AI is changing the expert network industry and has changed it more in five years than in the previous 25. What used to be a manual, human driven research model, sourcing experts through databases and cold outreach, has evolved into an AI-powered ecosystem where matching, vetting, compliance, and insight extraction now happen at a scale no human team could manage alone.
The question is now how fast it is changing the entire category.
The change is visible across the industry:
- GLG and Third Bridge: historically built on manual sourcing, now deploy AI-enhanced matching, transcript summarization, and compliance tools.
- New players like Techspert have developed AI-native platforms, such as ECHO Ask, which autonomously analyze interviews, extract themes, and generate insights.
The Traditional Expert Network Model
What expert networks were built to do
They enable:
- Investment due diligence
- Market research
- Competitive intelligence
- Operational guidance
- Strategic decision-making
In the past, clients submitted a request, and associates manually searched databases and LinkedIn to locate experts; today, however, this model is being fundamentally reshaped.
To know more about what these firms are, check out our comprehensive article here.
The limitations of manual sourcing
Traditional sourcing suffered from predictable issues:
- Slow turnaround times
- Heavy manual work
- Outdated or incomplete databases
As value chains expanded globally and industries became more specialized, the manual model simply couldn’t keep up with the changes.
How AI Is Transforming Expert Networks
It is now changing the expert network in every major stage of operations: matching, vetting, compliance, research, and workflow automation.
1. AI-Driven Expert Matching
Matching experts is no longer guesswork.
These systems achieve up to 95% match accuracy and understand user queries far beyond keyword matching.
2. Automated Expert Screening
AI screens experts using dynamic scoring models that evaluate:
- Technical expertise
- Communication clarity
- Responsiveness
- Past performance
- Relevancy to project context
This removes the old bottleneck where human teams manually evaluated every expert.
3. AI-Powered Compliance
It now acts as a virtual compliance monitor:
- Detecting risk signals during calls
- Flagging potential insider information
- Monitoring conversation patterns
- Tracking regulatory changes
- Ensuring GDPR/alignment with global frameworks
This significantly reduces legal risk for expert network users.
4. AI for Instant Insight Extraction
Instead of reading dozens of documents, users now:
- Identify thematic shifts in seconds
- Ask natural-language questions
- Get instant answers from billions of data points
- Summarize transcripts instantly
- Compare expert viewpoints automatically
Real-World Examples: AI in Action
GLG + Third Bridge
Both adopted AI to improve:
- Expert matching
- Search workflows
- Transcript summarization
- Automated compliance checks
Infoquest
Infoquest, for example, are building AI directly into its core workflows. It uses it to interpret client briefs, match experts with higher precision, and structure expert presentations automatically. AI models analyze experience patterns, detect niche expertise, and surface relevant profiles that traditional databases often miss.
Conclusion
The expert network industry has evolved from manual sourcing into a technologically sophisticated intelligence ecosystem.
AI now drives:
- Faster expert discovery
- Smarter matching
- Automated vetting
- Real-time compliance
- Instant access to insights
The core mission remains intact:
connecting people who have questions with people who have answers.
As AI matures and expert networks expand into new regions and sectors, the companies that pair machine intelligence with human wisdom will define the future of the industry, with significant changes seen in how expert networks are evolving.
