Most BI implementations fail. Not because the technology is wrong, but because nobody asked the right questions before building.
We've watched this play out dozens of times: a company drops £15,000-£30,000 on a Power BI project, the consultants deliver a technically perfect dashboard with drill-throughs and custom visuals, and six months later it's been viewed exactly twice. Once during the demo, once when someone clicked the wrong bookmark.
The industry average for dashboard adoption hovers around 12%. That means 88% of dashboards built are essentially shelf-ware—expensive, sophisticated tools gathering digital dust.
If your dashboards aren't being used, the problem isn't your users—it's the approach. We've never seen a dashboard fail because users were "too lazy" to check it.
The Real Cost of Failed BI Projects
Here's what nobody talks about: the cost of a failed Power BI implementation isn't just the money spent building it. It's the opportunity cost of continuing to make decisions based on gut feel, the hours still wasted manually pulling reports, and the credibility hit your IT team takes when they try to pitch the next technology investment.
One of our clients—a logistics company in Birmingham—had already spent £28,000 on a Power BI implementation before they came to us. Beautiful dashboard. Colour-coded. Real-time data refresh. Nobody looked at it. The ops manager was still exporting Excel files every Monday morning because "it's just easier."
We rebuilt it in three weeks. Same data, completely different approach. Usage went from 8% to 94% within a month.
Why Traditional BI Fails
The "Build It and They Will Come" Fallacy
Most BI projects start with technical teams building what they think the business needs. The dashboard gets designed in isolation, data models get built, DAX measures get written, and then—only then—users see it for the first time.
The result? Dashboards packed with metrics that:
- Are technically accurate but operationally irrelevant
- Answer questions nobody is actually asking
- Require context that isn't available at the point of decision
- Look impressive in a demo but fail the "Monday morning" test
We've seen dashboards showing 47 different KPIs on a single page. Conversion rates, customer lifetime value, inventory turnover, gross margin, net promoter score—everything crammed in because "it's all important." Nobody can process 47 data points before their first coffee. They just close the tab and go back to what they know.
The Mobile Blindspot
Here's a test: open your Power BI dashboard on your phone right now. Can you actually read it? Can you make a decision based on what you're seeing?
The Mobile Reality Check
67% of managers check business reports on mobile devices outside office hours. Yet only 12% of Power BI dashboards are optimised for mobile viewing. You're building for desktops in a mobile-first world.
We had a client—property management company—whose site managers needed to check occupancy rates and maintenance requests on the go. Their dashboard was designed for a 27-inch monitor. On a phone, it was completely unusable. They went back to phone calls and WhatsApp groups.
We rebuilt it mobile-first. The desktop version is actually just an expanded view of the mobile layout. Usage among site managers jumped from 5% to 89%.
Information Overload Kills Action
Just because you can show 47 KPIs on a single page doesn't mean you should. Decision-makers need clarity, not data dumps.
The human brain can hold roughly 3-5 pieces of information in working memory at once. When you present 20+ metrics simultaneously, you're not empowering decisions—you're paralysing them. Users fall back to whatever metric they're most comfortable with (usually revenue) and ignore everything else.
The AferStudio Approach
We've developed a methodology that consistently achieves 90%+ adoption rates. It's not rocket science. It's just asking the right questions in the right order.
1. Start With Decisions, Not Data
Before touching Power BI, before mapping data sources, before writing a single DAX formula, we spend time understanding:
- What decisions are being made weekly?
- Who makes them and when?
- What information would actually change those decisions?
- What's the current process for getting that information?
This seems obvious, but it's skipped in roughly 80% of BI projects. Teams jump straight to "what data do we have?" instead of "what decisions do we need to support?"
Map the decision points
Sit with managers and document every recurring decision they make. Don't ask what data they want—ask what they're trying to decide.
Identify information gaps
For each decision, ask: "What would you need to know to feel confident making this call?" Most of the time, they don't need more data—they need the right data presented clearly.
Design backwards from the decision
Only after mapping decisions do we touch Power BI. The dashboard design flows from the decision context, not from available data fields.
2. Design for Context
A dashboard viewed at a Monday morning management meeting has completely different requirements than one checked on a mobile device at 6pm. We design for the actual usage context:
Morning Management Review → High-level trends, exceptions that need discussion, week-over-week comparisons
Mid-day Check-in → Action items, urgent flags, "do I need to intervene?" indicators
Weekly Planning Session → Comparative analysis, forecasts, resource allocation insights
Same underlying data. Three different presentations. We've found that a single "do everything" dashboard satisfies nobody. Context-specific views get used.
3. The Three-Metric Rule
Every dashboard view should focus on three core metrics. Not five. Not ten. Three.
You can have drill-downs, you can have supporting detail, but the top-level view must answer one question with three numbers. If you can't articulate what those three numbers are, you're not ready to build.
Ask users: "If you could only see three numbers before making this decision, what would they be?" Their answer is your dashboard spec.
4. Make It Actionable
Every metric should have a "so what?" attached. If users can't take action based on the information, question whether it belongs on the dashboard.
