AI Technologies

The AI landscape changes weekly. New models drop. Frameworks evolve. Yesterday's cutting-edge becomes today's legacy.

Enterprise AI adoption has surged to 88% in 2025. Average GenAI investment reached $1.9 million per enterprise last year. But here's the sobering reality: only 6% of organizations qualify as high performers achieving significant enterprise-level value. 74% of companies have yet to show tangible value from their AI investments. 90% of GenAI pilots fail to reach production.

The technology isn't the problem. The approach is.

What's actually production-ready

Not all AI technologies are created equal. The Gartner Hype Cycle tells a clear story about where to place your bets:

AI code assistants are mature and delivering documented ROI. Forrester found 433% ROI over three years for GitHub Copilot implementations. The productivity gains are real—developers completing tasks up to 55% faster. But you need quality guardrails: rigorous code review, increased test coverage, active monitoring of code churn.

RAG (Retrieval Augmented Generation) systems are rapidly maturing. 86% of GenAI adopters use RAG frameworks to ground LLMs with enterprise data. If you want AI that actually knows your business, this is where to focus. Gartner recommends prioritizing RAG investments for organizations looking to leverage proprietary data.

AI agents sit at the peak of inflated expectations. 62% of organizations are experimenting with agents, but only 23% are scaling them. No more than 10% are scaling agents in any individual business function. Proceed with narrow, well-defined pilots—not broad autonomous systems.

The high performer difference

What separates the 6% who get real value from AI investments? Three things:

First, they're 3× more likely to fundamentally redesign workflows rather than add AI to existing processes. Bolting AI onto legacy ways of working delivers marginal gains at best.

Second, they're 3× more likely to have senior leadership ownership of AI initiatives. This isn't an IT project—it's a business transformation.

Third, they pursue fewer opportunities with higher expected returns. The rule of thumb from McKinsey: 10% algorithms, 20% technology and data, 70% people and processes.

A sober perspective

We've fully embraced AI in our development work. We see the value daily—faster prototyping, accelerated documentation, real productivity gains on routine tasks.

But we're also clear-eyed about the limitations. Developer trust in AI output is low for good reason: only 2.7% highly trust AI-generated code, and 66% report frustration with solutions that are "almost right." Debugging AI code often takes longer than writing it yourself.

The technology is ready for production use cases. The question is: are you implementing it in ways that actually move the needle, or just adding expensive tools to broken processes?

We help organizations answer that question honestly—and build AI capabilities that deliver measurable business value.