AI-based deal-sourcing tools can automate market mapping and company research, giving firms the time they need for higher-value activities. Ruth Eagle is head of origination at CBPE, a client of deal-sourcing platform Sourcescrub.

RD: To what extent are private equity firms embracing technology when it comes to deal origination today?

Ruth Eagle: I have been working in private equity for seven years and the adoption of technology within that timeframe has increased significantly. 

When I first started out in the market, the industry had access to a handful of data sources but origination was largely a manual process. Origination teams would trawl the internet looking for potential targets, stitching together maps in a long-winded and not particularly sophisticated manner. 

Fast forward to today, I think most midmarket firms have recognised that they need to be a lot more tech-forward when approaching origination. At the same time, platforms like Sourcescrub have emerged, allowing sponsors to be more sophisticated in the way that they identify investment opportunities. For us, engaging with Sourcescrub has helped to dramatically cut down on the time spent mapping markets, automating away many of the most laborious and manual tasks. 

As a result, many private equity firms today are now embracing technology in the origination function and elsewhere within the organisation. Adoption has also been driven by an increasingly competitive market – and technology can be a great differentiator. 

RD: Do you view true artificial intelligence as a game changer when it comes to deal sourcing? 

Eagle: There has been a lot of talk about artificial intelligence in recent years but very often, when people have used the terms AI or machine learning, they have actually been referring to something less sophisticated. It has only really been in the past 18 months to two years that we have witnessed a significant jump forward. 

Private equity firms are now able to use AI and machine learning models in a genuinely transformative way.

For example, we are currently using AI platforms to help us screen and profile businesses more efficiently. Instead of manually pulling together information to triage businesses, we are now using a generative AI programme to do the same job in half the time. Additionally, we have employed machine learning to map markets by identifying companies based on specific inputs.

Machine learning and AI will be a game-changer during the next couple of years and I expect to see more tools emerging that can be applied to deal origination. This is a journey, and the industry is still very much at the start.

RD: What are the first steps that any private equity firm thinking about embarking on this journey needs to be thinking about?

Eagle: Our strategy was to start by ensuring that all our underlying data was high quality and well structured. If you point AI or machine learning models at inaccurate data you will get inaccurate results, which will not improve over time. Once you have ensured the accuracy and quality of your data, only then will you be in a position to realise AI gains.

There are also some quick wins that can be achieved by using tools such as ChatGPT or Microsoft Copilot, which can make swift work of cutting out unnecessary manual tasks. 

Sourcescrub has implemented a secure GPT model in the platform that acts as a co-pilot for sourcing activities. There are also firms that are using machine-learning algorithms to help predict which businesses may be of most interest. For certain investment strategies these can be a quick and efficient way to get up the AI curve.

RD: As the number of potential technology partners continues to proliferate, how should private equity firms decide who to work with? What questions should they be asking?

Eagle: As mentioned just now, data accuracy is the most important factor for us; how is that data generated and where does it come from? If you delve into the core data model and discover that the data they are using is not accurate in the first place, then applying machine learning or artificial intelligence onto those data models is only going to produce the wrong outputs. 

This is one of the reasons we have chosen to work with Sourcescrub, and recently made the decision to use its data as a core part of our data warehouse initiative.

Focusing on the foundations – where the data comes from and how it is joined together – is absolutely critical.

You also need to think about how technology partners fit into your work processes. What gap are they filling and how will they improve your workflow? One of the reasons we chose to work with SourceScrub is that its solution was a great fit for some of the processes that we were performing manually and integrated easily with our CRM to help us join the dots.

RD: In addition to ensuring that the underlying data is sound, what other potential risks are involved in employing artificial intelligence in the origination function and how can these best be mitigated?

Eagle: One of the challenges that we have encountered stems from the fact that our investment strategy is broad. There are predictive models in the market that are designed to surface the most interesting targets for a given fund. We are considering carefully how to apply such models in a way that fits our broad investment strategy.

It is important to ensure that any technological tool and partner you are engaging with is a good fit for your strategy. Do not make the mistake of thinking that just because it involves artificial intelligence or machine learning that it is automatically going to solve all your origination problems. 

RD: How do you see the use of artificial intelligence in deal sourcing evolving going forward and what other functions within private equity do you see as ripe for transformation?

Eagle: I think that AI and machine learning models are going to become a lot more accessible and a lot easier to implement, particularly as tech giants such as Microsoft continue to introduce AI into their software. 

In terms of other parts of the business that can be enhanced with the use of artificial intelligence and machine learning, our portfolio team is seeing great results when it comes to improving the way that our businesses are managing and using their own data. There are significant gains to be made and that is something that we are also actively investigating.

RD: Where would you say we are currently in the AI ‘hype cycle’: peak hype, trough of disillusion, or slope of enlightenment?

Eagle: I think the industry is only at the start of the adoption curve. We are now starting to see tools emerging that are genuinely useful and that are actually producing positive results. 

During the past three to four years, there has been a lot of talk about AI, but practical implementation and use on a day-to-day basis have been far more limited. Now, however, we are on the cusp of seeing what AI can really do when it comes to improving our efficiency and enhancing origination functions. 

There is still a long way to go. We are still discovering which tools are the right fit for which strategies and what will ultimately enable us to reduce the time spent on manual tasks, but I believe that these tools could be transformational. 

It is important to note that we are not talking about automating away the origination team. It is about giving us back time that can then be spent on higher value activities such as forging relationships with businesses, conducting diligence or developing off-market deals.

This content was produced in association with Sourcescrub - click here to find out more about Sourcescrub on our Drawdown Service Provider Profile