Before ChatGPT changed everything, enterprise AI lived primarily in IT departments and back offices. Understanding this era explains why the generative revolution matters so much.
The Era of Back-Office AI
For decades, AI delivered value in specific, contained ways:
- Finance: Fraud detection and risk assessment
- Supply Chain: Demand forecasting and logistics optimization
- Operations: Payroll automation and inventory management
- Customer Service: Ticketing systems and basic chatbots
These systems worked. They saved money. They improved accuracy. But they were fundamentally limited.
Why It Wasn't Enough
High technical barriers meant only data scientists could work with AI. Regular employees couldn't access or influence these systems.
Siloed systems prevented cross-functional benefits. Finance AI didn't talk to supply chain AI, which didn't talk to customer service AI.
Data quality issues plagued legacy infrastructures. Garbage in, garbage out—and most enterprises had a lot of garbage.
Lack of adaptability was the critical limitation. These systems could only do what they were explicitly programmed to do. Novel situations broke them.
RPA: Automation Without Intelligence
By the 2010s, Robotic Process Automation (RPA) became popular for handling structured, rule-based tasks. If X happens, do Y. If this field contains that value, copy it there.
But while RPA could mimic human workflows, it couldn't think, learn, or improve. It was like having a very efficient but completely inflexible assistant.
The result? AI made existing processes more efficient but didn't fundamentally transform how businesses operated. It was optimization, not transformation.
The Missing Piece
What enterprises needed wasn't better algorithms running in back offices. They needed AI that anyone could use, that could handle ambiguity, that could adapt to new situations without reprogramming.
That's exactly what arrived in November 2022, when a product reached 100 million users faster than any app in history. The generative revolution didn't just improve AI—it revolutionized who could use it and what it could do.