Overview
When David Boyle set out to document what was truly happening with AI adoption in corporate insights, analytics and strategy departments, he faced a challenge familiar to anyone conducting qualitative research at scale: how to capture, process and synthesize dozens of confidential, hour-long conversations without drowning in data.
The project was ambitious: conduct 76 in-depth interviews with insights leaders across major global organizations to reveal the unvarnished truth about AI transformation. These weren't vendor case studies or press releases, they were candid conversations about what works, what fails and why most organizations struggle despite the technology working brilliantly.
The findings would inform multiple deliverables:
- A keynote presentation at TMRE (The Market Research Event) in Las Vegas for 1,000 industry professionals
- A comprehensive research report published after the conference
- Workshops with industry leaders exploring implementation strategies
- Ongoing consulting guidance for organizations navigating AI transformation
The constraint: David was working independently on this. No research team. No army of analysts. Just one person processing hundreds of thousands of words of confidential conversation while maintaining a global consulting practice and managing a complex transatlantic life.
The Challenge
Scale without support
"I knew from the start this would be the most ambitious research project I'd undertaken," David explains. "Seventy-six hour-long interviews means nearly two full work weeks just in meeting time, before you even begin analysis."
The math was daunting:
- 76 interviews with senior leaders at major corporations
- Hundreds of thousands of words of confidential dialogue
- organizations across consumer goods, pharmaceutical, financial services, entertainment and retail
- 3-month timeline from first interview to keynote delivery
- Multiple deliverables requiring different cuts through the same material
- Zero research assistants to help with processing
The confidentiality requirement
Beyond the scale challenge, David had committed to operating under Chatham House Rule: all participating organizations would be acknowledged, but no specific insight would be attributed to any individual or company. This meant he needed absolute clarity on what was said by whom, while simultaneously being able to synthesize patterns across conversations without losing track of sources.
"I'm asking people to tell me things they'd never say publicly," David notes. "Things like 'our AI implementation is failing despite spending millions' or 'we're quietly eliminating entry-level roles.' They'll only be honest if they trust I can keep their confidence while still using their insights. That requires meticulous record-keeping."
The repurposing imperative
The project's impact could only be maximised if David could efficiently repurpose the same core research across multiple formats:
- A 20-minute keynote presentation requiring punchy examples and quotable moments
- A detailed research report for those wanting deeper analysis
- Workshop content for hands-on implementation guidance with industry leaders
- Ongoing consulting insights for clients facing similar challenges
Traditional approaches would require different research projects for different deliverables. David needed the interviews to serve all purposes simultaneously.
The Solution: Transcription as Active Infrastructure
Discovery of the MCP integration
David had used Otter for several years to capture meeting transcripts. "I'd always found Otter valuable," he explains, "but transcripts lived in Otter's platform while my analysis happened in Claude Projects, the AI workspace where I do my thinking and writing. There was friction."
The breakthrough came when Otter released its MCP (Model Context Protocol) server, making transcripts directly accessible within Claude. "Suddenly, transcripts weren't stored somewhere else that I had to visit. They were infrastructure. Always there, always accessible, like having electricity."
The setup
Every interview followed the same pattern:
- Record via Otter during the conversation
- Automatic transcription completed within minutes
- Immediate availability in Claude via MCP
- Natural language search across the entire interview library
- Instant retrieval when patterns emerged
"I could be analysing Interview 23 about synthetic data failures," David describes, "suddenly remember Interview 7 mentioned something similar, and within seconds have both transcripts side-by-side. No switching tools. No breaking my train of thought. Implementation: From individual meetings to collective intelligence
Cross-interview pattern detection
The real power emerged in how David used the integrated system. Analysing one interview with an insights leader at a major retailer, he noticed references to avoiding junior hiring. A simple query of "Find meetings discussing junior analysts and hiring" surfaced six other interviews with similar themes.
"Within minutes, I'd identified a pattern," David explains. "Some organizations were explicitly avoiding hiring junior roles, with AI doing work previously done by entry-level staff. This wasn't about any single company's decision, it was an industry-wide shift happening simultaneously but quietly."
Without the ability to search across all transcripts instantly, the pattern would have remained invisible.
