One impact claim, three readings. Agreeing on what a target measures is a consortium decision, not a drafting task.
Take one illustrative line from an impact section: ‘We will reach 40,000 users.’
→ The university means people reached through communication.
→ The SME means potential customers.
→ The public authority means citizens expected to benefit from a service.
One number. Three different commitments. The wording looks consistent. The expectations are not. AI did not create the gap. It made it look finished. Before anyone validates the figure, someone has to get the partners to agree on what it measures.
Communication, dissemination and exploitation make that distinction essential: building awareness, sharing research results and putting them to use require different measures of success. An audience reached is not proof of adoption. A credible proposal explains who is expected to use the results, what would enable that use and which partner takes responsibility for the next step.
Resolving this takes negotiation from the start. Universities, companies and public bodies bring different missions, data policies and definitions of success. Alongside budgets and work packages, partners need to agree what information may enter AI tools, who validates claims against sources and assumptions, and who approves the commitments made in their name. Only then does the writing team know what it is writing.
Getting institutions to agree on what they promise, how they will demonstrate delivery and who takes responsibility has been central to my work. That agreement gives a proposal its credibility long before submission. Building it is what I want to bring to a research office or an institution with European partnerships at its core. For coordinators and research offices: who checks that a shared target means the same thing to every partner?
→ The university means people reached through communication.
→ The SME means potential customers.
→ The public authority means citizens expected to benefit from a service.
One number. Three different commitments. The wording looks consistent. The expectations are not. AI did not create the gap. It made it look finished. Before anyone validates the figure, someone has to get the partners to agree on what it measures.
Communication, dissemination and exploitation make that distinction essential: building awareness, sharing research results and putting them to use require different measures of success. An audience reached is not proof of adoption. A credible proposal explains who is expected to use the results, what would enable that use and which partner takes responsibility for the next step.
Resolving this takes negotiation from the start. Universities, companies and public bodies bring different missions, data policies and definitions of success. Alongside budgets and work packages, partners need to agree what information may enter AI tools, who validates claims against sources and assumptions, and who approves the commitments made in their name. Only then does the writing team know what it is writing.
Getting institutions to agree on what they promise, how they will demonstrate delivery and who takes responsibility has been central to my work. That agreement gives a proposal its credibility long before submission. Building it is what I want to bring to a research office or an institution with European partnerships at its core. For coordinators and research offices: who checks that a shared target means the same thing to every partner?