Artificial intelligence has transformed the expert network industry more in the past five years than in the previous 25 combined. In this article, we’ll explore how AI is changing expert networks and reshaping the way the industry operates. What began as a manual, database-driven research model — sourcing experts through LinkedIn searches, cold outreach, and static profiles — has evolved into an AI-powered ecosystem where matching, vetting, compliance monitoring, and insight extraction happen at a scale no human team could previously manage.
But the transformation is far from complete. In 2026, a new wave of AI capability — agentic AI, multi-agent research workflows, and real-time compliance automation — is pushing the industry into its next phase. This article covers both where AI has already changed expert networks and where the industry is heading next.
At a Glance: How AI Is Reshaping Each Stage of Expert Network Operations
| Stage | Old Model | AI-Powered Model in 2026 |
| Expert Matching | Manual database search + LinkedIn outreach | AI interprets brief, ranks profiles by experience patterns and contextual signals — up to 95% match accuracy |
| Expert Vetting | Human review of CVs and reference checks | Automated scoring across expertise depth, communication clarity, responsiveness, and project relevance |
| Compliance | Manual call monitoring and post-call review | Real-time MNPI detection, conversation pattern flagging, automated regulatory tracking across jurisdictions |
| Insight Extraction | Manual note-taking and transcript review | AI summarisation, theme detection, natural-language querying across thousands of transcripts instantly |
| Sourcing Speed | 2–5 days for a relevant shortlist | First shortlist in 2 hours or less for AI-native networks |
The Traditional Expert Network Model and Its Limits
Expert networks were built to do one thing well: connect clients with industry practitioners who could answer questions that reports and databases couldn’t. The core use cases — investment due diligence, market research, competitive intelligence, strategic decision-making — haven’t changed. What changed dramatically is how networks find, vet, and connect those experts.
In the traditional model, a client submitted a brief, and an associate team manually searched proprietary databases, LinkedIn, and industry contacts to find matching profiles. Each expert was reviewed by hand. Compliance checks were conducted before and after calls. Transcripts were delivered as raw documents for the client to read in full.
This model had predictable breaking points: slow turnaround times (typically 2–5 days), heavy dependence on the quality of static databases, outdated profiles, and a compliance burden that grew as regulations tightened globally. As value chains became more global and industries more specialised, the manual model simply couldn’t scale.
How AI Is Transforming Expert Networks: 5 Core Areas
1. AI-Driven Expert Matching
Matching experts is no longer a keyword search against a database. Modern AI systems interpret the full context of a client brief — the specific company being researched, the supply chain layer, the regulatory environment, the seniority of insight needed — and rank candidates based on experience patterns, not just job titles.
Leading networks now report up to 95% match accuracy using these systems. For clients, the difference is visible: instead of receiving a shortlist of people who worked in an industry, they receive profiles of people who have directly operated in the specific function, geography, or company type they are researching.
Real-world example: Infoquest uses AI to interpret client briefs, detect niche expertise patterns, and surface relevant profiles that traditional keyword-matching databases consistently miss — particularly in GCC and MENA markets where expert databases are historically thin.
2. Automated Expert Vetting and Screening
Before AI, expert vetting was a bottleneck. Human teams manually evaluated CVs, checked references, and assessed communication quality — creating delays between brief submission and the first approved shortlist.
AI vetting systems now score experts dynamically across multiple dimensions simultaneously:
- Technical depth and recency of experience in the relevant domain
- Communication clarity based on prior call transcripts and written responses
- Responsiveness and scheduling reliability from historical engagement data
- Relevance to the specific project context, not just general industry fit
- Conflict-of-interest signals detected against the client brief
This removes the manual bottleneck and enables networks to scale vetting without proportionally scaling their associate headcount.
3. AI-Powered Compliance: From Monitoring to Prevention
Compliance is one of the most consequential areas where AI has changed expert networks — and where the stakes are highest. The protection of Material Non-Public Information (MNPI) is central to how financial services clients evaluate any expert network provider. A single compliance failure can result in regulatory penalties, reputational damage, and lost clients.
In 2024, the SEC penalised a firm for inadequate controls around MNPI, reinforcing that simply having an insider trading policy on paper is no longer sufficient. In 2023, China’s crackdown on Capvision — a major expert network — for alleged violations of national security laws sent a clear signal that compliance risks are not theoretical.
AI compliance tools now operate in real time during calls, not just before and after them:
- Detecting risk signals and sensitive topic patterns mid-conversation
- Flagging potential MNPI disclosure in real time for review
- Monitoring conversation patterns against a continuously updated regulatory framework
- Tracking global regulatory changes across GDPR, EU AI Act, SEC rules, and regional data sovereignty laws
- Automating documentation and audit trails for client compliance teams
For regulated clients — financial services, healthcare, government — AI compliance infrastructure is increasingly a primary evaluation criterion when choosing a network provider.
