The AI Wave
Operating a business requires timing-waiting for transformative waves and riding them to success. Major waves over the past 30 years have included outsourcing, the internet, mobile, social media, and cloud computing.
AI is the largest wave yet.
But here’s the problem: many businesses are drowning in AI initiatives instead of riding the wave strategically. The difference between success and failure isn’t having more AI projects-it’s having the right ones, implemented correctly.
AI Will Take Away Jobs
Let’s address this directly: AI optimizes workflows by automating repetitive tasks. The technology doesn’t need perfection, just sufficient capability to accelerate work. Knowledge-intensive areas like market research and content creation become dramatically faster.
Real Examples
A media company reduced RFP response time from days to minutes. An art marketplace achieved 100x speed increases in biography creation, cut costs by 90%, and boosted page impressions by 50%.
These aren’t futuristic projections-they’re happening now.
The Disengagement Problem
However, Gartner research shows nearly 70% of employees feel disengaged. AI forces organizations to identify meaningful work, delegating routine tasks to technology. This is an opportunity, not just a threat.
AI Will Provide More Jobs
Historical precedent supports this. The internal combustion engine eliminated carriage-related jobs but created a $3.6 trillion automotive industry. Mobile phones destroyed telephone directories and film camera businesses while spawning billions in new value.
The Programming Example
Programming demonstrates this pattern clearly:
- Assembly language → C/C++
- C/C++ → Python/Java
- Python/Java → AI-assisted coding
Each abstraction level eliminated certain roles while opening programming to larger populations.
Critical Caveat
Strong architectural expertise remains irreplaceable. There’s no substitute for human judgment on complex system design. AI amplifies human capability-it doesn’t replace human wisdom.
Hire an AI Employee
Treat AI as a junior employee, not software. An untrained LLM lacks enterprise knowledge-it’s like a capable but uninformed intern.
The Transformation
Performance improves dramatically with domain-specific data. Fine-tuning on historical queries teaches the system your organization’s patterns and vocabulary, transforming it through accumulated experience.
Think about it this way: Would you hire someone and expect them to know everything about your business on day one? Of course not. The same applies to AI.
AI May Not Be for You
Many executives request AI additions because boards demand it or competitors use it. These are the wrong reasons entirely.
A Better Approach
Before implementing AI, answer these questions:
- Identify actual business bottlenecks - What’s really slowing you down?
- Establish clear business cases with ROI projections - How will you measure success?
- Determine if AI genuinely solves the problem - Is AI the right tool?
- Consider if smaller, specialized models work better - Do you need GPT-4 or will a focused solution suffice?
When Not to Use AI
Standard email templates outperform LLMs for routine refunds. Sentiment analysis doesn’t require knowledge of Taylor Swift lyrics. Match the tool to the job.
LLMs Are Dumb
Large language models need proper tools and data. “Garbage-in – Garbage-out” remains universal truth across all systems, deterministic or neural.
Tools Enable Human Capabilities
Think about how tools work for humans:
- Cars make humans faster
- Cranes provide strength
- Calculators enable complex math
Similarly, LLMs excel with quality data and appropriate tools. An LLM with access to your documentation, customer data, and business rules becomes dramatically more useful than a raw model.
LLMs Are Over-confident
They hallucinate-stating falsehoods with absolute certainty. This creates dangerous scenarios, especially with non-deterministic outputs.
The Misinformation Risk
The internet already contains AI-generated content indistinguishable from authentic sources. This creates compounding misinformation risks as AI systems train on AI-generated data.
What This Means for Your Business
You cannot blindly trust AI outputs. Every production deployment needs verification mechanisms, confidence scoring, and human oversight for high-stakes decisions.
Bet on AI but Know How to Win
Deploy gradually with human oversight. Don’t expect perfection; employees make mistakes too. Test with small samples before scaling responsibility.
Practical Implementation
Put AI in production like you would an employee:
- Start with supervised tasks
- Gradually increase autonomy as trust builds
- Maintain feedback loops for continuous improvement
- Have escalation paths for edge cases
Key Takeaways
The Wave is Here
Rapid AI innovation continues with decreasing costs and increasing adoption. AI will be the biggest business transformation force over the coming decade. Missing this wave isn’t an option.
It’s Becoming Real
AI functions as virtual agents alongside human employees. Organizations need cultures where AI agents thrive-with proper training, oversight, and integration into workflows.
Paradigm Shift Ahead
AI raises human potential through extreme operational efficiency, forcing meaningful work discovery. Some tasks automate; new opportunities emerge. The net effect on jobs will likely be positive, but the transition requires thoughtful management.
The Bottom Line
The winners in 2025 won’t be companies with the most AI projects-they’ll be those strategically implementing AI to solve genuine problems with proper oversight and realistic expectations.
Don’t drown in AI. Learn to ride the wave.