Tag Archives: SaaS

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 will not fix your Customer Success model – it will expose it

The wrong AI question in Customer Success

Most SaaS companies are asking how AI can make CS more efficient.

The better question is what broken post-sale process they are about to automate.

  • AI makes weak post-sale execution more visible
  • Unclear onboarding becomes faster confusion
  • Poor sales handoffs become cleaner-looking ambiguity
  • Activity-based CS becomes automated noise
  • Weak ownership becomes harder to hide
  • Efficiency is only useful when the model is right

Faster is not automatically better.

AI can help CS teams move faster, but only if the work is pointing at realised customer value.

What an AI-ready CS model needs

  • Clear customer outcomes
  • Faster time to value
  • Better handoffs
  • Value-based health signals
  • Commercial accountability

The real opportunity

The winners will not be the companies that automate the most.

They will be the companies that redesign post-sale around value first, then use AI to scale what works.

Before you automate CS, fix the model

I help B2B SaaS companies fix the gap between what is sold, what is delivered and what customers actually achieve. If retention, expansion or AI-enabled CS is on your leadership agenda, this is the work to start with.

See how I help SaaS companies improve retention, expansion and customer value.

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 have a sales problem – they have a value delivery problem

Your sales engine is running.

Pipeline looks healthy. New logos are coming in. Revenue targets appear achievable.

But your NRR is telling a different story.

Somewhere between contract signature and renewal, growth is leaking.

Customers are going live but not expanding. Adoption exists but commercial momentum stalls. Customer Success teams are busy, yet leadership still struggles to clearly connect post-sale activity to retention and expansion outcomes.

This is one of the most common problems in SaaS right now.

And most companies feel it long before they can properly articulate it.

Why SaaS growth leaks after the sale

Most SaaS organisations are heavily optimised around acquisition.

Sales, marketing and product investment often dominate strategic conversations because they are easier to measure in the short term. Pipeline growth feels visible. New ARR feels tangible.

Post-sale execution is different.

The warning signs usually appear gradually:

  • Renewals become reactive
  • Expansion becomes unpredictable
  • Onboarding drags
  • Customers stay “active” without achieving meaningful outcomes
  • Customer Success becomes overloaded with activity but commercially unclear

Over time, growth slows despite continued acquisition investment.

That is when leadership teams start asking harder questions about retention, customer value and operational efficiency.

The gap between adoption and customer value

One of the biggest mistakes SaaS companies make is confusing product usage with realised value.

A customer logging in regularly does not automatically mean they are successful.

Many organisations measure:

  • logins
  • feature usage
  • meeting volume
  • ticket response times

But customers do not renew because they attended QBRs.

They renew because the product helped them achieve a business outcome that mattered.

That gap between activity and realised value is where many SaaS businesses quietly lose expansion opportunities.

The strongest Customer Success organisations understand this clearly. They align onboarding, adoption and ongoing engagement around measurable customer outcomes rather than internal process metrics.

Why Customer Success becomes commercially unclear

In many SaaS businesses, Customer Success evolves reactively.

The function grows quickly as customer numbers increase, but ownership boundaries often remain vague.

Sales owns revenue.

Support owns problems.

Product owns features.

Customer Success ends up sitting somewhere in the middle trying to hold everything together.

The result is predictable:

  • unclear commercial accountability
  • inconsistent customer experiences
  • fragmented definitions of value
  • poor cross-functional alignment

This is why some CS teams appear extremely busy while leadership still struggles to see measurable commercial impact.

The issue is rarely effort. It is operating design.

We recently discussed this exact shift on our podcast Breakthrough SaaS Growth with The Jasons in our episode From Customer Success to Customer Growth – The Next Evolution of SaaS, where we explored why Customer Success is increasingly evolving into a commercial growth function rather than simply a post-sale support layer.

How AI is exposing weak post-sale execution

As I covered in my article on AI-enabled Customer Success, the companies gaining advantage are not simply automating support tasks. They are redesigning how customer value is delivered.

Not because AI itself is causing churn, but because it exposes operational weaknesses much faster than before.

Weak onboarding becomes visible earlier.

Poor adoption patterns surface sooner.

Customers expect faster time-to-value and more proactive engagement.

Companies that simply automate broken post-sale processes will struggle.

The organisations gaining advantage are the ones redesigning how customer value is delivered in the first place.

They are using AI to:

  • identify churn risk earlier
  • improve customer visibility
  • reduce onboarding friction
  • surface expansion opportunities
  • scale proactive engagement

But the technology only works when the underlying operating model is aligned around customer outcomes.

AI does not fix value delivery problems.

It exposes them.

What high-performing SaaS companies do differently

The strongest SaaS companies treat post-sale execution as a growth function, not a support function.

