Forget 'AI Fluency'. Market Research Needs 'Insight Engineers'.

Let’s be clear: the conversation in market research is no longer whether to use AI. That debate is over.

Proficiency with tools like Microsoft Copilot or Gemini isn’t just “AI fluency”; it’s the new baseline, the minimum requirement for professionals in our field. Achieving the 1.5x efficiency gain these tools offer is now simply table stakes.

But hitting that baseline is not the revolution. It fundamentally misunderstands the potential and, I believe, misdirects our focus if we stop there.

The real transformation isn’t about tool fluency; it’s about cultivating a deeper AI strategic fluency. This is about learning a new way of thinking and problem-solving with AI as a collaborator. This is the leap that creates the 10x “Insight Engineer” – professionals capable of achieving exponential impact. This isn’t just theory; it’s a pattern emerging not just in market research, but across the broader knowledge industry. Much of this thinking stems from our work and internal discussions here at Euromonitor International, inspired by colleagues pushing the boundaries.

This deeper fluency isn’t just about ‘learning AI’; it’s about ‘living AI’. It’s about fundamental curiosity, relentless experimentation, the ability to learn fast in a constantly shifting landscape, and seeing connections others miss – adapting innovations from adjacent fields to create novel MRX solutions.

I was inspired by a recent TikTok from Nate Jones (Nate Jones on LinkedIn) discussing this very gap between baseline proficiency and true fluency. It reminded me of a conversation with one of my team who admitted AI solutions were appearing in his dreams – that deep immersion signifies a different level of engagement. In practice, this means relentlessly experimenting with prompts, building small custom tools for personal workflows, pushing the boundaries of what the AI can do, and critically understanding why it fails when it does.

Baseline Proficiency vs. Strategic Fluency: AI as a Colleague

Treating AI adoption purely as tool training (Usage Fluency) is a strategic error. It gets you to the baseline faster but doesn’t unlock new capabilities or competitive advantage. True differentiation comes from architecting unique solutions with AI.

Strategic Fluency is about learning to collaborate with another intelligence. It involves fundamentally new problem-solving skills, enabling researchers to architect and leverage AI for advanced tasks like:

• Architecting Solutions: Designing and building custom AI agents (like we’re exploring with the Writer AI studio) for specific, rigorous research tasks. Consider synthesising qualitative feedback from thousands of open-ended survey responses. A user might leverage Copilot to generate summaries faster (achieving the baseline). An Insight Engineer builds a custom agent, trained on category-specific taxonomies, that not only summarises but quantifies emerging themes, identifies sentiment shifts across segments, and flags statistically significant deviations from benchmarks – achieving in hours what used to take days, with far greater depth and reliability. That is the path to 10x impact.
• Structured Data Extraction: Leveraging AI to pull consistent, structured data points (e.g., brand mentions, feature lists, pricing information) from vast amounts of unstructured text like product reviews, company reports, or news articles at scale.
• Work Decomposition: Learning how to break down complex research problems (like predictive modelling for market forecasting) into ‘chunks’ that an AI can effectively process.
• Prompt Engineering & Self-Correction: Moving beyond basic prompts to iteratively guide AI towards high-quality, methodologically sound outputs, whether for automated report drafting or complex data augmentation.
• Communicating Intent: Clearly defining the goal and constraints for tasks like cross-lingual data synthesis, asking the AI for help but critically evaluating the outcome.
• Critical Validation & Understanding Limitations: Possessing the skills to rigorously evaluate AI outputs, interrogate logic (understanding why closed-loop RAG is safer), spot biases in sentiment analysis, and crucially, recognise where AI currently falls short. For instance, knowing that AI struggles with true imagination (e.g., generating genuinely novel brand concepts) because it’s fundamentally predictive, not creative. It also involves ensuring outputs meet robust research standards before generating synthetic data.
• Rapid Learning & Constant Reassessment: Continuously mastering new models and techniques is vital because AI is not static. Capabilities like image generation, context windows, and hallucination likelihood evolve dramatically in short periods. What wasn’t viable six months ago might be game-changing now. Insight Engineers constantly re-evaluate what’s possible.

This is a new cognitive toolkit, far removed from simply mastering baseline tools.

The Career Calculus: A Choice with Consequences

Let’s be clear: deep domain expertise remains absolutely critical. You cannot become a 10x Insight Engineer without first understanding what great market research looks like. The most powerful individuals will combine industry experience with AI strategic fluency.

However, relying solely on traditional skills and only achieving baseline AI usage fluency is becoming a risky proposition. As Nate Jones highlighted, the difference between these two approaches will drive tremendous career differentiation in the coming years. Baseline proficiency, while necessary, risks locking analysts into operating increasingly commoditised workflows, potentially diminishing long-term strategic value and career trajectory. Insight Engineers, conversely, are positioning themselves as the indispensable architects of future insights capabilities.

Understanding this distinction is crucial because it presents a choice. Not everyone needs or wants to become an Insight Engineer. But everyone deserves to make that choice knowing the stakes and what true AI strategic fluency entails beyond just meeting the minimum standard.

Cultivating the Insight Engineer

This isn’t just about L&D rolling out another Copilot training module. It requires fostering a culture of deep curiosity, experimentation, and critical thinking. It needs leaders to provide the tools, the time, and the psychological safety for teams to move beyond simply using AI at a basic level to truly collaborating with it at a strategic level.

My question to my network is this: How do we develop better language to distinguish true AI strategic fluency from baseline tool proficiency? And how are we cultivating the ‘Insight Engineers’ our industry desperately needs?