Category Archives: Service

Why Customer Success Needs a “State of AI” Report

If you’ve spent any time on LinkedIn recently, you’ll have seen no shortage of posts about AI and Customer Success.

Some predict the end of the Customer Success Manager. Others claim AI will transform everything overnight. Most fall somewhere in between.

The problem is that there’s very little agreement on what’s actually happening.

After dozens of conversations with Customer Success leaders, SaaS executives and founders over the past year, I’ve noticed something interesting.

We’re all talking about AI.

We’re just not talking about the same thing.

Some teams are experimenting with meeting summaries and email drafts.

Others are building AI into customer workflows.

Some are measuring productivity.

Others are trying to rethink their entire post-sales operating model.

These are completely different conversations.

What seems clear is that AI has moved beyond experimentation. It is becoming part of day-to-day Customer Success.

The more interesting questions are no longer whether teams should use AI, but how they should use it and what impact it is really having.

From where I sit, a few themes keep appearing.

  • AI is removing repetitive work but increasing expectations.
  • It is exposing weak processes rather than fixing them.
  • It is making commercial judgement more valuable, not less.
  • It is forcing Customer Success leaders to rethink what great performance actually looks like.

I don’t think the biggest story is that AI is replacing Customer Success.

I think it’s changing what good Customer Success looks like.

That’s a much more interesting conversation.

Over the coming months, I’m going to capture the patterns I’m seeing through my advisory work, podcast conversations and discussions with Customer Success leaders.

The goal is simple.

To build a practical picture of how AI is really changing Customer Success, separating the hype from what’s happening in real SaaS organisations.

I’d love to hear what you’re seeing too.

What’s been the biggest change in your Customer Success team since AI became part of everyday work?

Why AI Is Exposing the Biggest Weakness in SaaS

Everyone seems to be asking how AI will change SaaS. But the bigger question is what AI Customer Success is already exposing about weak onboarding, poor adoption and broken customer ownership.

How will AI change SaaS?

I think they’re asking the wrong question.

The more interesting question is this.

What weaknesses has AI already exposed?

Because AI isn’t creating most of the problems we’re seeing.

It’s revealing the ones that were already there.

SaaS has relied on inefficient growth for years

For a long time, many SaaS businesses could grow despite themselves.

Products were new.

Competition was lower.

Customers accepted long onboarding projects.

Renewals often happened because switching was painful.

Growth covered a lot of operational weaknesses.

Today those weaknesses are becoming impossible to hide.

Customers now expect value immediately

AI has fundamentally changed customer expectations.

People no longer compare your onboarding to another SaaS vendor.

They compare it to using ChatGPT.

They expect answers instantly.

They expect software to guide them.

They expect adoption to happen naturally.

They expect measurable value much earlier.

If they don’t get it, they don’t wait six months.

They look elsewhere.

Most Customer Success teams cannot solve this alone

This is where many companies make another mistake.

They assume Customer Success owns retention.

It doesn’t.

Customer Success influences retention.

The business owns retention.

Poor implementation.

Weak product adoption.

Confusing pricing.

Low product quality.

Disconnected data.

Misaligned Sales promises.

These aren’t Customer Success problems.

They’re company problems.

Yet Customer Success is often expected to fix them anyway.

The companies winning with AI are doing something different

The most successful SaaS businesses aren’t simply adding AI features.

They’re redesigning how customers achieve outcomes.

They’re asking questions like:

  • Where does customer effort slow adoption?
  • Which moments predict long-term success?
  • What can AI automate without removing human relationships?
  • How quickly can a customer reach measurable value?

Notice the focus.

Not features.

Outcomes.

Customer Success becomes more commercial, not less

There’s a misconception that AI reduces the need for Customer Success.

I think the opposite is happening.

Routine work will increasingly disappear.

Status updates.

Basic reporting.

Simple customer queries.

Manual account preparation.

All of these become easier.

Which means Customer Success professionals have more time to do the work that actually moves revenue.

Driving adoption.

Identifying expansion opportunities.

Building executive relationships.

Helping customers realise measurable business value.

That’s where the future sits.

Founders should pay attention

If your AI strategy is focused only on product capability, you’re missing a much bigger opportunity.

