Deals in the age of AI: The people problem

Dealmakers face a transformation challenge on compressed timelines. Private equity (PE) sponsors must adopt AI and enable AI capability across their entire portfolios within a three- to five-year hold period. Corporate acquirers must capture AI-enabled synergies while managing integration complexity. Both hit the same constraint: human capacity.

Across our five-part series, Deals in the Age of AI, the unifying thesis is simple. The binding constraint is people, not technology. In this first piece, we identify three specific people problems that determine whether AI creates value: use case clarity, capacity to learn, and incentive alignment. The technology is advancing. What lags is whether people know how to use AI in their specific jobs, whether they have time and permission to learn, and whether the system rewards the behavior change.

But these levers don’t move themselves. Use case clarity requires leadership choosing where to focus. Creating capacity requires leadership allocating time and budget. Incentive alignment requires leadership redesigning how performance is measured and rewarded. Without leadership commitment showing up in resource allocation, organizational design, and personal modeling of new behaviors, AI initiatives become pilot programs that never scale. This is a leadership challenge, not an IT project.

PE ownership can help solve these problems through board mandate, clear accountability, and the ability to standardize playbooks across portfolios. Corporate acquirers can help solve them through integration governance, synergy tracking discipline, and the scale to invest in enterprise-wide capability building. The firms that solve these three problems systematically will convert AI from narrative to net present value.  The pieces that follow apply this lens across the deal life cycle—from evaluating targets to executing deals to creating value and designing organizations post-close. 

The productivity gains from AI are real, but they’re concentrated among the firms that have solved the absorption problem. Most haven’t. In our diligence work, leading firms aside, we see a consistent pattern. Many companies have launched pilots, purchased enterprise licenses, and hired data scientists, but financial impact remains modest. Pilots don’t scale beyond a single team. Automation delivers time savings that never translate into structural cost reductions.

Our Global Workforce Hopes and Fears Survey 2025 notes that while 54% of workers globally have used AI for their jobs in the past year, only 14% use AI daily. Daily users, it turns out, report dramatically better outcomes with 92% reporting productivity benefits compared to just 58% of infrequent users. Daily users are also more likely to report improved job security (58% versus 36%) and better pay outcomes (52% versus 32%). Capability and confidence compound over time. Despite these results, AI usage remains sporadic and uncertain for most workers.

The result is a value gap. For dealmakers, this gap translates directly into risk and opportunity. Buyers now expect AI narratives backed by evidence—metrics, dashboards, and a credible workforce story. If the CIM describes “AI-enabled transformation” but the data room shows limited adoption and unclear ROI, the story becomes a discount factor rather than a valuation premium. Synergy cases increasingly depend on AI-enabled productivity gains, which only materialize if the combined workforce actually adopts new ways of working.

The gap persists because technology and organizations operate on different clocks. AI capabilities compound rapidly as new models and features arrive every few months. But organizational change moves at human speed. Skills take time to build, trust requires repeated positive experiences, and incentive structures require deliberate redesign.  

The AI opportunity hinges on three people-centered levers: those human and organizational levers that determine whether AI adoption translates to financial performance.

Building use case clarity and workflow integration

In our experience, we see that many workers are still unclear how to use AI in their specific jobs. Analysts, for example, don’t need to know the specifics of how large language models work. They need to know how to use AI for variance analysis in the company’s close process. The problem is that most training is generic and doesn’t help employees connect AI to their specific workflows.

What leaders are doing: Leaders quickly move people from “I have access to AI” to “I know exactly what to do with it and how it fits in my job.” They might, for instance, provide specific guidance on how to use AI for contract review in the firm’s diligence process, target screening against investment criteria, or financial extraction into deal model templates. They help employees integrate AI directly into their workflows. Successful approaches start small, maybe just two or three high-ROI use cases with detailed playbooks including before-and-after workflow diagrams, example prompts, quality checks, and common failure points. The appropriate benchmark and usage metrics are also identified up front and tracked to refine training and enablement activities and demonstrate success.

