Andrew is in Italy on his honeymoon, a guest backed out, and Brian decided to go solo for the first time in 10 years of podcasting. No co-host, no guardrails, no agenda. Just 30 years of experience and a list of questions he has been sitting on for a while.

None of them has clean answers. Thats kind of the point.

Brian works through six questions the market research industry is not asking loudly enough: whether stacking fraud tools without coordination is quietly introducing a new category of data quality bias, who actually owns the definition of quality when nobody agrees, whether synthetic data is a legitimate solution or a convenient way to avoid the harder problem, where the next generation of researchers is coming from and whether the industry even knows what skills it needs to fill that pipeline, why market research is one of the only professions that shapes billion-dollar decisions without any required accreditation, and whether the M&A wave is actually good for research quality or just good for returns.

These are not gotcha questions. Brian is not here to throw anyone under the bus. But he is willing to say some things out loud that tend to get avoided in favor of AI hype cycles and vendor showcases, and this episode is the result of that.

Key Takeaways:

• Why stacking uncoordinated fraud tools may be creating invisible bias, not solving fraud

• Why the quality definition problem may eventually be settled by procurement departments instead of researchers

• The legitimate use cases for synthetic data and the less legitimate reasons adoption is accelerating

• Why the researcher talent shortage is really two problems bundled into one

• Why market research informs billion-dollar decisions with zero required accreditation

• Why consolidation looks like efficiency at first, and what history tells us happens next

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In this episode, Brian and Andrew sit down with Dan Entrup, co-founder of AggKnowledge, to talk about one of the most overlooked drivers of the data quality crisis: the respondent experience nobody is actually fixing.

Dan tracked every research outreach he received over 15 months and turned it into a SampleCon presentation. The numbers were brutal. 137 times asked his age. 57 dental requests sent to someone who has never been a dentist. An inbox so flooded with irrelevant surveys that even a motivated expert panelist nearly quit. His point is sharp: bad targeting is not just inefficient, it is actively killing the panels we depend on.

The conversation gets into what AggKnowledge is building to solve it upstream, why free-text profiling and stale data are quietly sabotaging research operations at scale, and why identity verification and workplace verification are not the same thing. There is also a candid take on the M&A and funding landscape, who is well-positioned, where the yellow flags are, and why human knowledge only gets more valuable as AI takes over everything else.

Key Takeaways:

• Why bad targeting is a fraud accelerant, not just a waste of budget

• What 15 months of tracking his own respondent experience revealed about how broken outreach really is

• Why the gap between identity verification and workplace verification is where embellishment hides

• How upstream profiling data can reduce fraud before a screener is ever sent

• Why companies beating on revenue are still seeing their stock drop, and what that signals for the research industry M&A

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• It's Pronounced Data (Newsletter)

This episode is the bonus round to Brian and Andrew's four forces webinar. They ran out of time on the live session, so they saved the best part for here: where all four trends are actually pointing and what it means for how research gets done.

Andrew lays out the clearest version of the webinar's thesis: capital, AI, and the data quality crisis are not three separate things. They are converging forces pushing the industry toward methodologies that are more transparent, more respondent-friendly, and more operationally feasible than what came before.

From there, the conversation gets specific on async qual, AI-led conversational interviewing, and agentic research. Andrew makes a sharp argument that the next step change in automated research will not come from software companies selling AI tools. It will come from agencies building proprietary workflows trained on their own data and methodological history. The agencies that do that work now will have a moat that an off-the-shelf product cannot replicate.

The episode closes with Brian's take on four things the disruption narrative tends to get wrong, including why qual is not dying and why the trust crisis will not be solved by more technology.

Key Takeaways:

• Why the four trends are a single converging story, not four things happening in parallel

• What AI-led async qual does better than scheduled IDIs and why it matters for panel health

• Why agentic research workflows will be built by agencies from the inside, not sold to them as products

• Why qual is not dying and why AI quant cannibalises quant budgets, not qual budgets

• Why the trust crisis is a culture problem, not a technology problem

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In this special episode, Brian and Andrew bring their live webinar to the podcast feed. Drawing from recent conferences, client conversations, and industry reports, they break down four forces reshaping how research gets done: Capital Influx, the Data Quality Crisis, AI Disruption, and Methodology Reframe.

Brian's core argument is that these are not four separate trends. They are one larger story with feedback loops accelerating each other, and the industry does not get to choose whether the shift happens, only whether it leads or follows.

The episode also features a live Q&A, including a sharp exchange on whether there are research categories where AI should not be used yet, and a direct answer to how you sell quality to a client who thinks $12 CEOs are fine.

Key Takeaways:

• Why OpenAI and Anthropic’s launch of deployment consulting firms is a competitive threat the insights industry needs to take seriously

• What the latest GDQ benchmarks reveal, including a 59.6 percent post-survey removal rate in B2B

• Why the biggest fraud risk is not bots but humans using AI to fake qualifications and game incentives

• Why synthetic data still needs proof, and why every credible synthetic platform still anchors to a verified human sample

• Why the qual and quant divide has not just blurred, it has effectively disappeared

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In this quick-hit episode, Brian and Andrew debrief fresh off the conference circuit. Brian just landed from Washington, D.C. and comes in hot with takeaways from two back-to-back events: the Insights Association Ignite Data Quality and the GreenBook IIEX conference at the Ronald Reagan Building.

