Experiment
DREAM BIG
WHAT IF
Farmers did not have to apply for support Defra already had enough evidence to know they were likely entitled to?
Proactive Funding
Bring evidence-led funding opportunities to farmers instead of making them find and apply for support Defra may already know they are likely entitled to.
01
Problem to address
The current application-led model places the burden on farmers and land managers to discover support, interpret eligibility, repeatedly provide information, submit an application and wait while Defra reconstructs and verifies circumstances it may already know. This can create avoidable effort for users and caseworkers, and it means policy relies on eligible people successfully navigating the application process before an intervention can happen.
02
What the evidence tells us
Farmers want earlier certainty about eligibility and repeatedly encounter fragmented information, repeated evidence and fear of getting something wrong. Staff reconstruct context across systems and spend time on manual case handling. The wider capability work asks whether information Defra already holds can be reused rather than requested again. The evidence supports the burden and the need for earlier certainty; proactively awarding or offering funding is a DREAM BIG service-model hypothesis that would require policy, legal and assurance validation.
03
The proposition to explore
The idea
Move from application-led funding towards proactive, evidence-led offers: where Defra already has sufficient trusted information to establish likely eligibility, bring the opportunity to the farmer, pre-fill or pre-approve what can safely be determined, and ask only for the information or consent still genuinely needed.
04
Hypothesis
If Defra proactively identifies likely eligible farmers and removes application steps that merely re-prove information it already holds, more appropriate support can reach people with less effort and delay, while RPA caseworkers can focus more of their time on exceptions, complex judgement and helping interventions succeed.
05
How it could work
Policy defines the target intervention and eligibility rules. Trusted connected evidence identifies farmers or land managers who appear to meet those rules and how confident Defra can be. Instead of waiting for an application, Defra contacts them with a transparent offer explaining why they may qualify, the expected funding, obligations, evidence already held and anything still requiring confirmation. Depending on risk and policy, the journey could range from a pre-filled invitation to a genuinely pre-approved offer. Exceptions and ambiguous cases remain with skilled caseworkers.
06
Example: bring it to life
A land manager receives a message from Defra: “Based on the land and agreement information we already hold, 14 hectares of your holding appear eligible for this intervention. This could provide approximately £8,400 a year. We already have the evidence shown below. Please check two things before deciding whether to accept.” Instead of finding the scheme, filling in a long form and waiting for Defra to reconstruct the same facts, the farmer reviews the offer, corrects one item and chooses whether to proceed. A caseworker only becomes involved because one parcel has an unresolved tenure issue.
07
AI opportunity
AI could help explain eligibility, identify missing evidence, summarise complex circumstances and support exception handling. It should not be the mechanism that silently determines entitlement. Eligibility rules, authoritative data, policy intent, legal basis, fairness and human routes for challenge are foundational. In many cases deterministic rules may be more appropriate than AI.
08
Potential value
- Much lower application burden for farmers and land managers
- Earlier access to support for people who may otherwise miss it
- Less repetitive eligibility casework
- More RPA capacity for complex, high-value and exception work
- Potentially faster policy intervention and improved uptake among intended groups
- A shift from administering applications towards proactively enabling outcomes
09
Reduce uncertainty without overcommitting
Smallest meaningful experiment
Select one existing funding intervention with relatively clear eligibility rules and a bounded cohort. Without changing policy or making real awards, use existing data to identify a small sample of people Defra believes would be eligible. Compare that assessment with actual caseworker decisions, identify what evidence is still missing, and prototype a proactive offer with farmers and caseworkers. Test whether the journey removes meaningful effort without reducing confidence, fairness or control.
10
Open questions and risks
What we still need to learn
- Legal or policy rules may require an application or explicit declaration
- Data errors could wrongly include or exclude people
- Proactive targeting can reproduce existing data bias and disadvantage less visible groups
- Farmers need a clear way to correct, decline or challenge the offer
- Entitlement, eligibility and environmental targeting are not always the same thing
- Trust could be damaged if Defra appears to know or infer more than users expect