For the last several years dealmakers have vacillated between fearing that AI might hurt them to hoping it will save them. As usual, the reality lies somewhere in between.
After the dramatic highs and lows of the global pandemic and its immediate aftermath, the investment landscape is beginning to stabilize — but not necessarily the way dealmakers expected. “Back to normal” feels anything but, with so much dry powder chasing so few companies willing to transact at current valuations. Meanwhile, firms that invested in companies at peak multiples must be more selective than ever to make up for longer holding periods and appease increasingly anxious LPs.
Competition has reached an all-time high, requiring dealmakers to push far beyond the status quo to succeed. Fortunately, as this truth is setting in, AI-powered technology is also entering what the Gartner Hype Cycle would refer to as the “Slope of Enlightenment,” or a period of greater understanding and more practical application.
Firms with realistic expectations are beginning to unlock the true value of AI by combining it with human intelligence to advance their sourcing, outreach, and other dealmaking efforts. And the results of these joint initiatives are far greater than what either people or robots are capable of achieving on their own.
As VSS Capital Partners’ Head of Business Development, Jordan Margolin, recently said, “Firms must use a combination of human capital and technology to become more specialized and thesis-driven in an increasingly competitive market.” But what exactly does this mean in practice?
According to SourceScrub’s Head of Software Engineering, Data Science, and Operations, Jonathan Dodson, it requires keeping a “human in the loop” of all AI-powered processes. The machine learning models firms use to help them understand and identify ideal acquisition targets are only as “intelligent” as the data and logic humans use to train them. They lack the contextual reasoning and discernment necessary to independently generate reliable insights.
Failure to keep a human in the loop results in debacles like Amazon’s 2018 AI-powered job recruitment tool. The underpinning machine learning model was trained using resumes from the preceding decade — the majority of which happened to be male. Over time, this resulted in unintentional discrimination against female candidates, requiring developers to step in and correct the issue.
Not only must initial models factor in uniquely human knowledge around historical experiences, societal norms, technology trends, and more, but, “If models aren’t retrained to ‘learn’ from current, real-world data, unprecedented or anomalous events can cause predictions to become flawed,” says Dodson. Keeping a human in the loop requires quality checking and validating the model’s outputs to ensure consistently accurate, complete, and relevant results.
The latest deal sourcing platforms use a combination of AI and data experts to connect and cross-check information across thousands of individual conference lists, buyer’s guides, and other sources. The resulting web of insight not only delivers robust, high quality company profiles, but it also offers the context necessary to derive deeper, less obvious insights about these companies’ growth intent. These insights would be impossible to uncover with AI alone.
For instance, private companies don’t publicly release revenue data. However, the ability to connect the dots across data signals like open job postings, an uptick in conference attendance, and increased headcount makes it possible for humans to infer revenue growth and transaction readiness, and then train AI models to evaluate and score targets accordingly.
While 79% of dealmakers have tried ChatGPT or other similar generative AI tools, they also recognize that relationships remain at the heart of successful dealmaking. And even the most sophisticated algorithms are incapable of shaking hands and striking up friendly conversations with ideal targets.
However, when trained and harnessed appropriately, AI can take on the most time-consuming and tedious part of dealmakers’ jobs. For instance, it can generate outreach emails faster, at greater scale, and with fewer errors than even the most productive business development representative. This then frees teams to focus on the more strategic and human-centric aspects of their work.
Generative AI entrepreneur and CEO of Add Value Machine, John Shaw, agrees: “The best way for dealmakers to leverage artificial intelligence is as a partner. Humans and AI have different strengths, and they complement each other. We should never worry about or count on AI replacing people, because there are a lot of human things that AI simply can’t do. However, we should definitely be concerned about competitors using AI to enable their teams to do more of those human things smarter and faster than us.”
For example, rather than spending days scouring conference lists searching for opportunities that match certain investment criteria, top dealmakers use AI to instantly surface attending companies with similar attributes to an existing top target. From there, they leverage custom scoring algorithms to rank these relevant companies in priority order based on thesis fit.
Firms like finnCap Cavendish use the time they save to focus on preparing to meet these companies in person and building human relationships with the opportunities that matter most. “If I were attending a conference of 500 businesses, it would take someone two days to manually research every company and know whether it’s a target,” shares finnCap Cavendish’s Head of Business Development, Jonny Burr.
“There was a huge conference in London with 1,500 exhibitors,” he recalls. “There’s no way I would have been able to talk to everyone and find the right targets, even in two or three days. So I ran the attendee list against my [investment] parameters…and it returned a list of 25-30 people I needed to talk to. I was able to email them and schedule meetings in advance, and I spent about two hours there instead of two days.”
The real value of AI is in reach as dealmakers descend the Peak of Inflated Expectations and emerge from the Trough of Disillusionment. The future belongs not to those who think AI will solve all their problems, nor to those who ignore its potential. It’s the firms that pair human reasoning, ingenuity, and communication with AI’s speed, precision, and operational excellence that will rise above the competition.
However, believing in the “magic” of AI is just one of the many data-related roadblocks holding firms back. From relying on single-source data to waiting too long to hire a data expert, our guide digs into the top five hidden “traps” harming dealmakers and hindering them from transforming data into a true differentiator. Give it a read here.
