Continuing our conversation with Varun Gupta from Pengana Capital Group, we shift from the technical side of AI implementation to something just as important: creating a workplace where employees are encouraged to solve problems. We earlier covered the business case behind Pengana Capital Group's AI fund project: why repetitive investor emails were the first target, the projected ROI, and the long-term vision for AI across the business.In this episode, we pick up where that conversation left off.Varun Gupta from Pengana explains what happened after the proposal was approved, how the project moved into implementation, and why getting buy-in across the business was just as important as choosing the right technology. We also discuss the role leadership played in backing the idea, why the project came from someone closest to the day-to-day work, and what other organisations can learn from the process.Rather than treating AI as an IT initiative, Pengana approached it as a business improvement project, starting with a clear operational problem and building from there.Key Takeaways✓ The hardest part isn't always the technology, it's getting the right people behind the idea.✓ AI projects don't have to start with senior leadership. The people closest to the work often see the biggest opportunities.✓ Internal support helped turn a business case into a funded project.✓ Successful AI implementation depends on collaboration between business teams and technical specialists.✓ Solving one operational problem creates momentum for future AI initiatives.✓ Building a culture where employees can improve processes makes long-term innovation much easier.

Your people are already using AI. The question is whether your business has the visibility, governance and support needed to make that usage safe, productive and scalable.In this conversation, Brendan Ritchie and Jack Behrns discuss how Hatz fits into the CORE managed services model, and why it is a strong option for businesses that already have staff experimenting with different AI tools.Unlike choosing a single AI platform and hoping it stays ahead, Hatz gives users access to multiple leading AI models through one secure, governed interface. That means finance, marketing, operations and leadership teams can use the models that work best for their roles, while the business keeps oversight, control and confidence around data use.They also cover how Hatz supports AI adoption, why the platform can be more cost-effective than individual AI subscriptions, and how CORE helps businesses turn AI from scattered experimentation into a managed, measurable productivity strategy.If your business is thinking seriously about AI, automation, data governance or secure adoption, CORE gives you the operating model to make it work.Learn more about CORE: https://www.firstfocus.com.au/services/core/Key Chapters:00:00 – Why Hatz matters inside COREBrendan and Jack introduce Hatz as one of the AI platform choices available through CORE, alongside ChatGPT Business and Microsoft Copilot.00:35 – Hatz vs ChatGPT and CopilotJack explains how Hatz suits businesses where different teams are already using different AI models, and how it brings that usage into a secure, governed environment.01:49 – Solving shadow AI without limiting usersThe discussion covers why businesses do not need to pick one winning LLM, and how Hatz lets staff keep flexibility while giving the organisation visibility and control.03:16 – What the Hatz user experience looks likeJack walks through the interface, onboarding experience, AI champion certification, and why users familiar with ChatGPT, Claude or other AI tools should feel at home.05:28 – Why Hatz can be more cost-effectiveThey unpack the commercial model, including pooled AI credits, enterprise buying power, token usage and why this can deliver AI access at SMB-friendly pricing.

In this AI AMA webinar, First Focus AI Strategy Lead Jack and host Brendan Ritchie answer some of the most common questions business leaders are asking right now, including:

✅ How to prepare your business for AI adoption
✅ Data governance and AI security best practices
✅ Reducing AI tool sprawl and shadow AI
✅ Measuring AI ROI and identifying quick wins
✅ Real-world AI case studies delivering results
✅ AI agents, automation and productivity gains
✅ Copilot vs ChatGPT – which is right for your business?

The discussion draws on First Focus' own AI journey, lessons learned from internal deployments, and practical experience helping organisations implement AI successfully.

Whether you're just starting your AI journey or looking to scale existing initiatives, this session provides practical guidance on governance, adoption, security, and measurable business outcomes.

⏱️ TIMESTAMPS

00:00 – Welcome & Webinar Overview
01:04 – How First Focus Adopted AI Internally & Lessons Learned
03:53 – What Does an AI Strategy Lead Actually Do?
07:44 – Why the Traditional MSP Model Is Changing in the AI Era
13:35 – AI Readiness: Data Governance, Security & Preparation
20:33 – Managing AI Risk, Shadow AI & Tool Sprawl
33:34 – Measuring AI ROI & Finding High-Impact Use Cases
43:28 – Real-World AI Case Studies, AI Agents, Automation & Copilot vs ChatGPT

🎯 KEY TAKEAWAYS

• AI success starts with clear business problems, not technology selection.
• Data governance remains the foundation of effective AI adoption.
• Shadow AI is often reduced by providing employees with secure, approved AI tools.
• Quick wins and iterative improvements drive stronger adoption than large transformation projects.
• Organisations should target measurable ROI and focus on practical productivity gains.
• Internal knowledge assistants are among the most common and impactful AI use cases.
• AI champions within teams accelerate adoption and innovation.
• Security, governance, and user enablement are just as important as the AI platform itself.
• Copilot, ChatGPT, and other AI platforms each have strengths depending on business requirements.
• AI initiatives should be assessed based on outcomes, scalability, and business value rather than hype.