Insights

AI is transforming the way we innovate, collaborate, and solve real-world problems.

AI is a fascinating field, but one thing that consistently frustrates me is the constant flood of poorly defined terms thrown at the public, often without any regard for clarity.

There are two ways to approach a challenge. One way is to meticulously plan every step and resource beforehand and execute the plan step by step. This works well for familiar, known territory, but what if you're venturing into uncharted lands? What if the landscape is rapidly evolving, presenting unforeseen obstacles, offering new opportunities and demanding constant adjustments? How do you account for such a dynamic environment in your approach?

Some financial regulations run past 700 pages, and spotting what changed between versions eats days of expert time. ReacLM compares two versions, then maps the specific impact on a client's sector and activities. A look at how a domain-tuned assistant outperformed a generic chatbot, on a budget under 50k euro.

Off-the-shelf chatbots can't see your internal documents, can't be tuned to find the right file, and send sensitive data to third parties. This piece walks through where generic assistants fall short, and when a custom-built one, connected to your data with full control over privacy and behavior, earns its cost.

Burning through the backlog on time can still mean you built the wrong thing. This piece separates output (code shipped, points closed) from outcome (value actually delivered), and argues the second is the only real measure. Seven lessons on measuring value, experimenting to learn, and not staying wrong for long.