Ask yourself:

  • How long does it take a customer to realise value?
  • Where do customers lose momentum?
  • Which teams own customer outcomes?
  • Can every leader explain why customers renew?

Because AI won’t fix a broken operating model.

It will simply expose it faster.

Final thought

The next generation of SaaS winners won’t be the companies with the smartest AI.

They’ll be the companies that remove friction from every stage of the customer journey.

AI is just the accelerator.

Retention is still the destination.

AI won’t fix your growth problem – it will expose it

The gap between what is sold, delivered and realised in SaaS customer success

Most SaaS companies have become very good at measuring what they sell.

Bookings.
ARR.
Pipeline.
Win rates.

Some are reasonably good at measuring what they deliver.

Implementation completed.
Training delivered.
Success plans created.
Support tickets resolved.

Far fewer can tell you whether customers are actually achieving the outcomes they bought the product to achieve in the first place.

That gap matters more than most leadership teams realise.

It’s the gap between what was sold, what was delivered and what was ultimately realised by the customer.

And it’s where churn begins.

The most dangerous metric nobody measures

Imagine a customer buys your platform to reduce onboarding time from 30 days to 10.

The sales team closes the deal.

The implementation team deploys the product.

The Customer Success team runs training sessions.

Usage looks healthy.

Everyone internally believes the account is a success.

But six months later the customer is still taking 28 days to onboard new users.

The software was deployed.

The customer logged in.

The project was completed.

Yet the outcome never happened.

From the customer’s perspective, the investment failed.

Most SaaS businesses have no systematic way of spotting this.

They track activity.

They track adoption.

They track engagement.

They rarely track whether the original business problem was solved.

Why AI is making this problem impossible to hide

Many SaaS leaders believe AI will help them improve retention.

They’re partly right.

AI can identify patterns humans miss.

It can analyse usage data at scale.

It can spot declining engagement.

It can flag customers who look likely to churn.

It can even suggest actions for Customer Success teams to take.

But there is a problem.

AI can only analyse the data it can see.

If your business isn’t measuring customer outcomes, AI cannot magically create them.

Instead, it exposes the weakness.

Faster.

More accurately.

And often more publicly.

AI might tell you a customer’s usage is falling.

What it cannot tell you is whether the customer ever achieved the outcome they bought your solution for.

Because most companies never captured that information in the first place.

The three gaps that quietly destroy growth

Gap 1: The sales reality gap

The customer buys based on one expectation.

The product delivers something slightly different.

Nobody notices until renewal.

The wider the gap between promise and reality, the greater the retention risk.

Gap 2: The delivery gap

The customer receives the software but never fully adopts it.

Features are available.

Processes remain unchanged.

People revert to old ways of working.

The implementation succeeds.

The transformation fails.

Gap 3: The value realisation gap

The customer uses the platform regularly.

Adoption metrics look healthy.

Yet the business outcome never materialises.

Usage exists.

Value does not.

This is often the most dangerous gap because traditional SaaS metrics make everything look healthy.

Why expansion revenue depends on realised value

Many leadership teams view renewals and expansion as separate motions.

Customers don’t.

A customer who achieves meaningful outcomes naturally becomes more open to expansion.

A customer who is still waiting to see value rarely wants to buy more.

This is why the strongest Net Revenue Retention figures usually come from organisations that obsess over customer outcomes rather than product usage.

Expansion is often a lagging indicator of realised value.

Customers buy more when they believe the first investment worked.

Simple.

The question every leadership team should ask

If I stopped one of your account teams in the corridor and asked:

“Why did this customer buy?”

Could they answer?

More importantly:

“What measurable business outcome has the customer achieved since buying?”

Could they answer that too?

If not, you may have a value realisation problem hiding behind healthy-looking adoption metrics.

What to do next

  1. What business outcome were they trying to achieve when they purchased?
  2. How will that outcome be measured?
  3. Has it actually been achieved?

Not implemented.

Not trained.

Not onboarded.

Achieved.

Then build your customer operating model around those answers.

Track them during onboarding.

Review them in customer meetings.

Report them to executives.

Use AI to help analyse them.

But don’t expect AI to create them.

Because AI won’t fix your growth problem.

It will expose it.

And if your customers are not achieving value today, the evidence is about to become impossible to ignore.

Final thought

The next generation of SaaS winners won’t be the companies with the most AI.

