Understanding customer behavior from metrics is a major challenge for any company. Companies have means to collect so much data these days that just dealing with the data, extracting the signals, and visualizing them is a full-time job.
While AI can help build new tools, it can also help ask better questions and explore patterns faster. In my previous 9-5 job, I worked with a young, talented data scientist to explore a large dataset of time-series metrics extracted from a large-scale website serving 35 million customers.
Over a year, that dataset became a data pipeline, and signals were turned into a KPI dashboard and eventually the source of truth for the organization, thanks to the weekly conversations we had about the past week's performance.
It was hard work that took time - not because of writing code but because of building shared understanding and trust in the numbers and what they represent, and how they impacted customers.
Asking the right questions started with customer impact details. We made significant progress in this area, thanks to the new AI tools that became available before I retired.
Low value adding tasks should be offloaded, and not wasting expensive human hours on them. Humans should focus on high value activities.
Now, the real question is: can everyone do those high value activities actually? And how long would it take for people to get used to this change of mode?
Thank you for sharing this, I find this type of reflection extremely interesting because it describes very well one of the deepest transformations we are currently experiencing in the industry.
It is certainly positive that technical execution is no longer the primary bottleneck, but at the same time I believe that the progressive detachment from a real understanding of technical processes may become a significant risk. In a way, it is similar to what happens in manual professions when experience leads someone to lower their attention too much during potentially dangerous activities. The fact that AI makes execution easier does not mean we can afford to understand less about what we are actually doing.
On the contrary, I am increasingly convinced that people with a strong practical mindset should be pushed even further toward improving execution itself, searching for genuinely innovative solutions instead of limiting themselves to the passive use of libraries, workflows, or automatically generated code. Just think about how even a small optimization in computational resource management, memory usage, or API calls could have an enormous impact when replicated across millions of executions globally.
If execution and workflows are progressively losing centrality in favor of our judgment capabilities, then we will probably need to work more and more on our ability to identify relationships, interpret signals, and understand which are truly the right levers to move within increasingly complex systems.
AI is pushing every one of us to be strategists and marketers.
Great article, Kessie.
Understanding customer behavior from metrics is a major challenge for any company. Companies have means to collect so much data these days that just dealing with the data, extracting the signals, and visualizing them is a full-time job.
While AI can help build new tools, it can also help ask better questions and explore patterns faster. In my previous 9-5 job, I worked with a young, talented data scientist to explore a large dataset of time-series metrics extracted from a large-scale website serving 35 million customers.
Over a year, that dataset became a data pipeline, and signals were turned into a KPI dashboard and eventually the source of truth for the organization, thanks to the weekly conversations we had about the past week's performance.
It was hard work that took time - not because of writing code but because of building shared understanding and trust in the numbers and what they represent, and how they impacted customers.
Asking the right questions started with customer impact details. We made significant progress in this area, thanks to the new AI tools that became available before I retired.
And this is the way it should be.
Low value adding tasks should be offloaded, and not wasting expensive human hours on them. Humans should focus on high value activities.
Now, the real question is: can everyone do those high value activities actually? And how long would it take for people to get used to this change of mode?
I think as everyone picks up this new workflow, there will be a new change in how sustainable it would be since AI is expensive.
Thank you for sharing this, I find this type of reflection extremely interesting because it describes very well one of the deepest transformations we are currently experiencing in the industry.
It is certainly positive that technical execution is no longer the primary bottleneck, but at the same time I believe that the progressive detachment from a real understanding of technical processes may become a significant risk. In a way, it is similar to what happens in manual professions when experience leads someone to lower their attention too much during potentially dangerous activities. The fact that AI makes execution easier does not mean we can afford to understand less about what we are actually doing.
On the contrary, I am increasingly convinced that people with a strong practical mindset should be pushed even further toward improving execution itself, searching for genuinely innovative solutions instead of limiting themselves to the passive use of libraries, workflows, or automatically generated code. Just think about how even a small optimization in computational resource management, memory usage, or API calls could have an enormous impact when replicated across millions of executions globally.
If execution and workflows are progressively losing centrality in favor of our judgment capabilities, then we will probably need to work more and more on our ability to identify relationships, interpret signals, and understand which are truly the right levers to move within increasingly complex systems.