The future of Customer Success in an AI-native world
AI is not replacing CSMs. It is rewriting what 'high-leverage CS work' looks like. Here is the operating model leaders should be building toward.
In-depth, no-fluff articles drawn from real engagements — covering AI consulting, Customer Success transformation, revenue operations, automation, and digital transformation.
AI is not replacing CSMs. It is rewriting what 'high-leverage CS work' looks like. Here is the operating model leaders should be building toward.
The hype cycle is consolidating. Here is what serious AI consulting will look like through 2027 — and what to watch out for.
Not every painful workflow is the right first one to automate. Here is the four-dimensional triage we use in client engagements.
If your RevOps team is mostly building reports, you are leaving the highest-leverage work on the table.
Retention is not one problem. It is four. The right investment depends on where the leak actually is.
The funnel ends at purchase. The experience does not. Here is how to design for the journey that actually drives retention and advocacy.
Growth-at-all-costs is over. The teams compounding now are the ones who internalised the new unit economics two years ago.
Transformations rarely fail because the strategy was wrong. They fail because the operating system around the strategy gave up first.
Pilots are easy. Operating models are hard. The gap between them is where most AI programs quietly die.
If your health score is a single number that everyone interprets differently, you have a colour-coded spreadsheet — not a predictive model.
You cannot model your way to forecast accuracy if the underlying inputs are noisy. Start with the operating habits.
Governance done well is enablement. Governance done badly is theatre. Here is how to design the first kind.
Expansion is a motion, not a hope. Here is how to design plays that compound in both PLG and sales-led environments.
Automation ROI does not live where most teams look. Here is where it actually hides.
Human-in-the-loop is not a fallback. It is a design discipline. Here are the patterns that hold up in production.
Dashboards do not make decisions. People do. Here is how to design the system around the decision, not the data.
CS owns the customer. RevOps owns the system. The fact that those are different sentences is the entire problem.
Models do not transform organisations. People using models well, inside a system that supports them, transform organisations.
Most disappointing AI engagements were destined to disappoint at the scoping stage. Here is how to avoid that.
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