What this can look like: PwC Deals helped a PE sponsor codify its investment thinking—the logic behind thesis generation, the questions that define its investment committee (IC) process, and the tone and structure of its memos. The result was AI-generated first drafts of IC memos across six sectors that reflect the firm’s way of evaluating deals, not generic analysis, creating hours saved and capacity to ideate.

Creating capacity and permission to learn

Many employees don’t have the time or permission to learn. Consider a PE associate pulling together diligence findings for an IC memo. Manual synthesis takes 15 hours. AI could cut that to four hours once the associate is proficient, but getting there requires experimentation. The first attempt might take eight hours, with a real chance the output misses nuance or hallucinates details, meaning sections will need to be redone manually anyway.

The math doesn’t favor experimentation. Factoring in the risk of rework, the associate (who took on variance and visible risk) is no better off than doing it the old way. Already underwater on other deliverables, the associate chooses certainty every time—and never builds the capability that would eventually save 11 hours per memo.

What leaders are doing: Leaders create protected time for experimentation and psychological safety when experiments fail. This means building learning time into project plans explicitly, not as slack, but as investment. It means managers celebrating failed experiments that generate insight, not just successful deployments. And it means senior leaders modeling the behavior themselves by visibly experimenting, sharing what didn’t work and reinforcing that proficiency comes through iteration, not perfection.

What this can look like: A corporate development department added a dedicated AI resource to every deal team. Not a data scientist, but a team member explicitly responsible for running AI workflows during diligence. The company understands the cost, but the learning happens on live deals with real stakes. As a result, prompts get refined, workflows mature, and the team builds institutional muscle.

Aligning incentives and measurement

In many companies, behavior change is difficult to motivate. Performance reviews are designed around old ways of working. Plus, for employees, time saved with AI just means having more work assigned. Without alignment between AI adoption and the systems governing performance, compensation, and career progression, behavior change won’t stick.

What leaders are doing: Leaders make AI the cornerstone of how work is measured and rewarded, moving people past “I tried it once and it didn’t work.” Firms are updating KPIs to measure outcomes, not activities, and adding AI proficiency to promotion criteria explicitly. Critically, they ensure that time saved through AI translates into visible benefits for the employee in the form of better projects, development opportunities, and recognition or rewards. When people see that AI adoption advances careers, adoption accelerates.

What this can look like: Within PwC Deals, AI adoption is embedded directly into how performance and progression are assessed. AI usage is monitored by team and individual. Our Deals Skills Matrix has been reworked to incorporate AI capability at every career level. AI-enabled execution is assessed through our performance review roundtables. Time saved through AI translates into higher-quality insights for the firm and stronger career progression for the team member.

All three levers must move together

Despite these levers being interdependent, leaders find a way to move them in unison. Otherwise, benefits are lost.

• Building use case clarity without creating capacity means people know what to do but have no time.
• Creating capacity without building use case clarity means people waste protected time on low-value experiments.
• Building use case clarity and creating capacity without aligning incentives means early experiments don't stick because the system doesn't reward behavior change.
• Aligning incentives without building use case clarity means rewarding activity rather than outcomes.

The advantage PE firms have in this process is forcing change by pulling all three levers simultaneously through board mandate, portfolio-wide playbooks, and compressed timelines that create urgency. For corporates, they can build into their integration plan a mandate for new ways of working, synergy tracking that creates accountability, and enterprise scale that justifies investment in capability building.  

The decisive questions for AI in dealmaking are human questions. Do people know how to use AI in their specific jobs? Do they have time and permission to build proficiency? Does the system reward the behavior change? And critically, is leadership committed to making these people levers move?

PwC Deals helps dealmakers find answers to these questions—and build AI capability that is real, embedded, and drives results.

The remaining pieces in the Deals in the Age of AI series apply this framework further across the deal life cycle.

• Evaluating businesses in the age of AI: How to assess a target’s AI trajectory—the acceleration opportunities and erosion risks—before you buy.
• AI and the deal process: How to embed AI into your own deal execution and build institutional capability that compounds.
• Value creation post-close: The operational playbook for executing AI transformation in portfolio companies.
• Integration, separation, and org design: How carve-outs and transformational deals create rare opportunities to design organizations for AI from scratch.