By Stephanie Simone
</span>
<span itemprop=”>
In today’s rapidly evolving technological landscape, organizations are sitting on vast amounts of untapped data potential.
At Data Summit 2026, Vinnie Saini,senior GenAI/ML specialist solution architect,Amazon Web Services, and Aditi Gupta,senior AI/ML specialist solution architect,Amazon Web Services, explored how agentic AI-driven systems are revolutionizing the way enterprises transform raw data into measurable business outcomes during their session, “Building With Purpose: Leveraging Agentic AI to Transform Enterprise Data Into Business Outcomes.”
The annualData Summitconference returned to Boston, May 6-7, 2026, with pre-conference workshops on May 5.
This session took place at theData + AI Leadership Forum, an exclusive space at Data Summit for business and technical leaders to explore strategy, governance, responsible AI, and value realization.
“You all have enterprise data, and the data is used for analysis,” Saini said. “We want to talk about how your data is making decisions for you.”
Agentic AI is the technology powering enterprise transformation, she said. It can plan, execute, and adapt to achieve defined business goals without human intervention at each step.
“Understand what it is, how it’s different, and common use cases,” Saini said.
Agentic AI comprises of three systems: autonomous decision-making, multi-step execution, and continuous learning.
“With every iteration, it’s getting faster and better,” Saini said. “Your data becomes your most strategic asset.”
Agentic AI is the bridge between realizing the ROI from AI in the enterprise and data is its backbone.
According to Gupta, key takeaways from various success stories include:
- Starting focused
- Building for compliance
- Measuring impact
Align initiatives to business outcomes and continuously measure feedback, she said. Best practices suggested by Gupta include setting “think big” goals, focus on delivering business priorities fast, grow business-IT ownership, increase agility across data producers and consumers, upskill and empower self-serve, and build trust and confidence with privacy, security, and compliance.
There are 8 dimensions of trustworthy AI:
- Fairness
- Explainability
- Controllability
- Privacy and security
- Governance
- Transparency
- Safety
- Veracity
“An internal use case may have different requirements than an external use case,” Gupta said.
Many Data Summit 2026 presentations are available for review athttps://www.dbta.com/datasummit/2026/presentations.aspx.