Forem: Kevin Campbell

I stopped setting goals. Here's what happened.

Kevin Campbell — Sat, 07 Feb 2026 11:13:12 +0000

92% of people fail their goals.

82% of employees are at risk of burnout.

$322 billion lost annually to burnout-related productivity drops.

Maybe the problem isn't people. Maybe it's goals.

Neuroscientist Anne-Laure Le Cunff spent years at Google before getting her PhD at King's College London. Her research led to a radical conclusion:

Linear goals in a nonlinear world create suffering, not success.

Her book Tiny Experiments proposes an alternative:

Replace goals with experiments.

The difference?

When a goal fails, you failed. When an experiment doesn't work, you learned something. Same outcome. Completely different psychological impact.

Three lessons from the book that changed how I think about work:

Procrastination is protection. Neuroscience shows we delay tasks to avoid negative emotions. Instead of fighting resistance, ask what it's telling you.
Hustle culture makes you dumber. Research shows burned-out employees have 60% reduced focus and 32% lower productivity. Working more doesn't mean achieving more.
Uncertainty is opportunity. When you stop treating life as a path to follow and start treating it as a laboratory to explore, fear becomes curiosity.

I've started applying this to my own work. Instead of quarterly goals, I run weekly experiments. The pressure is down. The learning is up.

What would change if you replaced your biggest goal with an experiment?

Harvard just proved AI tutors beat classrooms. Now what?

Kevin Campbell — Mon, 05 Jan 2026 20:57:53 +0000

AI Tutoring: The Promise and the Divide

The Classroom That Never Changed

I've been thinking a lot about my kid's classroom this week.

Walk in there and it looks... remarkably like my classroom did. And my parents' classrooms before that:

One teacher
30 kids
Fixed schedule
Same pace for everyone

The Breakthrough Study

Then I read the Harvard study published in Nature Scientific Reports last June, and something clicked.

The Setup:

Proper randomized controlled trial
Physics students learning from AI tutor vs. active learning classroom
Not passive lectures—active learning (already the gold standard)

The Results:

AI group: 2x+ learning gains
In less time
Higher engagement and motivation

This wasn't ChatGPT doing homework. The researchers carefully engineered the AI to follow pedagogical best practices:

Scaffolding content
Managing cognitive load
Giving immediate personalized feedback

The kind of teaching that simply cannot scale with one human and 30 kids.

The Global Crisis

UNESCO's Global Teacher Report warns:

Region	Need by 2030
Worldwide	44 million additional teachers
Sub-Saharan Africa	15 million teachers
Global education funding	Expected to fall 25% by 2027

The Promise

On paper, AI tutoring is exactly what education has been missing:

✅ Infinite patience
✅ Infinite personalization
✅ Near-zero marginal cost

The Uncomfortable Truth

But here's what keeps me up at night.

Home Internet Access:

High-income countries: 87% of students
Low-income countries: 6% of students

Current Reality:

AI tutoring market booming in North America, Europe, and Asia-Pacific
The regions that need educational transformation most urgently are the regions least equipped to access it

The Choice

We're facing a choice:

Path A: AI gives every child on Earth a personalized tutor in their own language

Path B: Two parallel education systems

AI-powered learning for the privileged
Overcrowded classrooms for everyone else

The technology is proven. The question is whether we'll choose to deploy it equitably.

That's not a technology problem. That's a policy problem.

Sources

Kestin et al., Nature Scientific Reports (June 2025)
UNESCO Global Report on Teachers (2024)
UNESCO Global Education Monitoring Report (2023)

If this resonated: Share it with someone thinking about the future of education.

I Tested 5 AI Customer Service Agents With the Same Complex Billing Issue – None Escalated Correctly

Kevin Campbell — Sun, 04 Jan 2026 17:51:35 +0000

I Tested 5 AI Customer Service Agents With the Same Complex Billing Issue — None Escalated Correctly

None escalated correctly. AI customer service systems are systematically failing to escalate complex issues to humans, creating customer frustration and eroding trust.