Framework development through iteration
David's "Six-Link Chain" framework of identifying why AI transformations fail despite the technology working emerged directly from querying transcripts. He could ask: "What barriers to AI adoption were mentioned?" and see references from interviews. Then drill into specific links: "What did people say about executive sponsorship?"
"The framework wasn't predetermined," David notes. "It emerged from the data because the data was genuinely queryable. I was discovering patterns during the research itself, not just during write-up."
Quote mining for authenticity
For the keynote presentation, David needed authentic voices with raw, candid observations that prove you've actually spoken to people rather than synthesized from articles.
Example query: "Find discussions about vendor disappointment"
Result: Eight interviews containing specific examples,from an insights leader describing AI that was "analysing vowels and articles of speech" to another executive noting they "had us meet with their top person, we were still having issues." These weren't abstract concerns; they were documented disasters with specifics.
"I could search for emotionally resonant moments," David explains. "'Find transcripts where people described feeling overwhelmed' or 'Show me where executives admitted failure.' The authenticity in my presentation came from having genuine quotes at my fingertips."
Validation and fact-checking
When David made claims in his keynote preparation like"Most organizations are failing at AI transformation despite the technology working",he could instantly validate them.
Query: "How many people said their AI initiative was failing?"
Cross-reference responses: Confirm the count.
Result: 76%,not rhetoric, but documented reality.
"The transcript library became both an evidence base and fact-checker," David notes. "I could make bold claims because I could back them up instantly."
The Outcomes
Evidence-based thought leadership
The TMRE keynote, "Candid Truths About AI in Insights," was built entirely on this transcript-powered research:
Scale and credibility
- 76 interviews across major organizations
- Quantifiable patterns: "76% of organizations failing" isn't rhetoric,it's documented
- Concrete examples: Every claim backed by specific quotes
- Industry breadth: Consumer goods, pharmaceutical, entertainment, retail, financial services
"I couldn't have done this work without Otter's MCP server. Not just more slowly,I genuinely couldn't have done it. The difference between having transcripts in one place and analysis in another versus having them integrated is the difference between possible and impossible at this scale."
Unexpected discoveries
The most valuable findings weren't what David expected when he started:
- The 70/30 trap: organizations achieving 30% efficiency gains but failing to capture the value
- The synthetic data disasters: Panel contamination causing entire studies to be thrown out
- The vendor accountability crisis: Major suppliers claiming AI capabilities whilst delivering unusable outputs
"These insights emerged from cross-referencing transcripts, following threads across conversations and identifying what people said when they thought no one else was listening," David explains. "I discovered them during the research process because the transcripts were accessible in my analytical environment."
Efficient repurposing across deliverables
The same transcript library served multiple purposes:
For the keynote (20 minutes):
- Punchy examples and memorable quotes
- Key statistics that captured attention
- Emotional moments that resonated with audience experience
For the research report (comprehensive):
- Detailed case studies with fuller context
- Extended analysis of patterns across organizations
- Methodological rigor for those wanting depth
For leadership workshops:
- Practical examples participants could relate to their situations
- Discussion prompts drawn from real challenges
- Framework materials grounded in evidence
For ongoing consulting:
- Quick reference to relevant examples for client situations
- Pattern recognition across similar challenges
- Validation of recommendations with concrete evidence
"I didn't do different research for different deliverables," David emphasizes. "The same 76 interviews powered everything. The transcripts were the foundation. I just queried them differently depending on what I needed."
"The ratio of input effort to output value is extraordinary when transcripts become genuinely accessible infrastructure."
The Broader Implication: Transcription as Knowledge Infrastructure
From storage to access
"Transcripts have always been valuable," David reflects. "What changed was accessibility. Having 76 interviews stored in Otter is useful. Having them instantly searchable and retrievable whilst doing analysis is transformative. It's the difference between having books in a library and having them materialize on your desk the moment you need them."
From individual meetings to collective intelligence
Each interview had value individually. But the real insights emerged from connections across interviews. Atterns are invisible in isolation, in aggregate, they become visible.
"The MCP server made 'analytics of conversation' practical," David explains. "I could treat dialogue with the same analytical rigor as numerical data. That's what enabled me to move from 'interesting interviews' to 'systematic evidence about organizational transformation.'"
