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How Defra accelerated the process of understanding complex needs and new policy within the environmental recovery space using AI
By using AI, the department was able to complete Discovery tasks more quickly, capture richer insights and improve vital collaboration between agencies
Defra is transforming how development interacts with the environment. Moving away from the project-by-project mitigation and offsetting, the Nature Restoration Fund will introduce a strategic model focused on active environmental recovery through projects such as habitat repair or water quality improvement works.
Underpinned by the Planning and Infrastructure Bill and new Environmental Delivery Plans, the funding will be collected and managed by Natural England and the approach will shift the planning system from a site-by-site focus to a strategic model that actively supports large-scale restoration, improvement and resilience of the natural environment. These reforms aim to streamline processes, provide developers with a clear and simple route to deliver housing and infrastructure, ensuring that recovery and growth go hand in hand.
The proposed programme requires huge collaboration across multiple teams and departments, bringing together policy, legislation, operations, digital services and industry.
Outcomes
56%
reduction in the time taken to complete and synthesise user research due to AI-supported efficiency gains
37.5%
efficiency improvement in discovery process by reducing team size
Improved collaboration
between key stakeholders through better information sharing
About the Department for Environment, Food & Rural Affairs
The Department for Environment, Food & Rural Affairs is responsible for improving and protecting the environment. They aim to grow a green economy and sustain thriving rural communities, and support the UK’s world-leading food, farming and fishing industries. Defra is a ministerial department, supported by 35 agencies and public bodies.
Industry
Government
Organisation size
20,000+
Location
UK
Project length
Ongoing
Challenge
Navigating a complex policy landscape slowed down delivery of new services
Defra asked Equal Experts to conduct a discovery that would provide a better understanding of the Nature Restoration Fund’s policy intent, and provide recommendations on proceeding to alpha in a way that would meet user needs in line with wider strategic objectives.
This was a significant challenge because of the volume of information spread across multiple teams and departments and the limited collaboration between these departments.
With more than 70 stakeholders, the challenge was to create a collaborative engagement that united the digital Nature Recovery delivery group, Defra policy, MHCLG (Ministry of Housing, Communities & Local Government) Policy, and its key client, Natural England..
The key objectives of the Discovery phase were:
- Understanding the full problem and identifying user needs across all channels (offline and online)
- Recognising constraints such as technology and legislation
- Engaging stakeholders and other services
- Ensuring the resulting service is inclusive, accessible and aligned with government goals for a consistent, user-friendly experience
Solution
AI-enabled discovery with human oversight and collaboration
In June 2025, Defra launched a discovery phase to analyse and collate this essential information from across multiple agencies and teams.
We built a lean multidisciplinary discovery team who worked closely with the new Defra AI Capability and Enablement (AICE) team who provide AI guardrails and expertise across digital delivery teams..
To kick off the discovery phase, a user-centred design lead was embedded in the AICE team for a month. This jump started the team’s enthusiasm for AI, while the AICE also held sessions on AI-enabled working, prompt engineering, the AI SDLC and approved AI tools.
The team worked to understand how AI could enhance the complex process of collating, analysing and understanding large volumes of information. For example, AI tools helped the team to rapidly search public government information to understand complex topics in this domain and create initial process flows.
As the Discovery progressed the team were able to craft better prompts, and use AI tools to elicit information using standard and deep research. AI became like a virtual-grad or research assistant for the discovery team.
As the team’s confidence in AI’s ability to understand information increased, and their prompts improved, communication and collaboration was also improved. Team members could be well prepared for stakeholder meetings, pulling out key stats and information in a matter of hours, rather than days.
Defra also wanted to capture and share experiences and lessons learned during the AI-enabled Discovery. The team created a shared AI prompt library that captured prompts along with details of what worked – and what didn’t. The team held a fortnightly show-and-tell event, which provided an opportunity to:
- Share discovery findings
- Detail which findings were AI-enhanced, how these findings were checked and validated
- Present an ‘AI tips for you’ section, providing useful prompts to our stakeholder audience to use in their own roles
Results
Efficiency gains, enhanced relationships, and valuable lessons learned
Adopting AI as part of the discovery process has allowed Defra to improve speed and efficiency in discovery.
In one example, a UCD lead was able to conduct user research with 21 participants and synthesise the findings in only 3.4 weeks. Before using AI, this process would usually take two people up to four weeks to complete.
The team was originally forecast to require eight experts but the work was delivered by a team of five senior experts, representing a 37.5% efficiency gain.
Meanwhile, the team captured what has been learned about using AI in Discovery which has been fed back and will be incorporated into the Defra AI SDLC playbook.
Other important results include:
- AI-enhanced discovery allowed us to deliver a broader scope with a leaner team over the 12-week Discovery phase
- The team built clear traceability to show how AI reaches its conclusions, reducing the risk of automation complacency
- Learning how AI can be used more effectively in the Discovery phase, and sharing this through the Defra AI SDLC Playbook for other digital initiatives to leverage the benefits
While AI has been a powerful accelerator in this Discovery, the team also identified some limitations, or areas where it’s not yet suitable, such as stakeholder mapping, or managing risks and issues (RAIDs).
The team also found that some AI tools worked better than others. For example, tools like Google and OpenAI have limitations on the amount of data that can be analysed, making them unsuitable for large-scale analysis.
A significant challenge was realising that MS Teams transcriptions could be unreliable, so the team created a rule that transcriptions would always be manually checked before they are used for generating AI outputs.
Conclusion
Successfully demonstrated AI's potential in discovery while building crucial stakeholder trust
The Defra Nature Restoration Fund Discovery team completed a successful discovery, and vastly increased their AI knowledge and experience in the process. Importantly, this process has built trust in digital among key stakeholders across a complex organisation, opening the door to future AI-enabled discoveries and initiatives..
It demonstrated that AI is an incredible tool for accelerating specific parts of the discovery process, especially research and understanding of the problem space and policy intent. However, it is a powerful assistant, rather than a replacement for human judgment.
The team is now working through the Alpha phase leveraging and evolving AI learnings from the Discovery.
I’m hugely thankful for the careful and considered way this team has boldly embraced the use of AI in the Discovery phase which has yielded a wealth of learnings for both those involved in the Nature Restoration Fund but also across digital delivery. I was inspired by the integrity with which the team members used the efficiency gains they realised to increase the depth of research and analysis they conducted over the 12 week period. I believe the outputs of this work, in terms of the rich insights uncovered, have set the wider NRF programme up for success.
Delivery Group Lead
, Defra DDTS Nature Recovery Delivery Group