Most AI discussions I encounter focus on programming. How much code can a model write? How many developers will a company need?
I work across analytics, marketing, and development. For me, one of the biggest changes is how many angles I can explore when a client brings me a problem.
Work that once consumed the available time now leaves room for more investigation. That creates an opportunity to provide better analysis. It also raises a question: as implementation becomes easier, where will an analyst's value come from?
I expect more of that value to come from anticipating possible outcomes and helping clients decide what to do next. Business understanding, judgment, and communication become central to that work.
Faster analysis still needs judgment
The DIKW hierarchy offers a useful way to think about this: Data → Information → Knowledge → Wisdom.
It separates records of what happened from useful information, practical knowledge, and judgment about what to do. Russell Ackoff also includes understanding as a separate level in From Data to Wisdom.
For a simplified analytics example:
- Data: Recorded visits, purchases, and campaign costs.
- Information: Those records arranged to show how conversion rates changed by acquisition channel.
- Knowledge: A tested understanding of how to measure and improve acquisition.
- Wisdom: A judgment about which improvement deserves the client's time and money.
In my work, AI has greatly reduced the time needed to prepare and explore information. I can compare approaches and investigate alternative explanations faster. That gives me more opportunities to challenge my first interpretation and bring the client a broader view.
