Workflow Woes: The Tedious Processes Bogging Down GLG Projects

For market researchers, product teams, and consultants, primary research is often the cornerstone of strategic decision-making. When deadlines loom and critical insights are needed, traditional expert networks like GLG (Gerson Lehrman Group) have been the go-to solution. But many teams are discovering that the very workflows designed to connect them with experts are creating significant bottlenecks in their research process.

The Traditional Expert Network Workflow Challenge

The typical workflow with GLG and similar expert networks follows a predictable pattern:

1. Submit your request with target criteria
2. Wait for a research manager to process it
3. Receive a list of potential experts (often with limited information)
4. Select experts you want to speak with
5. Wait for GLG to arrange the calls
6. Conduct interviews
7. Manually synthesize findings

This process, while established, creates multiple friction points that can extend timelines by days or even weeks.

Why the Broker Model Creates Inherent Inefficiencies

The Opaque Matching Process

According to a recent survey of research professionals, 67% report frustration with the "black box" of expert matching at traditional firms. When you submit criteria to GLG, you have limited visibility into how experts are being sourced or filtered. This lack of transparency can lead to misaligned expectations and multiple rounds of refinement.

"The back-and-forth with account managers to refine expert profiles can add 3-5 business days to a project timeline," notes a former research director at a leading SaaS company.

Schedule Coordination Delays

One of the most significant workflow challenges occurs during the scheduling phase. Since traditional firms own the expert relationship, all communication must flow through their platform or representatives. This creates a multi-party coordination problem:

• Your availability
• The expert's availability
• The broker's processing time

A McKinsey analysis of knowledge work inefficiencies found that intermediary coordination can add 30-40% to project completion times in professional services.

The Synthesis Bottleneck

After conducting multiple expert interviews, teams face another workflow challenge: turning raw conversations into actionable insights. Traditional firms like GLG provide the conversations but leave the labor-intensive synthesis entirely to you.

This typically involves:

• Manual transcription or note review
• Thematic coding across interviews
• Identifying patterns and contradictions
• Creating visualizations and summaries
• Developing actionable recommendations

This process can consume 15-20 hours of analyst time for a typical project with 10 expert interviews.

The Cost of Workflow Inefficiencies

The workflow challenges in traditional expert network projects create both direct and indirect costs:

Time Costs

According to research from Harvard Business School, knowledge worker productivity is significantly impacted by workflow interruptions and delays. For time-sensitive projects, the extended timelines of traditional expert network workflows can mean:

• Delayed strategic decisions
• Missed market opportunities
• Extended product development cycles

Financial Costs

The broker model creates financial inefficiency in two ways:

1. Direct markup: Traditional firms charge a substantial premium (often 50-100%) on expert compensation
2. Internal resources: Your team spends significant time managing the coordination process

Opportunity Costs

Perhaps most significantly, the traditional model creates long-term opportunity costs. Since the broker owns all the expert relationships, each project starts from zero in terms of network building. Your organization never develops a proprietary knowledge asset despite significant investment.

The Alternative: Owning Your Research Network

Innovative teams are shifting from renting access to building owned research networks. This approach fundamentally transforms the research workflow by:

1. Eliminating the middleman: Direct outreach to exactly the right experts
2. Streamlining scheduling: Self-booking through integrated calendar tools
3. Building lasting connections: Keeping the network relationships you develop
4. Accelerating synthesis: Using AI to quickly generate insights from conversations

Case Study: Marketing Team Transformation

A B2B SaaS marketing team recently shifted from GLG to a direct research network approach for competitive positioning research. The results were striking:

• Reduced project timeline from 4 weeks to 10 days
• Decreased per-interview cost by 61%
• Created a proprietary network of 75+ industry experts for ongoing insights
• Generated comprehensive insight reports within hours of completing interviews

Creating a More Effective Research Workflow

To overcome the workflow challenges inherent in traditional expert network projects, consider these strategic shifts:

1. Leverage Your Existing Network Assets

Your team likely has valuable LinkedIn networks that can be activated for research purposes. Modern tools can help pool these connections into a coordinated outreach engine.

2. Implement Self-Service Scheduling

Eliminate the three-party coordination problem by using calendar integration tools that allow experts to self-book based on your availability.

3. Adopt AI-Powered Synthesis

New technologies can transform raw interview content into structured insights, charts, and recommendations in hours rather than days.

4. Build for the Long Term

Design your research workflow to create lasting network assets rather than one-off transactions. The connections you make today should become valuable resources for future projects.

Conclusion: From Workflow Woes to Research Advantage

The traditional expert network model represented an important innovation when it emerged two decades ago. However, its inherent workflow inefficiencies are increasingly out of step with the pace of modern business decision-making.

By shifting from renting access to owning your research network, you can transform project bottlenecks into competitive advantages. This approach delivers not just faster and more cost-effective insights for today's decisions, but builds a growing knowledge asset that compounds in value over time.

As markets move faster and decisions become more complex, the ability to rapidly access and synthesize expert insights isn't just a workflow improvement—it's a strategic necessity.