Leading Change

What if the price you pay at the store depends on how much a company thinks you're willing to spend?
We've already seen the controversy surrounding AI-powered personalized pricing in the airline industry. Now, Walmart is making headlines over allegations involving surveillance pricing patents that could potentially use customer data to influence what people pay.
In this episode of Leading Change in the Wild, I unpack the controversy surrounding Walmart's patents, the growing use of AI in retail pricing, and the bigger questions this raises about consumer privacy, corporate responsibility, and trust.
Here's what I unpack:
The difference between dynamic pricing, personalized pricing, and surveillance pricing
What the Groundwork Collaborative report alleges about Walmart's technology patents
How retailers could potentially use shopping behavior, biometric data, and other personal information to influence prices
Walmart's response and why holding a patent doesn't necessarily mean using the technology
Why the FTC is paying attention to personalized and surveillance pricing
How AI-powered pricing could change the relationship between businesses and consumers
Why these practices are contributing to growing distrust of corporations and AI
What leaders should consider before implementing technology that benefits the business at the customer's expense
What concerns me most isn't necessarily the technology itself.
It's what companies are choosing to do with it.
There's a difference between using AI to create better products, improve customer experiences, and deliver more value, and using it to figure out the maximum amount someone might be willing to pay.
And when companies prioritize extracting more value from customers instead of creating value for them, they risk losing something far more important than a sale.
Trust.
As I've said before, trust is still the currency the world operates on.
And as AI becomes more deeply integrated into our everyday lives, the decisions leaders make about how they use this technology will have a lasting impact on how consumers view their businesses.
👇 Let's discuss:
How do you feel about AI-powered personalized pricing?
Should retailers be allowed to use personal data to determine what individual customers pay?
Do you think Walmart will ever use these patents?
At what point does personalization cross the line into exploitation?
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Companies are investing in AI, launching pilots, and telling employees to experiment. But too often, leaders expect transformational outcomes without actively participating in the transformation itself.
In this episode of Leading Change in the Wild, I unpack why so many AI initiatives are getting stuck in the messy middle and what you can actually do when the leader sponsoring your project doesn't seem to care.
Here’s what I unpack:
Why the “go experiment with AI” approach isn't delivering the transformational outcomes companies expected
The invisible work employees are taking on as organizations roll out AI
Why leadership participation matters just as much as the technology itself
How to connect an AI initiative to the outcomes your leader already cares about
Why you should stop asking leaders to “get more engaged” and start giving them specific actions and decisions
How to make the cost of executive inaction visible
What to do when you may simply have the wrong sponsor
When it might be time to let an AI pilot die instead of forcing it into production
The technology isn't enough.
If leaders want AI transformation, they have to actively participate in leading the change. That means setting direction, supporting the people doing the work, making decisions, and creating the conditions for an initiative to move beyond experimentation.
And if you're the person trying to move that initiative forward without an engaged leader?
Sometimes you might need a few Jedi mind tricks.
👇 Let’s discuss:
What do you do when a leader isn't engaged in an initiative they're supposed to sponsor?
Have you seen an AI pilot stall because leadership wasn't actively involved?
What has actually worked for you when trying to get leaders more engaged?
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What happens if we make the same mistake with AI that we made with digital literacy?
We gave an entire generation access to technology, but access didn't always translate into understanding how to use it effectively. Now, as Gen Alpha grows up alongside AI, are we at risk of creating another skills gap?
In this episode of Leading Change in the Wild, I sit down with Josh McDonald, co-founder of Gradebox, to talk about AI in education, the growing debate around student-facing AI, and what it actually means to prepare students for a future where these tools will be everywhere.
Here’s what we unpack:
The growing debate around banning student-facing AI in schools
Why banning AI and adopting it everywhere may both miss the point
The digital skills gap we're already seeing with Gen Z in the workforce
Whether we risk repeating that same mistake with Gen Alpha and AI
Why AI literacy needs to go beyond simply giving students access to the tools
The importance of foundational skills like reading, writing, reasoning, and critical thinking
What we can learn from the way our generation adapted to social media and other emerging technologies
The role teachers should play in building responsible AI literacy
How AI can reduce administrative work without replacing the human side of education
Why teachers need AI literacy just as much as students do
The question isn't simply whether AI belongs in the classroom.
It's how we prepare students to live and work in a world where AI exists without allowing the technology to replace the foundational skills they still need to develop.
We don't have all the answers. And that's exactly why I think we need more conversations like this.
AI is changing faster than our education systems can adapt, and the decisions we make now could shape how an entire generation learns, thinks, and eventually enters the workforce.