They’ll be the companies with the clearest understanding of customer value.

AI will simply make that difference visible.

The revenue leak starts after the sale

Most SaaS founders know revenue is leaking somewhere.

They can see it in the numbers.

Renewals are harder than they should be.
Expansion is slower than expected.
NRR starts to drift.
Customers look fine until they suddenly are not.

So the business looks at the usual suspects.

The renewal process.
The onboarding journey.
The sales handover.
The health score.
The CSM cadence.

All useful places to look.

But often, they are not where the leak starts.

The leak starts earlier.

It starts the moment a customer signs and nobody clearly owns whether they achieve what was promised.

The deal is not the outcome

A signed deal feels like progress.

And it is.

But for the customer, nothing has been realised yet.

  • They have not bought a contract
  • They have not bought access to software
  • They have not bought an onboarding plan

They have bought an outcome.

That might be faster reporting, lower risk, reduced manual work, better visibility, higher productivity or stronger revenue performance.

Whatever it is, someone inside your business needs to own whether the customer gets there.

  • Not loosely
  • Not culturally
  • Not “the team”

Actually own it.

Because when everyone owns the customer outcome, nobody does.

Where the gap opens

The gap opens between what sales commits and what the customer experiences.

  • Sales sells the promise
  • Onboarding gets the customer live
  • Customer Success runs the cadence
  • Support handles the issues
  • Product tracks usage
  • Finance watches the renewal number

But who owns the customer getting what they paid for?

That is the uncomfortable question.

Because in many SaaS businesses, the answer is vague.

  • Everyone is involved
  • Nobody is accountable

And that is where churn starts to build.

  • Not at renewal
  • Not when the customer complains
  • Not when usage drops

Much earlier.

It starts when the business loses sight of the original promise.

Usage is not proof of value

This is where many SaaS teams fool themselves.

A customer can be onboarded and still not be successful.

They can log in and still not see value.

They can attend QBRs and still not believe the product is critical.

They can use the software and still struggle to explain the commercial impact.

Usage matters.

But usage is not the same as value.

Activity is not the same as progress.

A completed onboarding is not the same as a customer who has achieved what they were sold.

That distinction matters because renewals are not won by activity.

They are won by evidence of value.

Customer Success cannot fix this alone

When retention weakens, companies often assume they have a Customer Success problem.

Sometimes they do.

But often, they have a post-sale ownership problem.

Customer Success cannot fully own value if the sales promise was never captured properly.

It cannot prove impact if success criteria were never agreed.

It cannot drive expansion if the customer never reached first value.

It cannot rescue a relationship if the business treated the sale as the finish line.

CS has a critical role.

But post-sale value is not a department.

It is a commercial operating discipline.

That means sales, onboarding, product, support, finance and leadership all need to be aligned around the same question:

Did the customer get what they paid for?

A simple test

Ask five questions after every deal closes:

  1. What outcome did the customer buy?
  2. What did sales promise or imply?
  3. Who owns first value?
  4. How will we prove value has been realised?
  5. When will leadership know if the customer is off track?

If the answers are vague, you have found the leak.

Not a theoretical leak.

A commercial one.

The kind that shows up later as slow adoption, weak expansion, renewal risk and declining NRR.

The real ownership question

The best SaaS businesses do not treat the sale as the finish line.

They treat it as the start of the value obligation.

That is the shift.

From “we sold it” to “they realised it”.

From customer handoff to customer ownership.

From activity tracking to value evidence.

So the real question is not:

“Do we have a Customer Success team?”

Most companies do.

The better question is:

Who in your business owns what happens after the sale?

If the answer is unclear, that is where the revenue is leaking.

SaaS isn’t dying – weak retention is

“SaaS is dead.”

We’re hearing this a lot this year.

And to be fair, some SaaS companies probably are in trouble.

But AI is not the real reason.

Most of the companies struggling right now were already vulnerable long before generative AI arrived.

They had:

  • weak onboarding
  • poor adoption
  • low product engagement
  • unclear customer ownership
  • expansion that depended on heroic account management
  • customers renewing because switching felt painful rather

AI didn’t create those problems.

It exposed them faster.

The real shift happening in SaaS

For years, many SaaS businesses benefited from inertia.

Once embedded, software tended to stay.

