Hg's Chris Kindt on AI Transforming Private Equity Value Creation

In Brief:

Private equity value creation is experiencing its most significant transformation in decades as artificial intelligence shifts from efficiency tool to fundamental business reinvention. Chris Kindt, Partner and Head of Value Creation at Hg, warns that companies failing to embrace "AI-first" culture risk falling on the wrong side of disruption as agentic AI delivers 10-20x efficiency gains in core functions. With 15 years architecting value creation strategies—11 at Hg managing a portfolio of 55 companies worth over $180 billion in enterprise value—Kindt leads a 60-person team that has become ground zero for understanding how AI reshapes everything from software development cycles to market entry strategies. Speaking from New York on the Alt Goes Mainstream podcast, he argues the current moment represents a rare window where incumbents with mission-critical software and deep vertical expertise can leverage AI to expand moats rather than watch them erode.

Big Picture Drivers:

• Portfolio Scale: Hg oversees 55 companies representing $180B in enterprise value, creating a laboratory for testing AI implementation across diverse software businesses at scale

• Organizational Agility: Company size matters less than cultural adaptability—some of Hg's largest businesses are moving fastest on AI while smaller firms struggle with organizational inertia

• Technology Inflection: The combination of reasoning models and agentic frameworks has shifted AI from 10-15% efficiency gains to 10-20x productivity multipliers in engineering and customer support

• Competitive Urgency: AI has lowered barriers to replicating software products, making deep vertical expertise and proprietary data the primary defensible moats

Key Themes:

• AI-First Culture: Organizations must embed AI as the default starting point for problem-solving rather than treating it as optional tooling, requiring systematic change management from executive buy-in through frontline adoption

• Value Creation Evolution: The firm has built an 80-person ecosystem combining internal specialists with external experts, operating more like a tech conglomerate than traditional PE ops team

• Agentic Transformation: The shift from co-pilot assistance to autonomous AI agents handling complete workflows represents the most significant value creation lever Hg has encountered

• Prompt Engineering Mastery: Effective AI deployment requires treating language models like mid-level analysts—providing extensive context, clear role definitions, and iterative refinement rather than expecting instant results

Key Insights:

• Engineering Acceleration: Hg portfolio companies using AI-first development approaches have compressed software product development cycles from 12-15 months to under 3 months, fundamentally changing build-versus-buy decisions for bolt-on acquisitions

• Cognitive Discipline: The greatest risk of AI adoption is intellectual laziness where professionals accept outputs without critical analysis, leading to "cognitive decline" that undermines the judgment and taste that creates competitive advantage

• Measurement Philosophy: Value creation teams balance rigorous impact tracking with avoiding self-serving administrative burden by establishing clear KPIs upfront and reviewing outcomes with CEOs and CFOs rather than elaborate internal reporting

• Talent Development Paradox: The traditional grind of mastering crafts through difficult repetitive work built expertise and grit in previous generations, raising unresolved questions about how younger professionals will develop taste and judgment when AI removes friction from learning

• Organizational Readiness: Successfully deploying advanced AI tools like autonomous coding agents requires sophisticated technical infrastructure—automated development pipelines, proper instrumentation, and unified systems—making implementation expertise as valuable as the tools themselves

• Strategic Positioning: Companies solving complex vertical-specific problems with proprietary data can uniquely deploy AI to create "moments of magic" for customers, while horizontal lightweight tools face commoditization as building software becomes radically easier

Memorable Quotes:

• "We are starting to hear from various corners that there's a bit of AI fatigue and AI disillusionment setting in... my goodness, we're only just starting to see the acceleration of the impact." - Chris Kindt, addressing market skepticism about AI's practical value

• "AI is a multiplier, not a leveler—it makes people who've got good instincts and good smarts multiply those, but people who try and turn off their brain and have AI do the work for them, you spot that and they get a lot less impact." - Chris Kindt, explaining why human judgment remains critical

• "For most prompts, unless it's a quick question or query, it should be about a page of A4... You should have your own personal prompt libraries, your own golden prompts—things that you use on a repeat basis." - Chris Kindt, on the rigor required for effective AI deployment

• "I sometimes on the weekends will experiment with three or four different AI agents to have a conversation with all of them to understand how do I prompt you best." - Chris Kindt, revealing his personal approach to mastering AI tools

• "It's not just eight, nine, ten things, because management teams are busy... it's about selectively understanding what are the two or three critical interventions that can really drive a differentiated outcome." - Chris Kindt, on the philosophy of focused value creation

The Wrap:

Kindt's perspective reveals private equity at an inflection point where AI expertise has become as critical as deal-making acumen. His team's evolution from four to 60 people, building proprietary AI platforms and running internal "incubators" for portfolio companies, suggests traditional boundaries between investor and operator are dissolving. The most striking insight is temporal: while markets debate whether AI hype exceeds reality, practitioners report 10x productivity gains already materializing in engineering and customer support. The winners will be firms and leaders who recognize that AI-first isn't about efficiency optimization but fundamental business model reinvention—and who move with urgency while competitors debate whether the transformation is real.