Over the past few months, I’ve had a noticeable uptick in private equity firms calling about a new role: 𝗮 𝗛𝗲𝗮𝗱 𝗼𝗳 𝗔𝗜 𝗮𝘁 𝘁𝗵𝗲 𝗳𝗶𝗿𝗺 𝗹𝗲𝘃𝗲𝗹.
The goal is usually similar: help portfolio companies figure out how to adopt AI, defend against disruption, and turn it into real operating leverage.
What many firms don’t realize is that there isn’t one obvious model for how to do this. In fact, 𝘁𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 𝘆𝗼𝘂 𝗰𝗵𝗼𝗼𝘀𝗲 𝗹𝗮𝗿𝗴𝗲𝗹𝘆 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲𝘀 𝘁𝗵𝗲 𝘁𝘆𝗽𝗲 𝗼𝗳 𝗽𝗲𝗿𝘀𝗼𝗻 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗵𝗶𝗿𝗲.
Earlier this year, I spent some time talking with peers across the investment community and looking at how firms are actually structuring this.
I see four common approaches emerging:
1. 𝗖𝗲𝗻𝘁𝗿𝗮𝗹 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝘁𝗲𝗮𝗺 𝗶𝗻𝘀𝗶𝗱𝗲 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗼𝗽𝘀
(Example: Blackstone BXDS)
Upside: A real internal team that supports both investing and portfolio value creation, with a focus on measurable impact. Many friends there speak openly about how this approach has driven meaningful bottom-line results.
Tradeoff: Expensive and heavier to build. Makes the most sense at a very large scale.
2. 𝗗𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝗔𝗜 𝘂𝗻𝗶𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸
(Example: EQT Motherbrain)
Upside: A structured internal group that assesses AI maturity, runs pilots, and helps with implementation across diligence and value creation.
Tradeoff: Requires sustained focus and commitment. If not tied to clear outcomes, it can drift into process.
3. 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗼𝗿 “𝗳𝗮𝗰𝘁𝗼𝗿𝘆” 𝗺𝗼𝗱𝗲𝗹
(Example: Vista)
Upside: Shared tools, standards, and partnerships that portfolio companies can adopt quickly. Particularly effective in software-heavy portfolios.
Tradeoff: Works best in firms that are comfortable being more prescriptive.
4. 𝗦𝗲𝗻𝗶𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿-𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿
(Examples: Berkshire, Silver Lake, Bain specialist model)
Upside: This is the dipping my big toe in the water model, and a great starting point, particularly for mid-sized firms. One senior operator sets the strategy, builds a repeatable playbook, and brings in the right internal and external resources when needed.
Tradeoff: Like most things in life, the model rests heavily on getting the right individual. EQ and credibility matter as much as technical depth.
My biggest takeaway: 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗲𝗮𝗿𝗰𝗵, 𝗳𝗶𝗿𝗺𝘀 𝗺𝘂𝘀𝘁 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗶𝗰𝗵 𝗺𝗼𝗱𝗲𝗹 𝗳𝗶𝘁𝘀 𝘁𝗵𝗲𝗶𝗿 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 (𝗮𝗻𝗱 𝗯𝘂𝗱𝗴𝗲𝘁).
Because each one implies a very different candidate profile.
Curious what others in the PE and data/AI ecosystem are seeing as this role continues to evolve.