Customers tolerated:

  • clunky workflows
  • slow implementation
  • weak reporting
  • poor support experiences
  • limited adoption
  • shelfware across departments

Not because they loved the product.

Because replacing it felt harder than keeping it.

That equation is changing very quickly now.

AI has introduced a completely different conversation into boardrooms and leadership teams:

“Why are we paying for this software at all?”

That question creates pressure everywhere:

  • product
  • pricing
  • onboarding
  • adoption
  • renewals
  • Customer Success

And the companies that cannot answer it clearly are starting to feel exposed.

AI is compressing the time between disappointment and churn

This is the bit many SaaS leaders still underestimate.

AI is accelerating customer expectations far faster than most operating models can adapt.

Customers now expect:

  • faster outcomes
  • simpler workflows
  • better automation
  • more intelligence
  • less manual effort
  • clearer commercial value

That means weak post-sale execution becomes visible much earlier.

Poor onboarding becomes obvious faster.

Weak adoption becomes measurable faster.

Slow time-to-value becomes commercially dangerous faster.

The old SaaS model could sometimes survive despite operational friction.

That gets much harder when buyers believe AI alternatives may exist.

Especially when CFOs are looking for areas to reduce software spend.

This is why retention now matters so much commercially

A lot of SaaS leadership teams still talk about retention as if it is a support metric.

It is not.

Retention has become a proxy for business quality.

Investors know it.
Acquirers know it.
Boards know it.

Strong retention signals:

  • real customer value
  • embedded workflows
  • operational dependency
  • trusted relationships
  • durable revenue
  • expansion potential

Weak retention signals the opposite.

That is why the valuation gap between strong-retention SaaS businesses and weak-retention SaaS businesses is becoming much more obvious.

The market is rewarding companies that genuinely keep and grow customers.

And increasingly punishing those that relied on inertia.

Customer Success teams are feeling the pressure first

This is where the conversation becomes uncomfortable.

Many Customer Success teams were never actually designed to drive commercial outcomes.

They were designed to:

  • manage accounts
  • run QBRs
  • maintain relationships
  • respond to issues
  • improve sentiment

That was often enough when SaaS budgets were expanding rapidly.

It is not enough now.

Because renewal conversations no longer start 90 days before contract end.

They start much earlier.

They start the moment a customer begins questioning:

  • platform value
  • adoption
  • efficiency
  • commercial return
  • internal usage
  • AI alternatives

That changes the role of Customer Success entirely.

The strongest CS teams now operate much closer to:

  • value realisation
  • operational alignment
  • commercial outcomes
  • adoption strategy
  • expansion enablement
  • executive stakeholder management

The role is becoming more strategic because the pressure on proving value has become more strategic.

The SaaS companies surviving this shift look very different

The companies navigating this period well usually have a few things in common.

They:

  • reach value quickly
  • embed deeply into workflows
  • align product usage to operational outcomes
  • create executive-level customer relationships
  • treat onboarding as revenue infrastructure
  • measure customer progression rather than customer activity

Most importantly, they make themselves difficult to remove because customers can clearly see the business impact.

Not because migration sounds painful.

That distinction matters far more in the AI era.

SaaS is not dying

Weak SaaS is.

The companies in trouble are often the ones that:

  • over-relied on feature growth
  • underinvested in customer outcomes
  • confused activity with value
  • treated Customer Success as a service layer rather than a growth function

AI simply accelerated the correction.

And in truth, that correction was probably coming anyway.

The SaaS businesses that survive the next few years will not necessarily be the ones with the most AI features.

They will be the ones that can consistently prove operational value after the sale.

Because in the end, customers do not renew software.

They renew outcomes.

Related reading

AI is exposing weak post-sale execution much faster than most SaaS companies realise.

You can also read more here:

Where this becomes a growth problem

Most retention problems are not renewal problems.

They are onboarding, adoption and value realisation problems that appear later in revenue numbers.

That’s exactly the gap I help SaaS companies fix.

The AI Tourist Effect

There’s a number circulating right now that should make every SaaS founder and leader sit up.

AI-native SaaS products have a median gross revenue retention rate of 40%. For context, the B2B SaaS median is 82%. That’s not a rounding error – it’s a 42-point gap and it’s prompting a lot of people to conclude that AI-native products are fundamentally harder to retain.

