AI in 2026: Five Strategic Shifts for Private Equity Value Creation

AI in 2026: Five Strategic Shifts for Private Equity Value Creation

For most portfolio companies, the AI question in 2026 is no longer “Should we use it?” but “What does it do to EBITDA, cash and exit multiples within our holding peri-od?”
 

Across recent reports and executive surveys, one pattern is clear: the hype phase is over. AI is becoming part of the value-creation toolkit, but only a small share of companies has turned experiments into material P&L impact. That gap is exactly where private equity can play a decisive role by setting clearer expectations, asking sharper questions in due diligence, and backing the capabilities that convert AI into cash flow.
 

This article highlights five strategic shifts in AI that matter most for private equity owners in 2026, and seven conditions General Partners should require from portfolio leadership teams to unlock value and protect downside risk.
 

Shift 1 – From Hype to Hard Work: AI Factories as Value-Creation Engines

The first shift is financial discipline. Across markets, AI spend is under pressure. CFOs are tightening approval thresholds and deferring a significant share of planned AI budgets into 2027 unless projects are clearly tied to revenue, margin or cost outcomes. At the same time, only a minority of companies can point to measurable EBIT uplift from AI, despite widespread experimentation.
 

The era of “let’s fund ten pilots and see what sticks” is ending. Funding now flows to organisations that behave more like AI factories: they standardise how use cases are sourced, evaluated, industrialised and governed, with shared platforms and reusable components instead of bespoke experiments.
 

Implications for GPs and operating partners

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• In due diligence

  • Treat AI as a value-creation lever, not a buzzword.

  • Quantify potential uplift in 3–5 core value drivers (pricing, sales productivity, service cost, supply chain, SG&A).

  • Assess whether the target has the foundations to industrialise AI, not just a handful of pilots or vendor demos.

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• In the first 100 days

  • Ask for a focused AI value-creation plan, not a shopping list of tools.

  • Anchor it in 3–5 high-value use cases, each with an owner, baseline, and impact target.

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• In portfolio reviews

  • Track AI as a discrete workstream alongside commercial, operational and financial initiatives.

  • Ask a simple question: “How many AI use cases are live in production, reused, and contributing to the value-creation bridge?”
     

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Shift 2 – AI Teammates in Every Role: Workforce Leverage, Not Just Cost-Cutting

By the end of 2026, a large share of enterprise applications will embed task-specific AI agents. Many roles, sales, service, finance, HR, operations, will routinely involve working with AI copilots and agents. These are not just chatbots; they are digital col-leagues handling entire flows, with humans overseeing exceptions and judgement calls.

The organisations that benefit most are not the ones buying the most tools, but those that redesign work around agents. Successful AI programmes consistently reflect a simple rule of thumb: roughly 10% algorithms, 20% technology and data, 70% people and processes. High performers redesign workflows from the ground up; laggards try to “AI-wash” broken processes.
 

Implications for GPs and operating partners​

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• Focus on leverage, not headcount cuts

  • Use AI to create workforce leverage: more quotes, more pipeline coverage, more first-call resolution, without a linear increase in FTEs.

  • Protect revenue while reducing unit costs.

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• Push for end-to-end redesign of critical flows

  • Identify 2–3 processes per year per portfolio company (e.g. claims, collections, order-to-cash, support, onboarding) and challenge management to redesign them assuming agents from day one.

  • Measure success in cycle time, cost per transaction, and error rates, not only productivity per head.

 

• Make “working with agents” a core skill

  • Treat AI collaboration as a capability to build across the portfolio, not a niche IT skill.

  • Encourage management teams to integrate AI usage into role descriptions, KPIs and incentives.

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Shift 3 – New Org Structures: CAIOs, CoEs and Leaner Pyramids Across the Portfolio

As AI moves into the core of the business, organisational design is following.
 

