But simply modernising legacy systems may not be a sufficient response to the uncertainty that continues to mark the AI landscape. Tech leaders are navigating a murky regulatory environment, continued geopolitical shocks, and security incidents that highlight the need for self-sufficiency. That’s why building greater internal control over AI infrastructure – rather than becoming overly dependent on any single third-party vendor – provides organisations greater flexibility, enhances their resilience through market shifts, and differentiates their work.
In fact, as we found in The future of AI, unfiltered, our global survey of 945 CIOs and CTOs at $500M+ organisations, 42% of respondents said they’re leaning toward building their AI infrastructure internally, rather than buying or using it from external providers. What’s more, 99% said that potential government restrictions on AI access increase the importance of avoiding dependence on a single provider.
As increasingly powerful AI models become more accessible, competitive advantage will stem less from access alone and more on an organisation’s ability to apply the right models to its own data, expertise, processes, and business context. That’s what modernisation facilities: The operationalised flow of intelligence across the business.
But when it comes to digital transformation, many organisations miss a key opportunity presented by AI. Because AI isn’t just the endpoint of modernisation – it can also serve as the engine of modernisation itself, enabling enterprises to understand legacy systems, automate complex transformations, and embed intelligence directly into their business processes.
As an example, consider our work with one of the world’s largest financial services organisations, whose legacy systems were causing significant delays in documentation, modernisation, and compliance reviews. Using Wynxx, the industry’s most flexible generative AI platform for enterprise software delivery, we helped the client modernise a 20-year-old Java system. Wynxx ingested the source code directly from the bank’s repository and generated structured outputs aligned with the bank’s standards.
The result? What used to take weeks was reduced to a single day, with individual documentation artefacts generated in minutes and validated by experts. Documentation time was reduced by 95%, while cost per artefact fell by 93%. All said and done, the project delivered €300,000 in value in a single day, and 500% ROI within the first month.
That outcome points to a way through the AI widening AI implementation gap. By using AI to accelerate modernisation, organisations can build the internal capabilities they need to integrate, govern, and scale AI successfully directly into the foundation of their tech stacks. The goal and the engine, driving transformation in tandem.
We’re looking forward to exploring that kind of transformation alongside the global tech community at this year’s AWS re:Invent, where we’ll showcase Wynxx and other AI-native solutions and host a range of events.
Download The future of AI, unfiltered for more insights in the lead-up to the conference – and learn more about our programming for AWS re:Invent here.
