Artificial intelligence is no longer a future concept in market research. It is embedded in day-to-day workflows, reshaping how studies are designed, executed, and analyzed. Yet for all the excitement surrounding AI’s capabilities, there is a growing gap between what AI can accelerate and what it can actually understand.

The real transformation underway is not about replacing researchers with algorithms. It is about redistributing where thinking happens in the research process and, in some cases, obscuring where it no longer does.

As AI adoption accelerates, the most important question is no longer whether to use it, but how and to what end. In this shop talk, Karen Barnes, Senior Research Advisor and Dave King, Vice President of Insights at The Farnsworth Group discuss their take on these matters and what you should be thinking about too.

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Why AI Feels Transformational Right Now

Market research has always involved a significant amount of operational labor: questionnaire and discussion guide development, data cleaning, transcription, coding, synthesis, and reporting. AI excels at precisely these tasks. It processes language at scale, identifies patterns quickly, and produces outputs that look polished and complete.

This combination of speed, scale, and fluency makes AI feel revolutionary. Studies move faster. Data feels more accessible. Insights arrive sooner.

But this moment is less about the sudden emergence of intelligence and more about the automation of work that previously required time and manual effort. AI did not change the fundamentals of research. It changed the economics of execution.

That distinction matters.

Where AI Is Genuinely Improving Market Research

Used thoughtfully, AI is delivering meaningful benefits across the research lifecycle.

Operational Efficiency

AI has reduced friction in areas that once slowed projects down:

• Faster transcription and translation of qualitative interviews
• Automated coding of open-ended responses
• Streamlined data cleaning and organization

In large-scale studies common to construction and home improvement categories, where surveys often include hundreds or thousands of open-ended responses, this efficiency gain is real and valuable.

Pattern Detection at Scale

AI can surface themes across vast datasets that would be difficult to detect manually:

• Enabling faster synthesis of ongoing feedback streams (social posts, call center data, returns, website activities, etc.)
• Highlighting emerging concerns and/or issue detection across regions or trades
• Supporting early hypothesis generation

This capability is especially useful in exploratory phases or when working with fragmented audiences.

Lower Barriers to Entry

AI tools have also made research techniques more accessible to non-researchers. Marketing, product, and insights teams increasingly use AI to:

• Run directional internal analyses
• Summarize feedback from customer touchpoints
• Explore early ideas before commissioning formal research

These applications are not inherently problematic. They become problematic only when speed is mistaken for rigor.

Speed and access are not the same as insight quality.

Where AI Introduces New Risks to Research Quality

The same characteristics that make AI appealing also introduce new risks.

The Illusion of Insight

AI-generated summaries are confident, articulate, and persuasive. Before research is executed, they can answer questions too quickly with familiar narratives - prematurely narrowing the scope before full exploration can be completed and validated.

When used to analyze primary research, AI can make weak data feel conclusive and shallow analysis feel robust. Fluency masks uncertainty. In practice, this means poor inputs can still produce outputs that look ready for decision-making.

Methodological Blind Spots

AI does not inherently question:

• Sample quality
• Question wording
• Response bias
• Survey context

Unless explicitly instructed and carefully guided, AI treats all inputs as valid signals. Experienced researchers know when to challenge data rather than summarize it.

Loss of Industry Context

Construction and home improvement research often involves:

• Technical language
• Long and fragmented buying journeys
• Multiple decision-makers
• Regional and regulatory variation

Generalized AI models struggle with this complexity. Without domain knowledge, they can misinterpret terminology, flatten nuance, or overgeneralize findings.

AI is very good at telling us what is being said. It is far less reliable at explaining why it matters.

AI in Market Research: Standardization vs. Adaptation

AI is not being implemented in one uniform way. How it is embedded into research workflows makes a meaningful difference in outcome quality.

AI as Pre-Programmed Platform Capability

Many technology-driven research platforms offer AI as a set of predefined features:

• Fixed workflows
• Templated analyses
• Standardized applications designed to work “out of the box”

This approach prioritizes scalability and consistency. However, market research is inherently situational. Every study differs in audience, category dynamics, survey design, and objectives.

The challenge is that pre-programmed AI must rely on assumptions that may not apply to a specific scenario.

For example, AI-driven data quality checks may flag obvious issues like straight-lining or speeders. But subtle problems, such as respondents misunderstanding technical terminology or misrepresenting professional experience, often require context-specific rules and judgment.

A templated approach can miss what matters most.

AI as Contextual, Researcher-Directed Tooling

In contrast, many services-led research teams use AI as a flexible accelerator rather than a fixed solution.

