Can Legacy Systems Support AI? What to Modernise First
Legacy systems do not automatically make an organisation unready for AI. The real question is whether the systems, data and integrations required for a specific AI use case are accessible, reliable and governable enough to support it.
AI readiness is broader than technology alone. Strategy, data, governance, delivery capability and adoption all play a part. For the wider picture, our AI readiness assessment looks at the six questions technology leaders should answer before committing to AI investment.
Here, we are focusing on one specific constraint: what happens when valuable data and critical processes sit inside legacy systems that were never designed with AI in mind.
How legacy systems can constrain AI
Artificial intelligence continues to dominate boardroom conversations, and with good reason. When implemented effectively, AI has the potential to accelerate decision-making, uncover insights and deliver genuine business value. But for many organisations, these opportunities remain out of reach.
One potential barrier is the existing technology estate. Ageing systems can make it difficult to reach the data and processes an AI use case depends on, particularly where integration options are limited or poorly understood.
To generate meaningful results with AI, businesses need to feed their models with the right data. Whether that data relates to customer behaviour, operational performance or internal workflows, it needs to be accessible, sufficiently accurate for the intended use and available at the frequency the use case requires.
The challenge is that, for many businesses, their most valuable data is locked inside ageing systems that were never designed for modern integration. These systems often pre-date standardised APIs, store information in fragmented or inaccessible formats and resist efforts to scale or automate. The result is that organisations can be sitting on valuable information without a practical way to use it in modern applications.
This is a significant problem, not just for AI, but for any data-led initiative. Without access to dependable, usable data, teams cannot generate insights with confidence. Attempts to manually extract data from legacy systems can also be slow, error-prone and resource intensive.
The UK Government’s guidance on making datasets ready for AI reinforces this wider point: useful AI depends not just on having data, but on its quality, accessibility, structure and governance. Read the UK Government guidance on making datasets ready for AI.
If data itself is the constraint, our guide to FAIR data principles and AI readiness looks in more detail at whether information is Findable, Accessible, Interoperable and Reusable.
Many businesses also rely on bespoke codebases, unsupported technology or ageing hardware that limit their ability to respond to change and can increase the risk involved in making updates.
Unlock legacy data without replacing everything
Supporting AI does not automatically require a full-scale system overhaul.
Before replacing a legacy platform, the more useful question is what the AI use case actually needs from it. UK Government technology guidance recommends understanding existing constraints and comparing replacement with alternative ways of accessing data or services before deciding on the scale of change required. Read the GOV.UK guidance on moving away from legacy systems.
In some cases, the answer may be to modernise just enough to make the required data or capability accessible. That could mean introducing secure APIs, migrating selected workloads to cloud infrastructure or building lightweight data pipelines to replicate information into a more flexible analytics platform.
APIs can be particularly useful where replacing a core platform immediately would create unnecessary risk. They can provide a controlled interface between modern services and older systems while allowing modernisation to progress incrementally.
By focusing on iterative improvements, businesses can unlock more value from legacy systems without unnecessarily disrupting day-to-day operations.
For a broader view of the available options, our guide to legacy system modernisation covers modernisation strategies, risks and how to decide what should be retained, improved or replaced.
This process also needs to be underpinned by strong governance.
As organisations expose more internal data to modern platforms and AI services, they need to ensure that information is appropriately structured, managed and protected. Governance should be built into how AI is designed, deployed and operated rather than treated as a final approval step.
AI as a tool, not a replacement
But it is not just about technology.
AI does not remove the need for domain expertise, ownership or judgement. Organisations still need people who understand the problem being solved, the context behind the information being used and the consequences of acting on an AI-generated output.
Just as introducing better tools changes how specialists work rather than removing the need for the underlying skill, AI can augment analysts, engineers and business leaders when it is applied to the right problems and supported by dependable systems and data.
Many of the challenges businesses now face around AI and data access are familiar from earlier business intelligence and analytics programmes. Then, as now, the objective was to make better use of organisational information.
What has changed is what can be done with that information. AI can work across far larger volumes of structured and unstructured content, but it still depends on a reliable operational foundation. If important information cannot be accessed, trusted or governed appropriately, the AI use case will inherit those weaknesses.
And solving the technology problem is not the end of the journey. A successful pilot still needs clear ownership, monitoring, integration, governance and user adoption before it becomes a dependable production capability. Our guide to why AI rollouts fail after successful pilots explains what leaders should test before scaling.
Catapult helps organisations tackle these challenges by aligning modernisation with business outcomes. That means identifying where legacy technology genuinely constrains an AI use case, exposing the data or capabilities that matter and avoiding unnecessary change elsewhere.
Our AI Diagnostic takes this approach across existing systems, data and workflows, identifying where AI can add value, what will constrain delivery and what needs to change first.
The objective is not to make every system “AI-ready”. It is to create the minimum dependable foundations required for the AI use cases that matter.
For the wider organisational picture, the AI Readiness Playbook explains the foundations across strategy, data, delivery capability, governance and adoption. Leaders who want a faster starting point can also use Catapult’s AI Readiness Scorecard to identify where those foundations are strongest and where constraints are most likely to limit progress.
