For CFOs and general counsels at private equity-backed companies, the next strategic transaction — whether an M&A exit, IPO or debt financing — is always on the horizon. As a result, PE firms demand disciplined execution, lean operations and transparent financial and compliance reporting.

They operate on defined timelines and expect their portfolio companies to be governance-ready and fully prepared for diligence at a moment’s notice. While PE investors offer capital and strategic direction, they have little tolerance for inefficiency, gaps in oversight or last-minute scrambles when opportunity knocks.

That’s why 'transaction readiness' isn’t a milestone — it’s a mindset. Here’s what PE investors expect from their portfolio companies — and how CFOs and GCs can meet those expectations with the right governance, risk and compliance frameworks, supported by 

1. Run lean, strategic, investor-ready board meetings

PE-backed boards are built for speed and efficiency. With investor-appointed directors juggling multiple portfolio companies, meetings need to be decision-driven, data-backed and free of administrative bottlenecks.

How CFOs and GCs can support investor expectations:

, including AI-powered board book summaries, key takeaways and action items that ensure materials are super-efficient, and that directors have the most relevant, actionable insights always at their fingertips.

 to prevent confusion over financial reports, performance updates and compliance disclosures.

, so investor-appointed directors have instant access to board minutes, resolutions, and key filings.

Tie in key financial KPIs to board materials using board-ready reporting templates, 

 to provide up-to-date financials with visual clarity and narrative commentary. 

PE investors expect board meetings to translate directly into execution—misalignment, inefficient reporting or outdated materials slow down decision-making and can ultimately create deal risks. 

2. Keep risk & compliance air-tight— without slowing growth

PE investors expect their portfolio companies to be fully compliant with financial, regulatory, cybersecurity and ESG obligations — but they also push for lean operations and cost efficiency. Compliance gaps or legal missteps can derail an exit, but adding headcount to manage governance isn’t always an option.

How CFOs and GCs can balance compliance with efficiency:

Automate compliance tracking for regulatory filings, 

 and policy acknowledgments to reduce manual effort and ensure accuracy.

, ensuring investor-appointed directors can instantly assess financial, regulatory and cyber and ESG risks.

 and conflict-of-interest tracking to preempt governance red flags before they create diligence delays.

PE investors don’t just want compliance — they want proactive risk mitigation that doesn’t slow down operations or deal momentum.

As a PE portfolio company, the next deal or IPO is always on the horizon. Get the checklist that helps CFOs and GCs get their governance ready before timelines tighten.

Stay ahead of PE expectations

3. Provide real-time financial & market insights to investors

PE investors need constant visibility into a company’s operational performance, financial trends and industry positioning. With exit strategies tied to market conditions, valuations and competitor activity, data delays or misalignment can impact decision-making and slow down transactions.

How CFOs and GCs can ensure investor confidence:

 that combine internal governance and financial performance with 

Ensure all financial disclosures and board reports are investor-ready, with automated workflows that maintain accuracy and version control.

 against industry peers, ensuring investors have full transparency into how the company compares to market expectations.

 to bring structured, board-level visibility to financial metrics — enabling faster, smarter decisions grounded in real-time data.

When PE owners have real-time access to accurate governance and financial data, they can move faster on exit decisions without unnecessary diligence hold-ups.

4. Work effectively with investor-appointed directors

Unlike independent public company boards, private equity-backed boards are highly involved and investor-driven. PE firms often install their own directors or advisors, ensuring hands-on oversight and direct alignment with exit planning.

How CFOs and GCs can navigate this structure:

 to ensure investor-appointed directors have the right expertise for the company’s next phase of growth.

Maintain transparency on governance decisions, ensuring all board-level strategy is well-documented for future diligence reviews.

benchmark executive compensation and board composition

, aligning with investor expectations and best practices.

Ensure director reporting includes clear insights into financial performance drivers, linking compensation outcomes to real business results.

