AI-Driven Software Engineering Roundtable in Santa Clara

Why Attend

AI in software development has made writing code dramatically cheaper. Everything surrounding it—requirements, review, release, and outcomes—was left exactly as it was. The result is not faster delivery; it’s a faster accumulation of unfinished work against every constraint your organization already had.

Agile shortened the cycle but kept verification, governance, and release as downstream gates—a design that doesn’t survive execution at machine speed. Hexaware’s Four-Loop Model moves beyond that: Intent, Implementation, Verification, and Value Realization run concurrently, making trust, control, and learning properties of how work executes, not stages it passes through afterward.

This exclusive AI roundtable explores what that shift looks like in practice and what it takes to move from an agile SDLC to a loop-native one.

What We’ll Explore

• Trust
  AI governance can’t live in a policy document. Every agent needs a named identity, bounded authority, and an evidence trail an auditor can inspect. Six decisions must stay human: intent, exceptions, proof sufficiency, residual risk, release, and value.
• Talent and Impact
  Specialist queues stop adding safety when machines execute across the SDLC. They just add delay. New roles compress around end-to-end accountability. KPIs shift from seats deployed to trusted throughput and developer productivity with AI measured against actual outcomes, not self-reported satisfaction.
• Productivity Gains: The Zero Backlog Ambition
  Cheaper AI-powered code generation admits more ideas into the pipeline but an unmanaged backlog becomes a liability that raises the cost of every future change. Technical debt reduction and backlog discipline are now financial decisions, not engineering ones.
• Ways of Working and Costs: Token Economics
  Machine execution cost behaves like cloud spend—budgets, attribution, and unit economics required. Two line items need explicit ownership: the cost of trusted change (verification infrastructure) and the cost of code automation and DevOps automation itself, which grows with every agent provisioned.
• Future Outlook: Autonomy Is Earned, Not Granted
  The organizations that gain most from AI-driven software engineering won’t be those that generate the most code—they’ll be those that can trust, verify, and learn from what they produce at the speed it is produced. Autonomy is what you spend once you’ve earned it.

Meet the Panelists

• Brett Sparks (Moderator): VP Analyst |  Gartner®
• Julie Lichty: SVP, Engineering & Software Development |  Consilio
• Rohit Gupta: Americas Partner Leader, Generative AI |  AWS
• Sanjay Salunkhe: President, Digital & Software |  Hexaware
• Matt Piekarski: Founding FE, Financial Services |  Cursor
• Guruprasad Raghavan: Founding Member |  Workfabric AI

Move past experimentation. Build the operating model that makes AI-driven software engineering trusted, governable, and value-generating at scale. Contact us to know more.