Extended holds: Sustain value when exits move

PE sponsors rarely underwrite investments expecting seven- or eight-year hold periods. Yet extended holds have become increasingly common as market conditions, valuation gaps, and other factors continue to affect exit timing.

How should sponsors adapt their value creation strategies when the original exit timeline no longer applies?

Top considerations include:

• Early vs. later-phase value creation: By Year 3, most organic levers have been pulled. The toolkit shifts to one of M&A and longer-term, higher-complexity initiatives like operational restructuring or business model evolution.

• Rigidity creates risk: Effective value creation during extended holds requires deliberate re-sequencing of initiatives. Reprioritize or retire projects that no longer make sense.

• Managing expectations: It may be tempting to delay difficult conversations as timelines extend. But delay breeds mistrust, speculation, and misalignment. Instead, establish transparency early and sustain it consistently.

Access our new guide (Opens a new window)for additional insights for extended holds – plus, a practical checklist and discussion guide for navigating these periods. 

Building AI trust starts with strong governance

CohnReznick’s ISO/IEC 42001 readiness assessment helps organizations understand where their AI governance capabilities stand today and what it will take to strengthen them. Organizations receive an executive summary of current practices and key risks, a maturity scorecard benchmarking governance and operational capabilities, a gap analysis against ISO/IEC 42001 requirements, and a prioritized roadmap for remediation, documentation, and certification readiness.

Organizations looking to assess or mature their capabilities should focus on:

• Evaluating governance and accountability: Assess policies, oversight structures, ownership, reporting mechanisms, and strategic alignment to determine whether AI is being managed effectively.

• Managing internal risks: Identify, prioritize, mitigate, monitor, and escalate AI risks before they become business, compliance, or reputational issues. 

• Strengthening data governance and security: Review practices around data quality, privacy, protection, retention, traceability, and security to support responsible AI use.

• Establishing lifecycle controls: Evaluate how AI systems are designed, developed, tested, deployed, monitored, and managed throughout their lifecycle.

• Managing third-party risk: Understand the risks associated with AI vendors, models, platforms, data ownership, and intellectual property before they become operational challenges.

Treasury's Section 987 proposal may simplify foreign currency reporting

Treasury and the IRS have proposed regulations that would simplify the application of Section 987 for controlled foreign corporations (CFCs) with foreign currency qualified business units (QBUs). The proposal would allow eligible CFCs to elect out of recognizing certain Section 987 gains and losses while retaining the underlying income and earnings calculations required under the rules, potentially reducing compliance complexity and administrative burden for multinational groups.

Organizations evaluating whether the election makes sense should consider:

• Election timing and mechanics

• Consistency requirements

• Treatment of pre-election gains and losses

• Application to partnership interests

• Gain recognition and anti-abuse provisions

Taxpayers may also be able to rely on the proposed regulations before they are finalized if the rules are applied consistently across relevant group members, creating an opportunity to revisit prior Section 987 calculations and evaluate potential compliance savings. 

How leading restaurant operators are turning data into action

By connecting AI to operational and financial data, restaurants can move beyond reporting what happened to understanding why it happened, identifying the drivers, and determining what to do next.

As adoption matures, leaders are focusing less on experimentation and more on: 

• Building AI understanding from the top: Operators who use AI themselves are better equipped to identify valuable use cases, build internal support, and make smarter investment decisions. 

• Creating a scalable data foundation: AI delivers more value when operational and financial data are connected, accessible, and reliable across the organization.

• Focusing AI on defined operating problems: The strongest applications help operators answer critical business questions around profitability, food costs, labor, demand forecasting, and unit performance.

• Keeping humans in the driver's seat: As AI takes on more analysis, employees remain responsible for reviewing outputs, applying judgment, and making decisions.

• Turning AI productivity into operating capacity: The real payoff comes when time saved on analysis is redirected toward leading teams, serving guests, and improving operations.