Artificial Intelligence to Win
Interactive Workshop

Using AI in Bids Without Breaking Things

AI can be a powerful helper through the whole bid journey. Used badly, it can quietly wreck a submission.

AI is increasingly used in proposal development to save time and improve efficiency. But misuse can lead to hallucinations, generic content, factual errors and non-compliant answers, often wrapped up in confident language that looks convincing but isn’t. Add privacy, data handling and customer concerns into the mix and it’s clear this needs a sensible, balanced approach.

What This Workshop Is About

This is AI with its feet on the ground.

We focus on how to use AI to support your teams, not replace them. Delegates learn where AI adds value, where it introduces risk and how to combine AI outputs with human judgement, experience and accountability.

The aim is simple: better use of AI, fewer hidden risks and no nasty surprises at review stage.

What We Cover

Everything is practical, current and rooted in real bid environments. Topics include:

Understanding where AI genuinely adds value in bid development
Recognising common AI risks, including hallucinations, bias and over-confidence in outputs
Managing compliance risks when using AI-generated content
Reviewing and refining AI outputs so they align with strategy and scoring criteria
Best practice for prompting to generate relevant, customer-focused content
Managing uploaded content and libraries to avoid garbage-in, garbage-out
Data privacy, confidentiality and intellectual property considerations
Understanding customer expectations and restrictions around AI use
Knowing when to use AI and when to step away
Putting the human touch back into responses to retain credibility, empathy and authenticity.

Delivery

This half day workshop is delivered virtually and is tailored to your organisation, your tools and your governance requirements.

What Delegates Leave With

A clear, practical understanding of how to use AI safely and effectively in bids.
Confidence in spotting and correcting AI-generated issues before they become problems.
And the knowledge to combine AI efficiency with human judgement.