How AI is helping sellers read buyer intent

Knowing which buyers are serious has always been one of the hardest calls in any deal process. Sell-side teams have traditionally relied on gut feel, advisor instinct and whatever signals a buyer chooses to reveal, which is rarely enough to go on. The information has sat on the buyer's side of the table, and that imbalance has shaped deal dynamics for as long as deals have been done. 

Anyone who has managed a transaction has seen it happen. A bidder appears highly engaged throughout a process, asks all the right questions and reviews large volumes of information, only to disappear before a final offer is submitted. Meanwhile, another bidder that seemed relatively passive emerges late in the process and ultimately completes the transaction. 

These situations are not unusual. They reflect one of the most enduring challenges in dealmaking: understanding buyer intent. 

Technology has transformed many aspects of transactions over the past two decades. Virtual data rooms have digitised due diligence, accelerated information sharing and created secure environments for collaboration. Yet despite these advances, one of the most important questions in any transaction has remained surprisingly difficult to answer: who is genuinely serious about doing the deal? 

AI is changing that.  

From activity to intent 

Deal processes generate an enormous volume of behavioural data. Every login, document view, question submitted, stakeholder added and interaction completed leaves behind a trace.  

Much of this information has traditionally existed in isolation. Deal teams could see activity taking place, but understanding what that activity meant required interpretation. A buyer reviewing dozens of documents might be highly engaged, or simply conducting routine diligence. A spike in activity could signal growing momentum, or it could indicate uncertainty and additional scrutiny. 

The challenge was never a lack of information; it was making sense of it. AI is bridging that gap by identifying patterns across thousands of individual actions and connecting them to likely outcomes. Rather than simply reporting activity, AI can help interpret behaviour and provide a clearer understanding of intent. 

This is where Ansarada's AI Predict comes in. Educated on more than 60,000 transactions across 170 countries, it analyses 57 behavioural signals, from login patterns to document activity to engagement trends, to identify which buyers are behaving like serious bidders. By day seven of a large transaction, our Bidder Engagement Score predicts serious buyers with up to 97 per cent accuracy. 

That is not a marginal improvement on instinct. It is a structural rebalancing of an information asymmetry that has defined how deals get run. Instead of relying solely on instinct, deal teams gain an evidence-based view of buyer engagement while a process is still underway. 

What changes when buyer intent becomes visible? 

Traditionally, understanding buyer intent has largely depended on intuition. Advisors develop instincts, management teams read between the lines and sellers look for signals that might indicate which parties are genuinely committed. Sometimes those judgments are right, sometimes they are not. 

By making intent measurable, everything changes. Rather than spreading time and resources evenly across all participants, deal teams can focus their attention where it is most likely to have an impact. Management presentations, advisor engagement and due diligence support can be directed towards buyers demonstrating meaningful levels of engagement.  

Importantly, the intelligence is available while the transaction is still unfolding. Instead of arriving as a retrospective report, it sits directly within the deal room itself. A live leaderboard can show which buyers are accelerating, which are maintaining momentum and which may be quietly disengaging.  

When a bidder moves from fourth to first in engagement rankings overnight, teams gain an immediate indication that something has changed. Trend data provides visibility into how interest evolves over time, allowing sellers to identify shifts in behaviour before they become obvious through more traditional channels. 

That matters as much on the way down as it does on the way up. Identifying declining engagement early can provide an opportunity to re-engage a buyer, address concerns or adjust strategy before momentum is lost entirely. 

What you get isn't just a sharper process, but a faster one, where every decision rests on what bidders are doing rather than what the room assumes they are thinking. 

It is why the same intelligence trusted by four of the Big 4 and the sell-side advisors of 85 of the ASX 100 earns its place in a mid-market deal just as readily. It does not key off the size of the transaction. It reads the behaviour inside it, and that travels across every deal there is. 

Levelling the playing field 

The most valuable aspect of buyer intelligence is not that it tells sellers who is likely to transact. It is that it helps teams make better decisions while there is still time to act on them. 

Successful transactions will always depend on relationships, negotiation and commercial judgement. AI does not replace those qualities.  What it does is show clearly what is happening in the room, so teams can tell motion from real intent. 

Understanding buyer intent has always been one of the most difficult parts of running a transaction. Now, it is one that can be measured rather than simply inferred.