Implementing AI-Driven Reviewer Selection: A Roadmap for Funding Agencies

Moving from manual reviewer selection to AI-assisted processes changes how grant evaluation works. Based on conversations with funding agencies across 30 countries, here's what the implementation process actually looks like.

Implementation Timeline: From First Demo to Full Deployment

The path from first contact to full deployment takes anywhere from 2 months to over a year. Here's the realistic breakdown.

Week 1: Initial demonstration

Most agencies schedule a demo within one week of first contact. This session covers the core functionality—how the system generates reviewer suggestions, applies diversity filters, and detects conflicts of interest automatically.

Program officers typically respond with interest at this stage. The challenge isn't convincing them the tool works. The challenge is finding time for what comes next.

Weeks 2-4: Free trial setup

We offer a 2-week unlimited trial. No restrictions on users, submissions, or features. The goal is practical testing with real grant proposals.

Here's where things get complicated. Many agencies want to test but lack the capacity. One French agency recently told us: "We're excited about Prophy, but this year we don't have the energy or resources to test properly. Let's reconnect next year."

This isn't about doubting the tool. It's about bandwidth. Grant evaluation teams already operate under tight deadlines with manual processes. Taking time to test something new—even something that will eventually save time—requires resources they don't always have.

For agencies that move forward, trial setup typically needs 2-4 weeks to coordinate schedules and compile user lists.

Weeks 5-10: Contract negotiation

After a successful trial, negotiation with procurement, compliance, and legal departments begins. This phase averages 3-6 weeks but varies widely.

One UK agency had program officers ready to proceed in January. Six months later, the contract still sat in the legal review queue. Not because of problems with terms, but because of limited legal department capacity and a backlog of vendor agreements.

A German agency initially rejected us outright. Their compliance team decided we weren't secure enough without discussion. After we requested a meeting to understand their specific concerns and walked through our security protocols, certificates, and government client experience, they reversed their position within 30 minutes. We now have an annual contract with them.

Total realistic timeline: 2-3 months minimum

When everything goes smoothly—engaged stakeholders, available internal resources, efficient procurement—you can go from first demo to active license in 2-3 months.

When it doesn't—summer vacations, competing priorities, lengthy compliance reviews—expect 6-12 months.

Managing Change: Building Confidence in AI-Assisted Grant Review

The program officers and scientific directors who actually use reviewer selection tools typically embrace them quickly. The resistance comes from elsewhere.

Building user confidence through trials

We don't ask agencies to commit before testing. The free trial includes testing with actual grant proposals from your current cycle, full access to all features, and no obligations.

Most agencies test with 5-15 proposals to evaluate how the system handles different research fields and proposal complexity.

This matters because we've seen the alternative. When organizations don't get hands-on experience, misconceptions persist. Compliance teams worry about security without understanding how the system actually handles data. Administrators question value without seeing the time savings.

After trials, the conversation changes. Users share specific examples: "Finding 10 qualified reviewers used to take me 3 hours. Now it takes 15 minutes."

Addressing department-specific concerns

Different stakeholders need different information:

Program officers and scientific directors care about recommendation quality. They want to know: Do the suggested reviewers actually have relevant expertise? How does the system handle interdisciplinary proposals?

We address this by letting them test with their own proposals. They see the semantic matching in action, apply discipline filters, and compare results to their manual selections.

Compliance and legal teams focus on data security and privacy. They ask about server locations, data segregation, backup protocols, and whether submissions train machine learning models.

We bring them into trials as users—not just to review documentation, but to see how the system processes sensitive, unpublished research. We demonstrate ISO 27001 infrastructure, explain our European hosting, and show how we create non-human-readable fingerprints of proposals.

Procurement departments evaluate vendor reliability and contract terms. They need proof the company won't disappear and the product won't be abandoned.

We point to 7 years of operation, 50+ organizations across 30 countries, and long-term clients like the European Research Council. We also participate in public tenders, which means we're experienced with thorough vetting processes.

The most effective confidence builder: Customization

Many agencies assume AI tools are rigid. They expect to adapt their processes to fit the software.

When they discover we customize features based on their feedback, it changes their perspective. One US partner suggested improvements during a call. Two weeks later, we'd implemented the features. That responsiveness—seeing their input directly shape the product—built more trust than any sales pitch.

Measuring Success: Key Performance Indicators for First-Year Implementation

Agencies measure success through four main metrics.

Speed of reviewer identification

Track how long it takes to generate a qualified reviewer list compared to manual methods. Most agencies see dramatic improvements here—one scientific director noted that Prophy could "replace an entire full-time equivalent role in some organizations."

Measure both average time per proposal and time for complex, interdisciplinary cases that typically take longest manually.

Diversity of selected reviewers

Before implementation, agencies often rely on familiar reviewers from specific geographies, institutions, or seniority levels. The system expands this pool automatically.

Track metrics like:

• Gender distribution
• Geographic spread
• Career stage representation (early vs. established researchers)
• Institutional diversity

The goal isn't just meeting diversity requirements. It's discovering qualified experts you didn't know existed.

