Many healthcare companies return to Opticom year after year for market insights—whether through in-depth HCP interviews, brand tracking, or testing the impact of new messaging. Consistency is key for meaningful comparisons, but it’s rare that the same questionnaire works year after year. Markets evolve—and so should your questions.

- How often do you use AI?

- Daily. New advances are dropping every week, and it is important to stay updated. Used correctly and with the right checks and balances, AI Is a useful tool to help us work more efficiently.

- What is AI actually good for?

- AI is brilliant at speed and repeatability. Generating a short explanation of a concept or process, or a first pass at clustering 3 000 open ends can be done in seconds. It can be a good sounding board for bouncing around ideas.

Here’s the thing: quality in = quality out. If the data it’s been fed is of high quality, the output will be too.

But the converse is also true: Garbage in = garbage out, which is where checks and balances come in. If the input data carry bias or gaps, the output only amplifies them - faster.

AI cannot be fully trusted because it can only ever be as good as the quality of its input data. I would never take what AI says in blind faith; I’ve been disappointed too often.

It’s been an interesting start to 2025 with disruption to global free trade agreements, continuing geo-political tensions and fluctuating markets. As we commented post pandemic, the new normal is - there isn’t one, so expect the unexpected.

Imagine your team is preparing to launch a new biosimilar. Internally, the belief is that price will be the primary differentiator. However, your commercial lead hears from hospital stakeholders that clinicians are hesitant due to perceived efficacy concerns. We ran a similar study and uncovered that trust in manufacturer reputation—not price—was the key barrier to adoption. Our insights enabled the client to tailor its messaging and physician engagement strategy effectively, avoiding a costly misstep based on assumption.

Many of our clients return to us year after year to take a snapshot of their market, whether that be in-depth customer experience interviews, or tracking the perception of a product, brand or message over time. Meaningful comparison involves repetition to ensure we are comparing like for like, but it’s rare that a repeat survey stays exactly the same when our clients return, simply because as the market evolves, so must the questions.

Let's say you would like to stop differentiating between certification systems when it comes to buying packaging, but the message from your customer's sustainability department is that they favour one system above another because they believe that's what consumers want. We conducted just such a study and found that in reality, consumers neither knew nor cared about the difference between schemes in the case we investigated and would not necessarily favour one label above another. We helped the client make better decisions based on real insight from their target group rather than presumption.

Before we sign off for the Summer break, let’s look ahead to the autumn. Trading conditions will likely remain complex and challenging. It can be tempting to batten down the hatches in moments of uncertainty, but shifting conditions give rise to new questions. Maybe you’re not even sure what questions you need answering, you just know there’s a gap in your view of the bigger picture.

Circular economy has become a sustainability buzzword in boardrooms and policy circles. It promises a world without waste – a win-win where growth and environmental responsibility harmonize. Yet this catch-all vision often glosses over hard trade-offs. Efficiency and recycling gains can create a false sense of security, even as total consumption keeps rising. In fact, research warns that the environmental benefits of circular strategies are often taken for granted, and rebound effects can counteract those gains by spurring more overall. In other words, chasing “circularity” without looking at the whole system risks turning into circular logic.

Does it matter who designs your questions?

- Yes. Extracting meaning from data starts with asking the right questions. Who you choose to write and ask the questions makes all the difference.