The PowerBI Promise vs. The PowerBI Reality

Remember the pitch? "Self-service BI! Empower everyone to make data-driven decisions! Democratize your data!"

Here's what actually happened:

• BI tickets still take a week to 10 days for anything that isn't already pre-built

• Non-technical users are locked out because drag-and-drop doesn't mean accessible

• Dashboards show what happened, never why or what to do next

• Enormous technical pre-requisites regarding infrastructure, data modeling and human (data engineering) effort to keep it alive

• Result: An expensive dashboarding platform that creates more meetings, not better, quicker decisions.

Companies spend roughly $650–$1,200 per FTE per year on BI software and headcount. Add infrastructure, and you're easily crossing seven figures annually. Yet most decisions still aren't backed by data, because by the time you get an answer, the window to act has closed.

The problem isn't that PowerBI is broken. It's that dashboards are the wrong paradigm for how modern businesses need to operate.

Why Dashboards Can't Answer Business Questions

Let's take a real example: "Which customers are at risk of churning?"

The PowerBI World:

1. Open a dashboard (if one exists for churn risk)

2. Stare at charts trying to interpret signals

3. Schedule a meeting with Customer Success and Customer Support

4. Loop in Sales to cross-reference pipeline data

5. Pull in BI to explain what you're actually looking at

6. Wait for Finance to validate ARR impact

7. 10 days later, you have an answer

8. By then, three at-risk customers have already churned

The Fundamental Problem:

Dashboards were built for reporting, not answering questions. They require you to:

• Know what you're looking for before you start

• Interpret correlations without understanding causality

• Manually piece together context from multiple tools

• Schedule meetings to turn charts into narratives

• Figure out next steps entirely on your own

PowerBI ends at visualization. The moment you need to understand why something happened or what to do about it, you're back to spreadsheets, Slack threads, and meeting invites.

And this doesn't just frustrate business stakeholders, it wastes your talented analytics teams on tedious, repetitive work that should be automated.

The Three Things PowerBI Can't Do (That Matter Most)

1. PowerBI Can't Explain "Why"

What PowerBI shows you: Revenue down 7% in EMEA.

What you actually need: Device mix shifted to mobile web (lower conversion); UK promotional pricing ended on the 15th; two enterprise deals worth £340K slipped to next quarter due to procurement delays.

Without causal explanations, every chart becomes a guessing game. You're paying for data visualization, but you still need humans to connect the dots, validate hypotheses, and build the narrative. That's why the same question triggers five meetings, no one trusts the dashboard alone.

Generative BI surfaces drivers with evidence: temporal alignment (what changed when), feature importance (which factors mattered most), and business context (campaigns, outages, pricing changes). It distinguishes "looks related" from "likely caused."

2. PowerBI Can't Handle Ad-Hoc Questions

Every new question requires:

• A new dashboard (if you're lucky)

• A BI ticket (if you're realistic)

• A 10-day wait (if you're being honest)

The painful loop:

• Raise a BI ticket

• Wait a week to 10 days (unless you're C-level)

• Hop on a few meetings to discuss what you actually wanted and what PowerBI can’t show or process (e.g. qualitative or conversational data)

• ...and the moment is gone, no action will ever be triggered

For non-technical users, PowerBI is basically useless for anything ad-hoc. You end up back in spreadsheets, copying data from three tools, trying to answer the question yourself.

Generative BI approach: Ask in plain English ("Why did churn spike in July?"), get a fresh analysis in 2–3 minutes, with context pulled from your warehouse, CRM, support tickets, and Slack. No pre-modeling required. No BI ticket.

3. PowerBI Can't Close the Loop

Here's the part that really hurts: PowerBI ends at a chart.

You get your answer (eventually), and then what?

• Manually flag at-risk accounts in CRM

• Draft a Slack message summarizing findings

• Schedule a follow-up meeting to assign owners

• Copy/paste charts into a slide deck for leadership

• Create a spreadsheet to track next steps

Every insight requires 4–6 manual steps to turn into an outcome. The execution latency gap, the time between seeing a signal and doing something about it, is where your BI investment goes to die.

Generative BI closes the loop: Once you have the answer, the system can update CRM, create owner tasks, post Slack summaries, generate partner-ready PDFs, and trigger follow-ups, all with approvals, RBAC, and audit trails. What → Why → Done, in minutes.

Generative BI: From Dashboards to Decisions in 2–3 Minutes

Let's replay the churn example:

You ask: "Which customers are at risk of churning?"

Generative BI (e.g., Milo) responds in 2–3 minutes:

• Identifies cohorts with usage dips, support ticket surges, failed payments, or contract renewals approaching

• Explains the drivers with evidence: "Pro plan customers in EU with <2 logins/week in the past 30 days and open 'billing' tickets"

• Then triggers actions:  

• Flags accounts in CRM with "Churn Risk - Low Engagement" tag

• Creates owner tasks for CSMs with context and recommended outreach

• Posts a Slack summary to #customer-success with cohort size and ARR at risk

• Generates a one-page PDF for leadership

(Plus a dashboard, if you really want one.)

The difference: You don't just get an answer. You get an explanation, evidence, and the next three steps already in motion, with governance, approvals, and receipts.

What "Dead" Actually Means

PowerBI isn't going to disappear tomorrow. It still has a role:

• Static reporting: Financial close, regulatory packs, board decks

• Curated KPIs: Metrics that don't change and don't need explanation

• Historical analysis: When you have time to explore, not urgency to act

But for operational decisions, the ~1,320 micro-decisions per knowledge worker per year (approvals, escalations, triage, follow-ups), dashboards are the wrong tool.

The $105 billion decision market doesn't belong to platforms that generate meetings. It belongs to platforms that generate outcomes.

BI isn't dying as a category. It's stopping being a standalone function and becoming embedded in how teams operate: natural language questions, causal explanations, and governed actions in one motion.

The Bottom Line

You can keep paying $650–$1,200 per FTE per year (plus infrastructure) for dashboards that:

• Take 10 days to answer ad-hoc questions

• Require five meetings to explain what you're looking at

• End at visualization, forcing you to manually trigger every next step

Or you can adopt decision infrastructure that answers questions, explains drivers, and closes the loop, in 2–3 minutes, with approvals and audit.

PowerBI isn't broken. It's optimized for the wrong job.

The future belongs to platforms that don't just show you what happened. They explain why it happened, and then handle what comes next.

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