How to Manage Large Volumes of Grant Applications

Most grant funds receive far more applications than they can fund. That is by design — open funding programmes are meant to attract a wide field. But the operational reality of processing hundreds or thousands of applications with a small team is a different matter entirely. Many funders find themselves caught between two pressures: the desire to be accessible and the practical limits of staff capacity.

The numbers are stark. Research by Pro Bono Economics for the Law Family Commission on Civil Society estimated that the UK charity sector spends between £442 million and £1.1 billion per year on grant applications, with two-thirds of those applications ultimately unsuccessful. On the funder side, the Directory of Social Change found that between a fifth and a third of applications received are ineligible — meaning staff time is spent reviewing proposals that should never have entered the pipeline in the first place. UK charitable foundations have reported application volume increases of 50 to 100 per cent in recent years, with some programmes seeing surges of up to 400 per cent, partly driven by AI-assisted application writing lowering barriers to submission.

This guide sets out practical strategies for managing volume without increasing headcount, without cutting corners on quality, and without closing the door to smaller organisations that lack the capacity to navigate complex processes.

Why application volumes are rising

Application numbers are not rising because more charities exist. They are rising because the cost of applying is falling while demand for funding grows. The proliferation of AI writing tools means that organisations can now draft a passable grant application in hours rather than days. According to a 2024 study by Instrumentl, 90 per cent of nonprofits have already implemented AI for at least one operational purpose, and grant writing is among the most common applications. For funders, this means more applications per round, but not necessarily better-quality ones.

At the same time, the funding landscape has tightened. A late-2024 survey of trust fundraisers reported an average success rate of around 35.6 per cent, down from roughly 40 per cent in 2020 (Hinchilla, 2025). Many charities now consider a 20 to 30 per cent success rate a realistic benchmark. When success rates fall, organisations compensate by applying to more funders, which further increases the volume each funder must process.

The result is a compounding cycle. More applications per round leads to longer processing times, which delays decisions, which frustrates applicants, which damages the funder's reputation, which makes it harder to attract high-quality proposals in subsequent rounds. Breaking this cycle requires structural changes to how applications are received, filtered, and assessed — not simply asking staff to work faster.

How much does it cost to process a grant application?

Before redesigning your process, it helps to understand where time actually goes. The table below breaks down a typical per-application cost for a mid-sized UK funder processing a standard programme grant.

For a fund receiving 500 applications, that is 1,000 to 2,500 hours of staff time — roughly 0.5 to 1.25 full-time equivalent posts dedicated entirely to one funding round. Research by Flexigrant found that reviewers can spend 3 to 5 hours per application when dealing with inconsistent formats, missing information, and manual scoring. The economics are clear: any reduction in the number of applications that reach the full-assessment stage, or any reduction in the time each assessment takes, delivers meaningful savings.

Understanding these costs also reveals where to focus. Eligibility checking and initial triage are high-volume, low-judgement tasks — ideal candidates for automation. Full assessment is lower-volume but higher-judgement — better suited to AI-assisted review than full automation. Decision communication is repetitive but sensitive — a good fit for templated responses with human oversight.

Building an eligibility gate

The single most effective intervention for managing volume is preventing ineligible applications from entering your pipeline. The Directory of Social Change found that between a fifth and a third of grant applications are ineligible. If your fund receives 500 applications and 25 per cent are ineligible, that is 125 applications consuming staff time for no purpose.

An eligibility gate is a set of hard criteria that applicants must meet before they can submit a full application. These are not subjective judgements — they are factual checks. Is the applicant a registered charity? Does it operate in the right geography? Is the request within the funding range? Does the work fall within the programme's theme?

The most effective eligibility gates are automated. Rather than asking applicants to self-certify and then checking manually, use a screening form with conditional logic. If the applicant selects a geographic area outside your remit, the form explains why they are ineligible and stops there. No application is created, no staff time is consumed, and the applicant receives an immediate, clear explanation rather than waiting weeks for a rejection.

This approach is consistent with the recommendations of the Law Family Commission, which urged funders to publish clear eligibility criteria and provide eligibility checkers to reduce the volume of ineligible applications. It also aligns with IVAR's Open and Trusting principles, which call for funders to be proportionate in their requirements and open about how decisions are made.

