For this week's Saturday Strategy, let's look at the "death of the dashboard" in light of AI and decision velocity. It is a provocative debate, but it misses the nuance of how enterprise needs are changing. Dashboards aren't necessarily dying, but they are changing focus now that AI is in play. AI can create data assets and code to help organisations make decisions – and making a decision can be the hardest part of all. In this post, let's take a look at a fresh perspective: decision velocity, which is the 'last mile' problem of the dashboard.
In the past, the constraint was data availability. We spent decades just trying to get data into a readable format, reshaping CSVs, and munging data together. AI has helped to remove some of that friction. Now, the constraint is the speed of decision and action. Most Chief Data Officers (CDOs) will tell you that they struggle with turning that insight into a tangible business result. Often, organisations don't know what problems they are solving, or what to prioritise first. Building a dashboard is a great first step in helping people on the path of digital transformation.
Now that AI has helped organisations to deal with the friction of data engineering, we are moving away from passive observation toward a model of Decision Velocity.
Beyond "What Happened?"
Traditional Power BI implementations often fall into the trap of hindsight. Dashboards are designed to be "reports behind glass", static environments where stakeholders look at what happened last week or last month. While hindsight provides necessary context, it is insufficient for a market that operates 24/7/365.
When a dashboard is merely a record of the past, it encourages a passive culture. It invites "data tourism," where users click through filters without a clear intent to change a business outcome. This is where the "data daisy chain" begins.
The Trap of the Data Daisy Chain
You have likely seen this in your own organization: a sophisticated Power BI dashboard is built with complex data modeling and beautiful visualizations, yet the first thing a manager does is click the "Export to Excel" button.
This "data daisy chain" is a major red flag for your data strategy. When people export data to Excel, they are essentially saying three things:
- They do not trust the dashboard to give them the final answer.
- They need to perform their own manipulations to reach a decision.
- The dashboard is a destination, not a tool for the flow of work.
This process introduces massive latency. By the time the data is exported, cleaned, pivot-tabled, and emailed, the window for an optimal decision has often closed. This manual intervention slows down your strategy and increases the risk of human error.
What is Decision Velocity?
Decision Velocity is the speed and accuracy with which an organization can move from a data signal to a concrete action. It is about completing the "Decision Loop": Signal → Context → Decision → Action → Learning.
In an AI-driven economy, signals arrive faster than ever. If your internal processes require a human to manually check a report once a day, you have a bottleneck. High decision velocity means embedding the ability to act directly within your data tools. It is about moving from "What happened?" to "What should we do next?"
Research indicates that organizations focusing on decision loops rather than static reporting see significant gains. For example, some enterprises have reported consultant throughput increasing by 3x when shifting from manual reporting to embedded decision systems (Source: Research on Decision Intelligence, 2024).
Improving Decision Velocity with Power BI
Power BI is often treated as a visualisation tool, but its true value in a modern Business Intelligence framework is its ability to facilitate action. To improve decision velocity, we must change how we design these systems so here's a few pointers to consider.
1. Transition to Exception-Based Reporting
Most dashboards show too much; one customer asked me to create a dashboard with 200-odd charts because "that was how they'd done it before". Busy dashboards might present "the whole truth," but that obscures "the urgent truth."
To increase speed, move toward exception-based reporting which answers the question "what do I need to know?" Exception-based reporting is a way of designing reports and dashboards so they surface what needs attention, rather than everything that could be measured. As the name suggests, the report focuses on exceptions: values outside agreed thresholds, unusual changes, missing data, rule breaches, or items that are overdue. This cuts noise and saves time because people spend less effort scanning “normal” results and more time acting on problems. It works best when the business defines clear rules up front (for example, acceptable variance, timeliness, and data quality checks), and when each exception has an owner and a next step, so the report drives decisions rather than just describing performance.
Instead of showing a table of all 500 open projects, design the interface to only show the five projects that are over budget and behind schedule. By focusing the user’s attention on where their intervention is actually required, you reduce the cognitive load and accelerate the time to action.
2. Leverage Power Automate for "Closed-Loop" Actions
One of the most effective ways to increase velocity is to allow users to act without leaving the report. Through Power BI’s integration with Power Automate, you can add "Action Buttons" directly onto a visual.
If a supply chain manager sees that stock levels are low, they shouldn't have to open an ERP system to fix it. A button within the Power BI report can trigger a Power Automate flow to reorder stock or alert a vendor immediately. This turns the report from a passive display into a control centre.
3. Design for the "Flow of Work"
Decisions rarely happen while someone is staring at a dedicated reporting portal. Decisions happen in meetings, on mobile devices, and in collaboration tools like Zoom, Notion, Slack or Microsoft Teams.
A high-velocity strategy brings data to the user. This means:
- Embedding Power BI tiles directly into the Teams channels where the relevant people are already talking.
- Setting up automated alerts (Data Activator) that push notifications to mobile devices when a specific threshold is met.
- Moving away from historical batch processing toward real-time signals.
4. Prioritise Real-Time Signals Over Batch Processing
If your data only refreshes once every 24 hours, your decision velocity is capped at one decision per day. For many industries, this is too slow. By utilising DirectQuery or Real-Time streaming datasets, you enable your team to respond to market shifts as they happen, rather than conducting an autopsy the next morning.
The Foundation: Data Fluency and Confidence
You cannot have high decision velocity if your team does not have the confidence to act. This confidence is built on two pillars: solid data foundations and data fluency.
I often work with enterprises where the technology is ready, but the people are not. They hesitate because they aren't sure if they can trust the signal. True data fluency is the organisational ability to interpret a signal and feel empowered to execute a decision immediately.
This shift requires a change in culture. It means moving away from a world where "the person with the highest salary makes the decision" to a world where "the person closest to the data makes the decision."
Decisions don't end with data analysis; they begin where the data meets the human intent to act.
Moving Toward a Predictive Future
As we integrate Artificial Intelligence more deeply into our operations, the role of the human changes. We are the decision validators, more than 'human in the loop'.
The goal of your data strategy should be to move the organisation from passive observation to active, automated decision-making. We must stop asking "What happened?" and start building systems that tell us "What should we do next?"
I help organisations navigate this transition. We focus on building the architectural foundations and the team fluency required to move beyond the static dashboard. We help you build the confidence to act on signals immediately, ensuring your organization leads rather than follows in an AI-driven market.
Everyone seems to be using AI, so it's not a differentiator in itself. However, high decision velocity is a competitive necessity. If your current reporting feels like a weight holding you back rather than a fuel for your strategy, it is time to switch your emphasis from tools.
Is your data strategy built for speed or for static reporting?
Please get in touch to discuss how we can accelerate your decision velocity and move your enterprise from insight to action.
