Artificial intelligence. It's unavoidable; in every newsletter, at every conference, and increasingly in our daily lives, both personal and professional. From the simplest (AI-enhanced search results) to the most complex uses (predicting protein folding), we are witnessing AI's triumphant march across various sectors, particularly in industries ripe with complex data and intricate processes like the life sciences. But a significant disparity exists between its successes in technical domains and its application in creative professional services. This gap, which we term the "technical-creative continuum," is especially evident in marketing research and warrants careful examination.
Let's start with the technical side. To be sure, AI has achieved remarkable breakthroughs in technical scientific fields, including biomedical science, pharmaceuticals, biotechnology, and medicine. This goes beyond the automation of tedious tasks; AI analyzes datasets so vast they would take more than a lifetime for humans or traditional computer systems and drives genuine breakthroughs. It enhances drug discovery by identifying new drug targets and optimizing clinical trials, accelerating the development of therapeutics. In diagnostics, AI-powered image analysis can detect subtle anomalies in medical scans, improving the accuracy and speed of diagnoses. AI is also helping advance personalized medicine, tailoring treatments to individual patients based on their genetic profiles. AI is even assisting surgeons with intricate procedures, enhancing precision and improving patient outcomes. AI's ability to analyze extensive biological data has accelerated research in genomics and proteomics, revealing and improving our understanding of disease mechanisms. These are not just incremental improvements; they're paradigm shifts, reshaping how we approach some of the most fundamental challenges in healthcare.
In contrast, the application of AI in professional services supporting these same healthcare and pharmaceutical clients—marketing research, marketing, consulting, and medical education—presents a different picture. While AI is making inroads, particularly through "wrappers" around general-purpose AI models from OpenAI, Anthropic, Google, and Meta, which facilitate information summarization, analysis, and repurposing, and "enhancer" products that leverage AI to improve specific processes or tasks (e.g., Bayesian analysis, image/object classification, transcription, translation), a truly transformative revolution has yet to fully materialize. Unlike the hard sciences, we lack radical, paradigm-shifting models and tools that fundamentally upend the status quo and reinvent the wheel of progress— we call this “the unfinished revolution.”
For instance, where is the AI capable of designing truly innovative studies that yield groundbreaking insights previously unattainable, even with unlimited human resources? Where are the AI-driven innovations that redefine our approach to marketing research? This is the core of the technical-creative continuum: the stark contrast between AI's dominance in technical scientific domains and its comparatively limited impact on creative professional service applications (consider splash vs impact, here). In other words, the insights industry has yet to see AI successfully leveraged in ways that represent such a dramatic acceleration of research capabilities that it feels like magic. Several factors likely contribute to this disparity. Data availability certainly plays a role; the proprietary nature of most marketing research limits the availability of tailored data for fine-tuning and research deliverables are created for human consumption, which may reduce the effectiveness of retrieval augmented generation (computer vision capabilities may need to be factored into how future reports are designed). To some extent, hard sciences may simply lend themselves more readily to the logic and pattern recognition at which AI excels, while creative domains may be inherently more subjective, nebulous, and difficult to codify and train into a neural network. Put more technically, current AI models’ limited ability to generalize may be less detrimental to technical tasks than to creative ones. Ultimately, these are complex questions, and while there are undoubtedly many contributing factors, we do not have exhaustive answers. We observe the disparity; explaining it fully is a task for another day.
It is only somewhat hyperbolic to say: the quandary of the technical-creative continuum is perhaps most evident in primary healthcare marketing research. While AI is being used in innovative ways, and market-research-specific tools are starting to shape our industry conversations, their impact has primarily focused on efficiency gains—assisting with or partially automating data analysis, reporting, social listening, and desk research, freeing researchers to focus time on more strategic tasks. While valuable (and the time savings will likely prove revolutionary for productivity), these advancements have not yet yielded the kind of transformative breakthroughs seen in the hard sciences. We want more. Where is the AI that can fundamentally improve patient journey research, something more than just making existing processes faster and cheaper?
Where is the AI that can generate truly insightful qualitative analysis and uncover nuanced themes and hidden connections? Imagine an AI that not only analyzes survey data but also understands the unspoken needs and motivations of physicians. Picture an AI that synthesizes disparate information sources—clinical trials, market trends, patient feedback—to generate truly novel insights. These are the areas where human creativity currently reigns supreme.
While AI is unlikely to replace seasoned marketing researchers anytime soon, we envision a future where each researcher becomes the maestro of a symphony of AI tools, enabling them to achieve and deliver value far beyond current capabilities—achievements comparable to those seen in the hard sciences. This is the potential of AI in marketing research, but realizing it requires a leap beyond current capabilities. It demands a shift from simply automating existing processes to fundamentally rethinking how we approach research. This is the future that we at MarketVision are preparing for; as we evaluate AI platforms and capabilities, it is with an eye toward ensuring that our researchers have a diverse array of tools at their disposal (AI and otherwise), such that they can always access the best available technology for a given use case.
The technical-creative continuum presents both a challenge and an opportunity. While AI has made incredible strides in technical scientific fields, its potential in creative professional services, particularly healthcare and pharmaceutical marketing research, remains largely untapped, especially when it comes to breakthroughs comparable to those in drug discovery. Why is AI so adept at solving complex scientific problems yet less effective at replicating genuine human creativity, rather than simply mimicking its patterns?
While definitive answers remain elusive, the implications are clear. For marketing research and other creative fields, the challenge lies in harnessing AI's power not just for efficiency but for genuine innovation. Bridging this gap will require more than just technical advancements. Developing research-specific foundation or frontier models is unrealistic, from the cost perspective alone. However, perhaps the path forward lies in research-specific distillation models, similar to DeepSeek R1. It is also possible that the scope of our imagination is limiting us, that because AI is designed to approximate human thought patterns and output, we tend to use it for tasks we already perform, albeit faster, with more complexity, or at greater scale. If we acknowledge this potential failure of imagination and dare to dream bigger, we can overcome that limitation.
We believe that the future of AI in marketing research lies not simply in automating and accelerating existing processes, but in unlocking new levels of creative potential, empowering researchers to ask bolder questions, explore new avenues of inquiry, and ultimately, deliver deeper, more impactful insights—in essence, accelerating human ingenuity with the power of AI. The journey has just begun, and it will be a long road, both in terms of technological development and successful, strategic corporate implementation. The possibilities are as exciting as they are challenging. We have an opportunity to collectively redefine the nature of creative work, and that, perhaps, is the most exciting prospect of all. What a time to be alive, indeed.
