AI automation is making the daily financial close a tangible possibility, moving beyond the traditional monthly cycle. This provides near real-time visibility into business performance, which is a powerful but potentially demanding capability for PE-backed companies.
Contrary to the narrative of AI startups destroying incumbents, established enterprise software companies will likely absorb and 'domesticate' AI. They will integrate AI capabilities into their existing platforms, leveraging deep customer relationships and distribution advantages to maintain their market position.
In early 2025, AI adoption in PE-backed companies was often performative. It focused on individual productivity hacks rather than creating quantifiable business value, especially for firms preparing for an exit who needed a good 'AI story'.
The true potential of AI agents is locked behind messy, disorganized corporate data. This has forced a renewed, urgent focus on foundational data work, like warehousing and cleanup, as companies realize that AI requires a data architecture built for agents, not just dashboards.
The speed and simplicity of AI development tools have led to a surge in 'vibe coded' products. These applications are often fun to build and appear impressive but lack the rigorous product thinking and engineering discipline required for long-term viability and maintenance.
A bold prediction that service-based businesses, especially consulting firms, that do not fundamentally reinvent their delivery models and cost structures using AI will fail. The core value of many services is being automated, requiring proactive self-disruption to survive.
A dramatic improvement in AI model capabilities in February 2026 rendered many existing AI strategies obsolete overnight. This event triggered a 'fever pitch' of activity, forcing companies to immediately abandon cautious pilots and urgently reconsider core business workflows.
The conversation around AI has evolved from adding simple features to existing processes. Companies are now grappling with fundamental organizational redesign, questioning the long-term need for roles like SDRs, junior developers, and large finance teams.
The future of the finance department involves a shift from manual execution to strategic oversight. Humans will act as orchestrators and quality control for a team of AI agents that handle the bulk of tasks like closing the books and generating reports, focusing people on exception management.
Companies are using AI agents to continuously scrape competitor pricing data throughout the day. This allows for near real-time, dynamic pricing experiments on their own e-commerce channels, leading to significant revenue increases that were previously impossible at scale.
Private Equity-backed companies are significantly behind their venture-backed counterparts in AI spending. This is largely because their CFOs and sponsors demand a clear, quantifiable return on investment and P&L impact, a difficult hurdle for emerging and experimental AI technologies.
Early AI adoption in software development saw a split: experienced engineers dismissed AI-generated code as low-quality 'slop', while junior developers, less able to discern its flaws, embraced it more readily. This highlighted a significant skill and trust gap.
AI development tools have radically compressed the product design cycle. Instead of presenting wireframes or mockups, teams can now arrive at initial stakeholder meetings with fully functional, data-connected demos, dramatically accelerating the feedback loop and decision-making process.