They’re wrong. And the data proves it.

What “AI Tourist” Actually Means

ChartMogul coined the phrase “AI tourist” to describe what’s driving this churn pattern. Users sign up because the product looks interesting, the demo is slick and the pricing is low enough to not require a procurement conversation. They experiment for a few weeks, hit a wall – maybe the use case isn’t quite right, maybe a competitor ships something shinier – and they’re gone.

This isn’t a story about bad AI. It’s a story about how people buy things they don’t need to commit to.

When the barrier to entry is $19 or $29 a month, the barrier to exit is equally low. There’s no contract to navigate, no internal stakeholder who championed the purchase, no workflow that breaks if the tool disappears. The customer signed up alone and they’ll leave alone.

That’s the AI tourist. They came for a look around. They were never planning to stay.

The Price Point Data Is Striking

Here’s where the conventional narrative falls apart.

ChartMogul’s data segments AI-native SaaS products by price point, and the retention picture is almost entirely determined by what you charge – not what the AI does.

Products priced above £200 (roughly $250) per month see 70% gross revenue retention and 85% net revenue retention. That’s essentially the same as traditional B2B SaaS. Products priced between $50 and $249 per month see 45% GRR and 61% NRR. Products priced below $50 per month see 23% GRR – meaning nearly 80% of revenue churns out within a year.

Same technology. Same category. Entirely different retention profile.

The variable isn’t the product. It’s who’s buying it, how they’re buying it and what’s at stake if they stop.

Why This Matters for CS Teams

If you’re a leader at an AI-native company and your retention numbers look ugly, the temptation is to reach for customer success playbooks. More onboarding touchpoints. Better health score models. Proactive outreach at 30 days.

Some of that will help at the margins. But if your core problem is that you’re selling a $29/month tool to individuals who don’t have a business outcome attached to it, no customer success motion fixes that. You’re putting a retention programme on top of a churn machine.

The commercial diagnosis here is uncomfortable but important: the customer success function can’t compensate for a flawed go-to-market model.

What you can do – and this is where customer success genuinely earns its seat in the room – is make the business case for moving upmarket. The data is unambiguous. When AI-native products sell to genuine business buyers at $250+ per month, they retain like traditional SaaS. The AI isn’t the risk. The self-serve, consumer-grade pricing model is.

What Moving Upmarket Actually Looks Like

Moving upmarket isn’t just raising prices. It’s a set of decisions about who you sell to, how they buy and how deeply embedded your product becomes in their actual workflows.

A few things tend to shift when you go from self-serve consumer to B2B buyer.

There’s a procurement process, which means there’s an internal champion. That person’s reputation is partly tied to the tool working. They don’t cancel without a conversation first.

There’s an integration layer. Your product connects to their CRM, their Slack, their ticketing system. Ripping it out takes effort. Switching costs go up.

There’s an outcome attached. The business bought the tool to achieve something specific – reduce support volume, increase renewal rates, speed up onboarding. You can measure it. You can show progress. That’s the foundation of a genuine retention conversation.

None of this happens at $25 or £25 a month. It happens when the deal is significant enough that both sides take it seriously.

The Lesson That Extends Beyond AI-Native SaaS

The AI tourist effect isn’t just a problem for AI-native companies. It’s a warning signal for any SaaS business that’s drifted toward self-serve growth without examining the retention consequences.

The economics look attractive at first. Low CAC, fast time-to-revenue, no sales headcount required. But if you’re churning 60%-70% of revenue annually, you’re running a leaky bucket – and growth is just masking the leak.

The companies that will build durable NRR in this environment are the ones embedding themselves into critical business workflows, selling to buyers who have accountability for outcomes and building customer success motions that tie directly to measurable value.

That’s not a new idea. But the AI tourist data gives it a specificity that’s hard to argue with.

The Practical Takeaway

If you’re a customer focused leader in an AI-native company, here’s the question worth putting to your leadership team: are the customers we’re churning the ones who were ever going to stay?

If the answer is no – if you’re churning self-serve, low-ACV, low-commitment buyers at the bottom of your customer base – then the retention conversation needs to start with pricing and ICP, not onboarding sequences.

The AI isn’t the problem. The market motion is.

