Articles and webinars

Humans are surprisingly good at working in imperfect environments. We adapt to broken processes, missing documentation, unclear requirements, and that one spreadsheet everyone is afraid to touch. We know when to make an educated guess and when to ask for clarification. Most importantly, we usually understand our own limitations. AI doesn’t work that way.

While many companies are still trying to get employees to use more AI, some are already starting to see surprisingly high monthly AI bills. It’s funny how quickly “innovation” turns into “please explain this invoice.” The biggest problem is that most companies still don’t actually know what kind of value they’re getting from AI. They track usage rates and know how people use the tools, but rarely calculate how much business value they get for every dollar spent. That’s where the discussion often goes off track.

For a long time in machine learning, one truth has been repeated almost like a mantra: data quality is everything. In many cases, the first step in any project has been to clean and structure the data before even attempting to find a solution. But I am starting to wonder whether that still holds true for modern general-purpose AI models.

The AI revolution and the democratization of software development have transformed the way many companies build tools. Increasingly, new tools and applications are being built by the people who best understand the business needs. This is a huge opportunity for companies. Ideas can be tested faster, internal processes can be automated more nimbly, and solutions can be developed right where they’re actually needed. At the same time, however, we must determine how to ensure that software produced with the help of artificial intelligence remains secure, reliable, and maintainable in the long term.

Organizations derive the greatest benefits from AI when expertise is developed on a role-by-role basis. When training is tailored to specific job requirements, what is learned is immediately put into practice. Product owners learn to directly leverage AI in producing specifications, and quality assurance professionals gain a concrete understanding of test generation and analysis without uncertainty regarding tool usage. When different roles learn to leverage AI specifically in their own work, tasks take less time, decision-making is more data-driven, and team workflows become more consistent and clear.

Are your specialists spending all their time on endless menial tasks and jumping from one assignment to the next? Is your company’s tacit knowledge leaking to competitors or disappearing with retiring employees? Is your current AI assistant a bit clueless and impractical in real-world work? In this blog, we’ll explain how more comprehensive AI agents tailored to your company can improve your organization’s operational capabilities and support its growth.