AI for customer service.

What the writers we follow are discussing right now.

• AI adoption in customer experience

  Companies are rushing to use AI without first fixing the underlying customer experience problems it is meant to solve. AI tools are most valuable when built on a strong service foundation, not as a shortcut around one.

• Customer success as a revenue-critical function

  Customer success leaders need to bring hard, defensible numbers to finance and the board, not vague metrics. Renewal plans and QBR agendas only work if they survive direct CFO scrutiny.

• Career moves into customer success

  People coming from other fields, such as content writing or product management, are well placed to move into customer success, and often earn more than staying on their original track. The skills that transfer best are executive communication and structured documentation.

• Customer problems as signals of business failure

  A recurring customer issue is usually a sign that something inside the business is broken, not just a ticket to close. The harder task is getting the right internal team to own and fix the root cause.

• Why CX tools fail in practice

  Tools like journey maps do not fail because the workshop was done badly. They fail because nothing changes after the work is handed over. The gap is in execution and accountability, not in the frameworks.

• A high AI deflection rate doesn't mean your customers got help

• Why Salesforce's contact center AI won't work until you fix your data

• When Cisco automates your customer support, what is customer success for?

• How much freedom should you give an AI customer service agent?

• Why customers reject your chatbot even when it gives the right answer

• Your AI agents will get worse over time. Make sure someone is watching.

79 AI customer service tools, grouped by what they do, with how each one is built, the use cases it leads with, and what analysts make of it.