AI adoption is no longer the difficult part. Turning AI into a dependable product is. McKinsey’s 2025 State of AI survey found that 88 percent of respondents said their organizations regularly...
Technology
AI adoption is no longer the difficult part. The harder problem is turning experiments into products that can survive production traffic, security reviews, changing models, budget scrutiny, and...
Artificial intelligence has entered a different phase of enterprise adoption. For years, businesses used machine learning inside fraud models, recommendation engines, forecasting systems and document...
Enterprise AI adoption has moved faster than enterprise AI scale. McKinsey’s 2025 State of AI survey found that 88 percent of respondents said their organizations used AI in at least one business...
Enterprise AI can fail while the application still looks healthy. A service can return HTTP 200, stay within infrastructure limits, and still produce an answer that is wrong, unsafe, poorly grounded...
Enterprise engineering teams have spent years trying to close the gap between application demand and delivery capacity. Low-code helped by abstracting repetitive development work. Generative AI...
For enterprise technology leaders, the argument around vibe coding is easy to misread. The real question is not whether developers should use generative AI. Most already do. The question is how much...
Enterprise technology teams increasingly use both terms in the same planning meetings, vendor briefs, and budget requests. That creates a practical problem. A team may ask for AI software development...
AI adoption is no longer a useful proxy for AI maturity. The harder question for large enterprises is whether an AI idea deserves to become a production...
Enterprise AI has moved past the stage where a successful demo counts as meaningful progress. The harder question for engineering leaders is whether an AI...