We had a retail client tracking "footfall by hour." Interesting data. Completely useless. Nobody could act on it. We changed it to "staffing vs. demand mismatch" with a red/amber/green indicator showing when they were over or understaffed based on traffic. Same underlying data. Suddenly actionable.
5. Iterate Based on Usage, Not Opinions
We don't launch and walk away. Power BI tracks usage. We look at which reports get viewed, which filters get used, which pages get ignored. Then we refine based on actual behaviour—not what people say they need in meetings.
Usage Tracking Reveals Truth
In one project, executives insisted they needed detailed supplier performance metrics. Usage logs showed they never clicked past the summary page. We simplified, removed three unnecessary drill-down levels, and adoption actually increased.
Real-World Example: Manufacturing Company Turnaround
A precision engineering firm in Coventry came to us after their first Power BI project flopped. They'd spent £32,000 with a large consultancy. The dashboard had every metric imaginable: OEE, cycle times, defect rates, material costs, labour efficiency, machine utilisation. 47 different charts across 12 pages.
Shop floor managers never used it. They kept using their Excel trackers and whiteboard.
We started from scratch:
Week 1: Shadowed three shift managers for a full shift each. Watched what decisions they made and when. Noted what information they currently used.
Week 2: Built three mockups in PowerPoint—not Power BI—showing how we'd present the key information. Got feedback. Iterated.
Week 3: Built the actual dashboard. One page. Three metrics: "Lines running below target," "Quality issues today," "Material shortages blocking production." That's it. Everything else was a drill-down.
Result: 94% adoption within three weeks. Shift managers started checking it multiple times per shift. When we asked why it worked, one manager said: "Because it tells me what's broken, not how the whole factory is doing."
Common Objections We Hear
"But executives asked for all these metrics"
What executives ask for in scoping meetings and what they actually use are often wildly different things. We've found that starting with a minimal, focused dashboard and expanding based on actual usage requests works far better than overwhelming users upfront.
Executives are used to software vendors saying yes to everything. When you push back and say "let's start with three metrics and see what you actually need," they're often relieved.
"Our data isn't clean enough for BI"
Perfect data is a myth. Every company we work with has data quality issues. The question isn't whether your data is clean—it's whether it's clean enough to support the specific decisions you're trying to make.
Revenue figures slightly off due to timing differences? Probably fine for a trends dashboard. Completely unacceptable for a financial close report. Clean the data that matters for the decision at hand, not everything.
"We've tried BI before and it didn't work"
That's exactly why you need a different approach. The tools aren't the problem—Power BI is perfectly capable. The methodology is what fails.
If your previous BI project flopped, ask yourself:
- Did we start with user decisions or available data?
- Did we show the dashboard to real users before building it?
- Did we track usage after launch and iterate?
Most failed projects answer "no" to all three.
Getting Started: The Power BI Adoption Diagnostic
If you're sitting on underutilised Power BI investments, here's a simple diagnostic you can run today:
Check your usage stats
Power BI tracks who views what and when. Go look. You might be surprised how little engagement you're actually getting.
Interview three non-users
Don't ask why they don't use it—that gets defensive responses. Ask what information would help them do their job better. Note whether any of it is actually in your current dashboard.
Map decisions to data
For each key business decision made weekly, document what information currently supports it. If it's not coming from your dashboard, that's your adoption gap.
Run the three-metric test
Can you articulate the three most important numbers for each user role? If not, your dashboard is probably trying to do too much.
The Technical Details Nobody Tells You
Here's something we've learned from dozens of implementations: technical excellence doesn't drive adoption. Simplicity does.
The dashboard with the most impressive DAX measures, the fanciest custom visuals, and the most complex data model is rarely the one that gets used. The dashboard that answers Monday morning questions in three seconds—that's the one managers bookmark.
We've stopped trying to impress clients with technical sophistication. We impress them by building something their teams actually open every day.
What Good Looks Like
Dashboards that succeed share these traits:
- Load in under 3 seconds - Any slower and people give up. Optimise your data model, use aggregations, remove unnecessary visuals.
- Answer the main question above the fold - No scrolling for key insights. If users need to scroll to see whether they have a problem, you've failed.
- Have obvious next steps - Drill-down paths that make sense. Filters that don't require a manual. Actions users can take based on what they see.
- Update automatically - Manual refresh = guaranteed abandonment. If users need to click "refresh" to see current data, they won't bother.
- Look good on mobile - Not an afterthought. Design mobile-first, expand for desktop.
Conclusion: It's Not the Tech, It's the Approach
The gap between 12% adoption and 90%+ isn't about better technology or more experienced developers. It's about better methodology. It's about understanding that BI success is measured not by technical sophistication but by business impact.
Every unused dashboard represents wasted money, yes. But more importantly, it represents missed opportunities—decisions that could have been better, problems that could have been spotted earlier, efficiency that could have been gained.
If your dashboards aren't being used, don't blame your users. Don't blame the technology. Rethink your approach.
Start with decisions. Design for context. Keep it simple. Track usage. Iterate relentlessly.
That's how you get from 12% to 90%+.
Sitting on a Power BI investment nobody uses? We've rescued dozens of failed BI projects by rebuilding them around actual user needs. Check our BI services or view our pricing to discuss how we can help turn your dashboards into tools people actually rely on.