For decades, getting insights from your data meant one of two things: either you knew SQL and could query databases directly, or you submitted requests to your data team and waited for reports. Both approaches had limitations. SQL queries required technical skills and only worked with structured data. Custom reports took time, and by the time you got answers, you often had new questions. These pains did not go away when we got BI tools - it was not easy to get insights out of distributed and diverse sources of data.
Generative AI fundamentally changes this relationship with data. You can now ask questions in plain language and get answers that draw from both structured databases and unstructured documents - reports, emails, meeting notes, PDFs. This isn't just about convenience. It's a shift in who can access insights and how quickly organizations can act on them.
This fits squarely in the first bucket of our AI value framework: Boosting Productivity.
Traditional analytics tools excel at structured data - numbers in databases, transactions in systems, metrics in dashboards. But much of your organization's knowledge lives in unstructured formats: customer feedback in support tickets, insights in strategy documents, decisions captured in meeting notes, expertise in technical specifications.
Gen AI can synthesize across both. You can ask "What are our top customer complaints this quarter and how do they correlate with our NPS scores?" and get an answer that pulls from your support ticket system (unstructured text) and your customer satisfaction database (structured metrics).
With traditional approaches, you needed to know what questions to ask upfront. With natural language interfaces, you can follow threads of investigation naturally: ask a question, examine the answer, ask a follow-up, and go deeper based on what you learn. This exploratory approach often surfaces insights that wouldn't emerge from predetermined reports.
When only technical teams can access data, they become bottlenecks. Product managers wait for analysis before making decisions. Sales leaders can't quickly verify hunches. Operations teams rely on last week's reports instead of current patterns. Natural language data access lets domain experts get answers themselves, freeing technical teams for more complex work.
Insurance companies typically have claims data in structured databases - dates, amounts, claim types, customer IDs - while detailed claim descriptions, adjuster notes, and supporting documentation exist as unstructured text. Weather data comes from external sources. Previous investigation attempts required IT to build custom queries joining multiple systems.
With natural language data access, claims adjusters can ask questions like: "Show me water damage claims in the week following heavy rainfall events over the past two years, broken down by property age." The system pulls from claims databases, extracts relevant details from claim descriptions, and correlates with weather data - all in seconds.
This enables adjusters to spot patterns they'd suspected but couldn't easily confirm: whether older properties in certain regions consistently show claims after specific weather thresholds. These insights can lead to proactive customer outreach before predicted weather events, reducing claim severity and improving customer satisfaction. The insight was always possible with the data - but the friction of accessing it meant it rarely happened.
Technology consulting firms often have project data scattered across multiple systems: timelines and budgets in project management tools, client communications in email, technical decisions in documentation, team capacity in resource planning systems. Project managers traditionally spend hours each week manually compiling status updates for leadership.
Natural language interfaces let leadership directly ask questions like: "Which projects are at risk of missing deadlines, and what are the primary blockers mentioned in the last two weeks?" or "Show me projects where scope changes have occurred but budgets haven't been updated."
Such a system synthesizes structured data (timeline, budget, resource allocation) with unstructured sources (meeting notes, email threads, Slack discussions). What previously required a project manager to spend half a day compiling information now takes 30 seconds.
More importantly, it can surface early warning signs that get lost in weekly status reports. When multiple team members mention the same technical challenge across different projects, the system can flag it as a pattern requiring attention - something that doesn't emerge when each project is viewed in isolation.
I created this framework to help customers systematically identify opportunities where natural language AI can connect their scattered data sources to deliver faster insights. It guides you through mapping your business question, identifying both structured data (databases, spreadsheets) and unstructured sources (documents, emails, notes), and understanding the manual effort currently required to answer that question. Then it helps you think how AI could synthesize these sources and quantify the expected value in terms of time saved, insights gained, and decisions improved. Use this canvas in workshops or planning sessions to evaluate multiple use cases and prioritize which natural language data access opportunities will create the most value for your organization.
I've built an interactive tool with Claude that brings this framework to life through a guided, three-step experience. You start by exploring an example to understand how each section works, then build your own use case with contextual guidance and tooltips. To use it, you'll need a Claude accounts (even a free one should work).
Click here or on the image to access and use the interactive canvas.