From Reactive Dashboards to Proactive AI with ThoughtSpot
Every data leader knows the BI waiting room. Someone in operations needs an answer, files a request, and waits a day, sometimes three. By the time the dashboard arrives, the moment that mattered has moved on.
That waiting room was the quiet subject of one of the sharpest sessions at the Agentic Analytics Playbook event in London.
Brian Reynolds, VP of Embedded at ThoughtSpot, sat down with Sam Greenhalgh, Chief Revenue Officer at Zencargo, for a fireside chat titled "The Competitive Edge: Driving Business Outcomes Through Embedded Intelligence."
The theme? A company that decided answers should reach its customers before anyone thinks to ask the question.
Here’s how Sam's story maps to the shift you’re probably chasing right now.
Why Is Real-Time Supply Chain Visibility So Difficult?
Start with the freight itself. Zencargo is a digital freight forwarder, moving goods around the world by sea, air, road, and rail.
"Moving goods is still quite archaic," Sam told the room, describing a global system split across time zones, carriers, ports, and customs authorities, with no single party holding the full picture.
For Zencargo, the edge comes from data pulled out of hundreds of APIs across all those parties, then stitched into something a customer can actually question.
Sam called data and insight early in the supply chain "the number one lever for how businesses can outperform their competitors," and that belief sat underneath everything else the session covered.
How Do You Move From Reactive Dashboards to Proactive Analytics?
Picture the old way of working. A customer needs to know whether a shipment will miss a retail launch date, so they route the question to a person, dig through a spreadsheet, or wait for BI. Every step adds delay, and delay in a supply chain is expensive.
Ask Luca, Zencargo's embedded assistant, was built to close that gap. Its name has an unglamorous origin - ask longtime customers what they did before Luca, and they name a person: "I'd ask Jack," or "I'd ask Bob."
So the assistant became the colleague you can always reach, or in Sam's words, "an analyst in your pocket."
Here is the line that framed the whole conversation: Sam described the goal as turning "what ifs into what next," and that phrase captures the reactive-to-proactive shift every data leader is weighing in 2026.
Zencargo had analytics in its product for years, but moving to ThoughtSpot a couple of years ago let the team ship faster and customize far more of what customers saw.
Sam described the shift as a move from "data plus visualization" to "data plus brain and visualization.” The brain is the semantic layer, and it encodes the context of global shipping, what good performance looks like, and what counts as a risk or an opportunity. Ask Luca sits on ThoughtSpot Spotter, which supplies the natural-language layer on top.
The payoff is proactive. Instead of waiting for a person to ask, the system can say: this is what's happening, these are your opportunities, these are your risks, and these are the decisions you need to make.
What Happens to Customer Retention When You Embed AI Analytics?
Adoption is the real test. Did it land?
Ask Luca launched in October. By the time Sam took the stage, more than 100 Zencargo customers had adopted it, and the beta program now has a waiting list for additional models.
"We're the victim of our own success; customers are addicted to Luca," Sam said.
That stickiness matters more here than at a typical software vendor. Different business model, different stakes.
Zencargo is a service business that transacts per container rather than per seat, and technology customers lean on every day becomes the reason they stay. The analytics layer stopped being a reporting feature and became a retention engine.
There is a second-order effect worth noting. Customer Success conversations moved away from tactical KPI readouts toward strategic, root-cause discussions, which makes Zencargo far harder to replace.
How Do You Know If Your Embedded Analytics Is Actually Working?
The proof point Sam kept returning to was dual use: two audiences, one assistant. Ask Luca faces customers, and it runs inside Zencargo's own operation too, with roughly 150 people in ThoughtSpot every day as part of their normal workflows.
A polished customer demo proves little on its own; the real tell is different.
Your own teams reaching for the platform without being told to, day after day. At Zencargo, merchandising, supply planning, and trading all pulled up a chair once the answers came easily.
Should You Build or Buy Your Analytics Layer?
Every data leader in the room knows the build-versus-buy question, and Sam was blunt about getting it wrong at first.
What was the mistake? Zencargo assumed the moat was data plus visualization, when anyone can build a visualization.
"The real moat was the data," he said. A simple line, a hard-won one. So Zencargo went deep where it could differentiate: execution, global supply chain knowledge, and context.
For the analytics layer, it chose ThoughtSpot Embedded over rebuilding one. Note the division of labor: Zencargo builds and owns its core live data platform, and ThoughtSpot owns the analytics layer on top.
Why Does AI Analytics Fail Non-Technical Users?
Not everything is solved yet, and Sam said so plainly. The request he hears most is cross-functional.
It’s typically items like purchase order SKUs and marketing launch dates, read alongside shipping, supply chain analytics, and customs data. Joining those sets cleanly is the work ahead.
He was careful about the framing. Treat it as a data structure and design consideration, not a software failure. Natural-language questioning breaks down when the underlying data sets have inconsistent structure, and that breakdown frustrates the non-technical users you most want to reach.
That kind of honesty about the boundary is what makes the rest of the story credible.
What Does the Future of Agentic Analytics Look Like? Sam’s Advice
Sam's vision for Ask Luca 2.0 moves past asking altogether. Imagine Luca looking across a customer's past decisions, their supply chain, and outside signals like geopolitical events, news, and port disruptions, then surfacing a recommended action: move it by air, or split the shipment.
But get the data structure right before you fall in love with the interface—the analytics layer is only ever as good as what sits beneath it. The companies turning "what ifs" into "what next" are the ones that treated their data model as the product.
Want to see what that looks like on your own data? Start your free ThoughtSpot trial today.