Get that diagnosis right and the customer success playbook becomes much more straightforward.

Want to Talk Through What This Means for Your Business?

If you’re a SaaS leader wrestling with retention in an AI-native product – or advising a company that is – I work with customer success and revenue teams to build operating models that hold up under board scrutiny.

Book a 30-minute conversation and let’s look at the actual numbers together.

Or follow me on LinkedIn where I write about this kind of thing every week – commercial customer success, retention mechanics and what’s actually changing in the post-sale world.

Sources: ChartMogul SaaS Retention Report – The AI Churn Wave (2025/2026); ChartMogul The New Normal for SaaS Retention

Most SaaS Companies Don’t Need A Bigger Customer SuccessTeam

Why Hiring Another CSM Rarely Fixes Churn

Most SaaS companies respond to churn the same way.

They hire…

  • Another CSM
  • Maybe a Head of Customer Success
  • Sometimes a VP

It feels like progress, but it rarely fixes the real problem.

Because churn is not always a capacity issue.

More often, it is a design issue .

Infographic to download and save below…

The Real Problem

If your customer success function is not clearly driving retention and expansion, adding more people usually creates more activity.

More calls
More QBRs
More dashboards
More internal updates

But not necessarily better outcomes and often, no clear link to revenue.

That is where many SaaS companies get stuck.

They build a bigger customer success team before they have built a proper customer success model.

Where A Customer Success Advisor Fits

A customer success advisor does not simply add more hands.

They help fix the operating model.

That usually means:

  • defining what customer value actually looks like
  • aligning sales, customer success and product around that value
  • building a repeatable system for retention and expansion
  • creating earlier visibility of renewal risk
  • making customer success commercially accountable

This is not about doing more.

It is about doing the right things consistently.

The Pattern I See Repeatedly

Across SaaS companies, the pattern is often the same.

  • Sales sells one version of value
  • The customer expects something slightly different
  • Customer success tries to bridge the gap
  • Product is not always close enough to what is happening after the sale
  • Leadership only sees the issue when churn appears

By then, the problem has usually been building for months.

Why This Matters Now

AI is making this more obvious.

It is now easier than ever to:

  • automate activity
  • generate insights
  • scale customer outreach
  • produce reports
  • identify signals

But none of that fixes a broken model.

In fact, AI can make the problem worse.

If the customer success model is unclear, AI simply helps you scale unclear work faster.

That is not transformation. That is organised noise.

What Good Looks Like

When customer success is working properly, the business feels different.

Retention becomes more predictable.

Expansion becomes systematic, not opportunistic.

Customer success has a clear commercial role.

Sales, product and CS are aligned around the same customer outcomes.

Leadership trusts the numbers because the numbers connect to reality.

That is the shift from customer success as support to customer success as a growth engine.

Practical Takeaway

Before hiring another CSM, ask one question:

Do we know exactly how customer success drives revenue in our business?

Not in theory.

In practice.

  • By segment
  • By customer stage
  • By renewal risk
  • By expansion opportunity
  • By measurable value delivered

If the answer is not clear, another hire will not fix it.

It may simply make the current problem more expensive.

Final Thought

If you are trying to improve retention or make customer success more commercially accountable, the real work usually starts before the next hire.

It starts with the model.

The team can only scale what the business has designed properly.

👉 Customer success advisor

Why SaaS Growth Breaks After the Sale (and How to Fix It)

Introduction

Most SaaS companies think they have a pipeline problem.

They don’t.

They have a conversion problem after the deal.

Revenue doesn’t just come from winning customers. It comes from what happens next. – adoption, value realisation and expansion.

And this is where growth quietly breaks.

The Real Problem Isn’t Sales

SaaS leaders spend a lot of time optimising:

  • Lead generation
  • Conversion rates
  • Sales efficiency

But once the deal is signed, things become far less structured.

The assumption is simple:

“We’ve sold the value, now the customer will realise it.”

In reality, that rarely happens consistently.

Where Growth Actually Breaks

Growth doesn’t fail at the point of sale.

It breaks in the gap between:

  • What was sold
  • What is delivered
  • What the customer actually achieves

This shows up in familiar ways:

  • Slow or partial adoption
  • Customers using features but not achieving outcomes
  • Expansion opportunities that never materialise
  • Churn risk appearing late in the lifecycle

By the time it’s visible, the damage is already done.

