Over recent engagements, our work advising on AI, particularly within NetSuite-driven environments, has increasingly brought us into close collaboration with private equity firms and their portfolio companies. This shift has revealed a consistent pattern: while interest in AI is accelerating, execution is often misaligned with the realities of portfolio operations.
Private equity firms are under growing pressure to demonstrate tangible progress in AI. Sponsors expect it. Limited Partners (LPs) are asking about it. Meanwhile, portfolio companies with varying levels of data maturity, infrastructure, and risk tolerance are waiting for clear direction.
In response, many firms initiate a search for a “Head of AI” to drive transformation across the portfolio. However, in practice, this is where execution often stalls. The profile being hired frequently does not match the breadth of the role required.
Where Most AI Leadership Models Fall Short
The typical hiring approach tends to focus on one of two profiles:
A technologist with some operational exposure
An operator with enough technical fluency to participate in discussions
While each brings value, neither fully addresses the dual challenge of AI transformation at the portfolio level. These gaps are not isolated; they compound across multiple companies, increasing execution risk and cost.
The Missing Technical Perspective
AI transformation is not purely a strategic or financial initiative; it is deeply technical. When this perspective is underrepresented, critical risks emerge:
Security and compliance exposure
AI introduces new data risks, privacy concerns, and regulatory obligations across all portfolio companies. Without technical ownership, these risks remain unmanaged.
Weak data foundations
AI initiatives depend on robust data architecture. Poor data quality or fragmented systems will undermine even the most promising use cases.
Ineffective vendors and platform decisions
Evaluating AI tools, models, and vendors requires hands-on technical expertise. Financial analysis alone is insufficient.
Unrealistic roadmaps
Without technical validation, AI strategies often prioritize ideas that perform well in presentations but fail in production environments.
The Missing Financial Discipline
Conversely, organizations that lean too heavily on technical leadership encounter a different set of challenges:
Lack of measurable ROI
AI initiatives may demonstrate capability but fail to connect to revenue growth, cost reduction, or operational efficiency.
Unstructured investment decisions
Without financial oversight, budgets expand based on enthusiasm rather than expected returns.
Limited credibility with sponsors and LPs
Investors evaluate AI through the lens of margin improvement and exit valuation, not technical sophistication.
Premature scaling
Expanding AI programs before proving value leads to embedded costs and unclear outcomes.
AI initiatives that cannot be measured cannot be sustained. Those that cannot be justified will not survive future budget cycles.
What Works: Proven Approaches to AI Leadership
From our experience, firms that successfully operationalize AI across their portfolios typically adopt one of three models:
1. Cross-Functional Leadership with Shared Accountability
A joint mandate between the CFO and CIO/CTO ensures both financial discipline and technical governance. This model works best when both leaders are empowered equally and aligned under a common sponsor.
2. A True Hybrid Leader
This is not a CFO with surface-level AI knowledge, nor a CTO with limited financial exposure. The right profile has deep experience in both domains, capable of translating technical initiatives into financial outcomes and vice versa.
3. A Fractional Head of AI
This model is often the most practical and underutilized.
Private equity firms already rely on fractional leadership, interim CFOs, operating partners, and advisors. Applying this model to AI is a natural extension.
The key is the profile:
A practitioner, not just a strategist
Proven experience implementing AI in operational environments
Ability to align technical execution with financial outcomes
A fractional leader can:
Deliver early, visible wins to build confidence with sponsors and LPs
Establish governance, infrastructure, and repeatable frameworks
Define the long-term role requirements based on real implementation experience
In many cases, portfolio companies are not yet ready for a full-time AI leader. A well-scoped fractional engagement provides a lower-risk, high-impact starting point.
Key Questions to Guide AI Investment Decisions
Before committing to any AI initiative, firms should be able to clearly answer:
Will this investment improve margins in the near term and contribute to a higher exit valuation?
Is our data infrastructure, security posture, and vendor strategy capable of supporting this at scale?
If either answer is unclear or deferred, it indicates a structural gap that should be addressed before further investment.
Final Perspective
AI transformation at the portfolio level is not a single-discipline challenge. It requires tight integration between financial strategy and technical execution.
Optimizing one while neglecting the other consistently leads to underperformance.
The firms that succeed are those that align both from the outset, ensuring that every AI initiative is technically feasible, financially justified, and operationally scalable.
How We Help
We work with private equity firms and portfolio companies to move AI from concept to measurable value. Our approach is grounded in both technical execution and financial outcomes:
- AI strategy aligned to margin improvement and exit readiness
- Data and architecture assessments to ensure AI feasibility
- Hands-on implementation across NetSuite and operational systems
- Vendor selection, build vs buy decisions, and governance frameworks
- Fractional AI leadership to deliver early wins and scalable foundations
