AI-Ready Data Agile Automation perfectly sums up my first year at Cognizant—and the projects that lit me up in 2025. Following with gratitude for work that has me jumping out of bed to see what we can do next. I love my work with all of you, and this pretty much captures my first year at Cognizant and every project that energized me in 2025.
I worked where ServiceNow Data Quality, Workflow Data Fabric, RaptorDB analytics, and Agile Test Automation all meet—and it changed how I see AI.
Wow—thank you for all the messages celebrating my 1-year anniversary with Cognizant! 💙
Over 2025, my work centered on two connected missions: ServiceNow Data Quality for AI, so every AI agent, dashboard, and workflow runs on clean, trusted front-end data; and Agile Test Management Automation, so Cognizant can lead in hyper productivity.
- First, we launched the ServiceNow Data Quality solution at Knowledge 2025, proving how hyper-performance analytics and AI-ready data drive real outcomes.
- Next, we showcased the U2 Usage Dashboard at ServiceNow World Forum New York, turning usage intelligence into real-time executive decisions.
- Then, Cognizant’s Tony Fugere, Chris Pope, and AutomatePro highlighted AI-driven DevOps and Agile test automation for ServiceNow.
Together, these milestones send a clear message: AI-ready data demands modern data trust—and manual testing simply does not scale. As we wrap 2025 and step into an even bolder 2026, I’m grateful for the support and even more energized by what we’ll build next, together.
AI-ready data demands modern data trust, and manual testing is not Agile, and does not scale.
Why GenAI Demands Data Quality Trust
Today, GenAI doesn’t just use more data. It uses more data, faster, with far less manual review. As a result, when we scale analytics and AI, Data Quality for AI and GenAI data trust become absolutely non-negotiable.
Therefore, we actively push front-end ServiceNow Data Quality, modern data trust frameworks, and Agile Test Automation for AI platforms to the front of every design, sprint, and release conversation. This way, every AI agent, AI-powered dashboard, and workflow automation runs on clean, governed, AI-ready data—not guesswork.
Because in the case of dirty data: GenAI quickly and confidently serves and scales the wrong answer.
If the data is trusted, Data Quality at the front of GenAI unlocks better decisions at hyper speed.
Knowledge 2025: Data Quality as the Front Door to AI
At Knowledge 2025, ServiceNow Innovation Labs offered the solution that doubled down on Workflow Data Fabric, RaptorDB, and the Workflow Data Network. Started as a portal as pictured above and then rapidly iterated for modern workspace 100 % focused on improving all dimensions of Data Quality.
The message was clear: AI agents are only as good as the data and insights they can reach.
Our ServiceNow Data Quality solution fit that story and matched Cognizant’s focus on AI-ready data:
- Front-end quality first. We treated intake forms, reference data, and ownership as the first line of AI defense, not an afterthought.
- Modern data trust. With RaptorDB and Workflow Data Fabric patterns, we moved from static batch reports to near real-time Data Quality scores and trending views.
- Actionable workspaces. Instead of static DQ PDFs, we built Data Quality Management Workspace views where issues become work items, routed to the right owners with clear remediation paths.
This work mattered to me on a personal level. I’ve carried one question for years: How do we turn data quality from a scolding into a superpower? At Cognizant, the answer is simple and powerful: make it visible, make it actionable, and wire it directly into AI and workflow decisions.
U2 Dashboard at ServiceNow World Forum New York
Last month, the U2 Usage Dashboard took the stage at ServiceNow World Forum New York. Here, RaptorDB, Workflow Data Platform, and AI-ready usage intelligence stood front and center.
This work aligned perfectly with Cognizant’s strategic aims:
- Usage intelligence, not just uptime. U2 shows what’s running where, who owns it, and how it’s used. It bridges the gap between platform performance and real business outcomes.
- Closer to real time. With RaptorDB as the analytics engine, leaders explore usage, adoption, and risk with far less latency—and far more confidence.
- Fuel for better backlogs. These usage signals help agile teams decide what to fix, what to simplify, and what to retire, so roadmaps stay honest and data-driven.
For me, this dashboard tied everything together. It’s one thing to talk about AI, Data Quality, and usage analytics. It’s another to put live usage, trust metrics, and governance into one story that executives can act on in the room.
Tony Fugere, AutomatePro, and Automating Agile
A third highlight came from watching Tony Fugere represent Cognizant alongside AutomatePro‘s brilliant Chris Pope, in conversations about AI-driven DevOps. Those sessions showed how AutoTest, AutoDoc, and AutoDeploy can make ServiceNow releases faster, safer, and much more intelligent.
Key themes from those conversations reshaped how I frame my own work in Agile Test Automation:
- AI-powered test automation. Using AI to generate test plans, accelerate coverage, and document flows turns testing from a bottleneck into a force multiplier.
- 5–10x faster time-to-value. When AI-driven DevOps replaces manual spreadsheets and ad hoc scripts, platform teams deliver upgrades and features at the speed Cognizant promises.
- Thrive-style platform management. Together, AutomatePro and Cognizant shift the story from “run tests” to “run a healthy, evolving ServiceNow platform.”
So I state with conviction:
If we are manual testing, we are not automating agile, we are not doing our best work—for our teams, our customers, or agile itself.
Cognizant’s push for hyper productivity fits this perfectly. Agile Test Management Automation is the engine that lets our ServiceNow, AI, and Data Quality stories scale without burning people out.
Why This All Fits at Cognizant—and Why I’m Excited for 2026
Taken together, these threads form a clear picture of Cognizant’s direction—and my role in it:
- AI-ready data powered by modern data trust, using ServiceNow Data Quality, Workflow Data Fabric, and RaptorDB.
- Usage intelligence and U2 dashboards that turn platform telemetry into better product and service decisions.
- Agile Test Management Automation with AutomatePro and leaders like Tony Fugere, proving that AI-driven DevOps is not a buzzword—it’s a working pattern.
I’m excited because this is not technology for technology’s sake. Instead, it’s a better way to:
- Respect people’s time by automating repetitive work.
- Protect customers by baking Data Quality and automated testing into every release.
- Unlock AI’s potential with the one thing it absolutely needs: clean, trusted, well-governed data.
Thank You—for the Year and the Journey
As I mark my one-year anniversary at Cognizant, I want to say a sincere thank you—to everyone who sent a note, shared encouragement, invited me into these ServiceNow and AI initiatives, or trusted me with a piece of this AI + Data Quality + Agile Test Automation story.
Because of you, AI-Ready Data, Agile Automation is more than a tagline. It’s the work I get to do every day—and the foundation for an even more exciting, AI-powered, hyper-productive 2026.
Other AI-Ready Data Agile Automation Resources
- Assuring the Rewards of Generative AI
- AutomatePro ServiceNow Test Automation
- Data Quality Dimensions Metrics
- DQ Product Features.pdf
- Data Quality – ServiceNow Store
- How AI Drives DevOps: Faster Testing and Smarter ServiceNow Platform Management | LinkedIn
- New York, October 29 – ServiceNow World Forum 2025
- ServiceNow World Summit – Chicago Highlights
- ServiceNow World Forum NYC
- Workflow Data Fabric Hub | Getting started
