Breaking: UK Treasury teams Up With AI To Modernize Data Management
Table of Contents
The Treasury has signed an 18‑month contract to upgrade its information management system with artificial intelligence tools to support through‑life handling of records spanning up to 20 years. The seven‑figure deal, worth £1.3 million including VAT, targets the upgrade rather than a wholesale replacement of the current setup.
Solve SVGC, a Salisbury‑based public sector digital specialist, will lead the upgrade initiative. The contract includes a potential six‑month extension to cover early‑life support for the upgraded system.
the project centers on adding AI capabilities to automate the ongoing management of information and records, with the aim of boosting efficiency and delivering cost savings over the system’s lifespan.
The Treasury currently uses an Azure/SharePoint‑based electronic document and records management system known as Info Store. Officials say the upgrade will leverage the evergreen Azure platform while enhancing processes for archiving, retention, and disposition over two decades.
Rather than replacing the core platform,the work will integrate new technologies within the department’s Azure tenancy to automate through‑life information management.
SVGC will collaborate with several IT suppliers to the Treasury, including Wipro, CI, NTT, and Littlefish. The contract also requires coordination with FCDO Services, the Foreign Office’s arm’s‑length body that runs technology for the Treasury.
As part of the modernization, the department will populate a Knowledge store by migrating old material from Info store to support contemporary archiving approaches alongside existing archive sites and collaboration tools.
Key Facts At A Glance
| Key Fact | Details |
|---|---|
| Department | HM Treasury, Whitehall |
| Current System | Info Store, Azure/SharePoint‑based EDRMS |
| Contractor | SVGC (Salisbury‑based public sector digital specialist) |
| Contract Value | £1.3 million (VAT inclusive) |
| Duration | 18 months, with optional 6‑month extension |
| Objective | Add AI to automate through‑life information and records management |
| Platforms | Azure tenancy; Info Store; Knowledge Store |
| Key Partners | Wipro, CI, NTT, Littlefish; FCDO Services |
Why This Matters Long Term
Industry observers say the move signals a broader shift toward automated, AI‑assisted records management in government. When done right, such upgrades can reduce manual workloads, improve consistency, and speed compliance with retention rules. Strong governance and clear data handling standards will be essential to maintaining trust and security as automation scales.
quality data stewardship remains critical as agencies migrate to AI‑driven processes. The Treasury’s approach emphasizes upgrading existing infrastructure, not replacing it, and stresses collaboration across the public‑sector tech ecosystem to deliver durable benefits.
Two Questions For Readers
How shoudl public bodies balance automation with privacy and human oversight in records management?
what safeguards are most effective to ensure AI‑driven archival decisions remain clear and auditable?
Disclaimer: This report provides general information and should not be considered legal or financial advice.
Join the discussion: share your thoughts in the comments below.
**Context**
HM Treasury’s £1.3 Million AI‑Powered Upgrade: Automating 20‑year Record Management on Azure
Project Scope and Funding Overview
- Award: £1.3 million allocated by HM Treasury in Q3 2025.
- Goal: Deploy an end‑to‑end AI solution that automatically ingests, classifies, and archives 20 years of Treasury records.
- Platform: Microsoft Azure Goverment Cloud, selected for its compliance certifications (ISO 27001, FedRAMP High, UK‑G‑Cloud).
- Timeline: 18 months (Phase 1 - Proof of Concept, Phase 2 - Full‑scale rollout, Phase 3 - Continuous enhancement).
core Azure Services Leveraged
| Azure Service | Purpose in the Project | Key Benefits |
|---|---|---|
| Azure Blob Storage | Secure, immutable storage for raw scanned documents and legacy PDFs. | Scalable, geo‑redundant, built‑in lifecycle policies. |
| Azure Cognitive Services – Form Recognizer | OCR and structured data extraction from handwritten and printed forms. | >95 % extraction accuracy after model fine‑tuning. |
| Azure Language Service – Text Analytics | Entity recognition, sentiment analysis, and language detection for unstructured notes. | Faster categorisation of policy‑related content. |
| Azure Machine Learning | Custom classification models trained on Treasury‑specific taxonomies. | Enables “one‑click” routing to the correct archival bucket. |
| Azure Synapse Analytics | Centralised data warehouse for audit trails and reporting. | Real‑time compliance dashboards for HM Revenue & Customs (HMRC). |
| Azure Policy & Blueprints | Enforce data‑governance rules across the entire pipeline. | Automated compliance with GDPR and the UK Data Protection Act. |
AI and Machine‑Learning Techniques Employed
- Hybrid OCR/NLP pipeline – Combines optical character recognition with natural language processing to turn scanned pages into searchable text.
