Stories From Elite AI Engineering Teams

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 an AI bubble.

So, we brought together senior AI leaders, engineers, and ML specialists to learn from what others are doing. Our expert panel who’ve built and led early AI teams at companies including Meta, Google, Palantir, Monzo, and Unruly, shared insider lessons on joining, building, and leading world-class AI engineering teams that deliver at the highest level.

The session explored what great AI teams look like today, how to bridge the gap between research and production, and what businesses need to do to get AI-ready.

Below are the key takeaways from the discussion, covering how to hire AI engineers, structure machine learning teams, and scale AI capability in a way that drives measurable outcomes.

A huge thank you to our panellists and to our hosts Viator & Tripadvisor, who welcomed us into their brand-new rooftop office near Liverpool Street.

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, and the reason usually is usually down to strategic misalignment, rather than technical issues.

What do the best AI teams do differently?

• Anchor AI work in value: Every project starts with a clear business problem, measurable outcomes, and executive sponsorship.
• 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.
• Prioritise simplicity: Many AI engineers instinctively gravitate towards the most technically advanced and expensive solutions, even when a simpler model would suffice. Effective teams resist this temptation, choosing the simplest solution that solves the problem.

As one panellist put it:

“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.

Most panellists agreed that cross-functional pods - combining product, data, ML and software engineering - work best. This structure gives engineers context, autonomy, and ownership from prototype to production.

The hallmarks of high-performing AI teams:

• High agency: Engineers own problems end-to-end rather than waiting for a brief.
• Breadth with depth: Full-stack ML engineers who understand data pipelines, APIs, and monitoring help teams stay agile.
• Shared KPIs: Data, product, and engineering functions align around one measurable business goal to avoid the “throw it over the wall” problem.
• Leadership buy-in: Executive sponsorship ensures AI projects are protected from shifting priorities and receive proper resources to mature.

Balanced teams combine deep specialists with systems thinkers. This “team of hybrids” model, where data scientists, ML engineers, and product managers each bring context and can speak the language of both model accuracy and commercial impact is becoming the gold standard for building AI teams that scale.

3. Redefining productivity in AI Engineering

AI-assisted tools like GitHub Copilot and Cursor are changing how engineering work gets done. The question is no longer whether engineers use them, but how teams measure success when they do.

Key insights from the panel:

• AI amplifies, it doesn’t replace: Human judgment, design thinking, and quality assurance still define excellence.
• Redefine KPIs: Code volume no longer equals value. Instead, track review quality, delivery reliability and speed to business impact.
• Normalise tool use: AI tools shouldn’t carry stigma. Leaders must be explicit that using them is encouraged, not “cheating.”
• Upskill lighthouse engineers: Empower early adopters to guide peers and embed AI tooling best practices across the team.

Ultimately, AI-augmented productivity is about outcomes, not output.

The panel agreed that definitions of productivity are evolving fast. AI-assisted coding tools can now generate usable code in seconds, but speed doesn’t equal value. Quality assurance, maintainability, and business integration remain human-critical stages. As one panellist put it:

“Traditional KPIs no longer capture what effective engineering looks like.”

For leaders, this means shifting coaching and performance conversations to focus on model interpretability, cross-team collaboration, and risk management, not just raw coding metrics.

“Don’t repeat the same adoption mistakes we’ve seen in previous booms. Pressure to deploy AI should never outrun proper training, integration or security assessment.”

4. Closing the gap between prototype and production

Many AI teams excel at experimentation but falter when it comes to turning ideas into production-grade systems.

Where teams get stuck:

• Poorly defined problems and unclear ownership
• Disconnected data science and production workflows
• Missing infrastructure for monitoring, observability and governance

How to fix it:

• Design for production early. Think versioning, rollback, and deployment from day one.
• Own the full workflow. Integrate ML directly into business processes instead of a bolt-on down the line.
• Build on solid foundations. Governance, compliance, and auditability are non-negotiable, especially in regulated sectors.

As one speaker shared:

“Only about 20% of the work is core AI. The other 80% is the integration, operations, and monitoring where value is actually realised.”

5. Building careers & leadership in AI

Technical brilliance alone won’t future-proof your career in AI. The discussion highlighted that the engineers who thrive and go on to lead, are those who combine empathy, influence and curiosity with technical excellence.

Practical advice for AI engineers:

• Start: exploring broadly, mastering tools, owning business outcomes.
• Stop: chasing frameworks for the sake of it.
• Continue: staying commercially aware and flexible.

Mid-career engineers need to stay adaptable. Today’s dominant frameworks could be obsolete within a year, so focus on the fundamentals:

• Reasoning about data
• Evaluating trade-offs
• Understanding system behaviour at scale

The best AI engineers “see both the code and the company.” They understand how technical outputs influence revenue, risk, and customer outcomes, aligning engineering priorities with business goals.

To build sustainable careers, diversify your skills across model development, system integration & AI governance

“AI can replicate, but it doesn’t reason.”

Current models mimic intelligence, but human oversight, creativity, and judgment still drive real impact.

For leaders, success lies in creating environments where individual contributors and managers can grow in parallel, valuing deep technical expertise just as much as people leadership.

6. The future: AI as infrastructure

As models scale, so do risks. While AI can work impressively at scale, it can also fail catastrophically at scale. Hardware imitations, energy costs, and human oversight bottlenecks remain major constraints. Teams must start thinking about AI with the same rigour as civil or mechanical engineering - factoring in safety margins, governance and failure modes.

Key forward-looking themes:

• Governance is leadership: Compliance and ethical design are no longer afterthoughts.
• Strategic agility: Make multiple AI bets - some will miss, and that’s okay.
• Continuous learning: Frameworks will change, but curiosity, problem-solving, and adaptability will always be in demand.

AI engineering is shifting from a research function to a core operational capability, and organisations that treat it that way will be the ones that 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.