How AI Helps PwC’s Deals Team Get to Insight Faster

As M&A and transaction advisory engagements grow more data-intensive, the challenge is no longer access to information, but how quickly teams can turn it into confident, evidence-backed judgment. For Deals practitioners, this is redefining where value is created, shifting effort away from manual collation and toward earlier insight, sharper hypotheses, and conversations that directly inform deal decisions. AI is increasingly central to that evolution, not as a replacement for expertise, but as a way to proactively apply it sooner with a broader evidence base.

Kapila Haughey and Simon Bradford, two senior leaders in PwC’s UK Deals practice, have been at the center of this transition. Haughey, a Deals Partner focused on financial due diligence for corporate and private equity clients, describes her role as spanning early-stage proposal support through to deep diligence execution, work that increasingly happens “end-to-end in Harvey.”

Simon Bradford, a Financial Due Diligence Partner and the Chief Technology Officer for Transactions Services, operates at the intersection of client delivery, product strategy, and firmwide AI adoption. His work combines running complex diligence engagements with a “passion for using Harvey to accelerate analysis, pressure test hypotheses, and improve the quality and speed of client insights.”

In some cases, that shift has played out under intense deal pressure, including short competitive buy-side auction processes, takeover bids, and large complex transactions. As Haughey and Bradford explain, those moments reveal where AI-enabled work moves from convenience to necessity.

Together, their perspectives offer a window into how AI, powered by Harvey, is reshaping the modern deals workflow.

A New Pace for M&A Due Diligence

The most visible impact of AI in deals work is speed and importantly, speed that enables better judgment, not shortcuts.

“AI is changing the speed, depth, and repeatability of M&A work,” Haughey explains. “Where a team member might previously have spent a day pulling key facts and context on a target, they can now get to an informed perspective in an hour.”

Bradford sees the same effect across his projects. “In the short to medium term, AI’s most immediate impact is efficiency,” he says. “That’s how I sell it internally, the technology helps busy teams move faster on the tasks that consume significant amounts of time without compromising quality.”

That speed, however, isn’t the result of a single breakthrough use case. It comes from how Harvey is embedded across the Deals workflow. From early target familiarization and background research to ingesting and interrogating full data rooms, reviewing contracts at scale, synthesizing initial drafts of reporting outputs, and responding to live client questions, Harvey supports multiple moments throughout a deal’s lifecycle.

As Haughey puts it, “During execution, Harvey lets us ingest data room materials at scale including contracts, financial reports, and operational documents. We can then query, cross-reference, and collate what matters. That removes manual collation and accelerates the point at which we can apply judgement.”

For Bradford, the deeper value of this integrated approach isn’t just pace, but perspective. “We can assemble a clearer, more commercial narrative earlier in the process,” he explains, “and respond to client questions in real time with better referencing.”

How Trust Enables AI-Powered Diligence at Scale

Both partners emphasize that for AI to be truly valuable in Deals work, it must be verifiable. “The most powerful Harvey feature for us is source-grounded analysis,” says Haughey. “Harvey’s ability to cite exactly where a data point comes from and let you click straight into the underlying source document creates trust and shortens verification cycles. For diligence, traceability is everything.”

That trust is also what allows teams to move beyond experimentation and use AI at scale. As Haughey notes, the ability to interrogate full contract sets has fundamentally changed review processes. “We can review 100 documents where we used to review five,” she says. “Harvey lets us surface relevant information in minutes rather than hours and move from raw materials to structured insights far faster.”

Bradford sees the same dynamic through Harvey Vault, which his teams now use as their default environment for exploration, search, and first-draft synthesis. Instead of searching document by document, deal teams can ask targeted questions across the entire corpus of information, piece together cross-document narratives, and generate first-draft report sections with confidence.

“Vault materially outperforms generic data room search for our use cases,” Bradford says. With source-grounded analysis in place, teams can move from document overload to insight at scale, something Haughey describes as “turning a document flood into a navigable dataset.”

Using Harvey in Live Deal Environments

For both Haughey and Bradford, Harvey’s value becomes most tangible under deal pressure, when timelines are tight and teams need evidence-backed answers quickly.

