The End of BI Bottlenecks: How AI Is Redefining Business Decision-Making
Thirty years ago, if you told a hotel manager that one day every guest’s opinion would be public, searchable, and factored into booking decisions worldwide, they’d have laughed. Gathering feedback at that scale would’ve sounded absurd, too slow, too expensive, too impractical. And yet today, you wouldn’t dream of booking a room without checking reviews, which can be gathered and published automatically and nearly instantly. The internet turned the unthinkable into the everyday.
The same is about to happen in business decision-making.
Until now, advanced data analysis was a privilege. To get beyond surface-level dashboards, business stakeholders would need to ask their analysts and BI teams to translate questions into SQL, run the queries, and package the results. Forrester1 puts this bluntly: “With self-service, the [percentage] of non-IT professionals able to fulfill their own BI requirements slowly but surely went up to about 20%. But that’s where it has stayed for the last decade.” Despite “user friendly” investments perhaps intended to empower decision-makers with essential data, the tools aren’t fulfilling the promise. These analysts remain the gatekeepers – not to mention they are in short supply, overburdened, and forced to prioritize analysis from the most senior employees. Asking for deep analysis on a routine decision isn’t just discouraged among non-technical professionals, it can be humiliating.
But it’s 2025. AI is rewriting the economics of data analysis. What once required specialized skills, hours of work, and even political capital now takes seconds to execute — and any team member can do it in plain language. From “create this itinerary” to “edit this report,” AI makes complex tasks simple. The barrier has always been data itself: massive, uniquely structured, and difficult for general-purpose AI to understand.
Enter Alkemi. With platforms like Alkemi’s DataLab – an AI-native workspace that lets anyone query structured data in plain English – any user can query their data just as they would ChatGPT, and Alkemi’s proprietary AI can parse complex databases and sources to deliver rigorous, on-demand answers in seconds.
This is the gap businesses have lived with. For decades, insights moved at the speed of BI queues, not the speed of decisions.
The Old World: Gatekeepers, Bottlenecks & “Good Enough”
For the last two decades, businesses have conditioned their people to accept glacial timelines for insights. If you needed real analysis, not just a top-line metric from a dashboard, but a nuanced view of customers, pricing, or margin drivers, you were looking at weeks, not hours.
Analysts, BI teams, and data scientists were essential, but became the overwhelmed toll booth operators of business intelligence. A simple question about customer profitability might sit in the BI queue for three weeks. By the time the report landed, the quarterly planning cycle had moved on, the budget meeting had passed, or the competitive window had closed.
Entire companies normalized making decisions on partial data, shifting marketing spend, launching promotions, allocating sales headcount, because waiting for real analysis meant missing the opportunity altogether.
Dashboards offered “directional” numbers that updated daily, but they couldn’t answer the urgent questions that allow us to address the numbers in the moment. The truth is, we weren’t really running on data. We were still running our businesses on hunches. Just educated hunches.
The Absurdity Test
Looking back, it feels almost absurd. Imagine a Sales Manager submitting a BI ticket asking: “Before I decide on next year's commission structure, can we run a historical analysis on how commission payouts have impacted pipeline conversion rates?” Or a product manager requesting: “Can we model out the profit impact if we cut SKUs with negative contribution margin by category?”
In most organizations, that request would’ve amounted to wishful thinking, not because they weren’t valuable, but because the backlog meant answers would arrive too late to matter.
It’s similar to customer reviews. Thirty years ago, the idea of collecting feedback from millions of strangers before making a purchase seemed laughable; the juice didn’t justify the squeeze. But the internet changed that forever.
Now AI is doing the same for data analysis. What was once rare, costly, and reserved for the most strategic decisions can now be instant and ubiquitous. And it should be. Because in the AI era, running a business on hunches isn’t bold – it’s outdated.
Why doesn’t every business person have their own Palantir — a real-time, AI-powered decision platform? Why shouldn’t every decision, from the boardroom to the front line, be backed by advanced analysis that used to cost millions to access?
That’s the shift DataLab represents. Even as traditional BI tools bolt on AI features, they remain tied to the same top-down model that’s kept adoption stuck at about 20%. Those tools are like Photoshop: powerful but built for specialists. DataLab is Figma: collaborative, intuitive, and built for every business user.
The New Baseline: Data Conversations at Scale
AI doesn’t just change the economics of analysis. It changes the speed. What once required specialized skill, analyst hours, and weeks of waiting now takes seconds in natural language. Analysis stops being a scarce resource, rationed for board decks and annual planning, and becomes something that every business user can access in real time.
Examples:
Take a CFO. In the old world, asking “Which customers were unprofitable last quarter, and why?” meant days of back-and-forth with finance analysts, assuming the request even made it to the top of the backlog. With AI-native tools like DataLab, the CFO can just ask. Within seconds, the data comes back: a clear view of which customers are dragging margins, whether it's due to discounting, chargebacks, or operational costs, and even scenario models to test corrective actions.
Or a VP of Marketing trying to decide whether to double down on LinkedIn or shift spend to YouTube. Historically, this required weeks of reporting, attribution modeling, and analyst interpretation. Now, the questions flow conversationally: “Show me my customer cohorts and which I should focus on acquiring. What’s the ROI differential between LinkedIn and YouTube? Which audience segments are breaking even fastest?” The answers surface instantly, and the follow-ups are just as easy.
