market data around salaries and how to compete for top talent (even when market leaders are offering sign-on bonuses in the millions).
It’s a wild market, and we’re all navigating the AI bubble with optimism and caution.
Through our conversations with AI leaders, engineers, and machine learning specialists who’ve built early AI teams at companies including Meta, Google, Palantir, Monzo, and Unruly, we’ve gathered some valuable lessons on what it takes to join, build, and lead world-class AI teams that deliver at the highest level.
1. Start with the problem, not the model
The strongest AI teams are business-first, not model-first.
95% of AI projects fail to reach production or deliver ROI — often due to strategic misalignment rather than technical issues.
What do the best AI teams do differently?
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Anchor AI work in value: Every project starts with a clear business problem, measurable outcomes, and executive sponsorship.
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Translate, don’t just build: The most effective AI engineers act as translators between tech and the business — shaping requirements, managing expectations, and connecting outcomes to value.
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Prioritise simplicity: The simplest model that solves the problem usually wins.
“You can have amazing technical people, but if they’re not solving the right problem, it doesn’t matter.”
2. How to structure AI teams for scalability
Team design can make or break your ability to scale AI initiatives.
Cross-functional pods — combining product, data, ML and software engineering — tend to work best. They give engineers context, autonomy, and ownership from prototype to production.
The hallmarks of high-performing AI teams:
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High agency and ownership
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Breadth with depth (full-stack ML engineers)
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Shared KPIs across functions
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Strong executive sponsorship
Balanced teams combine deep specialists with systems thinkers — a “team of hybrids” who understand both model accuracy and commercial impact. This is fast becoming the gold standard for AI teams that scale.
3. Redefining productivity in AI engineering
AI-assisted tools like GitHub Copilot and Cursor are transforming how engineering work gets done. The question is no longer whether to use them, but how to measure success when you do.
Key insights:
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AI amplifies, it doesn’t replace.
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Redefine KPIs: move away from code volume to review quality, reliability, and business impact.
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Normalise AI tool use — it’s not “cheating.”
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Upskill early adopters to embed best practices across the team.
“Traditional KPIs no longer capture what effective engineering looks like.”
Leaders must shift coaching and performance conversations toward model interpretability, collaboration, and risk management — not just coding output.
4. Closing the gap between prototype and production
Many AI teams excel at experimentation but struggle to operationalise models.
Where teams get stuck:
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Poorly defined problems
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Disconnected workflows
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Missing monitoring and governance infrastructure
How to fix it:
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Design for production from day one
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Integrate ML directly into business processes
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Build strong governance and compliance foundations
“Only about 20% of the work is core AI. The other 80% is integration, operations, and monitoring — where value is actually realised.”
5. Building careers and leadership in AI
Technical brilliance alone won’t future-proof your career. The AI engineers who thrive combine empathy, influence, and curiosity with technical depth.
Advice for AI engineers:
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Start: exploring broadly and owning business outcomes
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Stop: chasing frameworks for their own sake
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Continue: staying commercially aware and adaptable
“The best AI engineers see both the code and the company.”
For leaders, success lies in creating environments where technical excellence and people leadership are equally valued.
6. The future: AI as infrastructure
As models scale, so do risks. AI must now be treated with the same rigour as other core infrastructure — with safety margins, governance, and failure mode planning.
Key forward-looking themes:
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Governance is leadership
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Make multiple AI bets, some will fail, and that’s okay
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Continuous learning and adaptability will always be in demand
AI engineering is shifting from a research function to a core operational capability. The organisations that treat it that way will be the ones to sustain long-term value.
Final reflection
The AI field’s rapid evolution demands not just technical brilliance, but judgment, adaptability and ethics.
Businesses that win in this space will be those that hire engineers who can bridge the technical and the commercial, structure teams around value, and scale AI responsibly.
At Burns Sheehan, we partner with tech-led organisations to help them hire and build AI-ready teams - from early AI hires to scaling entire machine learning functions.
👉 Want to learn more?
Explore our latest AI hiring insights here or get in touch to discuss how we can help you build your next AI team.
You can also read customer stories from companies we’ve helped scale their AI and engineering functions, and see what’s worked in practice to attract, hire, and retain top talent.
🧠 These insights were born out of real conversations with the people building AI at scale. The lessons shared in this blog come from our AI Leadership Panel, a live discussion bringing together senior AI and engineering leaders from Meta, Google, Palantir, Monzo, Viator, and Unruly. Together, they unpacked what it really takes to build and scale high-performing AI teams that deliver real business impact.
You can read the full event summary and meet the speakers here: 👉 Stories From Elite AI Engineering Teams
Written by Becca Davies
