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AI Forum Sydney made one thing clear: AI conversations are becoming less about possibility and more about execution. Across sessions on governance, customer experience, security, skills, and agentic AI, leaders focused on the common challenge of turning AI investments into results. That theme came into sharp focus during our closing predictions keynote, a Shark Tank-style debate featuring Jinan Budge, Martin Gill, Fred Giron, and Sharyn Leaver. The discussion reinforced a simple idea that successful investments balance bold innovation with evidence, practical outcomes, and a clear view of where AI, CX, security, and technology are headed next. Thank you to speakers from NAB, Canva, Suncorp Group, Commonwealth Bank, Serco, and Lendi Group for sharing their experiences with AI adoption and governance. And thanks to everyone who challenged assumptions, asked thoughtful questions, and helped make the discussions stronger throughout the day. Swipe through for highlights. ⬇️ Missed it? Join Forrester at an upcoming event near you: https://forr.com/3StYZ7K

Welcome Kasia Jakimowicz to Forrester! Kasia joins us as a senior analyst covering AI governance. Her work sits at the intersection of policy and implementation, tackling a question many organizations are now facing: As AI systems become more capable and autonomous, how should governance evolve with them? In her first Forrester blog, she explores why yesterday’s governance models may not be enough for tomorrow’s AI systems. A few themes from the research: ✅ Governance must scale with AI capabilities, not constrain innovation. ✅ Trust, resilience, and accountability are becoming competitive differentiators. ✅ Organizations need new approaches to governing autonomous and agentic systems. ✅ Successful AI transformation requires balancing innovation with oversight. Read the blog: https://forr.com/3U85H3Q

As AI adoption accelerates, so do the costs. From infrastructure and data pipelines to model usage and inferencing, traditional cost management approaches weren’t designed for today’s AI reality. Recent headlines from Uber, Microsoft, and Tesla highlight a growing leadership challenge: AI spending is becoming harder to predict, govern, and optimize. Forrester analysts Tracy Woo and Kevin Adeniyi Ogunsua, say leaders should focus on these five pillars of AI cost management: 1. People: Establish clear ownership and accountability. 2. Knowledge: Build expertise in AI cost drivers and optimization. 3. Visibility: Understand where AI spending occurs and who owns it. 4. Optimization: Continuously improve cost-performance trade-offs. 5. Operations: Embed cost management into workflows and governance. 📖 Read the blog for the full analysis: https://forr.com/4gt6jbt 🎤 Join Kevin at Technology & Innovation Forum East for more insights on AI cost control: https://forr.com/4wR1kb0

It’s a wrap on AI Forum Singapore! It was a sold out event for a reason. Our analysts dove deep into how to operationalize AI at scale, build trust into every initiative, and prepare for an agentic future. Srividya Sridharan unpacked what’s required to move from AI experiments to customer outcomes. Guest speakers Nimish Panchmatia (DBS Bank), Nicholas Kontopoulos (Twilio), Daryl Pereira (Google Cloud), Jon Stona (Airwallex), Ken Wong (Grab), Rashish Pandey (Fortinet), Jemma Renshaw (Cyber Compass Consulting), Manoj P. (CLP), and Bailing Zhang (CLP) shared firsthand lessons on AI transformation, AI-powered marketing, security, and enterprise readiness. A few ideas echoed throughout the day: • The organizations seeing the greatest value from AI are treating it as an operating model shift, not a technology project. • Trust, governance, and security are quickly becoming competitive differentiators. • Agentic AI is raising new questions about how businesses work, scale, and create value. One message came through loud and clear: The advantage is shifting from experimenting with AI to turning AI into measurable business outcomes. Missed it? Check out our upcoming Forrester events: https://forr.com/4gJhcY8