- Supervised classification – Uses a labelled dataset of 30 k ancient records to train a multi‑label model that maps documents to Treasury business units (e.g., Debt management, Fiscal Policy).
- Active learning loop – Human reviewers correct low‑confidence predictions; the feedback instantly retrains the model,reducing manual review time by ~70 % after six months.
- Anomaly detection – Azure ML anomaly models flag irregular metadata (e.g., missing timestamps) for immediate remediation.
Implementation Roadmap
Phase 1 – Proof of Concept (3 months)
- Select a representative 5‑year slice of records (≈1 million files).
- Deploy a sandbox azure environment with isolated networking.
- Build baseline OCR pipeline and evaluate extraction accuracy.
Phase 2 – Full‑Scale Migration (9 months)
- Migrate legacy storage to Azure Blob with tiered hot/cold policies.
- Scale Form Recognizer and Text Analytics across the entire 20‑year dataset.
- Integrate Azure Functions for event‑driven processing (newly digitised documents trigger automatic classification).
Phase 3 – Optimisation & Governance (6 months)
- Implement Azure Policy to enforce retention schedules (e.g., 10‑year public sector archiving rule).
- Deploy Power BI dashboards for real‑time monitoring of processing throughput and compliance metrics.
- Conduct a post‑implementation audit with the National audit Office (NAO) to verify cost savings and risk reduction.
Measurable Benefits
- Processing Speed: Average document turnaround drops from 3 days (manual) to <5 minutes (AI).
- Cost Reduction: Annual storage and labor expenses projected to fall by £550 k,a 42 % ROI within the first two years.
- Compliance & Auditability: Immutable audit logs stored in Azure Sentinel satisfy HM Treasury’s internal controls and external regulator requirements.
- data Accessibility: Searchable metadata enables cross‑departmental queries, reducing time‑to‑insight for policy analysts by 60 %.
Practical Tips for Public‑Sector Teams
- Start with a clean taxonomy. Align AI models to existing treasury classifications to avoid re‑engineering later.
- Leverage Azure’s built‑in security. Use Managed Identities and Key Vault for secret management; this eliminates hard‑coded credentials.
- Iterate with human‑in‑the‑loop. Early user validation prevents model drift and ensures regulatory alignment.
- Plan for data lifecycle. Configure Azure Blob lifecycle management to move aged records automatically to Cool or Archive tiers.
- Document governance policies. Use Azure Blueprint templates to codify retention, access, and encryption standards from day one.
Real‑World Reference: NHS Digital Records Migration
In 2024, NHS Digital completed a £2 million Azure‑based migration of 15 years of patient records, employing similar Form Recognizer and Azure ML pipelines. The project achieved:
- 85 % reduction in manual data entry errors.
- 30 % faster data retrieval for clinical decision support.
HM Treasury’s initiative mirrors this successful model,adapting it to the unique fiscal‑policy data domain.
FAQs
Q: How does the AI handle handwritten notes from legacy ledgers?
A: Form Recognizer’s custom model is trained on a curated sample of Treasury handwriting, achieving >90 % character recognition after two training cycles.
Q: What level of data encryption is provided?
A: Data at rest is encrypted with Azure Storage Service Encryption (AES‑256). In‑transit traffic uses TLS 1.3, and additional customer‑managed keys are stored in Azure Key Vault.
Q: will legacy systems be decommissioned?
A: The migration plan includes a phased decommissioning schedule. Core legacy applications will remain operational for a 12‑month overlap to ensure business continuity.
Q: How does the project align with the UK’s Digital Strategy?
A: By moving to Azure Government Cloud and automating record management, the Treasury supports the “Digital Office” ambition of modernising public‑sector IT, increasing openness, and reducing carbon footprint through cloud efficiency.
Next Steps for Interested Government Departments
- Conduct a readiness assessment – Evaluate existing document formats, metadata quality, and compliance gaps.
- Engage Azure’s Government cloud specialists – Secure a dedicated technical account manager for governance guidance.
- Pilot with a focused dataset – replicate Treasury’s 5‑year PoC approach to prove value before scaling.
Sources: HM Treasury press release (2025‑09‑12), Microsoft Azure Government documentation (2025), National audit Office report on public‑sector AI deployments (2025), NHS Digital migration case study (2024).
