AI Adoption: Measuring Support
Continuing the previous post, let's look at how AI adoption can be measured for support teams.
The principles are exactly the same: cost, quality, and time.
Here are the metrics I'd track:
🔸 Team Budget. Same as for development teams: the cost of the team in $, mandays, or FTEs.
🔸 AI Cost ($). How much does the team spend on AI, including autonomous agents if you're using them.
🔸 Incoming Load. The number of incoming tickets. At the beginning, this metric probably won't change much. But in the future it can show whether the overall quality is improving or getting worse. I recommend tracking it as a percentage change from the baseline before AI adoption.
🔸 Backlog Size. The number of non-resolved tickets. This metric should always be viewed together with the team budget and SLA.
🔸 Time to Resolution. How quickly customers receive a solution to their problem.
🔸 Reopen Rate. Has the percentage of reopened tickets increased? I've seen exactly this happen when teams introduced AI-based ticket assessment. Resolution time went down, but the number of reopened tickets increased several times. A clear sign that support quality had dropped.
As you can see, none of these metrics are AI-specific. Most tracking systems can calculate them out of the box, while a few may require collecting and analyzing historical data.
And one final point: never look at these metrics in isolation. Resolution time is decreased -> reopen rate doubles, team budget decreased -> backlog increased -> resolution time increased.
So it's the combination of metrics that tells you whether AI is actually improving support process or not.
#ai #engineering #ai4sdlc
Continuing the previous post, let's look at how AI adoption can be measured for support teams.
The principles are exactly the same: cost, quality, and time.
Here are the metrics I'd track:
🔸 Team Budget. Same as for development teams: the cost of the team in $, mandays, or FTEs.
🔸 AI Cost ($). How much does the team spend on AI, including autonomous agents if you're using them.
🔸 Incoming Load. The number of incoming tickets. At the beginning, this metric probably won't change much. But in the future it can show whether the overall quality is improving or getting worse. I recommend tracking it as a percentage change from the baseline before AI adoption.
🔸 Backlog Size. The number of non-resolved tickets. This metric should always be viewed together with the team budget and SLA.
🔸 Time to Resolution. How quickly customers receive a solution to their problem.
🔸 Reopen Rate. Has the percentage of reopened tickets increased? I've seen exactly this happen when teams introduced AI-based ticket assessment. Resolution time went down, but the number of reopened tickets increased several times. A clear sign that support quality had dropped.
As you can see, none of these metrics are AI-specific. Most tracking systems can calculate them out of the box, while a few may require collecting and analyzing historical data.
And one final point: never look at these metrics in isolation. Resolution time is decreased -> reopen rate doubles, team budget decreased -> backlog increased -> resolution time increased.
So it's the combination of metrics that tells you whether AI is actually improving support process or not.
#ai #engineering #ai4sdlc