Liam Lawson

You don’t need seniority to earn trust, just depth.

He wasn’t hired as an expert. He became one.

By going deep into the product, obsessing over the work, and becoming a resource others relied on, he earned influence most 20-somethings don’t get.

This is how you become indispensable, regardless of age.

Watch the full episode here:
https://lnkd.in/eVbTAdbM

#EarlyCareer #TrustedByExpertise #DepthOverTitle #YoungLeadership #ProductKnowledge

Most companies blame lazy employees for low AI adoption.
The real problem is understanding what's actually blocking them.

Here's what's really happening:

When fear drives resistance:

31% of employees are scared.
Job security anxiety keeps them from trying.
They're terrified of looking incompetent.
Making mistakes with new technology feels risky.

How to fix it:
Leadership needs to message "AI augments, doesn't replace."
Create private learning environments for safe practice.
Celebrate early adopters to normalize usage.

When confusion drives resistance:

28% of employees are overwhelmed.
Too many tools with unclear purposes.
No clear use cases for their specific role.
Technical jargon shuts them down completely.

How to fix it:
Build role-specific use case libraries.
Keep tutorials to 15 minutes maximum.
Ban technical jargon in all communications.

When inertia drives resistance:

24% of employees see no reason to change.
Current processes work "well enough" for them.
No time allocated for learning anything new.
Zero accountability for adoption.

How to fix it:
Make AI usage part of manager check-ins.
Block learning time directly in calendars.
Link AI usage to performance reviews.

When multiple forces combine:

11% experience both fear and confusion.
They're terrified they won't understand it and will fall behind.
Solution: Pair them with patient power users.

8% experience both fear and inertia.
Change feels dangerous, staying put feels safer.
Solution: Show small wins with real numbers.

6% experience both confusion and inertia.
They don't understand it and don't have time to figure it out.
Solution: Give them pre-built workflows to copy-paste.

5% experience all three at once.
These are your hardest employees to convert.
They need one-on-one coaching and extended timelines.

The biggest mistake is treating all resistance the same.
Fear needs reassurance.
Confusion needs clarity.
Inertia needs accountability.

Figure out what's blocking your team.
Deploy the right solution for that specific problem.

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Most companies throw money at AI tools randomly.
Then wonder why ROI never materializes.

Here's the allocation that actually works:

For a $100K AI budget:

Tools: $35K (35%)
→ AI platform subscriptions and API costs
→ What this covers: ChatGPT Enterprise, Claude, automation platforms
→ Common mistake: Spending 60%+ on tools, nothing left for training

Training: $25K (25%)
→ Employee education and skill development
→ What this covers: Workshops, certifications, internal training programs
→ Common mistake: Buying expensive tools then skipping training entirely

Consulting: $15K (15%)
→ External expertise for implementation guidance
→ What this covers: Strategy sessions, workflow design, best practice audits
→ Common mistake: Hiring consultants before understanding internal needs

Infrastructure: $10K (10%)
→ Integration, security, and technical setup
→ What this covers: API connections, security audits, monitoring tools
→ Common mistake: Assuming tools work out-of-box with no setup

Testing: $10K (10%)
→ Pilot programs and proof-of-concept validation
→ What this covers: Small-scale rollouts, A/B testing, performance measurement
→ Common mistake: Enterprise-wide rollout without pilots

Contingency: $5K (5%)
→ Buffer for unexpected costs and pivots
→ What this covers: Additional licenses, emergency consulting, tool switches
→ Common mistake: Zero buffer means failure when things go wrong

For a $500K AI budget:

Tools: $175K (35%)
Training: $125K (25%)
Consulting: $75K (15%)
Infrastructure: $50K (10%)
Testing: $50K (10%)
Contingency: $25K (5%)

For a $1M AI budget:

Tools: $350K (35%)
Training: $250K (25%)
Consulting: $150K (15%)
Infrastructure: $100K (10%)
Testing: $100K (10%)
Contingency: $50K (5%)

The pattern that separates winners from losers:

Failed implementations: 70% tools, 10% training, 20% other
Successful implementations: 35% tools, 25% training, 40% other

Tools don't drive adoption.
Trained employees drive adoption.

Most companies flip this ratio and waste millions.

P.S. Want to learn more about AI?

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