PE-backed leadership teams need to collaborate effectively with investor-appointed directors, ensuring decisions align with exit goals and governance remains scalable.

For private equity-backed companies, an exit or major transaction can happen at any time—whether through debt financing, an M&A deal, secondary buyout or IPO. Investors expect CFOs and GCs to have all governance, risk and compliance materials investor-ready at all times.

How to ensure ongoing transaction readiness:

 with governance documents, board materials, and compliance records readily accessible.

 and audit trails, ensuring potential valuation-impacting issues are identified before diligence begins.

Streamline financial and regulatory disclosures, eliminating last-minute bottlenecks in deal execution.

Connect finance and governance workflows with real-time data integration — so critical financial insights flow seamlessly into diligence-ready reports.

 The companies that command higher valuations and faster deal timelines aren’t scrambling to prepare for due diligence—they’re always ready.

So, in summary, private equity investors expect portfolio companies to be fast-moving, transparent and always prepared. For CFOs and GCs, this means working in lockstep to maintain airtight governance, risk and compliance frameworks while ensuring operational efficiency.

, agile leadership teams like yours can centralize reporting, keep board business highly secure and eliminate transaction bottlenecks, giving PE investors the confidence to execute deals smoothly at maximum valuation— ultimately paving the way for a seamless transition into the company’s next phase of growth.

 for CFOs and GCs to ensure your company is prepared for whatever comes next.

Five governance fundamentals that private equity expects from portfolio companies

 found that while 79% of leaders agree AI adoption is critical to competitiveness, 60% worry their company lacks a vision and plan to implement it. How do organizations accelerate AI to support transformational objectives and manage 

“Boards are racing to harness AI’s potential, but they must also uphold company values and safeguard the hard-earned trust of their customers, partners, and employees,” says Dale Waterman, Principal Solution Designer, at Diligent.

Here, we’ll explain how to develop an AI governance approach that helps you harness innovation without exposing your organization to undue risk, including:

What AI governance is and why it’s important

Ethical guidelines for responsible AI governance

AI governance encompasses the frameworks, policies and practices that promote the responsible, ethical and safe development and use of AI systems.

Boards will collaborate with key technology and risk stakeholders to set guidelines for 

 and fairness in AI technologies to prevent harm and bias while maximizing their benefits operationally and strategically. Responsible AI governance considers:

AI governance policies should promote human centric and trustworthy AI and ensure a high level of protection of health, safety and fundamental human rights

Boards should also consider compliance with applicable legal frameworks that govern AI usage where they operate, or intend to operate, such as the EU’s AI Act.

Organizations should assign responsibility for AI decisions to ensure human oversight and prevent misuse.

Chief technology officers, risk officers, chief legal officers and their boards must develop a governance approach that protects data, prevents unauthorized access and ensures AI systems don’t become a cybersecurity threat.

 of all key stakeholders — leadership, employees, customers, investors and more — fairly, transparently and for the company's good. AI governance is similarly important because it prioritizes ethics and safety in developing and deploying AI.

“The corporate governance implications of AI are becoming increasingly understood by boards, but there is still room for improvement,” says Jo McMaster, Regional Vice President of Sales at Diligent.

, AI systems could lead to unintended consequences, from discrimination and misinformation to economic and social disruptions. Having a strong AI governance approach:

AI models can inherit biases from training data, leading to unfair hiring, lending, policing and healthcare outcomes. Governance proactively identifies and mitigates these biases.

When AI makes decisions, who is responsible? Governance holds humans accountable for AI-driven actions, preventing harm from 

. PwC’s Head of AI Public Policy and Ethics Maria Axente says, “We need to be thinking, ‘What AI do we have in the house, who owns it and who’s ultimately accountable?’"

