5 questions companies are asking about AI in tender management

For companies bidding on public tenders, the question is no longer whether to adopt AI, but how to govern it, measure it, and trust what it produces.

Commercial teams and tender offices in the Life Sciences sector are asking different questions about AI than they were a year ago. Whether artificial intelligence has a place in the tender process is no longer up for debate: for most companies, that decision has already been made.

The conversation has moved on. Commercial leaders are thinking about return on investment. The people running operations want to understand the impact on turnaround times and workloads. Tender office managers are under pressure to show what is working, where adoption is stalling, and how the results hold up in front of leadership.

Talking with tender offices across pharma and medical devices, one theme comes up again and again: the most pressing questions are no longer about access to AI, but about the ability to see its reach and impact. How do we handle more tenders with the same team? Which answers can we trust? Where is measurable value being created? How do we keep from losing the know-how of the people who have run our tenders for years? And how do we prove all of this to the people who decide?

These are the questions increasingly shaping AI strategy inside Life Sciences & Healthcare companies today.

For most tender offices the problem is not finding new opportunities, it is keeping up with them. The flow of tenders a supplier could bid on is almost always larger than the team's capacity to respond with the quality it takes to win.

The problem is structural, not organizational. Each tender is usually split into lots, each with its own description; each response means reading dense tender specifications and, given the weight of the technical score, cannot be standardized. The workload grows with the number of tenders, the headcount does not. Historically there were two levers, both uncomfortable: hire, or decline to bid and hand a share of the market to a competitor.

This is where AI changes the equation. Not by making people work more, but by raising the ceiling on how many tenders an office can handle well with the same resources: collecting and normalizing the relevant notices, reading specifications and pulling out requirements and deadlines in a fraction of the time, and starting responses from material the company already has rather than from a blank page. Capacity stops being a fixed constraint.

For the tender office manager, the question changes accordingly. No longer "are we catching enough tenders", but "how many can we actually respond to well, and how do we raise that number without growing costs at the same rate".

In many settings, an inaccurate output from a language model is an annoyance to fix. In public tenders it is a risk with immediate consequences: a misread requirement, a wrong regulatory reference, an overlooked eligibility requirement can turn into a non-compliant bid or a disqualification.

That is why, in the tender process, the fluency of an answer matters less than its verifiability. The question managers ask is not "can the AI write a plausible answer", but "can I trace that answer back to its source and check it in thirty seconds". An output that reads well but is not anchored to the tender document does not save time: it just moves the checking further down the line.

It is the reason our thinking behind Document Intelligence starts from anchoring to the source, not from generation. Every statement traceable to the tender specifications, the tender rules, the contracting authority's documentation, so that whoever is working the tender can trust what they read because they can check it, not because they have to take it on faith. A system that invents a requirement that does not exist is worse than one that fails to find it: the second you know about, the first you do not.

As AI moves into the heart of the process, trust stops being a preference and becomes an operational requirement. The companies moving fastest are not the ones that trust more, but the ones that have demanded tools built to be verified.

One of the biggest challenges for anyone running a tender office is telling activity apart from impact. A team can have dozens of active users and hundreds of documents processed every week, but those numbers do not answer the question leadership cares about: where is AI creating value, and how can we measure it?

More and more companies are looking beyond usage metrics and focusing on specific points in the process. They want to understand where AI cuts response times on a tender, where it improves consistency across the documents produced, where it frees up capacity that used to be absorbed by reading specifications by hand, and where it makes it viable to bid on tenders that would not have been worth pursuing before.

The areas where this shift is most visible are consistent ones: document analysis, drafting responses, compliance checking.

The point, for leadership, is to understand whether these gains are isolated wins or repeatable patterns. Which activities produce results consistently? Which processes have genuinely absorbed AI into daily work? And where, elsewhere in the organization, do similar opportunities exist that have not been tapped yet? Visibility into the value created at each step of the process is becoming as important as visibility into overall usage.

Tenders are won on tacit expertise. Knowing how a given authority phrases its requirements, which clauses recur in a particular market, how the bid that won three years ago was built, which mistakes not to repeat: this is knowledge that is rarely written down anywhere. It lives in people, and often in a few people.

It is a fragile asset. When the person who holds it changes roles, retires, or is simply buried in another tender's deadline, that knowledge is not available when it is needed. Many companies find that their most valuable asset is not a response template or a checklist, but the ability of a handful of people to read a tender and understand it quickly.

Here AI plays a different role from the assistant that writes faster. It captures that know-how and makes it available to the team: turning how the company has responded in the past, the requirements that recur, the way a given authority awards, into something the whole office can lean on. This is the direction behind Tender Agents: not to replace the judgment of the people who run the tenders, but to make sure that judgment does not depend every time on the memory of a single person.

For the tender office manager, framing it this way raises the stakes. The question is no longer just "how fast are we", but "how much of what we know survives the individual who knows it today".

As adoption matures, the pressure to demonstrate value grows, and it comes from several directions. Senior management wants to understand the return on the technology investment. Commercial leaders want proof that AI is improving how the work gets done. And the market as a whole is raising the bar on efficiency and transparency.

The difficulty is that value is hard to measure consistently. You can point to convincing examples of tenders handled in less time or won thanks to sharper analysis, but few companies have built a reliable way to measure impact at the level of a single tender. Usage data is getting easier to track; quantifying the effects on bid quality, on turnaround, on the award rate, and on the overall economics of the office is another matter.

It is worth saying plainly: few companies have a perfect way today to measure AI's impact on tenders. Most are piecing the story together from the signals available to them: usage trends, activity across the process, tender outcomes, and examples of concrete results. Over time, those signals become the foundation for a more credible conversation about value.

Sooner or later every tender office will be asked the same question, by management or by leadership: what is AI actually doing for us? Are we using the right tools? The companies best positioned to answer will be the ones that started collecting the data today.