The first stop is data quality. The Global Data Quality Initiative unveiled its latest benchmarks at IA Ignite, and the numbers are hard to ignore. Post-survey removal rates of 46.5% in B2B and 30% in healthcare patient research paint a picture of an industry with serious work to do. Brian and Andrew also dig into findings from Verisol, who analyzed 50 million survey clicks and found that the biggest threats are not true bots, but humans armed with AI using VPNs and location spoofing to game incentives.

Then it's on to IIEX, where Brian played a little game with Andrew: name the companies. Out of roughly 30 AI companies with booths or presentations, Andrew recognized maybe two. Outset, Neurons, Dialogue AI, ConvoTrack, Riley AI, and dozens more are flooding the market, and Brian makes the case that nobody can keep up with all of them. The second mouse gets the cheese.

The episode closes with a plug for the Signal and Noise webinar on May 12th at 2 PM Eastern, where Brian and Andrew will go deeper on trends, AI, and everything they held back from this one.

Key Takeaways:

• Why the IA Data Quality Day may be the single most important gathering in the industry right now

• What the GDQ benchmark numbers actually reveal about the state of B2B and healthcare research sample

• Why the biggest fraud threat is not bots but humans with AI using proxies and spoofed locations

• The overwhelming volume of new AI companies entering the market and why being strategic matters more than being comprehensive

• Why the second mouse gets the cheese when it comes to evaluating early-stage AI tools

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In this episode, Brian and Andrew sit down with Mike Courtney, founder and principal of Aperio Insights and a practicing futurist. Mike brings a rare perspective to the Signal and Noise table, equal parts market researcher and strategic foresight practitioner.

Mike kicks things off with his "Land Man oil analogy," framing AI not as something happening to us but as a new drilling tool for human knowledge and intelligence. Just as early oil pioneers could not have imagined the thousands of uses petroleum would eventually unlock, we are likely only scratching the surface of what AI makes possible.

From there, the conversation goes deep on what futurists actually do, why exponential growth is so hard for humans to comprehend, and what the five to ten-year picture actually looks like.

Key Takeaways:

• Why the right frame for AI is not what it will do to us, but what we will be able to do with it

• How futurists think about possibility and change management rather than prediction

• The barbell effect coming for knowledge workers and why AI fluency is not optional

• Why lowering the floor does not automatically raise the ceiling

• What will be genuinely scarce and therefore valuable in an AI-abundant world

• Why the Michelangelo of market research will be the person who asks the right question, not the one who executes the fastest

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In this episode, Brian and Andrew sit down with Jase Bumgardner, a 25-year partner at The Link Group, a healthcare-focused market research consultancy. Jase leads complex, multi-year research work streams in neuroscience and new product planning, most notably alongside Eli Lilly, following products from early concept all the way through FDA approval and launch.

Jase brings a grounded, refreshingly calm perspective to a conversation the podcast usually approaches with a bit more urgency. Where Brian and Andrew often find themselves in the AI doom spiral, Jase comes in as a self-described pragmatist. He estimates AI has changed roughly 10 to 15 percent of what his team actually does day to day, and he sees it primarily as amplification, not replacement. The real disruption, in his view, is still mostly theoretical for firms doing elite, high-stakes consultative research.

The conversation covers how The Link Group made AI a formal priority years ago through a structured task force, a five-year strategic plan, and ongoing sentiment checks to see if it's actually moving the needle with clients. Jase also pushes back thoughtfully on the rush to adopt, citing data showing only 13 percent of brand-side clients are satisfied with generative AI results, and warns against what he calls the "great de-skilling," where reflexively outsourcing thinking to AI erodes the very capabilities that make great researchers irreplaceable.

Key Takeaways:

• How The Link Group built a deliberate, mission-aligned AI strategy rather than chasing every new tool

• Why the "any benefit" mindset around AI adoption is a problem, and how to think about net benefit instead

• The risk of de-skilling as reading, writing, and independent thinking get offloaded to AI tools

• Why market research is still fundamentally a talent and relationship game, and why that is not changing anytime soon

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In this episode of Signal and Noise, Brian and Andrew sit down with Erin Sowell, owner and principal consultant at Thoughtful Research. Erin brings a background that is genuinely uncommon in the insights world, starting in environmental science and entrepreneurship before discovering market research through the product development process and going on to earn her master's degree from the University of Georgia's highly regarded MMR program, where she now serves on the advisory board.

The conversation digs into what it actually looks like to run a small, independent research consultancy built around integrated insights, bringing together qual, quant, and existing client data to answer business questions rather than just execute studies. Erin walks through a real project where a B2B client had a product that customers asked for and then refused to buy, and how a phased qual-to-quant approach helped uncover that the problem was not the product but the positioning and targeting strategy around it.

Brian and Andrew also explore one of the more underappreciated challenges in the industry: working with clients who have never done primary research before. Erin talks candidly about what it means to not just sell a study but to introduce an organization to research itself, the white-glove education involved, the sticker price shock, and why getting that first impression right matters so much for the long-term relationship a client develops with insights as a function.

Key Takeaways:

• How Erin built Thoughtful Research around integrated insights and a consultative, business-first approach rather than a methodology-first one

• The unique challenge and opportunity of working with clients who are brand new to primary research

• How Erin is using AI for qualitative analysis and building toward always-on forecasting and leading indicator capabilities

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