AI relies on vast amounts of data, a particular risk for healthcare and financial organizations handling sensitive information. Governance establishes guidelines for data protection, encryption and ethical use of personal information.

environmental, social and governance (ESG)

, requiring massive amounts of electricity and water for every query. It also reshaped job markets and corporate operations. Governance helps create policies that balance AI’s opportunities with its 

Many AI systems are considered “black boxes” with little insight into their decision-making. Governance encourages transparency and helps users trust and interpret AI outcomes.

While AI holds immense potential for progress in healthcare, finance and education, governance weighs innovation alongside possible ethical considerations and public harm.

Master the five factors influencing AI governance today to help your board navigate the complex interplay between innovation and risk.

What does AI mean for the boardroom

General Data Protection Regulation (GDPR)

, but the U.S. does not, AI governance frameworks vary by region. Countries often take a different approach to what it means for AI to be ethical, safe and responsible.

“The issue of competing values is not a new one for governments or the technology sector.,” says Waterman. “During a time of regulatory uncertainty and ambiguity, where laws will lag behind technology, we need to find a balance between good governance and innovation to anchor our decision-making in ethical principles that will stand the test of time when we look back in the mirror in the years ahead.” 

 currently lack harmonization. For instance, certain countries like the United States and UK currently emphasize guidelines and focus on innovation and maintaining a competitive edge on the global stage. In contrast, the 

 is a comprehensive law that places a greater emphasis on assessing and mitigating the risks posed by AI to foster trustworthy AI and ensure the protection of fundamental human rights.

Some significant frameworks around the world include:

 was passed in 2024 and classified AIU systems into risk categories based on the industry and how AI is developed and deployed. All AI applications under the act are subject to transparency, accountability and data protection requirements.

. Rather than instituting a single law, the UK took a pro-innovation and sector-based approach to AI. The document encourages the self-regulation of ethical AI practices in industry, focusing significantly on safety, transparency and accountability in AI development.

New Generation Artificial Intelligence Plan

 is one of the most detailed AI regulatory systems. It includes strict AI controls, safety standards and facial recognition regulations. China also implemented the 

 in 2023 to ensure AI-generated content aligns with Chinese social values.

Developed by think tank NIT Aayog, India’s 

National Strategy for Artificial Intelligence

 focuses on the ethical adoption of AI in sensitive industries like healthcare, agriculture and finance. It proposes self-regulation and public-private partnerships for AI governance.

 include eight guidelines, including fairness and accountability. The country also passed the Online Safety Act in 2021 to specifically address AI-generated misinformation and harmful content.

See how the regulatory and governance response to AI’s opportunities and concerns has varied globally.

AI regulations around the world

The importance of technical standards for AI

, industry bodies and standards organizations have developed technical AI governance guidelines. While voluntary, complying with relevant technical standards can help your organization foster quality, safe and efficient AI-powered products, services and innovation.

Most guidelines attempt to strike a balance between these easily conflicting interests. These include:

The National Institute of Standards and Technology (NIST) AI Framework:

 focuses on addressing bias, explainability and security with AI. This is particularly important given the 2023 White House Executive Order on AI, which reinforces AI’s safety concerns.

The International Organization for Standardization (ISO) released international AI standards for data management, algorithmic transparency and security in 2017. The ISO has published 

This industry-led group established a committee on AI in early 2021. The 

 focus on technical standards that enable AI governance within specific sectors.

International Telecommunications Union (ITU): 

 to assess the need and requirements for future AI standards. These efforts have focused on AI for digital agriculture, natural disaster management, 

, environmental efficiency and autonomous and assisted driving.

Despite the value of AI governance, getting it right can be difficult. Standards must evolve as rapidly as technology does and consider the distinct regulatory approaches across jurisdictions — not to mention ethical concerns.

Boards working to govern AI may also need to confront:

Technological advancements outpacing regulations: 

AI is growing at an unprecedented rate, making it difficult for policymakers and regulators to keep up. With regulations one step behind innovation, organizations can easily expose themselves to the misuse of AI, lack of accountability or unforeseen ethical dilemmas.

