Where Cutting-Edge AI Tools Meet Real-World Solutions

With hundreds of AI platforms emerging monthly, business leaders face growing pressure to separate transformational innovation from shiny distractions. The most successful organizations are now developing structured frameworks for evaluating tools based on scalability, interoperability, and measurable ROI. This session explores how to build a sustainable AI stack that aligns with your company’s goals, workforce readiness, and data maturity to ensure technology investments deliver real business value rather than just buzz.

Join this keynote presentation to learn how to:

• Build an AI evaluation framework that prioritizes impact, integration, and ROI
• Avoid the “tool trap” by aligning purchases with long-term data and automation strategy
• Leverage pilot programs and proof-of-concept testing to validate AI performance before scaling

Aswini Atibudhi

Distinguished AI Architect

Walmart Global Tech

Root cause analysis in on-premise environments has not evolved with the growing complexity of enterprise systems. When critical platforms fail, investigations still rely on manual correlation and tribal knowledge, leading to prolonged war rooms and delayed recovery.

This session presents a new approach to on-premises incident investigation using AI-driven root cause analysis. Rather than reacting to alerts in isolation, AI can reason across telemetry, infrastructure state, and configuration intent to form hypotheses, gather evidence, and converge on a root cause with clear justification.

Using a live demonstration of an ERP outage on VMware vSphere, we show how this model reduces investigations from hours to minutes without exposing sensitive operational or configuration data to large language models. Attendees will see how connecting observability data with infrastructure-as-code context enables AI to surface issues traditional monitoring tools miss, while remaining practical for regulated enterprise environments.

You will learn:

• Why traditional root cause analysis breaks down in complex on-premises environments and what a better AI-driven model looks like
• How combining observability with infrastructure intent enables faster, evidence-backed investigations instead of alert chasing
• What a security-first AI architecture must include to be viable for regulated, on-premise enterprise operations

Francois Martel

Field CTO

Neubird.ai

Artificial Intelligence (AI) has revolutionized many industries, and the finance function is no exception. AI enables organizations to automate processes and controls, improve forecasting and decision-making, manage risk, and drive efficiency. The evolution of AI in finance can be understood in maturity stages, each marked by advances in technology, changes in operations, new skills, and shifting management priorities.

This presentation uses several use cases to explores those stages, with expanded attention to governance, predictive technologies, and the responsibilities of finance leadership — not only as budget sponsors but as educators, stewards of risk, and champions of responsible AI use.

For CFOs and FP&A leaders, this maturity model is best used as a diagnostic tool: a way to assess where your organization stands today, identify gaps in readiness, and sequence investments with intention.

Larry Maisel

Former Adjunct Professor

Columbia University, Graduate School of Business.

Anna Tiomina

Founder

Blend2Balance