A growing share of larger companies is appointing a dedicated AI leader (CAIO or equivalent), accountable for turning AI strategy into business outcomes and for managing risk. AI Centres of Excellence are moving from advisory roles to delivery engines that define architectures, build reusable assets and enable the business.
 

At the same time, the workforce pyramid is shifting. Entry-level roles with high automation potential are quietly disappearing, not via mass layoffs, but through non-renewal and redefinition, while experienced professionals with AI skills command significant wage premiums. The result is a move towards leaner pyramids and more “AI orchestrator” roles.
 

Implications for GPs and operating partners​

 

• Clarify ownership at the top

  • In key assets, ask: “Who owns AI?”

  • Ensure there is a senior leader with a clear mandate and enough authority to say no as well as yes.

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• Balance local capabilities with shared platforms

  • For platform plays and roll-ups, explore a shared AI capability (a hub or CoE) that services multiple portfolio companies.

  • Avoid each asset building small, under-resourced AI teams in isolation.

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• Build AI into the operating model and people plans

  • Encourage management to rethink spans of control, role definitions and career paths assuming that agents will take over a significant share of routine work.

  • In cost-take-out programmes, consider pyramid reshaping as a lever: fewer transactional roles, more high-value “AI-enhanced” experts.

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Shift 4 – Governance and Risk: Protecting Equity Value

AI is moving rapidly into the risk and regulatory spotlight.

By the end of 2026, AI governance will be tested not only in internal committees, but in regulatory investigations and courtrooms. Companies face growing exposure from opaque models, biased decisions, data-protection failures and over-reliance on third-party AI vendors.

At the same time, frameworks like the EU AI Act are transi-tioning from legislation to enforcement, especially in high-risk domains such as healthcare, finance, HR and public services.
 

For private equity, the question is straightforward: how much equity value is at risk if AI governance is weak?
 

Implications for GPs and operating partners
 

• Make AI a formal risk class in portfolio oversight

  • Include AI in the standard risk and compliance framework for portfolio companies.

  • Require boards to classify AI use cases by risk level and to document controls and human oversight.

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• Look ahead to exit diligence

  • Assume that buyers and their advisers will scrutinise AI usage, data handling and vendor dependencies.

  • Ensure that AI governance – policies, inventories, controls – can be confidently shown in the data room as part of the equity story.

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• Watch for contractual and vendor lock-in risk

  • Review key contracts with AI and cloud vendors for lock-in, IP ownership, liability and audit rights.

  • Weak contracts or opaque black-box systems in critical processes can directly hit valuation at exit.
     

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Shift 5 – The Data Quality Bottleneck: Asset or Liability in Your Investment Case?

Underneath the infrastructure and the models, one constraint matters more than any other: data quality.

Most companies have plenty of data. Very few have AI-ready data: unified, well-governed, timely, with clear definitions and ownership. Across studies, poor data is emerging as the main reason AI initiatives fail to meet ROI expectations. Left un-addressed, it becomes a silent drag on productivity and a source of execution risk.
 

For private equity, data quality is both a hidden intangible asset and a potential liability.
 

Implications for GPs and operating partner

 

• Include data in due diligence, not just systems

  • Go beyond “what systems do they use?”

  • Ask: “How consistent and trusted is the customer, product, asset and transaction data behind these systems?”

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• Fund targeted data clean-ups as part of the VCP

  • Prioritise 1–2 key data domains per asset that unlock multiple AI use cases (e.g. customer master, product master, asset registry).

  • Treat this as enabling capex for AI-driven value levers.

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• Make AI-ready data part of the equity story

  • At exit, being able to demonstrate that the company has clean, well-governed data and scalable AI use cases can support multiple expansion and buyer confidence.
     

​Seven Conditions for Successful AI Implementation in 2026

Pulling these shifts together, there is strong convergence across major consultancies and practitioner reports on what separates winners from the rest. For private equity, these become conditions to require and reinforce across the portfolio.
 

