Why Your AI Rollout Will Amplify Whatever Your Frontline Already Is
Every executive team is running the same experiment right now: hand frontline and customer-facing teams AI copilots and see what happens to productivity, cost-to-serve, and experience quality. The results are wildly inconsistent, and the reason has almost nothing to do with the AI. It comes down to a question that predates generative AI by decades:…
AI Promised to Finally Solve “We Don’t Really Know Our Customers.” Has It?
For twenty years, the honest answer to “do we understand our customers” has been: some of them, based on whoever complained loudest or filled out the survey. Generative AI has changed what’s technically possible here faster than almost any other CX capability, and that’s exactly why it’s worth a hard look at where your organisation…
Customers Now Expect AI to Remember Everything. Most Organisations Can’t Deliver That.
Consistency has always been hard: making sure a customer gets the same tone, standard, and outcome whether they call, chat, email, or walk in. What’s changed is the bar customers now hold you to. When a company deploys an AI chatbot or voice agent, customers assume, reasonably, that it has access to everything: their history,…
Ticket Resolution Is the Most Obvious AI Win in CX, and the Easiest to Get Wrong
If there’s one dimension every executive already has an opinion on when someone says “AI in customer service,” it’s this one. Ticket Resolution, whether customer issues get solved cleanly, ideally on the first contact, is the most visible, most measurable, and most heavily automated corner of the entire maturity model. It’s also where AI deployments…
Personalisation Used to Be a Cost Center. AI Just Turned It Into a Baseline Expectation.
For most of the last decade, real personalisation, beyond a first name in a subject line, was expensive enough that only the largest, most sophisticated organisations could justify it at scale. That excuse is gone. Generative AI has collapsed the cost of producing individually tailored content, offers, and interactions to the point where the constraint…
The Feedback Loop Your Organisation Has Been Meaning to Close: AI Can Actually Close It Now
Every CX leader has said some version of “we collect a lot of feedback, we just don’t always act on it fast enough.” That gap between collection and action, not the volume of feedback itself, is what the Learning & Improvement dimension measures, and it’s historically been one of the hardest to fix because closing…
Time-to-Value Is Still the Single Best Predictor of Retention. AI Just Made It a Lot Faster to Fix.
If you could only track one leading indicator of retention, most CS leaders would pick time-to-value: how quickly a customer reaches their first meaningful outcome after purchase. It predicts churn earlier and more reliably than almost any lagging metric, which is exactly why it’s weighted as a Foundational dimension, equal to Employee Enablement, in the…
Your Customers Are Paying for More Than They’re Using. AI Is the First Realistic Way to Close That Gap at Scale.
Under-adoption is one of the quietest, most expensive problems in any subscription or platform business: customers paying for capability they never activate, sitting invisibly until renewal, when the mismatch between price and perceived value suddenly becomes very visible. The Adoption Depth/Breadth dimension measures exactly this: how much of what customers are paying for they’re actually…
“Success” Isn’t a Feeling. It’s a Number You Should Be Able to Prove, and AI Makes Proving It Cheaper.
Ask most Account or Customer Success teams whether their customers are succeeding and you’ll get confidence. Ask them to prove it with a number, tied to a goal the customer actually agreed to, and the confidence usually thins out fast. That gap, between assumed success and demonstrated success, is what the Value Realization dimension measures,…
Churn Shouldn’t Be a Surprise Anymore. AI-Driven Health Scoring Is Why.
Of every dimension in the maturity model, Health & Risk is where AI and machine learning have been quietly doing real, load-bearing work the longest, well before generative AI made headlines. Predictive health scoring is one of the most mature applied-AI use cases in customer success, and it’s also one of the clearest places where…
Expansion Revenue Shouldn’t Depend on Someone Remembering to Ask. AI Can Spot It First.
Expansion revenue is one of the cheapest forms of growth available to any business with an existing customer base, and one of the most inconsistently captured, because it has historically depended on an account owner noticing a usage pattern, remembering to raise it, and timing the conversation well. The Expansion dimension measures how systematically that…
AI Can Find Your Best Advocates. It Can Also Make Your Outreach to Them Feel Fake. Both Are True.
Trust and advocacy are the compounding payoff of everything else in this model done well, and they’re also, deliberately, the lowest-weighted dimension in the entire framework. That’s not because they don’t matter, it’s because they’re a lagging outcome. You don’t build advocacy directly. You build it by getting enablement, understanding, resolution, time-to-value, and value realization…