Systems | Development | Analytics | API | Testing

As a cloud engineer, I’ve seen organizations rush to implement Generative AI, only to hit a brick wall when the Chief Information Security Officer (CISO) asks about data residency or PII leakage. In the public sector and regulated industries like healthcare or finance, moving fast and breaking things isn't an option.

Every few months, a frontier AI lab makes a move that says the quiet part out loud: agents are only as useful as the systems they can reach. The latest example is Anthropic's acquisition of Stainless, the company behind the tooling that turns API specs into SDKs and MCP servers. Anthropic's own framing is direct. Agents need to connect to data and tools, and the path from an API to an agent-ready interface needs to get shorter. We agree. We've been making a version of this argument for two years.

You ask Claude what your MRR was last month. The answer comes back fast, formatted cleanly, stated with total confidence, and completely wrong. Not because Claude is broken, but because it was guessing. Claude has no live connection to your business data by default. It cannot query your CRM, pull from your ad platforms, or check your billing system. So when a marketing manager asks about their numbers, Claude either refuses or generates a plausible-sounding figure based on patterns in its training data.

Customer relations excel when support teams can access knowledge at scale.

At Qlik Connect, one question kept coming up in conversations with leaders: “Which AI vendor should we pick?” But I think that’s the wrong starting point. The better question is: What kind of company do you want to become over the next three years? Right now, most organizations are heading down one of two paths with AI. Some are bolting it onto existing workflows to improve efficiency.