AI adoption in private equity is accelerating, but implementation isn’t always straightforward. From strategic fit to risk, governance, and value creation, here are 10 key considerations to help firms deploy AI effectively and avoid common missteps.
10 Key Considerations When Deploying AI in Private Equity:
- Strategic Alignment
- Use cases
- Buy vs build
- Sponsorship
- ROI
- Risks
- Governance
- People
- Value Creation
- Implementation
Consideration 1: Strategic Alignment
AI initiatives must directly align with your firm’s broader strategic objectives. While impressive AI tools frequently surface, prioritize those clearly supporting your long-term goals.
Examples of strategic alignment include:
- Efficiency-driven strategy: Tools extracting data from unstructured documents to scale AUM without proportional headcount increases.
- White-glove client service: AI content-generation tools to produce engaging, tailored client communications.
- Investor engagement: Web scraping tools to monitor LP trends, enabling timely and relevant outreach.
Aligning AI with strategy ensures your firm realizes meaningful, long-term outcomes from its technology investments.
Consideration 2: Use Cases vs Outcomes
AI use cases are frequently discussed at industry events and among peers, but outcomes should be equally emphasized. Clearly defining outcomes, such as efficiency gains, cost reductions, or strategic benefits, can help firms prioritize and select the right AI tools.
Examples of outcomes include:
- Tangible results: Efficiency improvements, cost savings, headcount reductions.
- Intangible benefits: Strategic advantages, enhanced client engagement.
Focusing on desired outcomes rather than just potential applications provides a practical framework to evaluate and prioritize AI investments.
Consideration 3: Buy vs Build
The classic “buy versus build” debate continues in AI deployment. Historically, private equity firms favoured buying solutions, especially with the rise of SaaS. While buying ready-made AI tools remains practical, don’t dismiss building your own, particularly for unique, edge-case scenarios.
Consider:
- Buy: Ideal for established, widely-available solutions.
- Build: Suitable when no existing product fully addresses your unique needs, offering potential competitive advantage.
With quality development partners now widely accessible and affordable, building a tailored AI tool can provide your firm a distinctive edge in the market.
Consideration 4: Sponsorship
Executive sponsorship, buy-in from the C-suite, is critical for AI initiatives. Senior executives generally recognize AI’s potential, providing essential resources and funding. If securing sponsorship is challenging, consider these points:
- LP expectations: Investors may question a lack of internal tech investment, especially if your strategy includes investing in tech companies.
- Competitive risk: Failing to adopt AI could mean falling behind peers and accumulating long-term technology debt.
Strong sponsorship from leadership ensures AI projects have the necessary backing to succeed.
Consideration 5: ROI
ROI provides valuable mathematical grounding for assessing AI opportunities, particularly appealing to private markets firms. However, avoid overly complex or purely scientific approaches. Difficulty in precisely calculating ROI shouldn’t halt promising AI initiatives.
Instead:
- Quantify clear financial returns when feasible.
- Consider qualitative benefits, such as strategic advantages or operational enhancements.
Balancing measurable ROI with broader strategic factors ensures your firm appropriately evaluates and leverages AI opportunities.
Consideration 6: Risks
AI introduces many familiar technology risks, such as data security, privacy, and ethics – but also unique challenges. Generative AI, for example, produces novel information each time and can hallucinate, necessitating specific mitigation measures like human oversight.
Key risk considerations include:
- Generative AI risks: Hallucination, novel outputs requiring oversight.
- Data diligence: Ensuring robust processes for data storage, processing, and provider vetting.
- Regulatory compliance: GDPR, DORA, and EU AI Act.
Engaging legal and compliance teams early helps effectively manage these risks and supports defensible diligence with LPs.
Consideration 7: Governance
Governance is critical in managing AI-related risks and ethical concerns. Given AI’s rapid evolution, clear accountability is essential.
Key practical governance actions include:
- Dedicated AI responsibility: Assign an individual or team to manage and maintain AI strategy and policy alignment.
- Team accountability: Ensure users verify AI model outputs and disclose their use clearly.
- Usage monitoring: Track tool adoption and usage patterns to identify best practices and facilitate knowledge sharing among teams.
Proactive governance ensures AI remains transparent, compliant, and strategically aligned.
Consideration 8: People
People and AI are closely interconnected, yet often overlooked in favour of technology, systems, and data. Your team’s AI literacy and capability directly impact the AI tools you can leverage and how swiftly your firm can adopt them.
Key people-related considerations:
- Team capability: Assess and invest in AI literacy and training.
- Tool selection: Choose AI solutions that enhance team skills.
- Future talent strategy: Recruit proactively, prioritizing skills aligned with future AI requirements.
Integrating people into your AI strategy ensures sustained adoption and competitive advantage.
Consideration 9: AI for Value Creation
When evaluating AI solutions, consider their potential as value creation tools across your portfolio. Successful internal AI initiatives can often be scaled or replicated within portfolio companies, enhancing overall returns.
Examples of value-creation strategies include:
- Content creation tools: Implementing AI in your digital marketing strategy, then training portfolio companies to adopt similar solutions.
- AI Value Playbooks: Developing standardized frameworks or guidelines that can be leveraged broadly.
Maximizing AI value across your portfolio multiplies the benefits from your internal investments.
Consideration 10: Implementing AI
Implementing AI differs from traditional technology projects, which typically require extensive governance, long timeframes, and significant testing (e.g., treasury or accounting systems). AI – particularly for discrete, edge-use cases – allows for quicker, more entrepreneurial approaches.
Consider:
- Rapid Pilots: Build internal capabilities to swiftly test and evaluate AI tools.
- Low-cost experimentation: Assess feasibility quickly without extensive upfront investment.
Adopting this agile approach enables faster validation and integration of AI, efficiently determining its fit for your firm.
Talk to us about your AI plans
Holland Mountain’s Matt Guy can support your firm, from strategy to implementation, to achieve your AI objectives. Contact us today for an introductory call.