They align sales, product and Customer Success around shared customer outcomes.

They focus on:

  • faster time-to-value
  • measurable business impact
  • operational clarity
  • scalable customer engagement
  • retention and expansion as board-level metrics

Most importantly, they understand that sustainable SaaS growth does not break at acquisition.

It breaks after the sale when customers stop progressing.

By the time NRR starts falling, the underlying problems have usually existed for months.

Sometimes years.

That is why the companies outperforming right now are not necessarily the ones automating the most.

They are the ones aligning their organisation around realised customer value.

Many SaaS companies already know something is breaking post-sale. The challenge is diagnosing where the operational gaps actually sit.

That’s typically where a Customer Success advisor can help bring clarity.

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

Customer Retention vs. Acquisition in SaaS – What Actually Drives Growth

Most SaaS companies are built to acquire customers. Very few are built to keep and grow them.

That’s the problem.

Because in SaaS, growth doesn’t come from how many customers you win. It comes from what happens after the sale.

The traditional thinking

The default model is simple:

  • Invest in marketing
  • Drive pipeline
  • Close new customers
  • Repeat

It works. Until it doesn’t.

Because over time:

  • Customer acquisition gets more expensive
  • Sales cycles get longer
  • Conversion rates drop

And suddenly, growth slows.

The reality of SaaS growth

In SaaS, retention is not a support metric. It’s the foundation of growth.

If customers don’t stay, nothing compounds.

If they don’t expand, revenue stalls.

That’s why the strongest SaaS companies focus on:

  • High retention
  • Consistent expansion
  • Fast time to value

Not just new logo acquisition.

Why retention beats acquisition

Acquisition creates revenue. Retention protects it. Expansion multiplies it.

Without retention:

  • You’re constantly replacing lost revenue
  • Growth becomes fragile
  • Forecasting becomes unreliable

With strong retention:

  • Revenue compounds
  • Customer lifetime value increases
  • Growth becomes predictable

Where most SaaS companies get it wrong

They treat Customer Success as a support function.

Not a growth function.

So you see:

  • Reactive customer teams
  • No clear ownership of expansion
  • Metrics focused on activity, not outcomes
  • No alignment between sales and Customer Success

The result?

Retention underperforms and expansion never quite materialises.

The shift from Customer Success to Customer Growth

The best SaaS companies are making a shift.

From:

👉 Customer Success as support

To:

👉 Customer Success as a revenue engine

That means:

  • Defining customer value early
  • Aligning sales and post-sale around outcomes
  • Tracking leading indicators, not just churn
  • Building expansion into the lifecycle

What this means for founders and CEOs

If you’re leading a SaaS business, this is not a trade-off.

You need both, but the balance matters.

If your growth is driven only by acquisition:

  • You’ll always be under pressure to sell more
  • Your cost of growth will keep increasing

If your growth is driven by retention and expansion:

  • Revenue compounds
  • Growth becomes more efficient
  • The business becomes more valuable

Bringing it together

Customer acquisition gets you started. Customer retention and expansion are what scale the business.

That’s the difference between:

👉 chasing growth and…
👉 building a growth engine

CTA (link this properly)

If you’re thinking about how to improve retention, reduce churn or build a Customer Success function that actually drives growth:

👉 See how I work as a fractional Customer Success leader.

AI in Customer Success: A Practical Guide for SaaS Leaders

AI is reshaping customer success. Not by replacing people, but by changing how decisions are made.

For SaaS companies, growth no longer depends on sales alone. It depends on what happens after the deal. This is exactly how I approach customer-led growth.

Retention, expansion and customer value are now the real drivers of revenue.

AI is accelerating that shift.

What AI In Customer Success Actually Means

AI in customer success is not about automation for its own sake.

It’s about improving how teams:

  • Identify risk earlier
  • Spot expansion opportunities
  • Guide customers towards value
  • Focus on what actually matters

The goal is not more activity. It’s better decisions.

Why AI Is Changing SaaS Retention & Growth

Traditional customer success models rely on:

  • health scores
  • manual check-ins
  • reactive engagement

These approaches are limited.

They depend on lagging indicators and human bandwidth.

AI changes this by:

  • analysing usage data in real time
  • identifying patterns across customers
  • surfacing signals before problems are visible

This shifts customer success from reactive to proactive and is already visible in how teams are evolving, as outlined in how AI will impact customer success teams.

Where AI Really Creates Value

The real impact of AI in customer success comes from a few specific areas.

Risk detection:

AI can identify churn risk before the customer feels it. Changes in behaviour, usage drops or engagement patterns can be flagged early, giving teams time to act.

Expansion signals:

AI can highlight where value is already being created. This allows teams to focus expansion conversations on real outcomes, not assumptions.

