AI is moving fast—but most enterprises are still struggling to scale beyond experiments. In this opening session, Rowan Curran from Forrester will set the stage with the latest research on how IT leaders are adopting AI, where progress is accelerating, and what’s holding organizations back. We’ll cover the realities behind the hype: governance gaps, data readiness, security concerns, and the operational barriers that stall momentum.

What you’ll leave with: a clear picture of the “state of AI” for IT today, plus the key decisions and foundations IT needs to put in place to move from pilot activity to measurable outcomes.

Most AI governance programs were built for a world where AI advises. Today, AI acts. It books appointments, approves transactions, routes decisions, and operates without a human in the loop. That changes the risk equation entirely. 

In this session, Manoj Saxena, Chairman of the Responsible AI Institute, walks through a practical framework for classifying, governing, and verifying AI and AI agents based on what they actually do, not what they are called. Designed for IT and business leaders actively deploying AI, this session bridges the gap between high-level AI principles and the operational systems required to keep deployment secure, compliant, and defensible.

What you’ll leave with: A risk-based governance model you can apply immediately, giving you the clarity and control needed to deploy AI with confidence and accountability.

Most organizations can write AI principles. Fewer can operationalize them. EY will cover what it looks like to translate policy into day-to-day execution—decision rights, accountability, risk management workflows, and audit readiness. We’ll discuss how IT, security, legal, and the business align on what’s allowed, what’s not, and how to move faster without losing control.

What you’ll leave with: a practical operating model view. Who owns what, how to govern at scale, and how to avoid bottlenecks that stall adoption.

15-minute break

The digital workplace is crowded, and workplace AI can add new layers of complexity. In this panel, IT leaders will share what is actually driving adoption and measurable ROI as they move from pilot to scaled rollout. Expect candid lessons on where initiatives stall, how they earn employee trust, and which decisions make the biggest difference early on.

We will cover the practical levers leaders are using to get results, including governance maturity and responsible AI guardrails, security and access control readiness, and the measurement approach that proves impact. You will leave with a clear set of patterns you can apply to increase usage, reduce tool sprawl, and connect workplace AI investments to business outcomes.

What you’ll leave with: A practical checklist of adoption and ROI drivers, plus examples of KPIs, enablement tactics, and governance moves that keep momentum after launch.

Organizations are racing to deploy AI in the digital workplace, but successful implementation depends on having the right infrastructure, governance, and measurement frameworks in place. This session, hosted by Daryl Cartwright (AWS), examines the technical and organizational challenges that derail AI implementations, and why 80% of companies are revising their data strategies to support AI initiatives. 

We’ll explore practical frameworks for selecting high-impact use cases, implementing security and compliance guardrails, and defining what success looks like through meaningful KPIs that prove ROI. Real-world examples demonstrate how organizations leverage AWS infrastructure and services to build scalable, secure foundations for AI-powered digital workplace transformation. 

What you’ll leave with: A practical blueprint for building AI-ready infrastructure, so you can deploy securely, scale with confidence, and measure real business impact.

This closing session turns the day’s insights into a concrete plan for IT leaders scaling workplace AI. Simpplr and Forrester will translate the summit’s core themes into a sequenced set of actions you can start immediately. Topics include measurable outcomes, security and access control, responsible AI guardrails, and unified knowledge and context. Expect practical guidance on what to prioritize first, what to standardize, what to instrument, and where teams typically get stuck when moving from pilots to enterprise rollout.

What you’ll leave with: A pragmatic checklist and sequencing plan covering KPI baselines and ROI measurement, permissioning hygiene and auditability, governance standards, and the data and knowledge unification steps that improve relevance and trust.