When I first took responsibility for due diligence at Freshfields, the approach was simple: throw as many people at the task as possible.
It didn’t work.
- People might not have touched DD work for months, then suddenly be dropped into an urgent project.
- Quality was inconsistent.
- Each new project felt like starting from scratch — pushing a boulder uphill, only to watch it roll down again when the work finished.
Together with colleagues, we decided to rethink the whole process.
STEP ONE: Build Specialist Teams
Instead of everyone doing DD occasionally, we created a pool of trained specialists who worked on it consistently. That meant expertise compounded over time.
STEP TWO: Change the Workflow
Rather than reviewing everything blindly, we asked associates to confirm a small sample of documents first. That became our model for the rest. It saved time, improved consistency, and gave associates more confidence in the output.
STEP THREE: Structure the Data
Instead of subjective summaries (“10 people, 10 different answers”), we broke contracts down into specific yes/no questions. This turned mountains of text into searchable, filterable data. Feedback from associates was clear: “This is actually useful.”
Over time, these improvements became the foundation for something bigger — our Dynamic Due Diligence platform (D3). At first it was slow for manual work, but once AI was integrated, we could apply the same principles (sampling, structure, specialist oversight) at scale.
The result? Not just faster DD, but better DD. Associates saw clearer insights, and clients received work that matched — or exceeded — what they expected from Freshfields.
The surprising lesson for me was this: improving due diligence wasn’t about working harder. It was about building systems that people could trust and replicate. And that, more than anything, is what makes DD sustainable.
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If you need more insights into AI and its impact on management, please message me here, or reach out directly at Freshfields.