Summary

Last week, I tested a complex billing discrepancy across five different AI customer service agents on separate platforms.

Each system claimed it could resolve the issue.

Not one correctly escalated to a human.

This is not an isolated experience. It reflects a structural failure in how AI customer service systems are designed, deployed, and measured.

Key Data Points

39% of AI customer service bots were pulled back or reworked due to errors in 2024
Customer complaints about AI service rose 56.3% year-over-year in China
Resolution rates range from:
- 17% for billing issues
- 58% for returns and cancellations
75% of customers say chatbots struggle with complex issues
85% of consumers believe their issues require human assistance
Global trust in AI dropped from 62% (2019) to 54% (2024)

What Happened in Practice

Each chatbot behaved in nearly identical ways:

Confidently stated it understood the problem
Offered generic troubleshooting steps
Failed to detect when human judgment was required
Never triggered escalation logic

The escalation mechanisms vendors advertise simply did not activate.

This failure matters because companies invested $47 billion in AI customer service in the first half of 2025 alone, yet 89% of that investment delivered minimal returns.

The Trust Breakdown

The data tells a different story than vendor marketing:

Customer complaints about AI service increased 56.3% year-over-year
For billing problems, AI resolution rates drop to 17%
Users increasingly recognize when they are being deflected rather than helped

Global confidence in AI customer service:

62% → 54% globally (2019–2024)
50% → 35% in the United States

Users are not confused — they are frustrated.

Real-World Consequences: The Air Canada Case

Air Canada learned the risks the hard way.

Their chatbot hallucinated a bereavement discount policy
A customer relied on the information
Air Canada argued the chatbot was a separate legal entity
A tribunal rejected that argument
The company was required to honor the fabricated policy

AI hallucination rates range from 3% to 27%.

This is not an edge case — it is a known limitation.

Enterprise AI Failure Rates

The situation is worse inside large organizations:

Only 5% of enterprise-grade generative AI systems reach production
70–85% of AI projects fail outright
Gartner projects 40% of agentic AI projects will be scrapped by 2027

Despite this, deployment continues — often with reduced human access and deeper menu nesting for escalation.

What Actually Works

The organizations seeing success share common traits:

AI is used as assistance, not replacement
Humans remain accessible and visible
Success metrics include escalation accuracy, not deflection rates
Bots are trained to recognize complexity, not mask it

Most companies are doing the opposite:

AI used as a barrier
Slower response times
Hidden contact options
Endless conversational loops

By the time a human is reached, trust is already gone.

Conclusion

This outcome is not inevitable.

The technology can work — when customer outcomes are prioritized over cost reduction.

Today, 85% of consumers believe their issues require human assistance.

Given the evidence, they are probably right.

Key Sources & Citations

Investment & Failure Rates

CMSWire — $47B invested in AI initiatives (H1 2025), 89% minimal returns
ASAPP / MIT — Only 5% of enterprise-grade generative AI systems reach production
Gartner — 40% of agentic AI projects scrapped by 2027
Multiple sources — 70–85% AI project failure rate

Customer Complaints & Trust

China Daily — 6,969 AI customer service complaints in 2024 (+56.3% YoY)
Sobot — Global AI trust fell from 62% (2019) to 54% (2024)
Salesforce — Customer trust dropped from 58% to 42% (2023–2024)
Plivo — 75% say chatbots struggle with complex issues
Plivo — 85% believe issues require human assistance

Resolution Rates & Bot Performance

Plivo — Resolution rates: 17% (billing), 58% (returns/cancellations)
Fullview — 39% of AI customer service bots reworked or pulled in 2024

AI Hallucinations

CMSWire — Hallucination rates between 3% and 27%
EdStellar — 77% of businesses concerned about hallucinations

Legal Case

Moffatt v. Air Canada, 2024 BCCRT 149
Legal analysis: McCarthy, American Bar Association, Lexology