The Hidden Revenue Leak

Every SaaS business has a flow:

Sale → Adoption → Value → Expansion

If that flow is not connected and managed:

Revenue is lost.

Not always immediately.

But over time:

  • Deals don’t expand
  • Renewals become harder
  • Growth becomes unpredictable

This is why many companies feel like they are working harder for the same results.

Because they are.

Why Customer Success Alone Doesn’t Fix It

This is often labelled as a Customer Success issue.

It isn’t. It’s a system issue.

Common problems include:

  • Weak handover between sales and post-sale teams
  • No clear definition of what “value” actually means for the customer
  • Lack of ownership for outcomes
  • Misalignment between sales, CS and product

Customer Success can’t fix this in isolation. Because the problem doesn’t sit in one team.

What High-Growth SaaS Companies Do Differently

Companies that scale efficiently do one thing well:

They control what happens after the sale.

That means:

  • Clear alignment between sales, CS and product
  • Defined customer outcomes from day one
  • Strong ownership of value realisation
  • Early visibility of risk and opportunity
  • A structured path to expansion

In these organisations, growth feels more predictable. Because it is.

How to Fix Where Growth Breaks

If you want to improve growth, start here:

1. Define Value Clearly:

What does success look like for the customer? Not usage. Not features. Outcomes.

2. Strengthen the Handover:

Make sure context, expectations and goals carry through from sales into delivery.

3. Track Leading Indicators:

Don’t wait for churn. Track adoption, engagement and value signals early.

4. Align Teams Around Outcomes:

Sales, customer success and product need to be aligned around customer success, not just their own metrics.

5. Build a Path to Expansion:

Expansion should not be opportunistic. It should be designed into the lifecycle.

Final Thought

Most SaaS companies don’t struggle to win customers. They struggle to turn those customers into long-term, expanding revenue.

That’s where growth really happens. And that’s where it often breaks.

Call to Action

If growth feels harder than it should, it’s worth asking a simple question:

Where does it break after the sale?

How AI Will Impact Customer Success Teams

For the past two years there has been a loud debate about AI in Customer Success.

  • Will it replace Customer Success Managers?
  • Will AI run renewals?
  • Will customers even want to speak to humans?

These are great questions but they miss the real shift that is happening.

AI will not replace Customer Success. It will expose weak Customer Success teams.

Because when automation removes the operational work, what remains is the hardest part of the role. Helping customers achieve real business outcomes.

The Work That AI Will Remove

A large part of Customer Success work today is operational.

  • Preparing reports
  • Updating dashboards
  • Summarising meetings
  • Chasing internal teams for updates

This is not where value is createdIt is simply the administrative cost of running a customer-facing organisation. AI is already automating much of this work.

AI is changing how customer success teams operate. I’ve broken this down in detail in this guide to AI in customer success.

Modern Customer Success platforms can:

  • summarise customer calls
  • analyse product usage data
  • identify churn risk signals
  • generate account summaries
  • recommend next best actions

According to the Gainsight Customer Success Index, 52% of Customer Success teams already use AI in their workflows, and 91% expect it to significantly impact their strategy.

Source

In other words, the administrative layer of Customer Success is starting to disappear and that is where the real shift begins.

What Remains When Automation Arrives

When AI removes the operational workload, something interesting happens. The role becomes clearer. Customer Success is not about managing activity. It is about creating value.

The companies getting this right focus Customer Success on three things.

(1) Helping customers achieve measurable outcomes:

Not feature adoption. Not product usage. Actual business impact.

(2) Connecting product capabilities to customer strategy:

The best CSMs increasingly look more like advisors than account managers
They understand the customer’s business model and help align technology to it

(3) Identifying growth opportunities

Expansion revenue rarely comes from sales pressure
It comes from customers realising the product is solving real problems
When that happens, growth follows naturally

The Uncomfortable Reality For Many Teams

Many Customer Success organisations were built for a different era. An era where simply maintaining relationships created value.

That model is under pressure. AI will not remove Customer Success roles. But it will remove the work that previously hid weak operating models. Teams that rely on:

  • manual reporting
  • reactive engagement
  • relationship management without measurable outcomes

… will find it harder to justify their role. Because AI can replicate most of that activity.

