Legacy IT Is Holding Back AI in UK Government, Say 7 in 10 Government IT Leaders

7 in 10 UK Government IT Leaders Say Outdated Systems Are Hindering AI Adoption

Legacy tech is taxing your AI plans. If seven in ten IT leaders are calling it out, it's not a perception issue-it's a systems issue.

The fix isn't another pilot or a bigger model. It's a cleaner foundation: data you can trust, platforms you can scale, and controls you can defend.

Why legacy blocks AI

• Data silos: Inconsistent schemas, duplicate records, and unclear ownership stall training and deployment.
• Brittle integrations: Point-to-point connections break under real-time or high-volume AI workloads.
• Insufficient compute: On-prem constraints and long provisioning cycles slow experiments to a crawl.
• Security and compliance risk: Unknown data lineage and weak access controls make approvals hard.
• Procurement drag: Fragmented buying and bespoke contracts delay delivery.
• Skills gap: Teams trained on legacy stacks struggle with MLOps, APIs, and cloud-native patterns.

What "AI-ready" looks like

• Shared data layer: Cleaned, catalogued, and classified data with lineage and quality checks.
• API-first services: Standard interfaces, versioning, and event streams-not file drops.
• Cloud-smart approach: Clear landing zones, cost guardrails, and approved reference architectures.
• Security by design: Zero trust, role-based access, encryption, and audit trails as defaults.
• Model governance: Documented use cases, risk tiers, human oversight, and rollback plans.
• Reusable components: Templates for data pipelines, model deployment, and monitoring.

A 90-day action plan

• Days 0-30: Baseline
  • Inventory critical systems, data sources, and integrations tied to your top two AI use cases.
  • Map data contracts, owners, and quality gaps. Tag sensitive data.
  • Agree non-negotiables: security controls, evaluation metrics, and budget envelope.
• Days 31-60: Unblock
  • Stand up a secure sandbox (pre-approved patterns, restricted data, automated logging).
  • Build one high-value integration as an API, not a one-off script.
  • Containerize a pilot model with CI/CD and monitoring. Prove repeatability, not novelty.
• Days 61-90: Produce
  • Move the pilot to a controlled production tier with approvals baked into the pipeline.
  • Document the blueprint: infra, data contracts, security checks, and cost model.
  • Decommission one legacy step the pilot replaces. Show net time and cost saved.

Practical procurement moves

• Use modular contracts with clear exit ramps and reusability clauses.
• Prioritise outcome-based statements of work: data availability, latency, uptime, and cost per inference.
• Pre-approve a shortlist of platforms and patterns to cut review cycles.

Security and assurance without the slowdown

• Adopt standard security patterns once, reuse everywhere (authN/Z, network policies, key management).
• Run DPIAs and model risk assessments as part of the pipeline, not after it.
• Log everything that matters: data access, model versions, prompts, outputs, and approvals.
• Align to trusted guidance like the NCSC security design principles here.

Metrics that prove progress

• Time-to-deploy: Idea to production in weeks, not quarters.
• Data readiness: Percentage of priority datasets with owners, contracts, and quality SLAs.
• Legacy reduction: Integrations/API coverage and number of retired batch jobs.
• Cost-to-serve: Cost per model run and storage per active dataset.
• User outcomes: Measurable hours saved, queue times reduced, or accuracy improved.

Where to go next

If outdated systems are stalling AI, start by modernising the data layer and standardising delivery. One repeatable path beats three flashy pilots.

For public-sector specific guidance and training, see AI for Government.

Bottom line: AI won't fix legacy. Cleaning up legacy is what makes AI work-safely, quickly, and at lower cost.