Tools like Plinth support eligibility gating in three modes: rule-based conditions (hard filters on specific form responses), AI-evaluated eligibility (where the system assesses applications against fund criteria using natural language), or a hybrid of both. The hybrid mode applies hard filters first and then uses AI for more nuanced evaluation — for example, determining whether a project genuinely addresses the fund's thematic priorities rather than merely using the right keywords.

Using expressions of interest to reduce full applications

A two-stage process — expression of interest (EOI) followed by full application — is one of the most widely adopted strategies for managing volume. The principle is simple: ask for a short summary first, review it quickly, and invite only the strongest candidates to complete a full application. This saves time for both funders and applicants. Organisations that are unlikely to succeed do not invest hours in a lengthy form, and funders do not spend hours assessing proposals that were never competitive.

An effective EOI is short — typically 3 to 5 questions covering the organisation, the need, the proposed approach, and the budget range. It should take an applicant no more than an hour to complete. The funder then reviews EOIs against broad fit criteria and invites a shortlist to the next stage. A well-designed EOI stage can reduce the number of full applications by 50 to 70 per cent, depending on how tightly the fund's criteria are defined.

Plinth supports multi-stage application processes natively. Funders can configure any number of stages — such as EOI, full application, and interview — each with its own form, deadline, and review process. Applicants are explicitly advanced between stages, and information from earlier stages carries forward so that applicants are not asked to repeat themselves. Each stage can have its own assessment form and assessor assignments, allowing different team members or external assessors to review at different points.

The key risk with EOIs is adding burden without adding value. If the EOI is too detailed, it becomes a mini-application and defeats the purpose. If the criteria for advancing are unclear, applicants feel the process is arbitrary. Publish your EOI criteria. State how many organisations you expect to advance. Give a clear timeline. Transparency at this stage builds trust and reduces the volume of follow-up enquiries.

Structuring triage for speed and consistency

Even after eligibility screening and an EOI stage, the applications that reach full review still need to be triaged. Triage is the process of sorting applications into categories — typically "clearly strong," "clearly weak," and "requires discussion" — so that reviewers spend the most time where it matters.

Effective triage depends on three things: clear criteria, consistent application of those criteria, and speed. The criteria should be drawn directly from your published assessment framework. If your framework has five criteria, your triage should use the same five criteria — just with a quicker, higher-level pass.

A common triage model uses a three-tier system:

The goal is to move Tier 1 and Tier 3 applications through the process quickly, reserving detailed human assessment for the Tier 2 applications where judgement is genuinely needed. In a typical round, Tier 1 and Tier 3 together may account for 50 to 60 per cent of applications, meaning that structured triage can halve the number of applications requiring full reviewer time.

AI can accelerate triage significantly. Rather than having a programme officer read every application in full before categorising it, an AI tool can generate a summary and an initial assessment against the fund's criteria. The programme officer then reviews the AI output and confirms or adjusts the tier. This shifts the task from "read and assess" to "review and validate" — a faster, less cognitively demanding process.

Assigning and managing assessors at scale

Once applications are triaged, they need to be assigned to reviewers. For small funds, this is straightforward — the programme manager reads everything. For larger funds with external assessors, panel members, or specialist reviewers, assignment becomes a logistical challenge in its own right.

The standard approach is to assign each application to two independent reviewers, adding a third if scores diverge significantly. For a fund with 200 applications reaching full review and two reviewers per application, that is 400 individual assignments. Managing this manually — tracking who has been assigned what, chasing deadlines, reconciling scores — can consume as much time as the assessments themselves.

Bulk assignment is essential at scale. Rather than assigning one application at a time, select a batch of applications and assign them to a pool of reviewers, either giving each reviewer all selected applications or distributing them randomly so that each reviewer receives a manageable number. Plinth's bulk assignment feature supports both modes: assign all selected applications to chosen assessors, or randomly distribute a set number of applications per assessor across the pool.

External assessors add another layer of complexity. They need access to application materials but not to the funder's internal systems. They need clear instructions, a structured scoring form, and a deadline. Plinth supports external assessor groups with granular permissions — funders can control which application questions are visible to external reviewers and which are redacted, allowing sensitive financial or organisational information to be hidden from assessors who do not need it.

