AI consultants tend to pick a lane: building or teaching. But clients expect both. They're stuck in the unknown with pressure mounting and no clear starting point. The real question isn't whether to show or tell: it's that people need to see the vision before they can commit to the journey.

Your first pancake as a consultancy will suck, and we have to accept that’s part of the process of building a company. We consultants love control, but growth lives on the other side of experimentation. Every messy first attempt teaches you something you can't learn any other way. So read this article to learn our approach to managing that process!

Clients don't care about your methodology, they care about their pain. Stop obsessing over frameworks and start asking what's actually getting in their way. Learn how we get there in this article!

For us consultants in the AI world, the only thing to fear is fear itself, and fear is a triangle. Every AI project triggers three types of uncertainty: Personal (social pressure, preconceived notions), Financial (ROI and job loss concerns), and Organizational (infrastructure gaps). Most try to solve all three at once. That's a mistake. Pick the two fears that move the needle. Let the third wait. Momentum beats perfection. Read on to find out why.

The most foundational framework that we Data & AI consultants can use is the simplest one you can think of: impact vs complexity.

In this article, I explain the differences between the two and how they come together to enable Data & AI strategies to become real, how we can use it in workshops to serve clients, and how you should be thinking about them.

Data & AI leaders no longer just build insights, they roll snowballs, starting small and building unstoppable momentum through four key principles: momentum over perfection, small and early over big and late, building for humans not processes, and understanding that value is a feeling not a metric. Read on to find out why!

Data projects fail because consultants fix individual issues in isolation instead of addressing the entire ecosystem: a cycle where sourcing, ingestion, storage, governance, visualization, and automation all depend on each other.

Superposition's assessment workshops use structured facilitation and gamification to reveal these cyclical connections, transforming vague client problems into properly scoped projects with clear roadmaps clients actually understand and sustain.

Most SMBs struggle with data not because of technical limitations, but because they lack the human processes (alignment, trust, and translation between technical and business teams) that make data actually useful. In this post, we talk about the key to creating just enough structure so your team can make confident decisions, starting with one specific business problem where better data makes a measurable difference in 90 days.