4. AI for Instant Insight Extraction
The emergence of large transcript libraries — Third Bridge’s Forum, Tegus/AlphaSense’s 200,000+ transcript database — has created a new use case for AI: making prior expert knowledge searchable and queryable at scale.
Instead of reading dozens of call transcripts manually, research teams now:
- Ask natural-language questions and receive cited, synthesised answers from thousands of interviews
- Instantly identify thematic shifts across sectors and companies over time
- Compare expert viewpoints across multiple companies or supply chain tiers simultaneously
- Generate research primers and competitive landscapes in minutes, not weeks
Named tools in 2026: Tegus/AlphaSense’s AskTegus and Gen Search, Third Bridge’s Forum search, Techspert’s ECHO Ask — these AI layers are transforming transcript libraries from static archives into live intelligence platforms.
5. Workflow Automation: Scheduling, Briefing, and Presentation
Beyond matching and compliance, AI is eliminating friction across the entire client workflow. Networks like Infoquest now use AI to interpret unstructured client briefs and convert them into structured sourcing criteria automatically. Expert presentations — the documents summarising shortlisted profiles and their relevance — are generated by AI and reviewed by humans rather than written from scratch.
Scheduling, reminders, pre-call documentation, and post-call follow-up are increasingly automated. For clients, this reduces the coordination overhead that once made expert calls feel administratively heavy compared to reading a research report.
5 Trends Defining the Future of Expert Networks in 2026 and Beyond
AI is the engine, but it is powering several distinct industry trends simultaneously. These are the forces reshaping how expert networks operate and compete over the next three to five years.
Trend 1: Agentic AI Will Automate Multi-Step Research Workflows
The next evolution beyond AI matching and summarisation is agentic AI — systems that can autonomously complete multi-step research tasks on behalf of a client without requiring human input at each stage. In 2026, leading platforms are beginning to deploy agents that can receive a research brief, identify and shortlist experts, schedule calls, synthesise transcripts, and flag key themes — all in a single automated workflow.
AlphaSense’s Deep Research feature, for instance, can already automate competitive landscape analysis across expert transcripts, filings, and broker research simultaneously. For expert networks, agentic AI will progressively reduce the time between a client’s question and a usable, cited answer — moving the industry from on-demand access to near-instantaneous intelligence.
Trend 2: Data Insights Will Become a Core Product, Not a Byproduct
As networks accumulate thousands of anonymised expert consultations, the aggregated signal becomes valuable in its own right. Networks that can identify sector shifts, emerging investment themes, and regulatory patterns from patterns across their call data — without exposing any individual conversation — are building a new product category: network-level market intelligence.
- Trend dashboards showing where client interest is accelerating across sectors
- Predictive market signals derived from expert opinion patterns at scale
- Thematic intelligence layers that clients can subscribe to independently of individual calls
Clients will increasingly not just ‘ask an expert’ — they will combine live calls with data-backed directional signals extracted from thousands of prior conversations.
Trend 3: Hyper-Specialised Networks Will Challenge Generalist Giants
The era of one-size-fits-all expert networks is ending. Alongside generalist global players, a new generation of hyper-specialised boutique networks is emerging — focused on single industries (energy, life sciences, fintech, logistics), specific functions (procurement, policy, compliance), or deep-technical domains (AI, cybersecurity, biotech).
These specialists win because they vet more deeply, understand vertical nuances that generalists miss, and maintain tighter, more curated expert pools. For clients researching niche or highly technical topics, a specialist network consistently outperforms a large generalist database. Infoquest’s focus on the GCC and MENA region is a direct expression of this trend — regional specialisation as a competitive advantage over global scale.
Trend 4: Compliance as a Competitive Differentiator
Compliance used to be table stakes — something every network claimed to have and few differentiated on. That is changing. As regulatory scrutiny intensifies globally — the SEC’s 2024 MNPI enforcement action, China’s Capvision crackdown, the EU AI Act’s transparency requirements taking effect — networks that can demonstrate robust, auditable, and AI-powered compliance systems are winning enterprise clients that others cannot touch.
Networks investing in real-time monitoring, documented audit trails, jurisdiction-specific regulatory tracking, and proactive MNPI prevention are building a compliance moat that will widen as regulations tighten further in 2026 and beyond. For financial services clients, especially, compliance infrastructure is increasingly a primary procurement criterion rather than an afterthought.
Trend 5: Global Reach With Regional Precision Will Define the Decade
The geography of expert network demand is expanding. Emerging markets — the Middle East, Southeast Asia, Latin America, Sub-Saharan Africa — represent the fastest-growing segments of global business activity, but are systematically underserved by networks whose databases were built around North America and Western Europe.
