Systems | Development | Analytics | API | Testing

Most AI analytics projects are failing to deliver on their promises, and the cause isn’t what you might expect. This creates widespread project failures and undermines confidence in AI-driven analytics. What are the problems with AI analytics and how can organizations address them?

When you embed traditional BI tools, you work with platforms originally designed for internal analysts who expect to explore data directly. Embedded capabilities came later, and while these tools expose APIs, every variation in experience requires development work. The challenge is that traditional BI tools aren’t built for the full spectrum of embedded use cases. Most embedded analytics implementations must serve several distinct user types inside your customers’ organizations.

The buying journey is about to fundamentally change. Are you ready? Let’s start by setting up an example.

Finance professionals aren’t afraid of AI taking their jobs, they’re afraid of being left behind while keeping them. The real anxiety in today’s finance teams isn’t automation, but irrelevance: the fear that the profession is evolving faster than their skills. This sentiment is increasingly common across organizations that acknowledge AI’s rising influence but haven’t yet aligned their training, expectations, or workflows with the new reality.

Finance teams are smart, but not infallible. Even the most talented and dedicated finance professionals, like the ones on your team, are bound to make a mistake from time to time. But some mistakes are more costly than others. Have you ever been in the middle of a presentation and realized that you were talking about the wrong data? Or have you had an executive ask for data that you simply couldn’t find? With the right tools and automation, you can avoid these embarrassing moments.