Your AI Tool Isn’t the Problem. Your Data Is.

Too many AI adoption strategies start by judging whether an off-the-shelf tool would be a better option than a bespoke in-house solution. This is a reasonable question, but it’s the wrong place to start.

The choice between a commercial platform or a proprietary system is not the hard part – although I do, of course, have an opinion. More important is to work out your AI proposal data strategy i.e. how you will give whichever tool you adopt the data and context it needs to develop complete, compliant, and compelling responses.

What ‘data and context’ means in a bid setting

When we talk about the data and context in terms of proposal development, we mean three distinct things:

Evidence of how you do things and the results you’ve achieved

Performance metrics, case studies, project outcomes, client testimonials, and delivery data. This is your proof. Without it, your AI will generate non-specific statements resulting in the dreaded ‘We will deliver x’ type assertions rather than evidence-backed responses of exactly who will deliver it, when they will start and finish, using what tools and where they will do so. Without this sort of detail, the AI tool will simply generate unsupported claims that evaluators view with scepticism and earn few marks.

A summary of how you will do things on this specific bid

The technical solution needs to exist in written form before the writing starts. This means solution teams must commit their intended approach to paper. A structured summary will provide the AI with something substantive to work from. Asking AI to draft a methodology response without providing it with guidance is asking it to make stuff up (something proposal writers are very familiar with!).

The reference documents that demonstrate alignment with accepted good practice

ISO standards, government policy, relevant strategy documents and sector frameworks. These provide the credibility scaffolding that evaluators are looking for, particularly in defence and public sector procurement.

The problem with waiting to build your data set

If you are waiting until the RFP arrives to start thinking about your data set, you are already behind. The curation of your content library is pre-bid work and belongs in the capture phase, not in the proposal production phase.
This is a mindset shift that proposal leaders need to drive. The months of capture activity before an RFP is released are precisely when your library should be reviewed, updated, stress-tested and refined against the anticipated requirements. By the time the clock starts, you want a data set that’s ready to work.

AI amplifies what’s already there – good or bad

This is perhaps the most important principle to understand about AI in proposal development. It can only use what it’s given and it builds on that. It won’t necessarily know if it is fed rubbish – especially if the toggle switch for drawing from internet material is set to ‘off’. If your data is strong, current, specific, evidence-rich, and well-structured, AI will produce better responses faster than any team of writers working manually. If your data is weak, generic, out of date, or poorly organised, AI will happily reproduce and build on those weaknesses.

Your content library is not a filing cabinet

One of the most common mistakes we see is the content repository that has become a dumping ground. Every past proposal section, every old case study, every superseded policy document is in the library. All the documents are stored together with no curation discipline; there is no structure, metadata or quality control.

An AI tool drawing on that kind of library will pull outdated contract values, reference decommissioned programmes, and cite performance data that no longer reflects your current capability. It will do so fluently and with apparent confidence. The result will be a weak proposal at best, or a noncompliant / failed one at worst.

A well-governed content library should contain only what you want to see reflected in a response. Specifically, it must be:

• Current. The currency of evidence is critical. An outstanding project delivery from six years ago carries far less weight than one from last year, particularly when evaluators are assessing current capability.
• Relevant. Matched to the types of bids you are pursuing, not a general archive of everything you have ever done.
• Accessible. Structured so the AI can retrieve the right content. This means consistent formatting, appropriate tagging and metadata that tells the tool where to look.
• Governed. Someone owns it. There is a process for updating it, retiring old content, and adding new proof points after every significant delivery milestone.

A pre-bid data readiness checklist

Before you begin any AI-assisted proposal response, your leadership team should be able to answer yes to the following:

• Is the data we need available and accessible within our content library?
• Is the data accurate, complete and quality-assured?
• Is the data governed and version-controlled, with a clear owner?
• Is the data tagged with metadata so the AI can locate and retrieve relevant content?

Where the answer is no, you have identified a gap. In most cases, the gaps can be addressed through a targeted request for information to your technical, commercial, or delivery teams. Where the gap remains unfilled, you can assume your response will also have gaps.

Reinvesting the time AI saves

AI will save your writers significant time on drafting. But the instinct to simply use that time to write more, faster, misses the point. The extra time should be used by the writers to hone the content and apply good storytelling.

The writing time saved by AI also needs to be reinvested in building and curating the data set that makes the next bid and every subsequent bid better. Proposal managers who understand this will use that capacity to update the evidence library after every delivery milestone, to work with solution teams to document their approaches earlier in the capture cycle and to close the gaps that currently hold their responses back.

The case against using AI

Some will argue it’s simpler to stay analogue: keep writing everything from scratch, keep relying on experienced writers, keep doing what has worked before. There is a logic to that position.

The analogue route, however comfortable, is not a competitive advantage in a world where your rivals are investing in AI-enabled capability.

The teams that invest in building and maintaining a high-quality data library will consistently produce better first drafts, cover more requirements, and have more time for the strategic thinking and storytelling that genuinely wins bids.

Final thoughts

AI adoption in proposal teams isn’t just about buying licences and sorting out information security, important though those things are. It demands a new discipline around content quality and data governance. The proposal managers who build that discipline will produce winning bids more consistently. Those who don’t will find that their AI tool simply amplifies exactly the weaknesses they were hoping to overcome.

How Salentis can help

Our consultants and subject matter experts work with bid teams to design, build, and govern the content libraries that make AI-assisted proposal development genuinely effective. Whether you are starting from scratch or trying to bring discipline to an existing repository, we can help you create a data set that is current, relevant, and built to win.

Get in touch to find out how we can support your team.

Article published: June 2026

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