The expert network industry has transformed dramatically over the past two decades. What began as simple matchmaking services has evolved into sophisticated research infrastructure. Where is the industry heading next?

Based on current trends and emerging capabilities, here's our view of what's coming.

Near-Term Evolution (2026-2027)

AI Integration Becomes Table Stakes

The adoption curve for AI in expert networks is steepening rapidly.

Current state: Early adopters using AI for transcription and summarization.

Near-term future: AI capabilities become expected, not differentiating.

Implications:

Providers without AI features will struggle to compete
Focus shifts from having AI to using it effectively
Integration quality matters more than feature existence

By 2027, expert conversations without AI enhancement will feel incomplete.

Global Access Expands

Geographic barriers continue to fall.

Remote-first engagement: Video calls have become the default, enabling global expert access.

Translation capabilities: AI-powered translation opens previously inaccessible expert pools.

Time zone optimization: Asynchronous elements reduce scheduling friction.

Local expertise: Networks building deeper coverage in emerging markets.

The addressable expert population grows significantly.

Quality Measurement Matures

Moving beyond subjective ratings to objective quality metrics.

Prediction tracking: Monitoring expert forecasts against outcomes.

Value quantification: Measuring insight contribution to investment decisions.

Pattern identification: Understanding what expert characteristics predict quality.

Feedback loops: Systematic improvement based on measured performance.

Quality becomes more measurable and improvable.

Medium-Term Shifts (2028-2030)

New Expert Categories

The definition of "expert" expands beyond traditional industry consultants.

Practitioners: Active professionals sharing current operational experience.

Customers and users: Direct voices of target audiences.

Data specialists: Experts on specific alternative data sources.

Technology experts: Specialists in emerging capabilities.

Cross-domain connectors: People with insight across boundaries.

Expert value comes from diverse perspectives, not just industry tenure.

Unbundling and Rebundling

The value chain continues to evolve.

Unbundling: Specialized providers excelling at specific functions.

Expert identification platforms
Interview facilitation services
Transcription and analysis tools
Quality and vetting services

Rebundling: Integrated solutions combining components.

Research platforms with embedded expert access
Data providers adding expert context
CRM systems incorporating expert relationships

The shape of the industry continues to shift.

Automation of Routine Elements

More of the expert engagement process becomes automated.

Expert matching: AI-driven identification of relevant experts.

Scheduling: Fully automated calendar coordination.

Preparation: Automatic generation of briefing materials.

Documentation: Complete capture and processing without manual effort.

Follow-up: Suggested next conversations based on gaps identified.

Human involvement focuses on judgment, not logistics.

Longer-Term Possibilities (2030+)

Conversational AI Participation

AI systems may participate in expert conversations.

Real-time assistance: AI suggesting questions, flagging inconsistencies, surfacing relevant context.

Follow-up automation: AI conducting initial exploratory conversations before human engagement.

Knowledge retrieval: AI answering questions from historical expert conversations.

Synthesis: AI combining insights across multiple experts and sources.

The boundary between human and AI participation blurs.

Continuous Intelligence

Moving from episodic consultations to continuous insight streams.

Always-on monitoring: Ongoing tracking of expert-adjacent signals.

Proactive alerting: Experts reaching out when relevant developments occur.

Dynamic panels: Flexible expert groups assembled around evolving questions.

Real-time synthesis: Continuously updated views incorporating latest inputs.

Expert intelligence becomes more fluid and persistent.

New Economic Models

Compensation and access models continue to evolve.

Outcome-based pricing: Fees tied to value delivered.

Subscription models: Ongoing access rather than per-consultation fees.

Expert incentive alignment: Compensation structures rewarding accuracy and value.

Democratized access: Lower barriers for smaller investors.

Economic models better align incentives across participants.

Industry Structure Evolution

Consolidation Pressures

Forces pushing toward fewer, larger players:

Technology investment requirements
Global coverage demands
Compliance infrastructure costs
Data advantages from scale

Fragmentation Pressures

Forces preserving specialized niches:

Industry-specific expertise requirements
Relationship-based differentiation
Regional specialization advantages
Innovation from focused players

Likely Outcome

A market with both large platforms and specialized providers:

Global platforms offering broad coverage and technology
Specialists excelling in specific industries, regions, or methods
Technology providers enabling capabilities across the ecosystem
Integration connecting different components

No single model dominates; diverse approaches coexist.

Technology Trajectory

Infrastructure Evolution

Core technology capabilities advancing:

Speech recognition: Near-perfect accuracy across contexts.

Language understanding: Deep comprehension of financial discussions.

Knowledge graphs: Connected representation of expert insights.

Multimodal processing: Integrated analysis of audio, video, and text.

User Experience

How users interact with expert networks:

Conversational interfaces: Natural language queries to expert knowledge.

Embedded access: Expert insight integrated into research workflows.

Mobile-first: Full functionality on any device.

Collaborative features: Team-based expert engagement.

Analytics and Intelligence

Deriving more value from expert interactions:

Cross-conversation synthesis: Connecting insights across many discussions.

Trend identification: Tracking expert view evolution over time.

Predictive signals: Using expert patterns to anticipate developments.

Quality prediction: Forecasting expert value before engagement.

Implications for Investors

What to Look For

When evaluating expert network providers:

Technology roadmap: Is the provider investing in future capabilities?

AI integration: How sophisticated is their AI and how deeply integrated?

Global reach: Can they access experts wherever needed?

Quality systems: How do they measure and improve expert quality?

Integration capability: How easily does their platform connect with your workflows?

What to Build

Internal capabilities worth developing:

Expertise in AI-assisted research: Learning to work effectively with AI tools.

Expert relationship management: Building direct relationships that complement networks.

Quality tracking: Internal systems for measuring expert value.

Knowledge management: Capturing and leveraging expert insights institutionally.

What to Avoid

Risks in expert network evolution:

Over-dependence on single providers: Diversify network relationships.

Technology lag: Don't fall behind in adoption of new capabilities.

Relationship complacency: Direct relationships still matter.

Passive consumption: Extract maximum value, don't just receive.

Conclusion

The expert network industry is in the midst of significant transformation. Technology is enabling capabilities that weren't possible five years ago. Business models are evolving. The definition of expertise is expanding.

For investment professionals, these changes create both opportunities and imperatives. Those who master emerging capabilities will gain advantages. Those who don't adapt risk falling behind.

The firms that will thrive are those approaching expert networks strategically—investing in relationships, adopting new technologies, and continuously improving how they extract insight from expert conversations.

InsightAgent is building the future of expert interview technology. Learn more.

The Future of Expert Networks: Predictions for 2026 and Beyond