As government agencies and large corporations automate procurement processes, artificial intelligence (AI) is starting to play a more significant role in tender evaluation. AI is unlikely to replace human reviewers entirely (at least for high-value or sensitive contracts), but machine learning and other tools are increasingly used to screen and rank bids at the initial stages.
Businesses that want to stay ahead of the curve need to understand how these systems work and what to do about them.
AI Tools are Becoming Commonplace
Procurement agencies are adopting AI for a number of reasons:
- Efficiency. A large tender may receive dozens or hundreds of submissions, most of which won’t be shortlisted. AI is faster at scanning and sorting content than humans.
- Consistency. Algorithmic tools minimise the role of subjectivity and emotion in the evaluation process.
- Cost and time. The entire tender evaluation process is more cost-effective if parts of it can be automated. Shorter timeframes mean less cost to bidders as well.
Some basic tools are used only for document management and keyword extraction. More sophisticated AI uses natural language processing (NLP) to ‘read’ narrative responses and assess them for relevance, completeness, and even sentiment.
Demand for these systems is coming from all levels of government, and from councils and infrastructure agencies as well. In fact, some recent upgrades to procurement platforms that process Melbourne tenders have included AI features designed to streamline pre-screening and compliance checks.
AI Systems Look For Specific Information
AI systems will typically be programmed to match key information in each tender response against the published selection criteria. This can include:
- Keywords and Phrases that align with the scope of work
- Elements that show experience or capability
- Sentiment indicators (positive or negative language)
- Structure and Format (headings, bulleted lists, or other formats)
Pre-scoring algorithms may even assign a preliminary score to each section of a bid, for review or adjustment by humans later.
If the system’s NLP function is sophisticated, it may be able to recognise variations on relevant words, synonyms and related concepts to some extent.
The upshot is that tender writers need to pay attention not only to the substance of their submissions but also to the structure and language used. NLP systems trained on the same criteria may be given the job of weeding out low-quality bids in future.
Optimising Tenders for Machine Reading
Good tender writing has always involved clear and focused content, structured to align closely with the evaluation criteria. But AI evaluation adds new considerations.
Here are five key tactics to adjust your approach:
1. Answer the Tender Questions
A basic requirement of every tender is to provide specific responses to the questions asked. Machine readers will generally be very literal in their understanding of text, so algorithms will look for obvious indicators of relevance.
One implication is to use the same words as the tender documents in your response. If a criterion is phrased “previous experience delivering community infrastructure”, use those words in your answer rather than a variation like “public works projects” or “prior community infrastructure experience”.
2. Use Headings and Logical Structure
Algorithmic systems tend to look for simple structural elements such as headings, numbered responses, and bullet points as an indicator of readability and attempt to match it to an expected format.
Use a logical structure and be consistent with your use of heading levels. Include a question number or title in your responses to indicate alignment. This also makes your answers easier to find and follow for human evaluators.
3. Optimise for Keywords (without ‘Keyword Stuffing’)
AI and search engine optimisation have much in common. SEO writers aim to ensure that websites are easy to find in online search by matching key terms and using them strategically in content.
Tender writers should do the same.
In practical terms, this means making sure your tender includes the right keywords without ‘stuffing’ your document with repetitive terms. It also means not varying from commonly used words in the tender.
Read the specification and supporting information carefully. Note frequently used terms. Mimic them where it makes sense to do so, particularly in your content outlining your understanding of the project and your delivery approach.
4. Include Numbers and Data
Quantitative content is more specific and easier for both AI and humans to verify. Instead of saying “we have delivered several projects like this in recent years”, use “in the past five years, we have delivered 16 comparable projects, each valued at between $2M and $5M.”
AI is likely to look for terms that can be quantified (such as timeframes, dollar values, or project completion dates) to assess your capability. The more data you can provide, the more trustworthy you appear.
5. Avoid ‘Fluffy’ and Ambiguous Language
Persuasive writing has a role in tender documents, but AI doesn’t interpret sentiment or value judgments. Marketing-speak like “cutting-edge solutions” or “global best practice” is less useful for machines.
Don’t be vague. Stick to factual, demonstrable content. Focus on how you would meet the evaluation criteria. Plain, professional English is the easiest for both AI and humans to read.
AI isn’t the Whole Story – Humans Still Read Tenders
AI tools are starting to play a larger role in the initial screening and ranking of tenders, but their use is not universal, and they are unlikely to be the final arbiter. Human panels will typically review shortlisted tenders, assess value for money and make a final recommendation.
The need for human review also highlights the importance of optimising for both machine and human readers. If your bid fails to make it past the AI filters it won’t be seen by any human, but it will struggle if it’s flagged as low quality or irrelevant.
Planning for AI Tenders
With the role of AI in public procurement set to increase in future, businesses will need to adjust their internal processes to cope. This might include:
- Training and updating bid writers to write for both human and AI audiences
- Practice runs using AI-style evaluation checklists
- Using readability and keyword analysis tools to test tenders digitally
- Engaging with tender consultants who are aware of emerging evaluation technologies and tools
As AI becomes embedded in the evaluation of government tenders, businesses must adapt their approach to writing tenders to meet both technological and human expectations. While core tendering principles—clarity, compliance, and client focus—remain unchanged, the way submissions are crafted must evolve.
By optimising content for algorithmic screening, maintaining structural precision, and ensuring relevance to selection criteria, businesses can position themselves for success in this new era of tender evaluation, whether responding to tenders in Melbourne or across broader jurisdictions.
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