Sydney Data & AI Innovation Meetup 2025: Unleashing Business Potential May 1st

### Sydney All Things Data – AI Innovation Panel & Networking

📅 Thursday, 1st May 2025 | 🕔 5:00pm for 6:00pm start
📍 Microsoft Reactor, Sydney
In a time when data and AI are reshaping every industry, join us for an evening that goes beyond buzzwords and into the real-world impact of innovation.

Sydney All Things Data brings together leading minds across AI, data strategy, and platform architecture for a highly interactive, outcomes-focused event.
Whether you're a data leader, architect, engineer or executive, this is your chance to learn, share, challenge ideas, and shape the conversation.

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### 🔍 What to Expect

🎤 Expert Panel: “Innovating with AI – Balancing Vision and Practicality”
Our panel of renowned speakers will explore the complexities of building data and AI solutions that scale. You’ll vote in real-time on the most relevant subtopics, with panellists tackling your biggest questions live on stage.

✅ Confirmed Speakers:

• Brett Wilson – CIO50 2024, 2023, 2022, 2021, 2020 & 2019 | Chief Information Officer | Digital Transformation | Royal Australasian College of Physicians, Australian Red Cross
• Simon Aubury – Principal Data Architect, Qantas | Thoughtworks | Confluent Community Catalyst & International Speaker
• Mimi Chattopadhyay – CDAO, CTO, Program Leader in Gov, Health, and Finance. NSW Rural Fire Service | NSW Treasury | News Corp

⚡ Live Voting – You Decide the Questions
We’ll propose four headline data topics in advance, each with 3–5 sub-questions. Attendees vote live on what matters most, so the discussion is relevant, real, and responsive to your world!

💡 Innovation Challenge
Got a two-minute pitch or problem worth exploring? Step up and share it with the room.

🍷 Networking Mixer
From 5 pm, and after the panel, enjoy drinks, snacks, and open conversations with fellow data leaders and innovators.

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### 🌐 Platforms & Topics in Focus

• Multi-cloud strategies: Microsoft Fabric, Azure, AWS, GCP, MuleSoft, Salesforce Data Cloud - but with an agnostic approach
• Data integration and orchestration at scale
• AI automation and responsible deployment
• Real-world case studies across financial services, healthcare, government, and tech

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### 🧠 Topic 1: From Data Chaos to Strategy – Building Foundations for AI

Sub-questions:

• What are the most common blockers to becoming “AI-ready” across large organisations?
• Is centralised data governance still relevant, or should we embrace decentralised, domain-led models (e.g. Data Mesh)?
• How do you get buy-in for foundational data investment when the business wants "quick wins" in AI?
• What does a realistic 12-month roadmap to AI capability look like?
• When is too much data a problem, and how do you simplify?

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### 🛠 Topic 2: Real-World Data Integration – Breaking Silos with APIs, Events & Modern Platforms

Sub-questions:

• MuleSoft, Confluent, Fabric, Data Cloud, Microsoft and others - how do you choose the right tool for the job?
• What does “real-time” really mean in business use cases, and where does batch still win?
• What are the key lessons learned from large-scale integration projects across government and finance?
• How do you balance short-term delivery with long-term platform thinking?
• When should you embed integration into product teams vs centralising it?

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### 🤖 Topic 3: Responsible AI – Delivering Innovation Without the Hype

Sub-questions:

• What frameworks or checks do you use to validate AI use cases before delivery?
• How do you manage risk when your AI system impacts customers or citizens?
• What should every data leader understand about explainability and compliance?
• Is GenAI ready for real enterprise use, or is it still a prototype play?
• What should boards and execs be asking before signing off on AI investments?

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### 📈 Topic 4: Scaling What Works – Turning Pilot Projects into Sustainable Success

Sub-questions:

• Why do so many proof-of-concepts fail to scale in enterprise settings?
• What does it take to embed analytics and automation into day-to-day operations?
• What KPIs actually matter when measuring success in AI/data initiatives?
• Should AI/data teams report into IT, the business, or stand alone?
• How do you retain talent and momentum after the "initial hype" fades?

🎟️ Spaces are limited and filling fast – RSVP or update your status now.
We have an extensive waitlist and would love to offer any available spots to others in the community.