The Bronze Wooden Spoon: Outsourcing Final Interpretation to AI
Third place among my worst or most dangerous uses of AI in healthcare market research goes to outsourcing the final interpretation.
AI can organise evidence, identify patterns and suggest explanations. The danger comes when a company accepts those explanations without adequately challenging them.
A persuasive answer becomes a presentation headline. That headline becomes a strategy. And that strategy directs investment.
Mistaking symptoms for causes
Imagine physicians repeatedly describe a treatment as “difficult to use”.
AI might interpret this as a need for education and recommend training materials.
But closer examination could reveal that physicians understand the treatment perfectly. The difficulty involves funding, appointments or access to diagnostic services.
The company spends money addressing a knowledge gap that does not exist, while the actual barrier remains.
Turning interviews into forecasts
Suppose several specialists express enthusiasm for a product concept.
An AI-generated interpretation might describe “strong market demand” and recommend accelerating launch investment.
However, those specialists may work in unusually well-resourced centres. Their enthusiasm may depend on unconfirmed assumptions about price or reimbursement.
Exploratory interviews reveal possibilities. They do not automatically establish the size of an opportunity.
Treating them as a forecast could produce unrealistic targets and inappropriate investment.
Overlooking the minority that matters
An AI summary may emphasise frequently mentioned themes.
But frequency does not necessarily establish commercial importance.
A few patients might describe a practical problem that prevents them from continuing treatment.
If that issue disappears beneath a broader conclusion about satisfaction, the company could miss an opportunity to improve patient support.
What could it cost?
The consequences extend beyond the research budget.
Companies could invest in the wrong message, audience or service, then spend months implementing a strategy built on weak foundations.
Meanwhile, the real problem remains unresolved.
For research agencies, recommendations they cannot substantiate could damage client confidence and future business.
These hypothetical examples illustrate why interpretation deserves scrutiny.
Keep responsibility with researchers
Ask AI for alternative explanations, contradictory evidence and the sources supporting each recommendation.
Then have experienced researchers check the original material, examine sampling limitations and test the reasoning.
Being involved in fieldwork helps.
Someone must be able to explain how the evidence supports the decision, acknowledge uncertainty and take responsibility for the recommendation.
Before acting on an AI-generated recommendation, could your team defend the reasoning behind it?
If not, the apparent saving in research time could become a much larger commercial cost.