Discussion around AI rules and regulation is heating up in the US and the decisions that are made in the coming year will have significant implications on who can build models, how models can be built, how AI can be used, and how the industry will form.

Managing costs is easier said than done for portfolio companies whose operations rely on physical assets. The approaches that management takes to optimize asset throughput today are largely reactive and optimized for responding to asset malfunctions as quickly as possible.

Machine learning techniques, name anomaly detection is a technique that allows companies to proactively manage their capital expenses, reduce direct costs, and stabilize revenues. Ultimately, uplifting the company’s enterprise value and giving it a leg up over its peers.

LLM adoption behaviour is split between wholehearted endorsement (minority of the landscape), complete prohibition (a small but growing percentage), and indecision (the vast majority of firms).

In this Perspective, we explore how some of the risks associated with implementing third-party LLMs are driving decisions and what all companies can do to build more conviction behind their LLM technology decisions.

Like their portfolio companies, private equity and venture capital firms are under pressure to control costs during this year’s headwinds and many are turning their attention to controlling legal spend. Tried and true machine learning techniques can be implemented to augment legal activities during deal making that allow professionals to focus on strategic negotiation and complex due diligence. The key is to understand where to start and which use cases provide the greatest expected return when enabled by machine learning.

Businesses are starting to figure out how they can put an LLM-powered application in the hands of their employees to more easily traverse proprietary data. This week, we dive into building a “Verticalized LLM” system for private equity firms.

Private equity companies go to great lengths and put in tremendous effort to improve the value generated by their portfolio companies. Many have “operational playbooks” that they run to improve the efficiency and / or effectiveness of the companies in their portfolio. In this Perspective we argue that the implementation of machine learning systems should be included in that playbook and highlight a use case in which an industrial firm was able to generate significant value through the implementation of computer vision.

Buy and build strategies are a tried-and-tested tactic for private equity companies to generate substantial returns. But executing on these strategies is much easier said than done in what is likely the most competitive market in the last decade. Our Perspective on buy and build strategies discusses how machine learning can help address some of today’s challenges and help firms move quickly on their buy and build strategies.