👇 Let’s discuss:
Should students be using AI in the classroom?
Are AI bans protecting foundational skills or potentially creating a new skills gap?
What does responsible AI literacy actually look like for Gen Alpha?
How should schools balance AI adoption with reading, writing, reasoning, and human interaction?
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Recorded live at Third Stage Consulting’s Stratosphere event, Ema sits down with Eric Kimberling to talk about his new book, The Machine, and what leaders need to understand about the future of digital transformation. 
They unpack some of the biggest themes and takeaways from the event, including how AI is changing transformation, where organizations continue to get stuck, and why the human side of change matters more than ever.
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Remember the SaaS apocalypse?
Earlier this year, the idea sent software stocks tumbling as people questioned what happens to traditional SaaS companies if AI reduces headcount, eliminates per-seat licensing, and starts doing the work those platforms were built to handle.
Now, Salesforce seems to be leaning directly into that threat.
In this episode of Leading Change in the Wild, I unpack Cloudforce, the new expanded partnership between Salesforce and Anthropic, and why I think it tells us something much bigger about where enterprise software could be heading.
Here’s what I unpack:
What Cloudforce actually is and how Salesforce and Claude work together
Why Salesforce may be moving beyond the traditional per-seat SaaS model
The questions I still have about pricing and how companies will actually adopt this
Why trust is such a major part of the Cloudforce positioning
The data problem that AI integrations still can't magically solve
Why accurate CRM data becomes even more important when an LLM is reasoning from it
How Salesforce could position itself as the orchestration layer between enterprise data and AI
What Anthropic potentially gains from getting closer to Salesforce's enterprise customers
What I find most interesting isn't necessarily Cloudforce itself.
It's what this partnership could tell us about the future of SaaS.
Companies like Salesforce already have years of enterprise data, business logic, workflows, governance, and customer relationships. Instead of trying to compete directly with frontier AI models, we may see more established software companies reposition themselves as the layer that gives those models the context they need to actually work inside a business.
Maybe the SaaS apocalypse doesn't mean SaaS disappears.
Maybe it means SaaS has to become something different.
👇 Let’s discuss:
Does this change how you think about the SaaS apocalypse?
Would you trust an AI model to make decisions based on the data sitting inside your CRM?
Will established software companies become the orchestration layer for enterprise AI, or is AI eventually going to eat them anyway?
What do you think Anthropic really gains from this partnership?
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What happens when we stop questioning AI and start trusting its answers more than our own thinking?
New research from the Wharton School explores a phenomenon called “cognitive surrender,” where people begin accepting AI-generated answers as their own thoughts and decisions without critically evaluating whether they are actually correct.
In this episode of Leading Change in the Wild, I break down the research and explores what cognitive surrender could mean for decision-making, productivity, and the way companies measure the value of AI.
Here’s what I unpack:
What cognitive surrender means and how it shows up when we use AI
The Wharton research testing human reasoning with and without generative AI
Why people became more confident even when AI gave them incorrect answers
How expertise helps us recognize gaps and errors in AI output
The connection between cognitive surrender and the Dunning-Kruger effect
How AI-generated “workslop” creates more work for experts
Why cognitive offloading could be eating into companies’ AI ROI
How leaders can use AI intentionally without outsourcing critical thinking
The takeaway is not that we should stop using AI.
It is that we need to understand which parts of our work should be supported by technology and which parts still require human judgment, expertise, and critical thought.
Efficiency should not come at the expense of thinking.
As AI becomes more embedded in how we work and make decisions, leaders need to ask whether these tools are actually increasing human capability or simply making it easier to surrender our thinking to the machine.
👇 Let’s discuss:
Have you caught yourself trusting an AI answer without questioning it?
Where should we draw the line between cognitive assistance and cognitive surrender?
Could overreliance on AI be one reason companies are struggling to see ROI?
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AI companies spent years telling us to adopt AI or risk getting left behind. Now, using AI could get your content labeled, flagged, or even reported.
So, has the AI witch hunt begun?
In this episode of Leading Change in the Wild, I break down the growing push toward AI watermarking, LinkedIn’s AI content reporting features, and the broader effort to label content that has been created or even edited with artificial intelligence.
The goal is to rebuild trust. But are we actually solving the problem, or just shifting the blame to the people who were told to use these tools in the first place?
Here’s what I unpack:
Why Anthropic is introducing watermarking for AI-processed text
LinkedIn’s approach to reporting AI-generated content
Why AI-assisted content is not necessarily AI-created content
Where we draw the line between tools like spellcheck, Grammarly, and generative AI
How AI companies helped create the trust problem they are now trying to solve
Why labeling everything that touches AI may create even more distrust
The need to bring purpose and intentionality back into how we use AI
The takeaway is clear. The problem is not simply whether AI touched a piece of content.