Haughey recalls preparing for an initial meeting with a client facing a takeover bid. The team had hours to gather information on both the client and bidder prior to the meeting. “We used Harvey to run structured background research,” she says. “Analysis and research that would have taken a day was performed in a couple of hours”

Moments like this highlight an important distinction in deal work. Under time pressure, the challenge isn’t generating an answer, it’s producing insight that can be defended, revisited, and challenged as the underlying information evolves. Applying AI in that context requires persistent document context, clear sourcing, and the ability for teams to work from a common, evolving set of materials. Capabilities like source grounded analysis and environments like Harvey Vault make it possible to rely on AI not just for isolated questions, but for sustained, defensible analysis across the arc of a diligence process.

Bradford shares a similar experience on a vendor due diligence engagement. A client question about a recalled and later relaunched product had direct implications for the investment narrative, and the team needed a clear, defensible answer quickly ahead of the next round of discussions.

“We used Vault to interrogate the documents and assemble a coherent, referenced narrative,” he says. “In about twenty minutes, we produced a precise, insight led email draft that addressed the timeline and drivers, and we supplemented this with external regulatory context for validation.” The response landed strongly. “Two separate PE stakeholders replied to say the insight was genuinely helpful, and it moved the conversation forward faster than waiting for the next management session.”

The Impact on Teams and Clients

More broadly, both Partners see these moments as emblematic of a wider shift in how deal teams operate. Preparation windows that once stretched to half a day are now measured in minutes. Contract reviews move from selective sampling to broad coverage. First drafts of initial red flags reporting and diligence sections are produced in minutes for human refinement. Ad hoc client questions can be answered in near real time, often with stronger substantiation than before. For clients, that speed translates into better informed decisions at critical moments.

Haughey also notes that speed isn’t just a client benefit, it’s a talent and culture benefit. “Giving teams modern tools that reduce manual collation and late-night drafting makes the work more sustainable and appealing, while preserving the craft of analysis and negotiation,” she says. “That combination of depth and pace, with a better day-to-day experience, is where Harvey feels most transformative.”

For Bradford, he is confident that the accumulated efficiency and insight advantages gained from Harvey will translate into tangible commercial outcomes. As he puts it, “You can’t argue with faster turnaround, stronger proposals, and more compelling client conversations.”

Beyond immediate delivery benefits, Haughey also sees a broader commercial opportunity emerging. “We’ve introduced Harvey to a client to embed the tool as part of their own workflow,” she says. “While it’s early days, embedding our AI approach and toolset directly with clients can strengthen trusted adviser relationships and open new avenues for collaboration.”

Advice for Leaders Starting Their AI Journey

Both leaders are clear that value comes from embedding AI fully, not using it as an add-on. “Start using it end-to-end, not just as a point solution,” Haughey advises. “The compounding benefits come when research, document ingestion, analysis, drafting, and reporting all sit in one workflow.” She also emphasizes the importance of keeping humans in the loop, noting that “Harvey accelerates you to judgement, but doesn’t replace it.”

Bradford suggests anchoring adoption in efficiency. “Busy teams embrace tools that save them time today,” he says. “Give them a simple explanation of what this technology does for them in concrete terms with real life examples.” Haughey also recommends standardizing prompts and templates so the team can get to consistent, repeatable outcomes. And when it comes to measurement, “Measure where time is saved and where depth improves, those are the levers that will translate into tangible change for your business.”

Looking Ahead: The Next Phase of AI-Powered Deals

Bradford believes the most profound change in deal work is still to come.

“The real step change will come from data native workflows,” he says. “Pushing raw transactional data into AI-enabled engines that can autonomously assess data quality, conduct price volume analyses at SKU level, and answer natural language questions with sliceable outputs will refine our value proposition. I don’t think that’s decades away, it’s a realistic multi-year horizon.”

For PwC’s Deals leaders, the implication is clear: Investing in AI is not just about speed and efficiency today, but about preparing for a fundamentally different way of delivering insight in the near future.