What AI cannot replace (today) is commercial judgement and strategic thinking.

What Great CSMs Will Look Like In 5 Years

As automation spreads across the industry, the profile of a strong Customer Success Manager will change. Three capabilities will matter far more than they do today.

(1) Business understanding: CSMs will need to understand how their customers actually create value. Industry knowledge becomes more important than product knowledge.

(2) Commercial thinking: Great Customer Success leaders will understand how retention, expansion and customer outcomes connect. They will think like revenue leaders.

(3) Strategic communication: Customers do not need another dashboard walkthrough. They need someone who can translate product capability into business impact.

The role then becomes far less operational and more advisory and strategic.

What Leaders Should Do Now

If you run a SaaS business, AI should not be viewed as a headcount reduction tool inside Customer Success. It should be viewed as a capability shift. The goal is not to remove Customer Success.

The goal is to remove the operational friction around it. This allows teams to focus on the work that actually drives growth. Helping customers succeed.

Because in subscription businesses, customer success is not just a support function. It is one of the most powerful growth levers a company has.

The Real Impact Of AI On Customer Success

The future of Customer Success will not be defined by automation. It will be defined by clarity. Once AI removes the administrative work, the purpose of the function becomes obvious. Customer Success exists to help customers achieve outcomes that make them want to stay. And to expand.

The teams that can demonstrate that clearly will become more valuable than ever.

The teams that cannot will struggle to justify their place.

Because AI does not remove Customer Success, it exposes it.

Want To Go Deeper?

If you’re thinking about retention, expansion or how AI fits into your strategy, I work with a small number of SaaS companies on exactly this.

👉 If you’re rethinking your approach, let’s talk.

AI has already arrived in Customer Success, the question now is what we do with it?

AI isn’t coming for Customer Success jobs. It’s already here.

Most of the work we used to treat as essential has quietly moved into the background.
Health scores. Renewal reminders. Basic check-ins. The admin that once filled our days is now handled by systems that never get tired.

And that’s a good thing.

Because it forces us to confront something the industry has avoided for too long. The value of a CSM has never been in tasks. It’s always been in thinking.

AI in customer success.

The Shift Nobody Can Ignore

As automation expands across the customer journey, the gap between activity and impact gets wider. CS teams that still define value by volume will struggle. Teams that define value by commercial thinking will thrive.

In 2025, every CSM needs a commercial mindset. Not to sell. But to understand the economics behind every customer conversation.

That’s the real shift.

Why Commercial Thinking Matters

The best CSMs already know the answer to one simple question:

How does our product make this customer more successful in a measurable way?

  • They understand margins
  • They understand cost savings
  • They understand how technology changes the economics of a customer’s business

They talk about return not renewal. Impact not activity. Value not volume.

And that’s where AI draws a sharper line than many expected.

If you’re looking at how AI fits into customer success more broadly, this guide to AI in customer success breaks it down.

Automation Doesn’t Replace Judgment

AI can process. It can surface insights. It can flag risk.

But it can’t replace partnership, judgment or business acumen.

These are the skills that turn a CSM from a task executor into a strategic operator. These are the skills that get Customer Success a seat at the table. And these are the skills that AI only makes more important, not less.

The New Line Between Replaceable & Essential

As more of the workflow becomes automated, the industry is heading towards a split. Not between junior and senior roles. But between replaceable and essential roles.

Replaceable roles cling to tasks. Essential roles understand the customer’s economics and use that understanding to guide decisions.

That’s where the profession is heading. And it’s where the opportunity lies for every CSM who wants to build a durable career as AI scales.

Here’s The Real Question

If Customer Success wants to evolve, it has to speak the language of business, not tasks. It has to show how decisions drive outcomes, not how many activities got logged.

And that means one thing.

Every CS leader needs to help their teams build commercial muscle.

Every CSM needs to learn how to anchor conversations in value.

And every organisation needs to decide whether CS will be a cost centre or a growth engine.

AI has already changed the role. Now it’s on us to decide what Customer Success becomes next.

How are you helping your team develop that commercial edge this year?

Want To Go deeper?

If you’re thinking about retention, expansion or how AI fits into your strategy, I work with a small number of SaaS companies on exactly this.

👉 If you’re rethinking your approach, let’s talk.