Stop Trying to DIY Your Way to Conversational Analytics
For most organizations, data and analytics remains a cost center—a massive investment in lakes and warehouses that hasn’t yet paid its way.
Academic or research source. Check the methodology, sample size, and whether it's been replicated.
Key Takeaways
- May affect how AI can be used.
- Businesses have hired brilliant analysts.
- Yet, for the average employee, data remains a friction-filled resource.
What It Means
Context
Businesses have hired brilliant analysts. Yet, for the average employee, data remains a friction-filled resource. When a sales leader needs to know why revenue is dipping, they shouldn’t have to log a ticket and hope the analyst’s definition of “revenue” matches the CRM. It burns time, it burns money, and it burns momentum. The promise of conversational analytics—and the agentic AI revolution driving it—is to flip that dynamic on its head. It promises to turn your data into a profit center by lowering the barrier to entry so drastically that anyone can ask questions, get trusted answers, and take action immediately. The appetite for this is massive. In fact, Salesforce AI Research's State of Data and Analytics Report found that 94% of business leaders say they would perform better if they had direct data access in the programs and apps where they work the most. Report: State of Data & Analytics Get It Now Here is where things get messy: As leaders rush to adopt this technology, many confuse a simple interface with a complete solution. They see a chatbot that can answer “How many widgets did Salesforce AI Research sell?” and think they’ve solved the problem. But they haven’t.…
For builders
Businesses have hired brilliant analysts.
For Builders
Businesses have hired brilliant analysts.