USE CASE: How to Build a Decision-Support Dashboard That Actually Produces Decisions in a Web Design Agency

A real-world GenAI marketing use case: how a web design agency drowning in dashboards but starved of decisions used GenAI to rebuild its reporting into a three-tier decision-support dashboard, so data finally led somewhere.

A decision-support dashboard has one job the ordinary kind forgets: to end in a decision. This is what building one looks like in practice, how a web design agency with lots of data and no decisions rebuilt its dashboard into three tiers, so a wall of numbers became a path from “what’s happening” to “what do we do”.

The Context: Data Everywhere, Decisions Nowhere

A web design agency is a data-rich business by nature, with analytics on every client site it built and dashboards for everything. Reporting was not the problem; there was more of it than anyone could read. The problem showed up in the meetings, where all that data was on screen and yet the same question kept going unanswered: "So what do we actually do?".

The Challenge: Lots of Data, No Decisions Come Out of It

The agency didn’t have a data problem, it had rather the opposite. Dashboards on every client, reports full of metrics, analytics tracking everything that moved. And none of it produced a decision. That is the paradox of most dashboards: they are built to display data, not to support decisions, so they show everything and recommend nothing, leaving a human to stare at a wall of numbers and guess what actually matters. More data didn’t help; it made the guessing harder, because every metric sat at the same flat level with equal weight, and when everything looks equally important, nothing points anywhere. What was missing wasn’t data, but structure: a path from “what’s happening” to “what do we do”. Lots of data, no decisions, is exactly what you get without it.

A dashboard should help you decide, not just display: A dashboard’s job isn’t to show you data, it’s to help you decide. Most do the opposite: they show everything and recommend nothing, and a wall of numbers is a place to hide, not to act. More data doesn’t produce more decisions; structure does as it offers a path from what’s happening to what to do.

The GenAI Workflow: Rebuild the Dashboard in Three Tiers

The fix wasn’t more data or a prettier chart; it was to rebuild the dashboard around the decisions it should drive, in three tiers each level answering a question that leads to the next:

Tier 1 – Are we on track?

The few headline outcomes that tell you at a glance whether to worry at all, and nothing else at this level.

Decision: do we need to act?

Tier 2 – What’s driving it?

When a headline moves the drivers behind it, you can see why without hunting through everything.

Decision: where’s the problem?

Tier 3 – What do we do?

The specific, granular view that points to the actual action: the page, the client, the step to change.

Decision: what exactly do we change?

The team used GenAI to take its flat data dump and propose which metrics were headlines, which were the drivers beneath them, and which were the tactical detail at the bottom, turning a wall of numbers into a path. Then the team corrected what GenAI mis-sorted, tied each tier to the decisions the agency actually makes, and cut everything that supported no decision at all. The dashboard stopped displaying data and started leading somewhere.

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The GenAI prompt:

You are an experienced marketing strategist, helping a web design agency turn a pile of metrics into a decision-support dashboard with three tiers. Here are the metrics and reports we currently have, and the decisions we actually need to make: [metrics + the real decisions].

Sort our metrics into three tiers:
• Tier 1 – Are we on track?: the few headline outcomes that signal whether to act at all.
• Tier 2 – What’s driving it?: the drivers behind each headline.
• Tier 3 – What do we do?: the granular detail that points to a specific action.

For each tier, tie the metrics to the actual decisions I gave you, and name any metric that supports NO decision so we can cut it. Keep each tier to the few that matter; a shorter dashboard, not a longer one. Do NOT invent our data or numbers, and treat any reading of what the data “means” as a hypothesis for us to check, not a conclusion. Mark assumptions about our decisions as CONFIRM WITH ME.

The caveat that decides whether this works: A decision-support dashboard only supports decisions if it’s built around the right ones, and GenAI doesn’t know which decisions the agency actually makes. It will assume generic ones and sort your metrics against them, so the tiers are a draft to rebuild around your real decisions, not a finished structure.

Three more boundaries:

A dashboard supports a decision; it doesn’t make one, and GenAI reading a dashboard will offer confident interpretations that can be wrong, a correlation mistaken for a cause, a number missing its context; so treat its diagnostic read as a hypothesis to verify, never a verdict to act on.
The discipline is fewer metrics, not more: GenAI and every dashboard tool will happily add panels, and a longer dashboard is just the original wall of numbers with tiers drawn on it; every metric has to earn its place by supporting a decision.
The whole structure is only as trustworthy as the data beneath it: a tidy three-tier dashboard built on unreliable data just drives confident wrong decisions faster. GenAI structures the dashboard; the decisions it serves, the reading of what the data means, and the trust in the numbers stay human.

The Result: a Dashboard that Leads to Action

The dashboard started producing decisions. With the metrics sorted into three tiers, the agency finally had a path instead of a wall: a glance at the headline tier said whether anything needed attention; when something did, the diagnostic tier showed what was driving it; and the tactical tier pointed to the specific thing to change. The same data that had produced nothing, now produced action, because it was organised around the decisions it was supposed to serve rather than dumped at one flat level. And because everything that supported no decision had been cut, the dashboard was smaller and clearer; the opposite of the everything-at-once view it replaced. No invented figures here: the change is that the agency stopped mistaking having data for making decisions, and gave its numbers somewhere to lead.

Recommended KPIs to Follow

A decision-support dashboard is judged on decisions, not dashboards: does data turn into action, is the thing disciplined, do the calls pay off? Here’s where the evidence sits and the direction this should push things. The point is the direction of travel, not a promised number.

Decisions Taken from the Dashboard (and time to decide)

Whether looking at the dashboard actually ends in a decision, and how quickly you get from “open it” to “here’s what we’ll change”. It’s the whole point: data that doesn’t change a decision is decoration, however well charted.

Benchmark: The gap is well documented: only ~32% of enterprise data is ever put to work (68% goes unleveraged, Seagate/IDC), and even where data exists, nearly half of people default to “gut feeling” over it (Seagate / IDC; Market Logic). “Lots of data, no decisions” is the norm, not the exception.

Dashboard Leanness (metrics per decision)

Whether every metric on the dashboard is tied to a decision, or the panel count is creeping back up. A decision-support dashboard should get smaller as it gets better; a growing one is the old wall of numbers returning.

Benchmark: No public figure, an internal metric; count the metrics that map to an actual decision versus those that don’t, and drive the ratio toward one.

Decision Quality / Outcomes

The payoff: do the decisions the dashboard drives lead to better results – client outcomes, agency performance – than the gut-feel calls they replaced? Slow and multi-causal, so read it as a trend.

Benchmark: Direction, not a promise: organisations that consistently track and act on metrics are reported to be ~2.3× more likely to exceed their goals than those that don’t (aidigital). The value is in the acting, not the collecting.

Decisions-from-the-dashboard is the point, leanness is the discipline that protects it, outcomes are the payoff. The external figures are survey-based and correlational; treat them as direction, not proof. Your own “does this produce decisions?” test is what matters.

Why this Transfers

Almost every business now has more data than decisions; the dashboards multiplied and the deciding didn’t. The transferable move is to stop asking “what can we measure?” and start asking “what decisions do we make, and what would each one need to see?”, then build the dashboard in tiers around those decisions – headline, diagnostic, action – and cut everything that leads nowhere. Data that doesn’t change a decision was never insight; it was decoration.