Everyone’s chasing the AI dream. But for large, established businesses, adopting new tech — especially GenAI, LLMs, and automation — is a different game.

Here’s why:

1. Skills Gap Hits Harder in the AI Era
Training an employee to use AI tools isn’t enough — you need system thinkers, prompt engineers, data ops pros.
McKinsey (2024): 70% of enterprises say “reskilling” is their #1 challenge.
– TCS reskilled 200,000+ employees for cloud and AI.

2. Legacy Systems Can’t Talk to AI
Mainframes weren’t built for APIs, embeddings, or real-time inference.
Gartner (2024): 87% of companies still depend on old systems for core ops.
– SBI had to bridge COBOL systems with YONO app’s mobile-first AI recommendations.

3. AI Investments Are Expensive (and Risky)
Buying an LLM license is easy. Operationalizing AI across departments is NOT.
BCG (2023): 63% of tech projects blow past budgets by 30–60%.
– GE’s $4B bet on “industrial AI” needed massive pivots.

4. Integration Nightmares Are Real
Your CRM can’t just “talk” to your new AI agent out of the box.
– British Airways’ IT disaster? Partly caused by failed integration of modern layers onto old architecture.

5. Defining AI Success Is Tricky
Saving time? Reducing costs? Automating decisions?
PwC (2024): 53% of CIOs say “unclear ROI” kills transformation efforts.
– Ford’s early smart mobility projects struggled without clear AI-driven KPIs.

Key takeaway:
Adopting AI isn’t just installing a chatbot or plugging an LLM.
It’s about reskilling humans, rebuilding systems, rethinking KPIs, and managing cultural change.
Enterprises that win the AI race (like Netflix, Tata Steel, Goldman Sachs) do it by:
Starting small
Proving clear wins
Upskilling aggressively
Leading from the C-suite down

In the AI era, tech adoption is not a sprint — it’s a marathon with strategy.
Curious: If you’re leading or witnessing an AI transformation — what’s the biggest challenge you’re seeing? Drop it in the comments!