There’s a question I hear more and more from procurement leads, bid managers, and commercial directors across the defence and government contracting space: “Can’t we just use ChatGPT for this?”
It’s a fair question. Generative AI tools are remarkable. They’re fast, fluent, and increasingly capable of producing content that reads like it was written by a seasoned professional. For brainstorming, drafting internal communications, or summarising a news article, they’re genuinely transformative.
But in regulated industries, particularly government contracting and defence procurement, defaulting to general-purpose generative AI without understanding its limitations isn’t just risky. It could cost you a bid, a contract, or your organisation’s security clearance.
"The problem isn't the technology. It's the mismatch between the tool and the task."
The Generative AI Trap
Generative AI, at its core, is probabilistic. It produces outputs that are statistically likely, not outputs that are guaranteed to be accurate, complete, or traceable. Ask it to draft a proposal section and it will produce fluent, confident draft. Even if that draft subtly misrepresents your technical solution, omits a compliance obligation, or hallucinates a capability you don’t have.
In a commercial context, that’s an inconvenience. In a government tender, particularly one governed by frameworks like Australia’s ASDEFCON, the Commonwealth Procurement Rules, or equivalent standards across the UK and New Zealand, it can be catastrophic. Defence contracts carry explicit obligations around data rights, security classifications, Australian Industry Capability commitments, and precise statement-of-work language. A missed “shall” is not a grammatical oversight. It’s a compliance failure.
The teams keeping procurement leaders up at night aren’t worried about AI being too slow. They’re worried about AI being too confident, hallucinating “facts”, producing outputs nobody can audit, trace, or defend to an evaluation panel.
The Case for Deterministic Software in Regulated Environments
"This is where the conversation needs to mature. The choice isn't between AI and no AI, it's between the right type of technology for the right challenge."
Many tasks in a bid or contract process need 100% accurate and repeatable results. Generative AI is by its very nature is variable and probabilistic. It produces different results every time.
Many tasks need 100% accuracy and must be “deterministic”. This is where the same input produces the same output, every single time, without exception. Search (CTRL + F) in MS Word is a great example of a deterministic approach.
Think about what that means in practice.
- When compliance matrix software shreds a tender document, it should extract the exact same requirements in the same order on every run.
- When a document comparison tool flags a change between contract versions, that flag should be identical whether you run it today or in six months.
- When readability is scored, the result should be reproducible by any member of your team, auditable, and defensible to an external reviewer.
There is no probability involved. No hallucination risk. No variation based on how a prompt was phrased.
This is the category of tooling that often gets overlooked in the rush to adopt generative AI, and it’s precisely the category that regulated industries should be reaching for first.
In environments where an Australian National Audit Office review, a programme evaluation, or a legal dispute could scrutinise your process, the ability to demonstrate exactly how a compliance output was generated is as important as the output itself. Deterministic software provides that chain of evidence by design, not as an afterthought.
A Framework for Choosing
Before deploying any AI tool on a regulated bid or contract, consider these questions:
- Where does the output go? If AI-generated content is going into a government submission, a signed contract, or a document with legal standing, traceability is non-negotiable. General-purpose tools should not be the last step in that chain.
- What data is involved? Commercially sensitive bid strategies, pricing models, and technical solutions fed into consumer AI platforms may not remain within your control. For anything touching classified, CUI, or commercially sensitive material, data sovereignty must be a primary consideration – not an afterthought.
- Can you defend the output? If an evaluator, auditor, or legal team asked you to explain exactly how a compliance statement was generated, could you answer? If not, the process isn’t ready.
- Are you ready to adopt it, not just deploy it? Technology selection is only half the decision. The more important question is whether your organisation is prepared to use it well. That means having your content in order, past performance documents, previous proposals, contract libraries. These must be structured and accessible enough to actually power the tools you’re investing in. It means change management: bid and proposal teams are often under significant deadline pressure, and tools that require a steep learning curve or disrupt established workflows will be quietly abandoned after the first crunch period, regardless of their capability. And it means training that goes beyond a product walkthrough, people need to understand why a tool works the way it does, not just how to click through it, if they’re going to trust its outputs on a high-stakes submission.
- Is adoption a program or an event? The organisations that see the strongest return from any technology investment in this space are those that treat adoption as a programme, not an event. A phased rollout, internal champions, clear metrics for what good looks like. These matter as much as the software itself. In government contracting, where the cost of a non-compliant or poorly written bid can run into months of lost pipeline, the investment in getting adoption right is never wasted.
The Opportunity Is Real. If You Match the Tool to the Task.
None of this is an argument against AI adoption. Quite the opposite. Organisations that thoughtfully integrate AI across the full bid lifecycle, from opportunity qualification and document analysis through to proposal writing and contract review. They are already seeing material improvements in win rates, compliance quality, and the time their best people spend on high-value work rather than manual document review.
The Australian defence industry is navigating one of its most complex periods: AUKUS obligations, ASDEFCON template reforms, new Commonwealth Procurement Rules, and intensifying sovereign capability requirements. The volume and complexity of documentation is only increasing.
The teams that will navigate this best aren’t the ones who adopted AI fastest. They’re the ones who asked the right question first: not which AI is most impressive, but what technology is appropriate for this job.
That distinction is worth more than any prompt.
VisibleThread is a bid and contract intelligence platform with over 15 years of experience in government contracting. Trusted by 11 of the top 15 US government contractors – including Leidos, Lockheed Martin, and General Dynamics – VisibleThread helps capture, proposal, and contracts teams work faster and with greater compliance confidence.
Its platform combines deterministic document analysis with AI-assisted writing, and is built for the security requirements of regulated industries, with deployment options spanning private cloud, on-premises, and air-gapped environments. Visit us at www.visiblethread.com