Acceptance rates from invited reviewers

When you contact more relevant experts with genuine expertise in the proposal topic, more say yes. Track acceptance rates before and after implementation.

Higher acceptance rates mean fewer review cycles, faster funding decisions, and less administrative overhead from repeated invitations.

Efficiency gains per officer

Measure grants handled per program officer and total time saved across the team. This connects directly to operational capacity.

If officers spend 3 hours per proposal finding reviewers manually, and the system reduces that to 30 minutes, a team processing 200 proposals annually saves roughly 500 hours. That's time redirected to higher-value activities like strategy, stakeholder engagement, or processing more applications.

What these metrics reveal

Good performance across all four metrics indicates successful implementation. Strong performance in speed but weak diversity suggests the filters aren't being used effectively. High diversity but low acceptance rates might mean recommendations aren't specialized enough for complex proposals.

Funding Agency vs Publisher Implementation: Understanding Key Differences

Publishers and funding agencies both need peer review support, but their constraints differ significantly.

Data privacy and unpublished research

Publishers handle submitted manuscripts, which will eventually be public. Funding agencies evaluate grant proposals—research that may never be published or may contain commercially sensitive information.

This makes compliance teams more cautious. They dig deeper into security protocols. They ask about server locations, data segregation policies, and whether uploads train machine learning algorithms (they don't in our system).

We address this through:

• European-hosted servers with ISO 27001 certification
• Non-sharing policies between clients
• Creation of non-human-readable proposal fingerprints for matching
• Detailed documentation of data handling procedures

When one German agency initially rejected us on security grounds, a 30-minute technical discussion resolved their concerns. They needed specifics, not general assurances.

Integration preferences

Publishers typically want API integration with editorial management systems. They're processing high volumes of manuscripts and need automated workflows.

Funding agencies use the system more as a standalone application. Many don't have grant management systems robust enough for integration, or they prefer keeping proposal evaluation separate from other administrative tools.

We support both approaches. APIs exist for agencies that want integration, but most prefer the flexibility of a dedicated platform they can access without changing existing systems.

Regulatory and compliance requirements

Government funding bodies face procurement regulations, public tender processes, and additional oversight that commercial publishers don't.

This extends timelines. It requires extensive documentation. It means answering hundreds of questions about operations, security, and business continuity.

We've built expertise here through experience. Working with the European Research Council, various national foundations, and government agencies means we understand what compliance teams need. We have the certificates, documentation, and track record to satisfy thorough vetting.

Diversity and conflict detection priorities

Both publishers and agencies care about conflicts of interest, but agencies place heavier emphasis on reviewer diversity. This isn't just about fairness—it's often mandated by funding rules.

Our system handles both automatically. It detects co-authorship and co-affiliation patterns for conflict identification. It provides filters for gender, geography, career stage, and institutional diversity for selection.

Agencies that used limited reviewer pools before implementation discover qualified experts they'd never encountered through manual searching.

Integration Strategy: Fitting AI Tools into Existing Workflows

We don't force agencies to replace their systems. We fit into what they already use.

Flexible input options

Agencies can upload proposals multiple ways:

• One at a time through the interface
• Bulk uploads for processing multiple proposals
• Abstract-only submissions when full proposals contain sensitive budget or institutional information

This flexibility matters because agencies have different internal processes. Some evaluate proposals in batches. Others handle them individually as they arrive.

Configurable export formats

Many agencies maintain reviewer databases in CRMs or spreadsheets. Manually transferring information from our platform to their existing systems would eliminate the efficiency gains.

We provide configurable exports that match their existing data structures. If an agency's CRM has specific columns for author concepts, publication metrics, and contact details, we can export results in that exact format.

This takes minutes instead of hours of manual data entry.

API availability for integration

For agencies with grant management systems, we offer APIs for direct integration. This allows automated proposal ingestion and reviewer suggestion generation without manual file uploads.

Most funding agencies don't need this level of integration, but it's available for those who do.

Support during implementation

Each agency gets a dedicated customer success manager who:

• Provides onboarding sessions for new users
• Answers questions about features and best practices
• Gathers feedback for customization requests
• Conducts regular check-ins after deployment

This ongoing communication catches problems early. When users struggle with specific proposal types or don't understand how to use certain filters, we address it immediately rather than waiting for frustration to build.

Responsive to customization requests

When agencies identify missing features or process improvements, we implement them when feasible.

Recently, a US partner suggested several enhancements during a call. Within two weeks, we'd built and deployed the features. That responsiveness—seeing their input directly improve the product—strengthens the partnership and ensures the tool evolves to meet real needs.

Moving to AI-assisted reviewer selection takes longer than expected—not because of the technology, but because of procurement and compliance processes.

Program officers typically embrace these tools quickly after testing with their own proposals. When implementation goes smoothly, agencies see the value within the first few months: faster reviewer identification, broader expert pools, and time redirected to higher-value work. As one scientific director put it after her first demo: "I'm certain I'd have far less gray hair if I was an early adopter."