Building trust in AI-powered analytics isn't just about having accurate data: it's about understanding the human side of how people feel about the insights they receive.
If you’re navigating the complexities of AI adoption, the challenge goes far beyond technology implementation to cultural transformation, trust-building, and creating confidence in data-driven decisions.
In a recent episode of The Data Chief podcast with ThoughtSpot Chief Data & AI Strategy Officer Cindi Howson, Karen Stroup, Chief Digital Officer at WEX, shared her journey of putting people before technology in the age of AI.
Her insights reveal actionable strategies for driving cultural change and building trust in self-service analytics, whether you're leading a team or shaping data strategy.
WEX's People-First Approach to Self-Service Analytics
When WEX rolled out AI-powered self-service analytics for its health and benefits platform, they quickly realized: technical accuracy isn’t enough to encourage adoption.
The problem? Emotional and psychological barriers were keeping users from trusting the insights.
As part of their broader GenAI journey, the global B2B payments company, which operates across mobility, benefits, and corporate payments verticals, took a different approach. They decided to involve employees from day one and make AI feel empowering, not imposed.
To achieve this literacy, they focused on:
Early access, low risk: Licenses for generative AI tools gave employees room to explore
Safe spaces to experiment: Proprietary AI environments + an “AI accelerator” community fostered creativity
Training with intent: Upskilling programs helped users understand the tech, instead of fearing it
CEO Melissa Smith led by example, demonstrating AI usage in town halls with 300+ digital team members. By modeling behavior from the top, she helped normalize experimentation and drive trust across the org.
This foundation of trust and leadership modeling was critical to WEX’s analytics success.
🎧 Hear more from Karen Stroup on the Data Chief—listen to the podcast episode here.
How to Build Trustworthy AI Through Explainable Analytics
After laying the cultural foundation for AI adoption, WEX turned to the next challenge: helping users understand why they should trust the answers they received.
Using ThoughtSpot Analytics, WEX tackled two critical use cases in its benefits vertical:
1. Annual enrollment decisions (September-November timeframe)
2. Ongoing benefits management throughout the year.
These scenarios involved complex, emotionally charged questions like "How much money do I have left on my FSA card?" or "Which health insurance option is right for my family?"
To build that confidence, WEX relied on ThoughtSpot’s explainability features:
Follow-up friendly: Users can ask things like “Can you explain that?”
Clear lineage: Answers show exactly where the data came from and how it was calculated
Drill-anywhere access: All users can explore without getting stuck in rigid paths
Context-aware responses: Results reflect pending claims, delays, and day-to-day nuance
Addressing any uncertainty was critical, as Karen explains:
This level of transparency helped WEX move past historical skepticism and give users what they actually needed: confidence in the decisions they were about to make.
💡 There’s a new standard of trust for enterprise AI—get your own playbook for delivering accurate AI-powered analytics.
How to Prioritize and Scale AI Use Cases
Once WEX had user trust and adoption on its side, the next challenge was scale. That meant choosing the right use cases and building the systems to support them. What principles were most important for implementing self-service analytics successfully?
WEX took a pragmatic, problem-first approach to prioritization:
Every new AI initiative had to pass five filters:
Value assessment: Is this a big, meaningful problem?
Technical feasibility: Can we solve it with the data and tools we have?
Desirability: Are we eliminating painful work, or just automating what people like?
External proof: Have others solved this already? Can we learn from them?
Cultural fit: Are people ready to adopt and support the change?
Once the right bets were in motion, WEX focused on staying flexible.
Their architecture followed Amazon’s “two-way door” philosophy: easy to try, easy to reverse. They ran tight, 2–4 week learning cycles, set clear success metrics, and built resilient platforms designed to evolve, not just endure.
By putting people at the center and AI at their fingertips, employees could ask questions, get answers, and make confident decisions without waiting on a report, a dashboard, or IT.
WEX’s 5 Best Practices for Self-Service Analytics
With the right culture in place, WEX needed a platform that could keep pace—not just with their tech ambitions, but with the way their people actually worked. They chose ThoughtSpot to power that experience.
From natural language search to explainable answers to drill-anywhere exploration, ThoughtSpot gave every user—not just analysts—the tools to get trusted insights, fast. That shift was key to driving real adoption, not just surface-level usage.
“What do you want to solve? What data do you need? And how do you take that thin slice to create the clean data in service to that outcome?” said Stroup, reflecting on how they turned platform capabilities into practical outcomes.
To scale, WEX leaned on five best practices for self-service analytics:
Start with thin slices: Pick a single high-impact use case, clean only what you need, and prove value quickly.
Embrace “ready enough” data: Instead of holding out for perfection, use AI to improve data quality as you go.
Design for confidence: Build experiences that help users trust what they see via clear sources, transparent logic, and space to ask “why.”
Plan to iterate: Don’t wait for perfect—launch in short bursts, learn from real usage, and refine fast.
Celebrate learning: Normalize progress over perfection by sharing both wins and setbacks, so teams stay motivated, not paralyzed.
So, how does your mindset need to shift to scale like WEX?
With the right self-service tool, WEX didn’t just upgrade their analytics; they reimagined the entire experience by putting people first.
Build Trust at Scale with AI-Powered Analytics
By prioritizing transparency, explainability, and user confidence, you can move beyond accuracy metrics for genuine adoption and business impact.
Your path forward means building systems people trust: designed for continuous learning, built on usable data, and ready to scale with your business.
Ready to redefine your analytics approach with the same people-first strategies that drove WEX's success? Start your free trial today.
Self-Service Analytics - Frequently Asked Questions
1. What is self-service analytics?
Self-service analytics is a form of business intelligence (BI) that gives non-technical users the ability to explore data, run queries, and create reports without needing support from IT or data specialists.
As the self-service analytics platform for BI, ThoughtSpot lets users answer their own questions, generate insights quickly, and make data-informed decisions in real time without waiting for a report.
2. What is the difference between guided analytics and self-service analytics?
Self-service analytics gives business users the tools to independently explore data, create dashboards, and run queries themselves. The goal is speed, autonomy, and flexibility so users can answer questions on demand.
Guided analytics, on the other hand, refers to more curated experiences where users follow predefined paths, reports, or dashboards created by analysts or developers. This approach often limits user flexibility and data exploration, however.
3. What are the benefits of self-service analytics?
Self-service analytics empowers everyone from frontline teams to executives to make data-informed decisions quickly. By removing bottlenecks and enabling direct access to insights, it helps you:
Deliver data confidence and decision speed by putting answers in users' hands
Scale data use across departments without overloading data teams
Allowing more teams to explore ideas, test hypotheses, and learn from data
Shift the role of data teams toward higher-impact strategy and governance
The result is a more agile, data-fluent organization, not just faster insights.
4. Does self-service analytics replace data teams?
Not at all. Self-service analytics complements data teams; it doesn’t replace them.
Instead of fielding one-off report requests, data professionals can focus on higher-impact work: improving data quality, modeling, governance, and advanced analytics. Meanwhile, business users gain the autonomy to explore trusted data on their own, make better decisions, and iterate faster.
5. What should I look for in a self-service analytics platform?
A modern self-service BI platform should go beyond dashboards and drag-and-drop charts. Key capabilities to look for include:
Natural language processing (NLP) so users can ask questions conversationally
AI-assisted search to surface relevant insights automatically
Explainability tools that show where numbers come from and why they matter
Live access to cloud data without needing to move or copy it
Granular governance so data teams stay in control while users explore
