Part 2 of this companion series to the Tools of the Trade podcast picks up where ingestion leaves off by examining dashboards and data insights, the hype cycle of AI, and why the pharmacist remains central to the data life cycle and future of AI in pharmacy data.
Dashboards and Data Insights
The last decade saw dashboards proliferate as a channel for data insight. Dashboards do not produce better decisions by themselves. Instead, dashboards offer a way to test whether the right question is being asked. A chessboard shows every possible move with full transparency but the best next move is unclear. AI works similarly. It can surface many insights without making the value of those insights clear. Its real value through AI is the iteration speed it enables. Work that once took a multidisciplinary team six months to cycle through several dashboards can now be done by a small team, or a single contributor, in hours. This rapid iteration of prototype visualizations does not replace the pharmacist’s value to this role. As highlighted in the podcast episode (“Tools of the Trade: Data Visualization”), engaging end users as stakeholders from the beginning still remains a key component of the data science life cycle. There is still a real risk of AI only accelerating a process that once saw dashboards built that no one used to become a rapid cycle of quickly building dashboards that are being ignored at rates previously unimagined. AI can remove much of the build effort but it takes the insight and clinical judgment of a pharmacist to direct the important, small remainder of effort that matters.
Risks, Rewards, and Potential
AI is moving through the common technology hype cycle: trigger, peak of inflated expectations, trough of disillusionment, and plane of productivity. Excitement pushes the technology towards the peak while its limitations and risks act as a thoughtful counterbalance. Successes that make headlines, such as passing a licensing exam or predicting readmission rates, often carry limited external validity. When little is known about the data behind those stories, the black box of model development compounds the obscurity of how results were reached. Compounded obscurity feeds the doubt of new users and heightens the risk of “accepting AI as gospel”. This risk finds supporters following the hype and jumping on the train even when it leads off a cliff. The interesting counter to such a risk of uninformed support is AI's potential to shorten learning curves. Much like how Google, and other advanced search, lowered the barrier between users and the internet, AI can reduce the time needed to learn a concept, or at least open a highly adaptive path to learning it.
Small individual experiments can allow pharmacists to learn how to harness AI and sidestep its common pitfalls. A minor project, such as using AI to manage a personal calendar, builds the confidence that transfers to using AI to address a drug information question. The way a person already relates to a familiar technology, email, an open archive like Wikipedia, or a phone alarm, reveals the use habits, positive and negative, that carry over as AI evolves.
Concluding Thoughts
Working with data is a journey across quantitative skill and qualitative judgment. AI's role in this journey is similarly multifaceted and evolving. AI may change the speed of certain steps in the data life cycle but it does not replace the pharmacist within that cycle. Pharmacists can lead the implementation of these technologies across settings by engaging actively with their development, deployment, and iteration, just as they do in clinical and drug information services. The time invested now, whether an hour or a single project, will pay dividends in the years ahead.
Learn more about these topics in practice with the following Information Bytes podcast episodes:
Tools of the Trade: Data Visualization - https://www.ashp.org/professional-development/ashp-podcasts/informatics-bytes/2026/tools-of-the-trade-data-visualization
Tools of the Trade: Artificial Intelligence Across the Data Journey - https://www.ashp.org/professional-development/ashp-podcasts/informatics-bytes/2026/tools-of-the-trade-artificial-intelligence-across-the-data-journey
This article was co-authored by Ravi Patel, PharmD and Clay M. Cooper, PharmD