💼 At Dreamforce 2025, I sat down with Hannah Calhoon, VP of AI at Indeed, and learned what real AI transformation looks like at scale. Indeed serves 615 million job seekers across 60 countries, and they're not just experimenting; they're systematically reimagining work with AI. #Salesforcepartnership #Sponsored

📊 The measurement-first approach:

Hannah's philosophy is refreshingly practical: "95% of enterprise AI pilots generate zero business value. My job is making sure we're in the 5%." Every department at Indeed picks metrics that matter, then tracks AI's impact relentlessly. Legal cut turnaround time by more than half. Over 80% of software engineers now use AI coding assistants, dramatically improving velocity and code quality.

🤖 Real use cases, real impact:

Take employer support. Linda West, VP of Business Systems, shared that 22% of requests were tied up in routine onboarding and account verification. Now Agentforce agents handle these autonomously - employers can ask in natural language about flagged issues and get guided through resolution in real time. Data Cloud and MuleSoft provide the context agents need for accurate, instant support.

🎯 The adoption secret:

"You can't just throw AI at people," Hannah explained. While 10-20% are natural early adopters, real transformation requires function-specific training. Indeed doesn't just give tools - they show marketers, lawyers, UX designers exactly which three tools to use and the five most useful ways to use them in daily workflows. The result? 75-80% of Indeed's 11,000 employees now use AI daily or weekly, with 80%+ satisfaction.

💬 Honest about the journey:

What I appreciated most was Hannah's candor. Some experiments fail. Employees are understandably concerned. The technology evolves faster than anyone predicted. Indeed's commitment? "We're going to bring you along. We'll give you the tools and training so whether your next job is at Indeed or elsewhere, you're ready for this AI revolution."

🔮 On the future of work:

Indeed's economics team has a unique vantage point - they analyze millions of job postings and resumes. Their finding? AI skills are mentioned in low single-digit percentages of postings. About 50% of jobs will see moderate AI transformation, but it's primarily human-AI collaboration, not replacement. "Most people won't become AI engineers - they'll use AI-enhanced versions of tools they already know."

💡 Hannah's advice for any transformation:

Tie AI to metrics that already matter to your business
Don't wait for perfect data - start experimenting with what you have
Invest heavily in role-specific education (more than you think)
Be honest with employees about uncertainty and change

🚀 The vision:

Indeed is exploring 100 different Agentforce applications to systematically accelerate processes and free teams from repetitive work. The ultimate goal? Cut job seeker hiring time by 50% - accelerating opportunity for millions of people worldwide.

This is the Agentic Enterprise in action: specific use cases delivering measurable value, guided by a transformation strategy that brings every employee along. Not just efficiency - real impact on people's lives.

Are we approaching AI development all wrong? In this eye-opening conversation, Dr. Sean Kennedy from Nokia Bell Labs challenges four widespread beliefs that are actually slowing down AI innovation.

🔍 What We Cover:

Myth 1: Regulation Kills Innovation Discover why uncertainty, not regulation itself, is the real barrier to AI progress and how Nokia's six pillars of responsible AI (fairness, reliability, privacy, transparency, sustainability, and accountability) provide a framework for moving forward with confidence.

Myth 2: Scaling Models Will Lead To AGI Learn why simply making AI models bigger won't achieve artificial general intelligence and why we need "Gen AI Plus," combining fast intuitive thinking with symbolic reasoners, knowledge graphs, and physical world models.

Myth 3: General Models Create Maximum Value Understand the hidden costs of general AI models and their impact on economic divides, particularly in rural communities versus metropolitan areas.

Myth 4: More Data Equals Better AI Find out why quality trumps quantity when it comes to data and how medium-sized language models trained on clean, proprietary data can outperform massive general models while being cheaper and more sustainable.

💡 Key Insights:

✅ How to build responsible AI frameworks that enable rather than constrain innovation
✅ The future of AI lies in hybrid systems, not just bigger models
✅ Physical world models are essential for the next leap in AI capability
✅ Why companies need to think beyond immediate metrics to societal impact

Dr. Sean Kennedy leads artificial intelligence research at one of the world's most prestigious innovation labs. His work spans AI-native networks, generative AI applications, and responsible AI development.

🎯 Perfect for business leaders, technologists, AI practitioners, and anyone interested in understanding where AI is really headed and what actually matters for progress.

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Artificial intelligence promises to transform everything, yet it could also stall progress if we get it wrong. 🤖✨ In this conversation, I’m joined by Carl Benedikt Frey, Oxford economist and author of the new book "How Progress Ends", to explore why technological progress is never guaranteed and how AI could fuel growth or risk stagnation. We discuss the two phases of innovation, the impact of regulation, the battle between centralised and decentralised models in the AI race, what leaders must do to encourage experimentation, and how inclusion shapes the future of discovery. 🌍🚀