What are the three questions companies should ask before investing in AI?

The article says leadership teams should ask what they are actually trying to solve, what their team understands about AI today, and what working well would look like in six months. Christopher presents these as the questions that should come before any new AI purchase.

Why does the article say tools come second and alignment comes first?

The article argues that if answers differ wildly across divisions, the real problem is not the toolset but the lack of internal alignment. Without shared understanding, AI investment fragments across departments and struggles to produce coherent results.

Why is defining the actual problem so important before investing in AI?

Christopher argues that companies often begin with technology instead of a clear business problem. Starting with the problem helps leadership teams judge whether AI is actually the right response and what success should be measured against.

What does the article mean by asking what the team understands about AI today?

The point is to surface whether people across the organization are operating from the same baseline. If teams hold very different assumptions about what AI can do, how it works, or where it should be applied, implementation will drift before it starts.

Why does the article use a six-month definition of working well?

The six-month horizon forces leadership teams to translate AI ambition into concrete operational outcomes. It shifts the conversation from abstract excitement to practical criteria that can guide capital allocation and accountability.

What should executive teams take away from this piece right now?

The core takeaway is that companies should not spend another dollar on AI without first aligning across departments on purpose, understanding, and expected outcomes. Christopher frames this leadership-level clarity as the real prerequisite for effective AI investment.