Data & AI Architecture Melbourne schedule

In this 5-minute networking session, the goal is to connect with three new people. Enjoy the opportunity to expand your network!

• What does a practical blueprint for building AI products look like in real environments?
• How do you move from prototypes to reliable, production-grade AI systems?
• Which architectural decisions matter most for performance, governance, and long-term maintainability?

AI agents can be impressively capable, and still confidently wrong, when they don’t have the right tools. In this session, InfoTrack shares how InfoTrack is using Model Context Protocol (MCP) to securely connect AI agents to trusted tools and authoritative data, with governance, permissions and auditability built in. Expect practical lessons on moving from “chatty assistants” to productive operators, plus what it really takes—architecture, patterns, and resourcing—to scale intelligent workflows across teams.

• Are we building for hypothetical scale instead of real use cases?
• When does “future-proofing” become wasted effort?
• Simplicity vs flexibility: what actually scales better?
• Are modern architectures solving yesterday’s problems?

Speakers
Mehul Majethia Principal Solution Architect Victoria Police
Ujjwal Batra Tech Lead - Data Platforms and Engineering Carsales 
Salah Rubaie Enterprise Architecture and Platform Manager South East Water 

Discover how Informatica and Salesforce deliver trusted, AI-ready data. Hear real-world insights on leveraging AI innovation to achieve high-quality data that powers smarter decisions in the agentic era.

• Where should you optimise for performance versus cost - and how do you make those trade-offs deliberately?
• What architectural patterns enable flexibility without compromising control, governance, or scalability?
• How do you evaluate and communicate the impact of your architecture decisions in terms the business understands?

2025 saw rapid efforts to adopt AI with sobering number of failures with AI projects. As we enter 2026, the industry is pivoting from "building AI" to fixing the underlying data problems that cause project failure. This session addresses the shift from hype to practical steps how IT leaders can meet demands for interoperability and governance needed to manage the massive system loads of autonomous AI services. We will translate the core predictions of AI in 2026 into actionable design patterns, moving beyond the race to adopt and toward a strategy of architectural resilience.

This session unveils the Architectural Blueprint for an AI-Ready Enterprise, a framework designed for the era of automation. We’ll dive into the essential pillars of success: real-time event streaming, independent data planes, and context architecture layers. Learn how to build serving layers that move at the speed of AI. 2026 is the year to stop experimenting and start architecting—because your AI is only as powerful as the data infrastructure supporting it.

• Agentic architecture patterns: single-agent and multi-agent designs for natural language data access and workflow execution
• Trust and governance: traceability, evaluation, and control mechanisms for reliable enterprise AI outcomes
• Embedded user experience: bringing agentic workflows into chat interfaces like Teams, Slack, and ChatGPT Enterprise

• What architectural shifts are required as data platforms evolve to support real-time and agent-driven AI systems?
• How do you design data and AI systems together to ensure scalability, reliability, and responsible governance from the outset?
• What new pattern are emerging as the boundaries between data engineering and AI engineering blur? 

• Interpreting privacy obligations and standards through a risk-based lens, rather than as compliance requirements alone
• Understanding why business context, objectives, and data use are critical to identifying and prioritising privacy risk
• Using risk-informed governance to guide practical and proportionate decisions across controls, architecture, and service design

• When should you choose streaming over batch, and what are the trade-offs in cost, complexity, and performance?
• How do event-driven architectures enable real-time decisioning and AI at scale?
• What are the key patterns and pitfalls when operationalising real-time data for business-critical use cases?