Implementing Proposal AI into a Bid Function

An early key decision is whether you take a technology-led approach or a process-led approach.

A technology-led approach asks: which tool has the features we need, and how do we align our ways of working to it? This can be quicker and cheaper to implement, but it often forces standardisation around a tool that’s designed to satisfy generic market demand. That may be fine in a large, high-volume, repeatable bid environment. It becomes risky where bids are complex, or where your governance and approvals cannot bend around the tool’s workflow.

A process-led approach asks: what does our bid process need to look like, and which vendor will configure, integrate and support the tool to fit that reality? This can deliver better outcomes, but it comes with costs: more design effort, higher configuration burden, more dependency on the software vendor, and potentially more bespoke technical work. You also need to be confident that the vendor’s implementation capability is real, not just promised.

A key question to ask is, “What tool(s) do our other business functions use?” If everyone else is using Copilot, then should that become the de facto choice across the organisation, or is there a specific benefit for choosing a bespoke tool for bid management and tender responses? Careful consideration is required regarding whether a company-wide app will ‘do a job’ more cost-effectively, or whether there is a justifiable return on investment for going down the specialised route. You should test whether Copilot’s strengths (everyday drafting, summarising, email/document productivity) translate into bid-critical capabilities such as compliance, response planning, drafting, reusable libraries, collaboration, and auditability. If a specialist bid tool reduces bid risk and improves win quality, then an argument that says, “everyone else uses Copilot” is not a good enough reason to not invest in a specialist tool.

Equally, if you choose a bespoke bid tool, consider whether you require bespoke support. Bid teams do not operate 9–5 (this is when Bid Managers are helping everyone else before getting to their own actions in the evening). If the system goes down at 10pm before a submission, you need clarity on who fixes it, how quickly, and what happens in the meantime. Internal IT and outsourced providers are rarely resourced for bid-critical support unless you contract for it explicitly. You should require evidence of the vendor’s support model, escalation routes, service levels, and real-world incident performance. If the vendor cannot provide support at the times you actually work, they are not a great choice for the world of bidding.

Training is another area of hidden cost. While initial training gets budgeted; recurring training or on-demand training for new joiners can be overlooked. Plus, you are likely to need subject matter experts and operational colleagues to contribute at short notice. You therefore need a realistic plan for onboarding, role-based training, refresher training, and rapid enablement for occasional users. You also need to decide where the in-tool expertise lives: do you rely on vendor customer success or internal champions?

Will your governance regime and commercial teams need to adapt to keep pace with more deals going through the pipeline in the mistaken belief that AI (which could mean generative AI, AI agents, or even more advanced agentic AI) will let you crank the handle and churn out more bids to the same quality? How will bid teams curate the governance inputs that senior leaders demand, and reassure them that no one is accidentally committing the business to terms, obligations, or delivery claims that cannot be honoured?

Auditability matters. Can you trace who created what, when, from which sources, and who approved it? Can you evidence changes over time? Can you show decision points and rationale? These are not nice-to-haves in regulated or public sector environments; they are vital for assurance, dispute prevention, and post-mortem learning. If you’re in any doubt, visit the National Audit Office and ponder the role of the proposal behind such high-profile procurement failures.

Safety controls and protocols need to be documented and actively managed. What data can be entered? What can’t? What prompts are permitted? How do you prevent confidential content from being exposed or misused? How do you manage data retention, deletion, and model training policies? How do you prevent users from pasting in sensitive customer information without thinking? Most organisations assume (because the vendor tells them so) the system is secure and forget that the main vulnerability is people.

There is also the uncomfortable procurement question: what do you say when a government contracting authority asks whether you used AI to write the proposal? You need a commercially approved position in advance. Not a vague statement. A policy that is consistent with your internal controls, your legal view, the customer’s terms, and the realities of how your teams will work with AI tools and agents. If you don’t have a clear position, you’ll either over-disclose and spook evaluators, or under-disclose and create issues further down the line.

You also need to adapt to emerging procurement trends. The Competitive Flexible Procedure can reduce reliance on the written submission until later in the process, increase emphasis on dialogue, presentations, and iterative shaping, and shift the decision making to demonstrations rather than a perfectly written proposal. If you are buying a tool to write proposals, but the procurement decisions are increasingly being made based on behaviours, collaboration, videos, and live demos, you may be investing in the wrong thing in the long run.

One of the most overlooked risks is “marking its own homework”. If you generate copy with AI and then use the same tool to review it, you can create a closed loop where errors, bias and weak reasoning survive because the system is effectively evaluating itself. You need independent reviews conducted by a combination of internal experts and external shadow reviewers.

You also need checks and balances that protect quality, originality, and judgement. Over-reliance on a tool is not just a risk to accuracy; it is a risk to thinking. When teams start ‘asking the tool’ instead of working through the problem, human creativity and critical reasoning are the first casualties.

Handover to delivery is another weak point in many implementations. A good proposal tool should not just help you win; it should make it easier to mobilise. That means structured capture of commitments, assumptions, dependencies, risks, pricing rationale, and delivery approach – and a controlled handover to delivery teams that does not rely on someone’s memory or a rushed transition meeting. If the tool produces compelling narrative but does not help you formalise what you have promised, you are creating future pain.

Get it wrong and the damage will not be limited to wasted spend. You will lose confidence from leaders, frustrate delivery teams, weaken your governance posture, and erode the quality of your bids in subtle but compounding ways. Do nothing, and you will likely fall behind competitors who are learning faster, producing more consistently, and shaping opportunities at the speed of AI. The objective is not to ‘have AI’. The objective is to improve how you win using both people and technology.

This article was drafted by Jon Darby and checked by AI. Any errors or omissions are Jon’s. Any m-dashes and flowery words are (mostly) AI’s.