What Changed Because of Your Work?

Some weeks, we work long hours and still struggle to explain what we achieved.

Our calendars are full. Tasks keep moving. Everyone is putting in effort.

But one question deserves more attention:

What changed because of our work?

Did we help a client solve a problem? Did we reduce a cost? Did we improve quality or make someone’s work easier?

The hours we spend matter, but they do not tell us whether the work was useful. Our work should create a meaningful difference in our own business or our clients’ businesses.

A practical example

At Klizer, creating videos is a recurring marketing need. We had purchased tools to support that work, which came with recurring subscription costs.

Recently, one of our AI leader developed a Claude Video Skill that helps generate smooth videos through prompts. Using it has helped us cut some of those recurring tool costs.

Here, the connection between the work and its impact is clear: we built something that serves an ongoing business need and reduces the cost of meeting it.

The savings can be measured by comparing our subscription expenses before and after the change. We can also evaluate production time and output quality to understand the full value.

That is the kind of thinking I want us to bring to AI adoption and to our work in general.

Give effort a clear purpose

Before starting a task or building a solution, we should be able to answer three questions:

• What problem are we solving?
• What improvement do we expect?
• How will we know whether it worked?

Without that clarity, we can spend considerable energy producing very little value. Continuing the same approach simply because we have already invested time can waste even more of everyone’s time.

Sometimes the right decision is to improve the approach. Sometimes it is to stop.

Experiments will not always succeed. But even an unsuccessful experiment should leave us with useful learning that informs the next decision.

Look beyond completion

We often measure progress by what we finish: a feature delivered, a report submitted, an automation built.

Completion is a milestone. We still need to understand what happened after it.

Was the feature useful? Did anyone act on the report? Did the automation save time without creating more work elsewhere?

Our work needs to be measurable and meaningful.

This week, alongside asking yourself, “What did I complete?”, ask:

“What difference did it make?”

What are you building with AI, and how are you measuring its impact? Share an example of time saved, costs reduced, quality improved, or value created.