BC Partners bringing generative AI into deal sourcing

Generative AI technologies like ChatGPT can be very helpful 'in identifying add-on opportunities in the private markets', says partner and portfolio CTO Lakshman Charanjiva.

BC Partners is testing generative AI – the technology behind systems such as ChatGPT – in deal sourcing, Lakshman Charanjiva, partner and portfolio chief technology officer, portfolio operations, told PE Hub Europe.

A partner-level working group at the London-headquartered private equity firm is setting a strategy and direction for using generative AI. It is already exploring and trialling start-up companies’ tech for use in deal sourcing and other internal processes, including search and retrieval and data analysis.

BC, which closed its €6.9 billion Fund XI in February 2022, is also building APIs that will let trained generative AI large language models (LLMs) combine with its unstructured and unlabelled proprietary data, which otherwise “would be hard for an associate to weed through”, said Charanjiva.

“We can certainly use GenAI for easier information gathering and filtering on news, results, conference presentations, media statements, market whispers, probability/timing of businesses we like coming to market etc,” he added. “This is work that takes our associates a lot of time and effort can be made more efficient and faster and could potentially surface something we had not been aware/thinking of.

“In our sectors and geographies, we generally know which target companies meet our investment criteria; where GenAI can be very helpful is in identifying add-on opportunities in the private markets.”

This is especially timely. Several sources have told PE Hub Europe that making platform investments is harder than it has been for a few years thanks to the rising cost of debt, valuation gaps and other factors – meaning they have focused more on investing in and growing portfolio companies.

Data rooms

On due diligence, the technology “can be very useful in rapidly searching VDRs [virtual data rooms] and publicly available data via prompts, and in market scans and surfacing competitor information for our commercial diligence,” said Charanjiva.

“We can apply ‘code interpreter’ to use Python to rapidly create data cuts on large data sets we receive from the target (eg customer cube, spend cube) – without our associates needing to learn Python coding.”

At the portfolio level, BC Partners is working with “several” of its companies to leverage generative AI in software development, legal, data analysis and other areas, to improve operational performance and efficiency.

Charanjiva listed how generative AI could create value in the sectors BC invests in:

Software/IT services: GenAI allows for the creation of new code, potentially finding errors in code, creating testing scripts etc – all of which make the code generation process more efficient

Media: Democratisation and reduced cost of content generation

Consumer: The best use-case is in customer service, where GenAI can be used to provide better, more targeted, more personalised information to call centre reps (or chatbots) as they speak to customers with complaints or service issues; potentially higher ROI on target marketing; sentiment analysis

Healthcare/Pharma: can potentially help accelerate R&D and the discovery of new drugs/molecules; in health services, automated document processing and medical image recognition are some use cases

Functions such as sales and marketing, legal, customer service, IT, knowledge management can benefit from generative AI, he added.

Other areas beyond portfolio management where generative AI could assist include the deal cycle, ownership and internal opportunities, he said.

“We will experiment and iterate with a few technologies and partners,” added Charanjiva. “GenAI technology continues to evolve and has some real risks, and we do not want to get too far ahead of our skis.”

Investment interest

Charanjiva said that from an investment standpoint, the technology was more an area for big tech, venture capital and software companies embedding generative AI and LLMs into their products.

“We prefer not to take on technology obsolescence and disruption risk, so do not do venture investing as this technology is moving rapidly,” he said.

“Instead, we think that software companies that can effectively leverage and benefit from GenAI is where we should be spending our time to ride this wave; and certainly, we now ask questions about how GenAI – or even conventional AI – might benefit/disrupt target companies in all sectors we invest in.”

The companies that can leverage foundation LLM models into their products and workflows “will see a lot of interest and demand attractive multiples”, he said. “We think that the end-user facing application layer will see the most interest.

“It is possible that some of the tech-focused PE firms will lean forward and take some calculated risks on GenAI providers/services.”