Despite AI's proven capabilities, McKinsey's finding that About seventy percent of companies reported using generative AI in at least one business function as of 2024 in "at least one function" tells only part of the story¹³. Dig deeper and you find:
- Only 23% have moved beyond pilot programs - Just 11% report significant ROI from AI investments - Only 8% have successfully scaled AI across multiple functions Why the gap between potential and practice? The answer lies not in technology but in integration. Companies struggle because they're trying to insert AI into existing workflows rather than reimagining work around complementary intelligence.
The success stories share common patterns:
They Start with Human Outcomes: Rather than asking "What can AI automate?" they ask "What human capabilities do we want to amplify?"
They Redesign Workflows: Instead of dropping AI into existing processes, they rebuild processes around human-AI collaboration.
They Invest in Translation: They develop "bridge" roles—people who understand both human needs and AI capabilities.
They Measure Differently: Rather than tracking cost savings, they measure innovation velocity, employee satisfaction, and customer outcomes.
They Iterate Rapidly: They treat human-AI collaboration as an ongoing experiment, not a one-time implementation.