New McKinsey research reveals a significant AI adoption gap. While 88% of organizations use AI, nearly two-thirds haven't scaled it beyond pilots, meaning they are not behind their peers. This explains why only 39% report enterprise-level EBIT impact. True high-performers succeed by fundamentally redesigning workflows, not just experimenting.

While 88% of sales teams claim to use AI, it's often shallow adoption like using ChatGPT for emails. Only 24% have integrated AI into core revenue workflows, indicating a significant gap between perceived adoption and deep, systemic implementation that drives real business value.

The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

The most significant and immediate productivity leap from AI is happening in software development, with some teams reporting 10-20x faster progress. This isn't just an efficiency boost; it's forcing a fundamental re-evaluation of the structure and roles within product, engineering, and design organizations.

Block's CTO observes a U-shaped curve in AI adoption among engineers. The most junior engineers embrace it naturally, like digital natives. The most senior engineers are also highly eager, as they recognize the potential to automate tedious tasks they've performed countless times, freeing them up for high-level architectural work.

Companies can either augment existing processes with AI for incremental efficiency (e.g., co-pilots) or completely redesign workflows. While augmentation is common, the most transformative value and disruptive business models will emerge from a clean-sheet redesign of how work is done.

There is a significant gap between how companies talk about using AI and their actual implementation. While many leaders claim to be "AI-driven," real-world application is often limited to superficial tasks like social media content, not deep, transformative integration into core business processes.

Data on AI tool adoption among engineers is conflicting. One A/B test showed that the highest-performing senior engineers gained the biggest productivity boost. However, other companies report that opinionated senior engineers are the most resistant to using AI tools, viewing their output as subpar.

The focus on AI writing code is narrow, as coding represents only 10-20% of the total software development effort. The most significant productivity gains will come from AI automating other critical, time-consuming stages like testing, security, and deployment, fundamentally reshaping the entire lifecycle.