By James Bunting, CEO, Leighton
Artificial intelligence is rapidly becoming a strategic imperative for businesses and technologists alike. What once was framed as an experimental add-on is now becoming central to how organisations operate, innovate and compete.
As AI continues to gain momentum companies are being pushed to accelerate their adoption but it’s essential that this is done in a measured and systematic way. The opportunity is huge – from autonomous agents that support complex workflows to increasingly accessible custom models – but to take these developments companies need to ensure they have the right foundations in place to avoid issues later down the line.
Modernisation: A turning point for AI adoption
For organisations watching from the sidelines, recent developments in AI have highlighted that the gap between legacy systems and AI-ready technology stacks is widening.
Legacy systems – monolithic, inflexible, and data-poor – are illsuited to power AI at scale. Organisations that have invested in modern data architectures, APIs and scalable infrastructure are able to integrate AI into core processes more quickly and with fewer barriers.
This isn’t just technical preparation, it’s strategic positioning. These modern foundations make it feasible to integrate AI into existing workflows, scale AI functions reliably across teams and tap into advanced capabilities like real-time insights and predictive automation.
In effect, modernisation has become a prerequisite rather than a choice for organisations that are serious about leveraging AI.
Governance: Balancing speed and control
While AI can unlock remarkable capabilities, unmanaged adoption can quickly lead to chaos with ungoverned experimentation, fragmented solutions and data governance issues spreading quickly across your organisation.
Organisations that are realising value from AI aren’t just moving fast, they’re moving deliberately. They build within defined guardrails, prioritise strong governance and use robust policy frameworks to retain control for example, through clear rules around data access and model usage, strong observability and monitoring practices and methods for policing compliance and ethical standards.
Innovation must be balanced with accountability. By emphasising governance early, organisations can scale AI adoption without compromising security, reliability or trust, ensuring that tools are used in ways that align with business needs.
Redefining software engineering
One of the most notable shifts driven by AI in our sector has been the way in which the discipline of software engineering is evolving. The sheer volume of tools and ultimately options being made available to work with is completely reshaping how we can approach development.
Traditionally, engineers have worked with tools that assist in incremental productivity tasks for example, code completion, debugging and testing automation however, today’s AI systems are beginning to push those incremental boundaries even further. Advanced AI agents are starting to participate more meaningfully in the software development lifecycle, contributing to design, code generation, optimisation and even deployment.
Rather than simply being assistants, these AI tools will become collaborators – or team members – capable of understanding context, suggesting solutions and helping engineers navigate complex codebases.
This evolution promises major productivity gains through more informed code, faster iteration cycles, and shorter time to market. But it also raises important questions about how teams work, how skills evolve, and how organisations should structure their engineering teams in order to maximise on these opportunities.
The union of engineers and AI
There’s a counterintuitive insight emerging from these opportunities and that is that greater engineering efficiency is likely to increase demand for skilled software engineers, not reduce it.
As AI tools make the process of building software faster, more predictable, and more productive, organisations will likely look to explore new products, deeper integration, richer features and expanded digital platforms. The result? The demand for engineers who understand AI-enabled workflows, can guide architectural decisions and can shape how AI is applied in complex contexts will increase.
However, this fuels a looming skills challenge. Despite rapid advances, there’s a shortage of professionals with real-world experience in AI-first engineering. The pace at which the technology evolves makes it difficult for traditional educational and training systems to keep up.
Organisations that invest early in upskilling their engineers, especially by offering structured learning and hands-on experience with AI tools, will gain an advantage in capability and cultural readiness.
At Leighton we’re already working with customers to integrate AI-enabled and automated solutions within digital products and cloud platforms. We help organisations to identify the right use cases, implement AI responsibly, and turn experimentation into measurable impact without unnecessary complexity.
If you’d like to hear more about the work we’re doing, please get in touch: www.leighton.com