Dashboards vs. AI: 3 Tips on Applying Tools to Analysis in Healthcare

How to leverage tools that solve problems

A month ago I had a conversation about AI with the CEO of a GPS IoT company. Specifically, we focused on the role of data analysts as AI continues to make data analysis and insights easier.

What’s the default one thinks of when they hear the title “data analyst”? A dashboard wizard.

From the time I started in analytics, the dashboard has been the epitome of my data analyst skills. Originally when I stepped out on my own, thinking about the business instead of building a complex dashboard was a challenge.

Additionally, besides times when dashboards weren’t trusted, I’ve only seen the usability of them knocked once. This was in 2019 with a stubborn sales manager I worked with. They refused to adapt to Looker’s formatting and learning curve, sticking with an export of data into their Excel workbook.

This GPS IoT CEO challenged the idea of dashboards altogether. Leveraging MCPs, he’s been using AI to deliver insights on his marketing data. He prefers having conversations about specific questions he needs answered. A dashboard just provides generic, high level information in his eyes. It requires too much digging.

Couple this with a quote from one of

Joe Reis’s recent Substack articles: “We’re moving from dashboards to decision engines. From analysts to autonomous agents. From “let me know” to “just do it or fix it.””

I realized the paramount shift: As a data consultant, it’s not about the tools you provide. It’s about the problems you solve.

I talked about this in a successful LinkedIn post I made last week. (below)

The catch is that the rapid progression of AI tooling has made the lines even blurrier for what tool fits a given problem’s solution.

This is why I want to give you 3 tips on how to select a dashboard or AI for your data solutions. We’ve tailored it for the healthcare industry.

I realized quickly that, to bring real value, you need to start with the question the business is trying to answer. Then work backwards from that question to ensure you’re delivering the tools that will help answer that question and allow the business to take action.

1. Start with the decision, not the data

Before touching any tool, ask: "What specific action will be taken based on this analysis?" In healthcare, this might be adjusting staffing levels, changing treatment protocols, or reallocating resources. If there's no clear decision to be made, you don't need either a dashboard or AI - you need better problem definition.

For example, a hospital administrator asking for a "patient satisfaction dashboard" might actually need to decide whether to hire more nurses for night shifts. An AI conversation like "What factors are driving our low satisfaction scores between 10 PM and 6 AM?" delivers actionable insights faster than scrolling through charts.

2. Match the tool to the user, not the data

Dashboards work best for routine monitoring by teams who need to spot patterns over time. AI excels for ad-hoc investigation by decision-makers who need immediate answers to specific questions.

That stubborn sales manager from 2019? They actually had it right for their workflow. They needed to manipulate data, not just view it. Excel is great for that, and allows them the flexibility to self-serve. Just ensure you place proper governance on what the standard of truth is.

Meanwhile, the GPS CEO needs quick answers to evolving business questions - perfect for AI conversation.

In healthcare, a nurse manager might benefit from a real-time dashboard showing bed availability, while a chief medical officer might prefer asking AI: "Which treatment variations are showing better outcomes this quarter?"

3. Measure by business impact, not technical sophistication

The best solution is the one that gets used and drives decisions. Sometimes that's a simple Excel export. Sometimes it's a sophisticated AI agent for BI. The GPS IoT CEO doesn't care about the elegance of his solution - he cares that he gets answers that help him allocate marketing spend.

Track success by decisions made and problems solved, not by how impressive your technical stack looks. A basic AI chat that prevents one medication error is infinitely more valuable than an unused million-dollar analytics platform.

The bottom line

As AI continues to democratize data analysis, our value as healthcare consultants isn't in the tools we build - it's in the problems we solve and the clarity we bring to business decisions.

Keep extra awareness for the question and answer you’re solving for, the user personas who will interact with the solution, and the effectiveness of it. Stop asking 'What should we build?' Start asking 'What decision are we trying to make?

Christian Steinert is the founder of Steinert Analytics, helping healthcare & roofing organizations turn data into actionable insights. Subscribe to Rooftop Insights for weekly perspectives on analytics and business intelligence in these industries.

Feel free to book a call with us here or reach out to Christian on LinkedIn. Thank you!