I group them into three domains: Financial Foundations, Operational Excellence and Organisational Capability.
 

FINANCIAL FOUNDATIONS

 

1. Financial discipline first

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• Every AI initiative in the VCP must be tied to specific P&L metrics (revenue, gross margin, cost, working capital) with realistic assumptions.

• IC and portfolio reviews should ask: “Where is the AI impact in the bridge from entry EBITDA to year-3 or year-5 EBITDA?”

• Projects that cannot demonstrate credible value within 12–18 months should be killed or re-scoped, not left to linger as “innovation”.

 

2. Build an AI factory, not a pilot zoo

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• Require management to standardise how they source, prioritise and industrialise AI use cases.

• Focus AI funding on a small number of high-value domains rather than spreading it thinly over dozens of proofs of concept.

• Where possible, operating partners can help build common patterns and playbooks that can be reused across bolt-ons or multiple portfolio companies.​​

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OPERATIONAL EXCELLENCE

 

3. Redesign workflows around agents (the 10–20–70 rule)

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• Use the 10–20–70 mental model: only 10% of success is the model and 20% is tech/data; the remaining 70% is people and process.

• In board and VCP discussions, challenge teams with: “If we were designing this process today assuming we had AI agents, what would it look like?”

• Measure progress by processes simplified or eliminated, not just tasks automated.

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4. Treat data as a product

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• Ensure each portfolio company identifies a small set of critical data domains (e.g. customer, product, asset, transaction) and assigns clear ownership.

• Invest in making these domains AI-ready: consistent definitions, quality rules, lineage, access controls.

• Make data readiness a gating criterion for funding AI use cases: no clean data, no AI budget.

 

5. Governance by design, not retrofit

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• Require AI governance from day one: inventories of models and agents, risk classification, documented human-in-the-loop points for high-impact decisions.

• Build explainability and audit trails into systems upfront; black boxes are increasingly difficult to insure, regulate and sell.

 

ORGANISATIONAL CAPABILITY

6. Organise for AI: leadership, CoEs and agent operations

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• Clarify executive ownership (CAIO, CDO, or equivalent) with a mandate that extends beyond IT into the business.

• Encourage the creation or strengthening of an AI Centre of Excellence that can actually deliver: reference architectures, reusable components, coaching for business teams.

• Build agent operations capabilities to monitor, tune and govern fleets of agents across processes, treating them like a new class of digital workforce.

 

7. Commit to continuous upskilling

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• Assume a large share of current skills will materially change over the next five years; treat AI literacy and orchestration as mandatory for managers and knowledge workers.

• Support management in building structured upskilling programmes rather than relying solely on external hiring.

• At fund level, consider portfolio-wide initiatives: shared learning resources, communities of practice, and cross-company secondments for key digital and AI talent.

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The Bottom Line

For GPs in 2026 the question is no longer whether a company has an “AI story” in its investor deck, but whether AI shows up in EBITDA, cash generation and the quality of the business at exit, without introducing new, avoidable risks.

Funds that combine disciplined underwriting, focused workflow redesign, portfolio-level capabilities and credible AI governance will own assets that are simply more productive and more attractive at exit. Those that allow AI to remain a loose collec-tion of pilots inside portfolio companies will increasingly find themselves selling businesses that buyers see as AI turnarounds, not AI leaders.

The opportunity is clear: treat these five strategic shifts and seven conditions as a practical checklist for 2026, and AI becomes less of a wild card and more of a deliberately engineered engine for value creation.
 

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A Gentle Invitation

Many private equity investors will recognise parts of their own portfolio in these shifts. The challenge is rarely identifying AI opportunities, but translating them into disciplined value creation across diverse assets and operating contexts.

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We support GPs and operating partners in embedding AI as a value creation lever across portfolios, from diligence through value creation planning and governance design. Where helpful, we are glad to exchange perspectives on how these strategic shifts and conditions apply to specific investment situations.