It is especially acute in high-throughput, operations-heavy businesses where yesterday’s data can shift today’s plan. Brands like Instacart, Uber, McDonald’s, Amazon, and FedEx manage millions of daily interactions across distributed operations, where every data-driven micro-decision directly impacts revenue and cost.
Equipping operators to optimize in real time – rerouting inventory, adjusting prices, reallocating staff – can translate into millions in value.
Can AI solve the analytics bottleneck?
AI is often pitched as a silver bullet. In analytics, we’ve seen three main approaches emerge:
“Talk to your metrics” tools
These chatbots sit on top of dashboards and reports making them slightly easier to access. They’re good for surface-level metrics to simple questions, without clicking through charts or filters.
But they stop there – they can’t explain why something happened, connect signals across sources, or reason deeply. For high-throughput, fast-moving companies, that leaves the core real time insights bottleneck untouched.
AI on semantic layer (aka "talk to your metrics") tools lets users self-serve predefined metrics & reports. Flexibility is limited to existing metrics. Experts needed to maintain semantic layer.
SQL co-pilots
These help analysts write queries faster. Business users can’t use them, because they can’t verify if the SQL is accurate. Analysts may be more efficient, yet business stakeholders still wait.
Turnaround times don’t meaningfully improve. And because the dependency on analysts remains, they’re not strategic. The bottleneck just gets a little faster, not removed.
Text-to-SQL co-pilots speed query generation for technical users who can validate generated queries. But, non-technical users still rely on experts.
AI Analysts
This is the holy grail. Systems that reason across multiple sources, capture context, and blend analytics, data science, and ETL skills. In effect, AI that performs like your best analyst – but operates 24x7, faster, and at scale.
AI Analyst serves as a unified interface, learning from all data experts. It democratizes advanced analytics while shifting experts from doing analysis to teaching and governance.
The first two approaches are UX enhancements. They smooth the edges of the existing system but don’t fundamentally change it.
The third – AI Analysts – are a strategic unlock that could transform how organizations operate.
What AI Analysts must learn from humans
To be truly useful, an AI Analyst must embody the same qualities that make great human analysts invaluable:
Capturing tribal undocumented knowledge. Continuously absorbing context from conversations, docs, and usage so accuracy compounds over time.
Reasoning with depth – connecting dots, validating conclusions, and spotting edge cases instead of just returning raw query results.
Bridging silos – knowing what data exists, where it lives, and which sources can be trusted.
Understanding context – grasping the business domain, the metrics that matter, and the “why” behind every question.
Flagging & filling knowledge gaps – surfacing uncertainty, capturing missing context, and learning from it to improve over time.
Operating as a polymath – spanning business, analytics, engineering, and governance to deliver secure, accurate answers without multiple handoffs.
An AI Analyst that can reason, learn, and adapt represents an entirely new category. It doesn’t just speed up the pipeline – it collapses the pipeline, putting accurate answers directly into the hands of operators and business users.
This is how we built PromptQL.
Scaling accuracy is the breakthrough
Enterprise data is sprawling, messy, and full of evolving tribal knowledge. And when thousands of non-technical business users can ask direct questions of the data, trust is everything. Without it, adoption dies.
With accuracy, though, the impact compounds.
The more good answers people get, the more questions they ask. The more questions they ask, the better the system gets. Over time, the AI Analyst begins to outperform the systems it learned from.
This is the promise we’re delivering for our customers.
AI Analysts at the world’s largest companies
Global fast-food giant Across 40,000+ locations in 100+ countries, PromptQL enables local markets to analyze their sales and marketing data instantly and adjust strategies in real time – without relying on central analytics.
US grocery delivery leader With 5M+ daily transactions, business and analytics teams use conversational analytics to monitor performance and make the micro-decisions that drive daily and weekly targets.
Asian quick commerce powerhouse Managing millions of daily orders, hundreds of category managers rely on PromptQL to surface local trends, stay ahead of shifting demand, and meet their weekly goals.
The common denominator: massive, multi-source data. Large, diverse user bases. Complex and varied analytical needs.
The only reason PromptQL succeeds at this scale is that it delivers accuracy at scale – compounding with every use.
Ready to break the analytics bottleneck?
Forward-thinking leaders are already breaking free with PromptQL.
Getting started is fast and low-risk with our 30-day, no-strings trial.
Our AI engineers set up your project (connect data, define context)
Work with your SMEs to build high-quality evals
Pilot with a real team, measure and refine accuracy
At the end of 30 days, you’ll know if PromptQL works – not in a demo, but in your business.
And beyond the trial? It’s simple: month-to-month. If PromptQL isn’t delivering accuracy and impact as you scale adoption, you fire it.
Contact us todayto see how PromptQL can help your business break free from the bottleneck.