The bigger question is why we are using AI in the first place.
There is a massive difference between outsourcing our thinking and using technology intentionally to help us create, solve problems, and do things we could not do before.
If we want to rebuild trust, labeling people for using AI may not be the answer. We need to get back to purpose.
Let’s discuss:
Do AI watermarks actually make you trust content more?
Where should we draw the line between AI-assisted and AI-generated content?
Is labeling AI content solving the trust problem, or making it worse?
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What happens when an AI model attempts to cheat... and the response reshapes the future of the entire AI industry?
In this episode of Leading Change in the Wild, I break down the recent OpenAI security incident and explains why the biggest story isn't the model itself. It's the growing shift toward open-weight and open-source AI.
As companies rethink control, security, and data ownership, a new conversation is emerging about who should own the future of artificial intelligence.
Here's what I unpack:
What happened during OpenAI's cybersecurity test
Why Hugging Face turned to an open-weight model for defense
The difference between closed, open-weight, and open-source AI models
Why companies like NVIDIA, Microsoft, IBM, and SpaceX are backing open AI initiatives
How open-weight models give organizations more flexibility and control
Why data ownership is becoming one of AI's biggest competitive advantages
What this shift means for enterprise AI adoption and digital transformation
The bigger takeaway is that this isn't just a debate about one security incident.
It's about where AI is heading next.
As organizations adopt AI at scale, they'll need to make strategic decisions about control, customization, security, and who ultimately owns their data. The rise of open-weight models signals that many leaders are looking for a middle ground between building everything from scratch and relying entirely on closed AI platforms.
This is not just an AI conversation. It is a leadership conversation.
Because the choices organizations make today about their AI infrastructure will shape how they innovate, compete, and protect their knowledge for years to come.
👇 Let's discuss:
Do you think most organizations will adopt closed, open-weight, or open-source AI models?
Should companies prioritize flexibility over convenience?
How important will AI ownership and data control become over the next few years?
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What happens when technology stops being a tool for trust and starts becoming a tool for surveillance?
In this episode of Leading Change in the Wild, I explore the growing controversy surrounding Flock Safety cameras and why the debate extends far beyond law enforcement.
Because this is not just a story about surveillance cameras.
It is a conversation about what happens when organizations, governments, and leaders begin relying on technology to monitor people instead of building trust with them.
Here's what I unpack:
 - How Flock Safety cameras are changing modern policing - The recent controversies surrounding surveillance and false accusations - Why surveillance technology is raising new ethical questions - The growing misuse of monitoring tools by those with access - How workplace surveillance mirrors what's happening in society - The relationship between trust, accountability, and technology - Why leaders should think carefully before replacing trust with monitoring
The takeaway is clear. Surveillance may reduce uncertainty, but it cannot replace trust.
Whether we're talking about governments, police departments, or organizations, every new monitoring tool forces us to ask the same question:
Are we creating safer systems, or simply less trusting ones?
This is not just a technology conversation. It is a leadership one.
Because the strongest organizations are not built on constant surveillance. They are built on trust, transparency, and accountability.
👇 Let's discuss:
 - Where should we draw the line between security and surveillance? - Can organizations build trust while increasing employee monitoring? - When does technology become a substitute for good leadership?
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57% of all internet traffic is now generated by bots.
That statistic alone changes the way we think about the internet.
In this episode of Leading Change in the Wild, I explore Cloudflare's latest announcement to block AI crawlers and why it may mark the beginning of a fundamental shift in how the internet is funded, searched, and experienced.
Because this is not just about AI bots.
It is about the future of the internet itself.
Here's what I unpack:
Why AI bots now generate the majority of internet traffic
Cloudflare's new strategy to block AI crawlers
How Google's AI search is changing the economics of the web
Why the attention economy is beginning to break down
Cloudflare's new pay-per-crawl marketplace for AI companies
What happens when websites are no longer visited by humans
Whether this could be the beginning of the end of the free internet
The takeaway is clear. The internet was built on human attention.
As AI increasingly consumes information instead of people, the business model that has powered the web for decades is being rewritten.
This is not just a technology conversation. It is an economic one.
Because the future of the internet will depend on who creates value, who consumes it, and who ultimately pays for it.
👇 Let's discuss:
Should AI companies have to pay to access online content?
Will Cloudflare's approach change the balance of power between publishers and AI companies?
Is this the beginning of a paid internet for everyone?
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