Why AI Governance is Business Critical in Bids and Proposals

In recent months, we’ve written extensively about the potential of AI in bid and proposal management: what it takes for proposal professionals to stay relevant in an AI-enabled landscape, the cultural shifts needed, where AI can help in bids beyond writing and the importance of learning writing skills before using AI.
(The links to these are included at the bottom of this post.)

But now we need to talk about the less glamorous, but still vital, cousin: governance. As AI adoption grows, governance is essential to ensure it doesn’t create a legal, reputational, or ethical liability for your company.

This post builds on our previous work to explore why organizations using AI must have an AI governance strategy and what that looks like in practice.

Why governance matters in bids and proposals

Bid and proposal teams are early adopters of AI. We deal with tight deadlines, vast amounts of information, and the demand for persuasive, accurate writing. AI supports this through the creation of first drafts, mapping compliance matrices, or critically reviewing our output. But without a clear governance framework, the use of AI can introduce new risks:

• Introduction of unintended bias
• Use of flawed or out-of-date data
• Breaches of client confidentiality or misuse of sensitive material
• Regulatory breaches, especially in defence and public sector bids

Embedding governance into the proposal function is both a strategic safeguard and a competitive advantage.

What is AI governance?

AI governance refers to the frameworks, policies, and processes that ensure AI is used ethically, responsibly, and legally across an organization. It’s about:

• Ensuring the quality and traceability of data used to train and deploy models
• Managing risk, bias, and explainability
• Enabling accountability across technical and non-technical teams
• Staying compliant with emerging laws and expectations

Strategic objectives for AI governance

Any AI governance strategy should aim to:

• Ensure trustworthy, high-quality data for AI training, deployment, and monitoring
• Mitigate risks related to bias, privacy, and regulatory non-compliance
• Enable scalable, ethical AI innovation across business units

In the context of bids and proposals, this might mean:

• Documenting where proposal content originates from and how AI-generated sections are reviewed
• Assigning responsibility for AI-assisted tools used in compliance, pricing, or evaluation prediction
• Ensuring proposal managers and writers are trained on AI limitations and legal considerations

Key components of an AI governance framework

Below is a simplified table outlining the core components of a governance strategy:

How to implement an AI governance: A phased roadmap

Here’s an example of how organizations can approach governance without getting overwhelmed:

Phase 1 – Foundation

• Review current data governance policies
• Identify where AI is (or might be) used
• Form an AI governance council
• Define roles (e.g., data owners, ethics leads, proposal oversight )

Phase 2 – Building the framework

• Write AI-specific data policies, for example, around consent, traceability, and accuracy
• Introduce simple tools that help label and track data sources, so it’s easier to see where your information comes from and how it flows through systems
• Create easy-to-use templates to log what AI tools assume (e.g., expected user behaviour) and how you’ll track whether they’re delivering good results
• Set up clear, repeatable ways to check for bias in AI-generated content – e.g., running test cases or comparing results across different user groups

Phase 3 – Making it operational

• Embed governance checks into your proposal processes where they are using AI tools (instead of treating them as separate)
• Train staff on how to spot ethical risks or compliance red flags in AI-supported work
• Run your first formal review cycle to assess for fairness, effectiveness, and legal compliance
• Refine your plan for what to do if an AI tool gives a bad result or raises a red flag

Phase 4 – Keeping your AI governance strategy up to date

• Regularly review how your AI tools are performing and whether they’re starting to go off-track (known as ‘data drift’ – where an AI’s outputs become less accurate over time because conditions have changed)
• Update your governance policies to reflect new (and evolving) legal frameworks or internal lessons
• Compare your practices to industry standards and peer organizations
• Share updates internally or with clients to demonstrate accountability and transparency (e.g., a quarterly report or project summary explaining how AI is being used responsibly)

Tailoring AI governance for your organization

While the strategy above offers a general roadmap, different teams may need tailored approaches. For teams working across governance, risk, and operations, your AI governance plan should also:

• Support board-level reporting and integrate with risk registers
• Control how AI accountability is embedded into contract management and supplier oversight
• Align with relevant compliance frameworks such as ISO 27001, ISO 42001, and NIST AI RMF

This ensures AI oversight isn’t siloed within IT or compliance teams, but becomes a visible, measurable part of how the organization delivers value and safeguards its future.

Supporting tools and technologies

• Data catalogues such as Collibra and Atlan to track data lineage and metadata
• Model monitoring tools, such as Fiddler and Arize, to detect bias and performance issues
• Privacy tech such as OneTrust and BigID to support consent management and minimisation
• Audit automation to streamline compliance and reporting

What does this mean for proposal teams?

Proposal professionals don’t need to become governance experts, but they do need to:

• Understand the basics of AI risk
• Know which tools they’re using and why
• Be clear on who owns what in AI-supported workflows
• Ask better questions of suppliers, subcontractors, and internal teams

Governance should be part of how your team works: efficiently, ethically, and in full confidence that your bids comply with the evolving AI regulations.

Further Reading

Small Business, Big Rules: Why AI Data Governance Is No Longer Optional

EU Artificial Intelligence Act

Previous posts in the series

Bid Writers in the Age of AI: Why your job isn’t at risk – It’s evolving

Why proposal managers can’t ignore AI

Beyond Training: The cultural shift bid teams need to embrace AI

Article published: December 2025