Different countries have varying perspectives on AI regulation, privacy and data security. The EU, for example, has taken a strict regulatory approach with its AI Act, while the U.S. leans toward industry self-regulation. These variations make it challenging to anchor governance to any universal standard.

It’s nearly impossible to understand how AI systems make decisions. Lack of transparency erodes trust in AI and makes it difficult to govern it. How do you know an AI system for healthcare, for example, is making fair and unbiased decisions based on the data available to it? Those developing AI governance frameworks must consider how to balance developing AI systems with public accountability.

Determining responsibility when AI causes harm is complex. Is the developer, the user or the organization responsible? Current legal frameworks don’t clearly define AI accountability, particularly in cases where autonomous systems make independent decisions.

Data privacy, security and risk management considerations: 

AI systems require vast amounts of data, raising concerns about how personal information is collected, stored and used. This exposure to data also raised the stakes for cybersecurity. Non-Executive Director and Founder of C Squared Consulting Caroline Cartellieri says, “So it’s almost like today boards talk a lot about cybersecurity. Just add that to the power of X because now the risks are becoming so much bigger because nobody quite understands what Gen AI does, its capabilities, and how powerful it can be.”

AI governance should encompass more than specific processes for developing and using AI. Frameworks today should also consider five ethical principles to ensure AI is developed and deployed in a way that benefits society while minimizing harm. The principles below are also the foundation for emerging AI ethical guidelines.

AI systems should be designed to prevent discrimination and bias. This includes ensuring diverse representative training data, auditing algorithms for bias and implementing fairness-aware machine learning techniques. The 

 are an intergovernmental AI standard promoting trustworthy AI that respects human rights.

AI models should be explainable and understandable to users. Organizations should disclose how AI systems make decisions, particularly in high-stakes areas like finance, healthcare and law enforcement. The 

 is at the forefront of AI transparency, requiring certain disclosures for high-risk AI systems.

Determining who is responsible for AI decisions is challenging. Developers, businesses and policymakers should collaborate to ensure AI systems align with consistent ethical and regulatory standards. This is a pillar of the 

AI systems must follow strict data protection regulations to safeguard users’ privacy. This includes requiring informed consent and robust security measures. 

 are a compelling example of guidelines for the AI development process that put humans first.

All AI systems should be designed to prevent vulnerabilities and cyber threats. Developers must implement safeguards against breaches, attacks and unauthorized access. The UK’s 

 is an example of a standard that examines AI security closely.

An AI governance policy clearly outlines what an organization considers the acceptable development and use of AI systems. These guidelines should be clear, easy for employees to follow and align with compliance and risk management measures.

What these policies mandate can vary by organization. Some may prohibit entering proprietary information into AI systems; others may specify which tasks AI can support and which it can’t. Whatever the requirement, though, AI governance policies are important because they:

 with existing and emerging regulations or standards, such as the EU’s AI Act or the NIST Risk Management Framework.

 keeping in mind the five aforementioned principles.

by balancing business interests, ethical considerations, and the need to adapt to the future of AI.

Given the quick pace of AI evolution, writing a governance policy can feel daunting. What does it look like to manage AI proactively and ethically? Here’s a template to get you started:

This AI Governance Policy outlines the principles, guidelines and responsibilities for the ethical development, deployment and management of AI within [Organization Name]. We aim to promote the responsible, fair and transparent use of AI while aligning with legal and ethical standards.

This policy applies to all AI systems [Organization Name] develops, procures or deploys, including machine learning models, 

 and AI-driven analytics in business operations.

[Organization Name] commits to the following:

AI systems must be designed to prevent discrimination based on race, gender, age or other protected attributes.

Regular audits will be conducted to identify and mitigate bias in AI models.

AI-driven decisions must be understandable and interpretable by users.

Clear documentation on AI functionality and decision-making processes will be maintained.

An AI Ethics & Compliance Team must monitor and manage AI-related risks.

Human oversight will be required for high-risk AI applications.

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