The AI Intersection

Week of July 22, 2025

🔍 Insight at the Intersection

The AI Ethics Fatigue Crisis: Why Companies Are Quietly Abandoning Responsible AI as They Race to Scale

Here's the uncomfortable truth no one's talking about: As AI adoption accelerates, responsible AI principles are being quietly shelved. Companies that spent 2023 and 2024 establishing AI ethics committees and responsible AI frameworks are now bypassing their guardrails in the rush to deploy agents and capture competitive advantage.

The evidence is mounting: Over 40% of agentic AI projects will be canceled by the end of 2027, according to Gartner—but not because of ethical concerns. They'll fail due to "escalating costs, unclear business value or inadequate risk controls." Ethics isn't even making the list of failure reasons.

What's happening in boardrooms: 88% of C-suite executives say helping their business speed up AI adoption will be important over the next year. Meanwhile, many organizations are engaging in "agent washing"—rebranding existing products without substantial agentic capabilities—while their ethics frameworks gather digital dust.

The dangerous shift: Companies are moving from "responsible AI first" to "move fast and fix ethics later." Andrew Ng advised attendees at a recent AI summit to "leave safety, governance, and observability to the end of the development cycle" to foster rapid innovation. This mindset is spreading.

The strategic insight: The organizations that maintain ethical AI practices during this adoption frenzy aren't just building better technology—they're building the trust infrastructure that will matter when the inevitable backlash comes. As 68% of global citizens support increased regulation of AI systems, the companies with genuine ethics integration will have competitive moats, not compliance headaches.

🛠 Try This Tool

Test Your Organization's "Ethics Durability" Before It's Too Late

Before your team gets swept up in the AI acceleration race, try this revealing assessment that takes just 20 minutes:

Step 1: Pull up your company's AI ethics guidelines or responsible AI framework (if you can find them)

Step 2: Compare them against your three most recent AI deployments:

• Were ethics reviews actually conducted?
• How long did ethical evaluation add to deployment time?
• Were any features modified or delayed due to ethical concerns?
• Who was the final decision-maker when ethics conflicted with speed?

Step 3: Survey your team anonymously:

• "Would you feel comfortable reporting an AI ethics concern?"
• "How often do ethics considerations slow down our AI projects?"
• "Do you believe our leadership prioritizes responsible AI when it conflicts with competitive pressure?"

Why this works: Nearly 50% of employees report feeling embarrassed to use AI at work, with many stating that AI usage would make them appear lazy or incompetent. If your team can't trust your ethics stance, customers won't either.

The deeper insight: Companies with durable ethics practices aren't slowing down—they're building faster, more sustainable competitive advantages. They've learned that ethics-by-design is faster than ethics-by-retrofit.

Want to build genuine AI ethics competency? Check out my Responsible AI, Transparency & Ethics course on Coursera for comprehensive training on integrating ethical practices into AI development and deployment.

📈 Strategic Signal

The Trust Infrastructure Divergence: Why Ethical AI Is Becoming a Competitive Moat

Global AI adoption is expected to jump by another 20% and hit 378 million users in 2025, but beneath these impressive numbers lies a critical divergence in how companies approach responsible AI implementation.

The split that's emerging: Organizations are dividing into two camps as they scale AI:

1. The "Move Fast" Faction: Prioritizing speed over governance, often engaging in "agent washing" and bypassing established ethics frameworks
2. The "Build Trust" Faction: Integrating ethics into their AI architecture, seeing responsible practices as competitive infrastructure

Here's what the data reveals: While 68% of global citizens support increased regulation of AI systems, only 2% of firms are ready for AI across all five dimensions: strategy, governance, talent, data and technology. Most companies rushing to deploy are missing the governance piece entirely.

The strategic warning: Companies abandoning responsible AI practices are building technical debt that will become compliance debt. Meanwhile, the organizations maintaining ethical AI practices during the adoption frenzy are building something more valuable than speed—they're building institutional trust that will matter when regulations tighten and customer scrutiny intensifies.

Watch for: Organizations that separate their AI acceleration from their ethics integration. The winners aren't choosing between speed and responsibility—they're architecting systems where ethical AI is the faster path to sustainable competitive advantage.

🧭 From the Lab

Course Development Update: The Human-AI Workflow Design Patterns

While developing my 6G course, I've been studying how telecommunications companies handle network automation, and I have discovered something fascinating about successful AI integration that applies far beyond the telecom industry.

The pattern that works: Keep your agents in line by adding human-in-the-loop interventions for approval steps, safety checks, or manual overrides before AI actions take effect. The most successful implementations don't replace human judgment; they amplify it at precisely the right moments.

Three workflow design patterns I'm seeing across industries:

1. The "Trust but Verify" Pattern: AI handles routine decisions, humans approve exceptions. A global software provider uses an AI assistant to detect sentiment in incoming support tickets. Urgent or negative messages are prioritized and routed instantly to senior agents, while standard inquiries are handled by chatbots or level-one support.
2. The "Escalation Architecture" Pattern: AI attempts the task, escalates complexity it can't handle. This mirrors how network operations centers function—automated systems handle 80% of issues, specialists tackle the edge cases.
3. The "Continuous Learning Loop" Pattern: Human corrections become training data for the next iteration. The system gets smarter from every intervention.

Beta insight: The companies winning with AI aren't replacing their expertise—they're creating systems that capture and scale it. SanctifAI spun up its first n8n workflow in just 2 hours, thanks to n8n's visual builder and routing systems. That's 3X faster than writing Python controls for LangChain.

I'm building these patterns into both my AI and 6G courses because whether you're automating customer service or network slicing, the human-AI orchestration principles remain remarkably consistent.

Looking Ahead

Next week, I'll dive into the "Agentic AI Reality Check"—why 40% of AI agent projects will fail by 2027, and the specific organizational capabilities that separate the survivors from the casualties in the coming AI shakeout.

A preview: The companies that will dominate the agentic AI era won't be those that deployed the most agents—they'll be those that built the organizational foundations to make AI agents actually work at scale.

The AI Intersection is your weekly guide to thinking strategically about AI integration. Forward this to someone who's navigating the shift from AI experimentation to AI transformation.

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After 35+ years in technology and telecom leadership, I've learned that every major technological shift has three phases: excitement, disappointment, and transformation. With AI, we're moving from excitement to the disappointment phase—which means the real transformation is just beginning.