Managing conflicts of interest is also easier to handle systematically at scale. Rather than relying on individual declarations, build conflict checks into the assignment process. If a reviewer has a declared connection to an applicant, the system should prevent assignment automatically. This is particularly important for community foundations and place-based funders where personal connections are common.

Using AI to support (not replace) assessment

AI is not a substitute for human judgement in grant decisions. It is a tool for reducing the administrative burden that surrounds those decisions. The distinction matters — funders who position AI as a decision-making tool will rightly face scrutiny, while funders who use AI to help reviewers work more efficiently are simply adopting good practice.

The most valuable AI applications in high-volume grant assessment include:

Application summarisation. An AI tool reads the full application and generates a structured summary against the fund's criteria. The reviewer reads the summary first, then dips into the full application where needed. This is particularly valuable for applications that run to 15 or 20 pages — common for grants above £50,000.

Pre-populated assessment forms. AI reads the application and drafts answers to each question on the assessment form, along with a brief justification. The reviewer then adjusts, overrides, or approves each answer. Plinth's AI assessment feature (called Pippin) does exactly this — it reads the application content, generates draft scores and justifications for each assessment question, and presents them for human review. Funders can control the verbosity of AI justifications from a single sentence to a detailed paragraph, and choose whether the AI considers the full application or only the sections linked to each assessment question.

Rejection analytics. After a round closes, AI can analyse patterns across rejected applications — identifying common reasons for rejection, typical applicant profiles, and areas where the fund's guidance could be clearer. This feeds directly into improving future rounds. Plinth generates these insights automatically from the rejected applications in each funding round.

The key principle is human-in-the-loop. Every AI-generated assessment should be reviewed and approved by a human before it influences a funding decision. AI can draft; humans decide. This approach is consistent with the ethical frameworks for AI in grantmaking that are emerging across the sector.

Communicating decisions at scale

Telling 400 applicants whether they have been successful is a significant communication task. Doing it well — with clear reasoning, constructive feedback, and appropriate tone — is important for the funder's reputation and for the sector. Research from IVAR's Open and Trusting initiative emphasises that honest feedback is one of the qualities charities most value in funders.

The challenge is that personalised feedback for every unsuccessful applicant is impractical at scale. A programme officer cannot write 300 individual rejection letters. But a generic "we regret to inform you" letter with no explanation is unhelpful and damages trust.

The middle ground is templated feedback with structured personalisation. Define a set of common rejection reasons — "outside geographic scope," "budget exceeds programme limits," "insufficient evidence of need," "similar work already funded in this area." For each application, select the relevant reasons and let the system generate a letter that combines the template with application-specific details.

Plinth supports this through configurable rejection templates. Funders create a library of rejection reason instructions, each with a short label and an AI prompt. When declining an application, the programme officer selects the relevant reasons, and the AI generates a personalised feedback letter drawing on those instructions and the specific application. The result is feedback that feels considered and specific without requiring individual drafting for each applicant.

For successful applicants, the communication challenge is different — it is about clarity of next steps. Automated notifications with grant agreement details, payment schedules, and monitoring expectations ensure that the transition from "awarded" to "active grant" does not stall because of unclear process.

Staggering deadlines and managing peak load

One underrated strategy for managing volume is not receiving all applications at once. Many funders operate with a single annual deadline, which creates a predictable but intense peak of activity. All applications arrive in a two-week window, all must be reviewed before the panel date, and staff are overwhelmed for eight weeks before returning to relative quiet.

Rolling deadlines spread the load more evenly but introduce complexity — how do you compare applications fairly if they arrive at different times? A middle ground is multiple fixed deadlines per year (quarterly or biannual), each for a smaller batch. This reduces peak load while maintaining the ability to compare applications within each cohort.

Another approach is to stagger the process internally. Even with a single deadline, you do not need to start all applications at the same stage simultaneously. Run eligibility checks in the first week, triage in the second, assign reviewers in the third, and allow four weeks for assessment. This creates a steady flow rather than a bottleneck.

Pre-booking reviewers before the deadline is also essential. If you know your fund closes on 1 March and you will need ten external assessors, confirm their availability in January. Send them the assessment criteria, a sample application, and a calibration briefing. When applications arrive, reviewers can start immediately rather than spending the first week reading instructions.