For me, the “ah-ha” came while I was developing a new go-to-market strategy at a previous company. The natural questions weren’t just: What are my competitors doing?, but How does our strategy stack up? Where are they winning? Which channels should we double down on, and which should we cut?
Sure, I could ask ChatGPT those questions today and it would generate a lengthy report. But without the underlying data – traffic volumes, share-of-voice across channels, actual ad spend, org structures and titles, revenue trends – it’s just plausible-sounding words. What you get is opinion, not evidence.
That information has always existed in the data-as-a-service market. The problem wasn’t availability – it was accessibility. Unless you brought in a consulting firm or tied up your BI team for weeks, that intelligence stayed out of reach.
That frustration is what led me to build DataLab. In the AI era, every business stakeholder should be able to tap into the same intelligence that once required McKinsey, Deloitte, or a Palantir-style platform built for governments and Fortune 50 boardrooms.
With DataLab, now they can.
The Business Shift: Every Decision Backed by Data
In the AI era, speed isn’t just an advantage. It’s survival. Running a business on hunches isn’t bold; it’s a necessary risk when analysis takes weeks instead of seconds.
Most business leaders are used to making decisions with partial visibility. A revenue ops leader shifts budget among campaigns based on limited data, pattern recognition, and experience. A product manager greenlights features based on a handful of anecdotes. A sales director decides where to assign headcount guided by educated guesswork and gut. The reason isn’t a lack of curiosity – but a lack of access. Until now, the cost of getting real analysis always outweighed the benefit.
That math no longer holds.
With DataLab, advanced analysis is no longer gated by BI teams or buried in dashboards. It’s available on demand, in plain English.
Instead of saying: “I think LinkedIn is working better than YouTube,” you can ask: “Show me our customer acquisition cost by channel for the last two quarters. Which channel produced the fastest payback?”
Instead of guessing which customers are at risk, you can ask: “Which accounts are most likely to churn in the next 90 days, and why?”
The point isn’t that instincts go away. The point is that they don’t have to carry the burden alone. Every decision, big or small, strategic or tactical, can be backed by rigorous data in seconds.
That changes how organizations operate:
- Faster cycles. Questions don’t wait in ticket queues. They’re asked and answered in the moment.
- Better allocation. Budgets, people, and priorities are distributed with evidence, not just politics.
- Shared confidence. Decisions are easier to defend because they’re grounded in analysis everyone can see.
In short, analysis moves from being the exception to being the default.
In the AI era, making decisions without the most relevant data isn’t scrappy, it’s negligence.
That’s the shift we’re living through: the democratization of advanced analysis. It’s not about replacing analysts. It’s about giving every business user the same level of intelligence that once required a consulting firm, a data science team, and weeks of effort.
Conclusion: The New Default
Thirty years ago, booking a hotel without reading reviews was normal. Today, it feels reckless. In just a few years, making business decisions without AI-powered analysis will feel as outdated as making decisions without it.
The truth is, most organizations are still flying half-blind. They rely on dashboards that only scratch the surface, or analysts who simply can’t keep up with demand. The result? Billions of dollars in decisions still get made on instinct and politics.
AI changes that. It eliminates the friction from analysis, transforming what was once a consulting project into a conversation. Analysis that once demanded Fortune 50 resources and consulting-firm budgets is now accessible to every business user.
And it’s not just us saying this. A recent MIT study2 found that 95% of enterprise AI pilots have failed to deliver measurable ROI. The primary reason? Not bad models, but flawed integration and a lack of usable data in workflows. In other words, companies aren’t struggling with AI itself; they’re struggling with making AI useful. That’s precisely the problem DataLab was built to solve.
With DataLab, you get an always-on data analyst, a copilot for every decision, that works the way you do. A workspace where you can just ask, and get rigorous, evidence-based answers in seconds.
Because in the AI era, running a business on hunches isn’t bold or efficient. It’s malpractice. And soon, we’ll look back and wonder how we ever operated any other way.
FAQ
What is AI-powered business analysis?
AI-powered business analysis uses artificial intelligence to turn raw data into insights in plain language. Instead of relying on BI teams or SQL, any business user can ask questions and receive rigorous, real-time answers.
Why is traditional BI adoption stuck at 20%?
Forrester reports self-service BI has plateaued at ~20% adoption. Tools remain technical and analyst-driven, limiting access for non-IT professionals.
How does AI change business decision-making?
AI removes the bottlenecks of BI teams by making analysis instant, accessible, and conversational. This lets every decision – from budgeting to customer strategy – be backed by real data.
How is DataLab different from traditional BI tools?
Traditional BI tools work top-down through analysts, like Photoshop for specialists. DataLab is more like Figma: collaborative, intuitive, and built for every business user.
Why can’t ChatGPT replace BI or DataLab?
ChatGPT can generate narratives, but it doesn’t have access to current, structured business data. DataLab connects directly to real data sources, delivering evidence-based answers, not just plausible text.
1. Source: Forrester, ‘The Future of BI,’ 2023