In this model:

• Instructions and evaluation criteria change from study to study
• AI is guided by researchers who understand the category and research goals
• Outputs are reviewed, challenged, and refined based on research and industry expertise

For instance, identifying low-quality or fraudulent respondents often requires:

• Custom logic tied to the survey topic
• Knowledge of how professionals in a given trade speak
• Awareness of category-specific failure modes

AI performs best when it is directed, not left to operate autonomously.

True scalability in research comes from repeatable thinking, not repeatable templates.

Where AI in Market Research May Go Next and What to Watch Carefully

Beyond today’s applications, several AI-driven developments are emerging at the edges of market research. These technologies are not yet widely adopted, and their relevance will depend on how responsibly they evolve.

Some may meaningfully enhance research. Others may remain more theoretical than practical.

Synthetic Respondents and Modeled Insight

Synthetic respondents and digital twins represent one of the most discussed frontier applications of AI in research. These models are trained on historical datasets to simulate how specific audience types might respond under defined conditions.

Their potential value lies in extension, not substitution.

Appropriate future-facing applications may include:

• Elaborating on known quantitative patterns
• Exploring directional “what-if” scenarios grounded in past behavior
• Supplementing limited primary data with modeled perspective

Their limitations are equally important:

• They are likely to over-represent the norm
• They struggle with novelty, disruption, and emotional context
• They reflect assumptions embedded in the training data

The long-term relevance of synthetic respondents will depend less on technical sophistication and more on transparency, restraint, and methodological honesty.

Modeled insight should inform thinking, not validate decisions.

Predictive Scenario Modeling Beyond Survey Data

Another emerging area is the use of AI to integrate multiple data sources, such as historical research, behavioral data, economic indicators, and operational metrics, to simulate future market scenarios.

In theory, this could support:

• Scenario planning under economic or regulatory uncertainty
• Stress-testing strategic assumptions
• Exploring downstream impacts of upstream change

In practice, these models are only as credible as their assumptions. Without deep domain expertise, predictive outputs risk appearing more precise than they are reliable.

This area remains promising but immature.

Adaptive Research Systems

A more experimental frontier involves AI-driven systems that adapt research instruments in real time, such as:

• Modifying follow-up questions based on early responses
• Dynamically adjusting probes in qualitative research
• Iteratively refining hypotheses mid-study

While intriguing features in a qualitative setting, these approaches raise methodological and comparability concerns for quantitative validation.

Their future relevance will depend on whether rigor can be maintained alongside flexibility.

Data Governance, Provenance, and Insight Accountability

As AI-generated and AI-augmented insights proliferate, governance becomes a forward-looking differentiator.

Future-facing research questions will increasingly include:

• What data sources trained this model?
• What insight is observed versus modeled?
• Where did human judgment intervene?  
• What assumptions are embedded and undocumented?

Organizations that treat transparency as part of methodology, not compliance, will be better positioned to maintain trust as AI capabilities advance.

In an AI-enabled future, trust will be earned through clarity, not capability.

What This Signals About the Road Ahead

The most important future question is not which AI technologies will emerge, but which will prove useful in real decision-making contexts and how will it effectively incorporate human judgement and decision-making. Many will promise more than they deliver. Some will quietly become indispensable.

The research teams best positioned for that future will not be those chasing novelty, but those applying discipline, context, and judgment to every new capability.

What AI Will and Will Not Change About Market Research

AI is already reshaping how market research is conducted. It has accelerated analysis, lowered operational friction, and expanded access to research-like capabilities across organizations. These changes are not theoretical. They are now part of standard practice.

What AI will continue to change is how work gets done:

• Routine and time-intensive tasks will become faster and more automated
• Insight generation will feel more continuous and more accessible
• Research timelines and expectations will compress

What AI will not change is what makes insight valuable:

• Sound research design grounded in real decision-making needs
• Deep understanding of category and industry context
• The ability to distinguish signal from noise
• Accountability for conclusions and recommendations

As AI moves both upstream into research framing and downstream into interpretation, the role of human judgment does not diminish. It shifts. Judgment becomes more critical at the moments where AI is most convincing and most incomplete.

Future-facing capabilities such as synthetic respondents, predictive modeling, and adaptive research systems may extend what research can do. Their relevance will depend less on technical sophistication and more on how thoughtfully they are governed, bounded, and explained.

AI increases the speed of research. It does not change its responsibility.

The next era of market research will not be defined by who uses the most advanced tools, but by who applies them with the greatest discipline. As AI continues to evolve, insight will remain a product of context, reasoning, and experience.

AI can accelerate human understanding. It cannot replace it.

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