Measuring and improving your process

You cannot improve what you do not measure. For high-volume funds, the key metrics are:

• Time to decision: Days from submission to the applicant receiving a decision. IVAR's Open and Trusting principles call for funders to publish this.
• Cost per application: Total staff hours divided by number of applications. Include screening, triage, assessment, panel, and communication.
• Ineligible rate: Percentage of applications that fail eligibility. A high rate (above 20 per cent) suggests your criteria are not clear enough or your eligibility gate is not effective.
• Reviewer variance: The spread of scores between reviewers for the same application. High variance indicates that criteria or scoring guidance need improvement.
• Applicant satisfaction: Survey applicants (successful and unsuccessful) about their experience. Even a simple Net Promoter Score gives you a benchmark.

Track these metrics across rounds and look for trends. If your ineligible rate is falling, your eligibility screening is working. If time to decision is rising, you have a capacity or process bottleneck. If reviewer variance is high, invest in calibration.

Plinth provides dashboards that surface many of these metrics automatically — application volumes by stage, assessor progress, average scores, and rejection reason distributions. Funders managing high volumes need this visibility in real time, not in a retrospective report six months after the round closes.

FAQs

How many reviewers should we assign per application?

Two independent reviewers is the standard for most grant programmes. Add a third reviewer or escalate to a panel discussion if the two scores diverge by more than a predetermined threshold — typically 20 per cent of the total available score. For smaller grants (under £10,000), a single reviewer with a second reviewer for a random sample may be proportionate.

Can AI fully assess grant applications without human review?

No. AI can summarise applications, draft assessment scores, and flag potential issues, but it should not make funding decisions autonomously. The sector consensus, reflected in guidance from bodies like the Association of Charitable Foundations, is that AI should support human decision-making, not replace it. Every AI-generated assessment should be reviewed by a qualified human before it influences a funding decision.

What is a good ineligible application rate?

Below 10 per cent suggests your eligibility criteria and screening form are working well. Between 10 and 20 per cent is typical. Above 20 per cent indicates that your criteria are unclear, your screening process is insufficient, or your fund is poorly targeted. The Directory of Social Change found that between a fifth and a third of grant applications nationally are ineligible, so anything below 20 per cent puts you ahead of the sector average.

How do we handle conflicts of interest at scale?

Build conflict-of-interest declarations into the assessor onboarding process and automate assignment rules so that flagged assessors are not assigned to conflicted applications. For panel meetings, record declarations at the start of each session and exclude conflicted members from discussion and voting on specific applications. Software that tracks declared interests and prevents conflicting assignments is more reliable than relying on individuals to recuse themselves in the moment.

Should we use rolling deadlines or fixed rounds?

It depends on your fund size and team capacity. Fixed rounds are simpler to administer and allow direct comparison between applications, but create peak workload. Rolling deadlines spread the load but require clear criteria for when the fund is assessed (monthly batches, quarterly panels). A hybrid approach — two or three fixed deadlines per year — often works best, reducing peak load while maintaining comparability.

How do we give feedback to unsuccessful applicants without overwhelming staff?

Use templated feedback with structured personalisation. Define a library of common rejection reasons with associated feedback text. For each declined application, select the relevant reasons and generate a personalised letter. AI can draft these letters based on the selected reasons and the application content, producing feedback that is specific enough to be useful without requiring individual composition for every applicant.

What is the minimum viable process for a small fund?

A small fund (under 50 applications per round) can manage with an eligibility screening form, a single reviewer per application (with a second for borderline cases), and a standardised scoring rubric. As volumes grow beyond 100 applications, invest in multi-stage processes, external assessors, and AI-assisted triage. The threshold for needing dedicated grant management software is typically around 50 to 100 applications per round.

Recommended next pages

• How to Automate Reviewer Workflows — Practical approaches to reducing admin burden on assessors and panel members
• How to Write Clear Grant Criteria — Designing criteria that reduce ineligible applications and improve assessment consistency
• What Is an Assessment Framework? — Building a structured framework for scoring and comparing applications fairly
• How to Onboard Reviewers Effectively — Preparing external assessors for efficient, consistent grant review
• Reducing the Burden on Grant Applicants — Designing proportionate processes that work for applicants and funders alike

Last updated: February 2026