Technology is permeating every industry and blurring the lines between them, making traditional sector-based research obsolete. Wood advocates for structuring investment research departments around foundational technologies like AI, robotics, and blockchain to accurately analyze future growth drivers.

Organizations that default to treating AI as an IT-led initiative risk failure. IT's focus is typically on security and risk mitigation, not growth and innovation. AI strategy must be owned by business leaders who can align its potential with customer needs, talent decisions, and overall company growth.

Effective private equity boards function as strategic advisory councils rather than governance bodies. Board members are expected to be co-investors who actively help with strategy, networking, and operational challenges like procurement, making them a key part of the value creation engine.

C-suites are more motivated to adopt AI for revenue-generating "front office" activities (like investment analysis) than for cost-saving "back office" automation. The direct, tangible impact on making more money overcomes the organizational inertia that often stalls efficiency-focused technology deployments.

Many leaders focus on data for backward-looking reporting, treating it like infrastructure. The real value comes from using data strategically for prediction and prescription. This requires foundational investment in technology, architecture, and machine learning capabilities to forecast what will happen and what actions to take.

Great investment ideas are often idiosyncratic and contrary to conventional wisdom. A committee structure, which inherently seeks consensus and avoids career risk, is structurally incapable of approving such unconventional bets. To achieve superior results, talented investors must be freed from bureaucratic constraints that favor conformity.

Boards have a finite 'governance budget'—their collective time, skills, and capacity. This budget must be sufficient to oversee the portfolio's risk. A board with limited capacity cannot effectively govern a high-risk, complex strategy like private equity, creating a critical misalignment that jeopardizes returns.

The most significant hurdle for businesses adopting revenue-driving AI is often internal resistance from senior leaders. Their fear, lack of understanding, or refusal to experiment can hold the entire organization back from crucial innovation.

Treating AI as a technology initiative delegated to IT is a critical error. Given its transformative impact on competitive advantage, risk, and governance, AI strategy must be owned and overseen by the board of directors. Board ignorance of AI initiatives creates significant, potentially company-ending, corporate risk.