AI is changing the conversation around legacy modernisation, but successful transformation demands far more than powerful models and automated code generation. It requires engineering discipline, governance, and a clear-eyed view of where AI genuinely adds value and where it doesn't.

On a recent episode of Tech Transformed, host Christina Stathopoulos, founder of Dare to Data, sat down with Shodhan Sheth, Enterprise Modernisation Platform and Cloud Lead, and Alessio Ferri, Lead Software Engineer, both part of Thoughtworks' Global Legacy Modernisation Service Development Team, to unpack exactly that.

Legacy Modernisation With AI

The conversation around AI in legacy modernisation is often muddied by marketing noise. As Sheth puts it, "value and hype can coexist". Overpromising doesn't automatically mean a technology is worthless. The real question for technology leaders is whether AI meaningfully improves the cost-time-value equation for a specific problem.

This will always start with problem-solution fitness: has someone already solved a comparable challenge with AI, and does the proposed use case genuinely fit that pattern? As Sheth notes, "most things can be judged by cost, time, and value", which is a simple but effective filter for cutting through the noise.

Rethinking Legacy Modernisation

Generative AI is inherently probabilistic, while enterprise software has always relied on deterministic, predictable behaviour. Ferri unpacks this tension by separating two very different use cases: using AI to build systems, and embedding AI within operational systems.

When AI writes code, inconsistency is manageable; developers review, test and refine the output before it ships. Production systems are a different matter entirely, where unpredictable behaviour carries real operational risk. As Ferri explains, "AI in production requires different guardrails than AI for building."

The practical answer is controlled use. AI might suggest alternative products in a marketplace, for instance, while deterministic rules still guarantee that only in-stock items are ever shown. This lets AI add value within a firm, enterprise-grade constraints.

Why AI Alone Won't Modernise Legacy Systems

Technology is only part of the story. Sheth is clear that modernisation is fundamentally about change, and change is hard, especially across large enterprises with tangled, interconnected systems. Tasks that resist automation are often the hardest, like upskilling teams, explaining complex trade-offs, and winning buy-in; these cannot be solved with code alone. These human and organisational factors are routinely underestimated. Where the impact of a change is broad, he also advises either aligning teams properly across the business or breaking the change into smaller, more manageable pieces, a strategy that reduces resistance and smooths the path to adoption.

If you would like to learn more about this, visit Thoughtworks or connect with both Sheth and Ferri on LinkedIn.

Takeaways

Applying AI to modernise complex enterprise systems.
Distinguishing hype from practical AI applications.
Balancing probabilistic AI with deterministic enterprise software.
Organisational and leadership challenges in AI modernisation.
Building control, traceability, and abstractions in AI workflows.
Advice for CIOs and CTOs on AI adoption.

Chapters

00:00 Introduction to AI and Legacy Modernisation

01:18 Meet the Experts: Shodhan and Alessio

02:47 Distinguishing Hype from Value in AI

05:51 The Tension Between Probabilistic and Deterministic Systems

10:13 The Human Element in Modernisation

17:12 AI's Role in Enterprise Transformation

19:26 Distinguishing Hype from Value in AI

22:13 Probabilistic vs Deterministic Systems

27:45 People and Processes in Modernisation

29:03 Lessons in AI for Legacy Modernisation