Kris lewis

Every year someone declares a revolution. Cloud. Mobile-first. AI. It gets old. But 2026 is genuinely a weird moment, not because one big thing is changing, but because a bunch of things are changing at the same time, and they reinforce each other in ways that make the usual "wait and see" approach riskier than it used to be. For tech leaders, the question isn't really whether to pay attention. It's which of these shifts hit your organization first, and whether you've thought about them before they become urgent. Where does AI actually sit? Nobody is debating whether AI belongs in software development anymore. It's in code editors, test suites, CI/CD pipelines. The real question is the one worth time in leadership meetings is how deep to let it go and where you still need a human looking at the output. Healthcare is a useful reference point. Generative AI is in production at real health systems, handling clinical documentation and diagnostic support, under HI...

Three years ago, a global logistics company positioned what they called an “AI solution” to manage customer inquiries. Presently their system needs human help to reuse 68 percent of  customer requests. It costs $340,000 to maintain each year. Their customer satisfaction standing has dropped by 14 points. The company development staff selected inappropriate AI technology for AI in product development work because they failed to understand their actual needs. People need to know about AI agents and AI assistants and chatbots. Their understanding helps businesses decide which AI investment will bring 10x return or turn into another unprofitable digital transformation initiative.   The correct AI architecture enables companies to achieve 47 percent faster issue resolution and 31 percent lower functional charges. This compares to businesses that select technology incorrectly according to their functional requirements (Gartner, 2024). The guide presents detailed explanations of each...