The networks that win the next decade will be those that combine true global sourcing capability with genuine in-region expertise — not just names in a database from a geography, but experts who are actively operating in those markets and understand their specific regulatory, cultural, and commercial dynamics. Custom sourcing models have a structural advantage here over static databases, because they build the expert pool fresh for each brief rather than relying on pre-registered profiles.
Where the Major Networks Stand on AI Adoption
AI adoption is not uniform across the expert network industry. Here is an honest assessment of where the major providers currently stand:
GLG: The largest network by scale, GLG has layered AI-enhanced matching, transcript summarisation, and compliance tools onto its legacy infrastructure. Its AI capabilities are real but incremental — built on top of an existing database model rather than natively integrated.
AlphaSights: Added AI call summarisation and brief-processing tools in 2024. Strong service layer, but AI adoption is more client-facing enhancement than core sourcing transformation.
Third Bridge: Invested significantly in AI-powered search across its Forum transcript library. AI makes prior expert knowledge more accessible but does not yet drive the core sourcing model.
Tegus / AlphaSense: The most technically advanced platform in the industry following the 2024 merger. AskTegus and Gen Search represent genuine agentic research capability — natural-language querying across 200,000+ transcripts, financial data, and broker research simultaneously. The benchmark for AI-native insight extraction.
Techspert: An AI-native expert network platform. ECHO Ask autonomously analyses interviews, extracts themes, and generates insights. Purpose-built for technology and digital sectors where precise niche expertise matters most.
Infoquest: Builds AI directly into its core sourcing workflow. AI interprets client briefs, identifies experience patterns, ranks expert relevance, and generates structured shortlist presentations automatically — before human review. This AI-first sourcing model is particularly effective in markets like GCC and MENA where static databases are thin.
Frequently Asked Questions
How is AI changing expert networks?
AI is transforming expert networks across five areas: matching (AI interprets briefs and ranks experts by relevance rather than keyword), vetting (automated multi-dimensional scoring of expert quality), compliance (real-time MNPI detection during calls), insight extraction (natural-language querying across transcript libraries), and workflow automation (scheduling, presentation generation, and brief processing). The combined effect is faster, more precise expert access at significantly lower operational cost.
What is agentic AI and how will it affect expert networks?
Agentic AI refers to systems that can autonomously complete multi-step tasks without requiring human input at each stage. In expert networks, this means an agent that can receive a research brief, source and shortlist experts, schedule calls, synthesise transcripts, and surface key findings — all in a single automated workflow. In 2026, leading platforms including AlphaSense are beginning to deploy these capabilities. Over the next 3–5 years, agentic AI will progressively compress the time between a research question and a usable, cited answer.
Are AI-native expert networks better than legacy providers?
It depends on the use case. AI-native networks like Infoquest and Techspert outperform legacy providers on precision, speed, and niche sourcing — particularly in emerging markets or highly specialised domains. Legacy providers like GLG and Third Bridge retain advantages in scale, compliance infrastructure, and transcript depth built over decades. Most sophisticated research teams use a combination of providers rather than a single network.
How does AI improve compliance in expert networks?
AI compliance tools monitor calls in real time for MNPI risk signals, flag sensitive conversation patterns before they become regulatory violations, automate audit trails for client compliance teams, and track regulatory changes across multiple jurisdictions simultaneously. This shifts compliance from a reactive review process to proactive prevention, which is increasingly important as enforcement actions intensify globally.
What is the future of expert networks?
The future of expert networks is shaped by five converging trends: agentic AI automating multi-step research workflows, data insights becoming a standalone product derived from aggregated call patterns, hyper-specialised boutique networks challenging generalist incumbents, compliance as a true competitive differentiator, and regional precision becoming as important as global reach. Networks that combine AI-native technology with genuine subject-matter depth and regional expertise will define the industry’s next decade.
Conclusion: The Expert Network of 2026 Looks Nothing Like 2020
The core mission of expert networks has not changed: connecting people who have questions with people who have answers. What has changed is everything around it — how experts are found, how their relevance is assessed, how calls are monitored for risk, how insights are extracted and surfaced, and how quickly the entire cycle completes.
AI has compressed what used to take days into hours. It has turned static transcript archives into queryable intelligence platforms. It has shifted compliance from manual review to real-time prevention. And in 2026, agentic AI is beginning to automate entire research workflows end-to-end. The networks that will lead this industry over the next decade are not necessarily the largest. They are the ones that pair machine intelligence with genuine human expertise — combining AI speed and scale with the irreplaceable judgment of practitioners who have actually lived in the markets, companies, and functions their clients are researching.