From reactive to proactive
"I didn't wait until writing the keynote to find relevant quotes," David notes. "I discovered patterns during the research itself because transcripts were accessible in my analytical environment. This shifted interviews from passive data collection to active knowledge building."
When a mid-interview observation suggested a pattern, David could immediately query previous transcripts to test the hypothesis. "I'd be in Interview 45, hear something about training failures, and within 30 seconds confirm that 12 previous interviews mentioned the same thing. That changed how I asked questions in the remaining interviews."
What Made It Work
Three factors enabled this outcome:
1. Quality transcription
"Otter's accuracy meant I could trust the transcripts without constant verification," David notes. "I occasionally spot-checked, but I never worried whether what I was reading accurately reflected what was said. That trust is essential when you're building arguments on evidence."
2. MCP server
"This is the game-changer," David emphasizes. "Transcripts accessible where work happens, not in a separate system. The server eliminated friction that seems small but compounds massively across dozens of interviews. It's like the difference between having to walk to a filing cabinet versus having files instantly appear when you need them."
3. Semantic Search
"Natural language queries rather than keyword matching made the difference between finding relevant content and drowning in results," David explains. "I could ask 'show me discussions about executive resistance to AI' and get meaningful results, not just every mention of 'executive' or 'resistance.'"
Advice for Others
David's approach offers a template for anyone conducting systematic qualitative research:
Professionals building thought leadership
"If you're having client conversations, partner meetings or strategy sessions, you're generating insights constantly. The question is whether you're capturing them in a way that lets you build something larger."
Consultants synthesising patterns
"You're probably having the same conversation 15 times with different clients. Imagine being able to query across all those conversations to surface patterns, validate hypotheses or find the perfect example for a proposal."
Executives tracking themes
"Leadership meetings contain strategic insights that get lost because they're not written down. What if you could search back through six months of executive discussions to understand how your thinking evolved on a key decision?"
Researchers analysing interview programmes
"Any time you're doing systematic qualitative research. Whether that's 10 interviews or 100,the question is how you'll process them. Will it be practical to analyse across all of them? Or will you inevitably focus on the most recent few and miss patterns?"
The common thread
"Conversations contain insights," David concludes, "but only if you can actually work with them. Transcription transforms ephemeral dialogue into permanent, queryable knowledge,but only when integrated into your workflow, not isolated in a separate tool."
Looking Forward
The TMRE research project represents David's commitment to evidence-based guidance in an industry drowning in hype. "Everyone has opinions about AI transformation," he notes. "Very few people have systematically documented what's actually happening across dozens of organizations. That's what this research provides:evidence, not speculation."
The transcript library continues to generate value beyond the initial deliverables. "I'm still querying these interviews months later," David explains. "A client will describe a challenge, and I can search to see if anyone in my research faced something similar. The knowledge compounds over time."
For David, the project validated a thesis about modern knowledge work: "We're moving from document-centric to conversation-centric knowledge capture. The insights that matter increasingly emerge in dialogue, not in writing. The question is whether we're building infrastructure to capture and leverage those insights."
"Transcription isn't a feature anymore. It's infrastructure as essential as email or cloud storage. When done right, it becomes invisible. You don't think about whether you have transcripts any more than you think about whether you have electricity. You just use them to do work that would be impossible otherwise."
The Bottom Line
When you're on deadline to synthesize hundreds of thousands of words across dozens of confidential conversations, you need tools that don't just work,they disappear. Transcription must become infrastructure: essential, accessible, invisible. Otter's MCP server represents that vision realized.
From capturing the oral history of AI's transformation of corporate life to delivering evidence-based guidance for 1,000 industry leaders, this research wouldn't exist without transcription that genuinely becomes infrastructure for knowledge work.
About David Boyle: Founder of Audience Strategies, David helps organizations leverage AI for strategic advantage. His work spans Fortune 500 companies across sectors, combining data analytics, consumer insights and practical AI implementation. The TMRE keynote represents his commitment to evidence-based guidance in an industry drowning in hype.
About Otter: Otter provides AI-powered transcription and meeting intelligence for individuals and enterprises. The MCP server integration represents Otter's vision of transcription as infrastructure—essential, accessible, invisible—enabling higher-order work rather than being the work itself.
To learn more about how Otter can transform qualitative research at scale, visit otter.ai
