How DiligenceSquared Uses AI Voice Agents to Revolutionize M&A Research

When I first heard about DiligenceSquared, I was skeptical. The idea of relying on AI voice agents to perform interviews for M&A research rather than traditional management consultants sounded like a shortcut that might miss crucial human insights. However, after seeing how this approach works in practice, it became clear that this technology is reshaping how private equity firms gather diligence information affordably and at scale.

Mergers and acquisitions (M&A) involve complex decision-making, often requiring deep customer interviews to assess the reality of a target company. Traditionally, private equity (PE) firms have had to hire expensive consultants to conduct these interviews, making the process time-consuming and costly. DiligenceSquared offers a fresh alternative by using artificial intelligence (AI) powered voice agents that autonomously conduct conversations with the customers of these companies under consideration.

What Makes DiligenceSquared’s Approach Different?

DiligenceSquared replaces pricey human consultants with AI-powered voice agents programmed to call and interview customers. These voice agents use natural language processing (NLP) to understand and respond to customer inputs during conversations. By automating the interview process, M&A research that once stretched consultant budgets can be completed faster and at a fraction of the cost.

AI voice agents here refer to software systems designed to mimic human speech and interaction through phone calls. Unlike typical chatbots limited to text, these agents use speech synthesis and recognition to engage live on calls.

How Does DiligenceSquared’s AI Voice Agent Work in Practice?

Once a PE firm identifies a target company, DiligenceSquared sets up its AI voice agents to call the company’s customers. The agent interviews them on various topics — customer satisfaction, product performance, competitor insights, and more — exactly as a consultant would.

These AI agents listen in real-time, adapting questions based on previous answers to mimic a natural conversation flow. This dynamic interaction helps extract qualitative insights typically gathered by human interviewers.

All interview data is automatically transcribed and analyzed, allowing PE teams to receive detailed reports without lengthy manual work.

Why Are AI Voice Agents Making M&A Research More Affordable?

• Cost-effectiveness: No billable hours from human consultants means significant savings.
• Scalability: Multiple agents can run interviews simultaneously, accelerating research timelines.
• Consistency: AI agents ask the same unbiased questions every time, reducing variability.
• Data accuracy: Automatic transcription minimizes human error common in note-taking.

Common Mistakes to Avoid When Using AI for M&A Research

Based on practical observations, some pitfalls can reduce the effectiveness of AI voice agents in this context:

• Poor caller targeting: Calling the wrong customer segments dilutes insight quality.
• Overreliance on scripted questions: Stiff, non-adaptive scripts can make conversations feel robotic and limit valuable follow-up information.
• Ignoring compliance: Not transparently recording or notifying participants can lead to legal issues.
• Lack of human oversight: Skipping manual review of AI transcripts risks missing subtle signals AI may misinterpret.

When Should You Use AI Voice Agents for M&A Research?

AI voice agents excel in scenarios where cost efficiency and scale matter most. If you need quick customer feedback from hundreds of interviews, these agents provide a viable option. They're also suited when the goal is to uncover broad patterns rather than deep, nuanced insights.

However, if the target company operates in a niche industry requiring highly specialized knowledge or sensitivity, human consultants may still be irreplaceable.

Are There Alternatives to AI Voice Agents?

While DiligenceSquared leads with voice-based AI, other startups combine AI with human-in-the-loop models, where human reviewers validate or supplement AI-collected data. Also, traditional consultants remain preferred for highly strategic or relationship-driven diligence.

Some PE firms are experimenting with AI-driven text-based surveys or automated sentiment analysis on customer feedback data as complementary tools.

What Can You Learn from This AI-Driven Experiment?

DiligenceSquared’s approach highlights both the potential and limits of automation in qualitative research. It pushes you to rethink long-held assumptions: costly human interviews are not always the only option for valid customer insights.

That said, successful adoption requires careful orchestration — from defining proper interview flows, ensuring data quality, to legal compliance.

Try This Yourself

If you're curious how AI voice agents perform, try this quick exercise: Pick a simple set of questions about a product or service you use frequently. Using a popular AI voice assistant (like Google Assistant or Siri), conduct a simulated interview by asking your AI the questions and noting responses. Consider where the AI helps, where it falls short, and how natural the interaction feels. This experiment can give you a firsthand glimpse of how well voice AI can gather insights and where human touch remains essential.

Ultimately, DiligenceSquared’s model illuminates a new path for faster, more affordable M&A research — but it’s not a silver bullet. Like any tool, it works best when aligned with clear goals and critical oversight.