The AI “SaaS-pocalypse” narrative is missing part of the story. Much of the AI disruption conversation has focused on software licenses and seat compression. But that’s only one factor shaping the future of technology markets. Forrester’s AI Disruption Model looks beyond that narrative. It evaluates 17 technology and service categories spanning more than 200 markets to assess how AI is likely to accelerate, reshape, or disrupt the technology landscape. The central finding: AI’s impact will not be distributed evenly. Some markets face significant disruption, while others are positioned for growth. More findings: • Infrastructure, data & AI, and trust-related providers are positioned to benefit as AI adoption scales. • Labor-intensive markets, including technology implementation, custom software development, creative services, and training, face growing pressure from AI. • Many technology markets will be reshaped rather than replaced. 📖 Read the blog from by Craig Le Clair and Ted Schadler to learn more: https://forr.com/45DysI5

The cloud conversation is changing. Cloud strategy is moving beyond commodity infrastructure toward platforms purpose-built for generative AI and agentic AI. In their latest blog, Charlie Dai and Lee Sustar explain why two new categories are emerging within the AI-native cloud: 1. Neoclouds provide the AI-optimized infrastructure needed to support training, inference, retrieval, and agent execution. 2. NeoPaaS provides the platform layer to govern knowledge, standardize agent lifecycles, and operationalize AI-native application development. Together, they represent a broader shift in cloud strategy. Technology leaders now face decisions that extend beyond where applications run. They must determine how AI workloads will be built, governed, and operated at scale. Read the blog: https://forr.com/4zyNMUb

Forrester AI usage continues to grow year over year. Why? Because leaders need trusted research and guidance embedded directly into the tools where decisions get made. Forrester AI combines proprietary research, expert analysis, and workflow integrations to help leaders move from answers to action, faster and with more confidence. What sets Forrester AI apart? • Research-backed insights grounded in Forrester expertise • Answers that connect business and technology priorities • Support for over 200 languages • Unmetered usage with no token-based limits • Microsoft Teams, Copilot, and MCP integrations 📖 Learn more about Forrester AI: https://forr.com/4yXmIh0

Quantum computing is moving from theoretical curiosity to engineering reality. The challenge for technology leaders is determining where it belongs on the roadmap today. In his latest blog, David Mooter explores how technology leaders should assess quantum computing based on where it can create meaningful business value. A few implications: • The first wave of quantum value is likely to emerge in a narrow set of workloads, not across the enterprise. • Quantum computing is best viewed as a specialized tool for optimization, simulation, and probabilistic modeling. • Long-term adoption will depend on hybrid environments that combine classical and quantum computing. • Post-quantum cryptography planning should be a near-term priority as quantum capabilities continue to advance. For technology leaders, the question is less about when quantum becomes mainstream and more about where it can create value for the business. 📖 Read the blog: https://forr.com/4zoAVDE

Since 2020, Responsible AI has largely focused on governing how models make decisions through safeguards for bias, transparency, data provenance, privacy, and explainability. Recent incidents involving leading AI providers point to a new challenge: what happens when AI agents are given the ability to act? Agents can pursue assigned goals in ways organizations didn't anticipate, exposing gaps in governance, oversight, and accountability. For technology leaders, the issue often hides in plain sight: • Most responsible AI policies govern how models make decisions. • Few govern how agents take action. • Agents can carry flaws through an entire chain of decisions at machine speed. According to Brian Hopkins and Enza Iannopollo, responsible AI now extends beyond model governance to the rules, ownership, and controls surrounding agent deployment. Learn how to prepare your organization when an agent acts in ways you didn't expect. 📖 Read the blog: https://forr.com/4wvMtm2

As AI accelerates decision-making, the human element becomes even more important. Strategy. Judgment. Adaptability. Trust. These are the qualities that separate good teams from great ones, whether you're on a Formula 1 pit wall or leading enterprise transformation. That's why Ruth Buscombe's perspective is so relevant for leaders navigating the AI era. In a world where every organization has access to more data than ever before, the real differentiator is the ability to make confident decisions when the stakes are high. Drawing on her experience from one of the world's most data-driven and high-pressure environments, Ruth will share lessons on leadership, innovation, and translating insight into action. Join us at Technology & Innovation Forum EMEA to hear her advice on making confident decisions when the stakes are high. 🎟️ Join us in London: https://forr.com/4fUSkwd ➡️ Follow Forrester Technology & Security Events for the latest updates and insights.