• Why Salesforce’s AI driven layoff narrative never quite added up
• How mixed messaging from leadership is fueling confusion and skepticism
• What internal comments reveal about trust, accuracy, and AI readiness
• Why clean data, governance, and business logic are being re emphasized
• What Salesforce’s pivot back toward rule based automation signals for the broader market

• Why Microsoft Copilot isn’t seeing the adoption expected across enterprises
• How top-down directives fail to drive real AI adoption
• Why using AI as a “checkbox” leads to wasted licenses and disappointed teams
• The lessons leaders can take from hype versus reality when introducing AI tools

The year was 2021 and we were all told that virtual worlds were the future. Now, in 2025, Meta is reportedly preparing to cut its metaverse budget by up to 30%.

In this episode of Leading Change in the Wild, I take a closer look at what this shift really means, how investors are responding, and why Meta might be turning its full attention toward AI and wearable technology instead.

Here’s what I unpack: ✅ Why Meta is pulling back on a 60 billion dollar metaverse investment and why investors cheered ✅ Where that money is likely heading next, from AI infrastructure to wearable tech like Meta glasses ✅ Why wearables may become the next major data source for training AI models ✅ The privacy and ethical concerns tied to always-on, data-collecting devices ✅ Why the metaverse is a cautionary tale for leaders jumping into AI without a clear strategy ✅ How to avoid chasing hype and instead use technology to solve real business problems

For years, people made it clear they didn’t want to live in a virtual world built by Big Tech. Now we’re seeing what happens when companies chase hype instead of listening to their customers. The same warning signs are showing up in today’s AI race.

👇 Let’s discuss: - Is the metaverse really dead, or are we still in the early days? - Would you use AI-powered glasses in your daily life? - Is your organization leading with technology or strategy when it comes to AI?

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• Why carriers are asking to limit AI liability coverage
• How real-world AI mishaps—from chatbot hallucinations to deepfake fraud—are creating concern
• Why agentic AI introduces systemic risk that could trigger thousands of simultaneous claims
• What recent cloud outages at AWS and Microsoft reveal about scale, dependency, and exposure
• Why AI’s “black box” nature makes it nearly impossible to price risk accurately
• How this shift could impact AI-first companies that assumed insurance would back them up
• The key questions leaders need to ask their brokers before diving into AI

AI is showing up in every strategy deck, every board meeting, and every company roadmap. But if you look closely, most AI initiatives are quietly failing long before they ever deliver value.

In this episode of Leading Change in the Wild, I sit down with Brandy Ferrer to break down the real reasons AI projects fall apart and what leaders can do to keep their investments on track.

📉 Here’s what we unpack:

• Why “AI first” mindsets push teams to chase hype instead of solving real problems
• How unclear goals and poor communication derail even the best technical solutions
• The role culture plays in whether employees adopt or reject new AI tools
• Why companies overestimate the technology and underestimate the human side
• What leaders can do to design AI initiatives that actually stick

AI isn’t magical. It isn’t plug-and-play. And it isn’t a shortcut. It’s a tool that only works when we prepare our organizations to use it well.

👇 Let’s discuss: Where do you see AI initiatives breaking down in your industry? What’s one thing leaders should focus on before implementing AI?

• What Salesforce's new term (EGI) really means and why they introduced it.
• The argument for synthetic data: cost savings, compliance (HIPAA), and mitigating historical bias.
• The critical risk of Model Collapse when AI models are trained on their own generative outputs.
• When synthetic data makes sense (e.g., self-driving cars and fraud detection) versus general enterprise use.
• The paradox: Using synthetic data to smooth out models may introduce new, unverified bias and hurt trust.

AI is fueling record-breaking valuations, trillion-dollar companies, and endless hype. But… are we living in a bubble?

In this episode of Leading Change in the Wild, I dig into what’s really happening behind the scenes of the AI boom and what it could mean for leaders and organizations betting big on this technology. 📉

Here’s what I unpack:

• Why Nvidia’s $5 trillion valuation has leaders divided
• What Bill Gates and Sam Altman are saying about overhype and dead-end investments
• How “circular funding” between AI giants could unravel if investors start demanding returns
• The parallels between today’s AI surge and the dot-com bubble
• What business leaders can do to stay grounded and make AI work for them

AI isn’t going anywhere, but the way we use it will define who thrives and who disappears when the hype fades.

👇 Let’s discuss: Do you think we’re in an AI bubble? What steps is your organization taking to ensure AI investments create real value?

• Cloud was supposed to make uptime safer, but automation took the system down
• AI-first strategies are removing humans from the loop while infrastructure is getting more fragile
• Enterprises are rethinking disaster recovery when everything runs in the cloud
• Subscription fatigue is driving a return to building and hosting in-house
• The pendulum may be swinging back from SaaS and cloud to proprietary and on-prem