A More Sustainable Approach to AI in Market Research

Artificial intelligence and machine learning are transforming market research at an unprecedented pace. From automating analysis to accelerating insight generation, AI-powered tools promise speed, scale, and efficiency. However, as enthusiasm for generative AI grows, so do concerns about overreliance on technologies that may be volatile, opaque, or subject to rapid market shifts. The Greenbook article “An Alternative Approach to AI in Market Research to Hedge against a Potential AI Bubble Burst,”  written by our Head of Marketing, Michael Howard, addresses this tension and proposes a more resilient path forward for research organizations .

The Risk of Overdependence on AI

The article begins by acknowledging the value AI brings to modern market research, particularly in handling large datasets and accelerating exploratory analysis. At the same time, it raises an important caution: if AI becomes the sole engine behind research platforms, organizations may be exposed to significant operational and strategic risk. External AI models can change, degrade, become unavailable, or introduce inaccuracies that are difficult to detect or audit. In an industry built on trust, methodological rigor, and confidence in results, these risks cannot be ignored .

Market research clients ultimately care less about whether insights are “AI-generated” and more about whether they are accurate, transparent, and defensible. When AI outputs cannot be easily validated or replicated, confidence in the research suffers—regardless of how advanced the technology may appear.

AI as an Enhancement, Not a Replacement

Rather than rejecting AI, the article advocates for a balanced, parallel approach. In this model, AI is treated as an enhancement layered on top of a proven analytical foundation, not as a replacement for it. The core research engine remains stable, rule-based, and auditable, ensuring that essential analytical capabilities continue to function even without AI involvement.

One example highlighted is the use of a “double-prompt” or intermediary system architecture. Here, AI-generated instructions are interpreted and validated by a trusted internal engine before execution. This design preserves methodological integrity while still allowing researchers to benefit from AI-assisted exploration and automation.

Preserving Traditional Research Capabilities

A key theme throughout the article is the importance of maintaining non-AI workflows alongside AI-driven features. Tools such as manual variable creation, drag-and-drop analysis, and conventional scripting remain essential—not as outdated alternatives, but as safeguards. These capabilities ensure that researchers can continue producing high-quality insights regardless of external AI disruptions or market fluctuations.

This dual-track approach also empowers researchers with choice. Teams can selectively apply AI where it adds value—such as early-stage exploration or efficiency gains—while relying on traditional methods for high-stakes decision-making, regulatory-sensitive work, or client-critical deliverables.

Building Resilience in a Rapidly Evolving Landscape

The article positions this strategy as a hedge against a potential AI “bubble burst.” If enthusiasm for generative AI outpaces its long-term reliability or economic sustainability, organizations that have preserved independent analytical infrastructure will be far better positioned to adapt. Rather than scrambling to rebuild lost capabilities, they can continue operating with confidence and continuity.

Importantly, this is not framed as a defensive or conservative stance. Instead, it is presented as a responsible innovation strategy—one that embraces new technology while protecting the core principles of market research.

A Strategic Imperative for Research Leaders

For research leaders and organizations, the message is clear: the goal is not to choose between AI and traditional methods, but to integrate them thoughtfully. Platforms that balance innovation with transparency, flexibility, and resilience will be best equipped to serve clients in an uncertain technological future.

By treating AI as a powerful tool—rather than a single point of failure—market research firms can harness its benefits while safeguarding trust, quality, and long-term value. In an industry where confidence in insight is paramount, this balanced approach may prove to be the most sustainable advantage of all.

Read the full article.