Time to value:

AI can guide the next best action for customers. This reduces friction, shortens onboarding and helps customers reach value faster.

Focus & prioritisation:

Customer success teams often manage too many accounts with too little time.

AI helps prioritise where attention is needed most, improving both efficiency and impact.

What Most Companies Get Wrong

Adding AI on top of a weak customer success strategy does not fix the problem.

It amplifies it.

Common mistakes include:

  • unclear definition of customer value
  • misalignment between sales, product and customer teams
  • over-reliance on generic health scores
  • focusing on activity instead of outcomes

If you don’t know what value looks like for your customer, AI won’t solve that.

How To Implement AI In Customer Success

The starting point is not technology. It’s clarity.

1. Define customer value clearly

What outcomes does your customer care about? Revenue, efficiency, risk reduction, growth. Be specific.

2. Align teams around those outcomes

Sales, product and customer success need to agree on what success looks like. Otherwise, AI will surface data but not meaning.

3. Use AI for signal, not noise

Focus on:

  • risk indicators
  • expansion triggers
  • usage patterns

Avoid drowning teams in dashboards.

4. Combine AI with human judgement

AI prepares. Humans decide. The best teams use AI to reduce uncertainty, not replace thinking.

Final Thought

AI does not fix customer success. It exposes whether you understand customer value. The gap between average and high-performing teams is about to get much wider.

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.

Why SaaS Companies Outgrow Their Customer Success Model

Customer success rarely breaks overnight. What usually happens is much quieter.

  • The company grows
  • The customer base expands
  • Contract values increase

And the customer success model that worked in the early years slowly stops working.

Most leadership teams don’t notice this immediately.

  • The team still looks busy
  • Customers are still renewing
  • The product still works

But under the surface the system starts to drift.

The Moment SaaS Companies Start Feeling It

There are a few signals that usually appear around the same time.

The team feels constantly busy but mostly reactive.

Customer conversations revolve around product usage rather than business outcomes.

Renewals start to feel less predictable than they used to.

Expansion tends to happen through sales rather than emerging naturally from customer adoption.

None of these issues look dramatic in isolation but together they signal something more structural. The company has outgrown the way customer success operates.

What Worked Lower ACV Customers Doesn’t Work For Bigger Ones

In the early stages of a SaaS company the Customer Success model is usually informal. Customer relationships are close and founders are involved in the customer journey.

Customers are often smaller and more forgiving, but as the business scales two things change.

  1. Contract values increase
  2. Customer expectations increase with them

What worked when customers were small rarely works when customers are larger, more strategic and expecting measurable outcomes.

At that point customer success needs to shift from relationship management to value leadership.

When the Team Looks Busy but Value Feels Blurry

One of the most common symptoms is that customer success teams become highly active but struggle to consistently land the value story with customers.

  • They have data
  • Product usage dashboards
  • Activity reports
  • Feature updates

But customers are not buying activity. They are buying outcomes.

If the link between product adoption and business impact is not clear, renewal conversations become much harder than they should be.

This is rarely a capability problem inside the customer success team. It is usually a missing framework.

The organisation has never clearly defined how customer value should be articulated and reinforced across the customer journey.

Why Hiring Another Customer Success Leader Doesn’t Always Solve It

A pattern I see quite often is companies trying to fix these issues by hiring new customer success leaders. But if the underlying operating model has never been redesigned, leadership changes alone rarely fix the problem.

Customer success is operating inside the same system. The same segmentation. The same customer journey. And the same expectations placed on the team.

Without addressing those foundations the organisation simply repeats the same challenges with different people.

The Real Work: Designing the Post-Sales Engine

The real question leadership teams eventually face is this:

How should the post-sales engine actually work?

That normally means stepping back and looking at three things.

  1. How customers move through the lifecycle from onboarding to renewal
  2. How Customer Success is structured to support different customer segments
  3. How the organisation consistently connects product usage to measurable business value

When those foundations are clear, customer success teams can focus on proactive work that drives adoption, retention and expansion.

When they are not, the team inevitably ends up reacting to problems rather than leading customer outcomes.

Customer Success as a Growth Function

The most successful SaaS companies eventually recognise something important.

Customer success is not simply about protecting revenue. It is about creating the conditions where revenue can grow.

When the post-sales model is designed well:

  • customers adopt the product more deeply
  • retention becomes predictable
  • expansion becomes natural

And the company stops relying solely on new sales to drive growth. This is when customer success truly becomes a strategic function.

Helping SaaS Companies Reset Customer Success

At certain points in a company’s growth it helps to step back and look at the whole post-sales system. Not just the customer success team itself, but how the organisation connects product adoption, customer outcomes, retention and expansion.

That usually means examining the customer journey, the way the customer success organisation is structured and how value is articulated with customers.

Sometimes those changes are driven internally.

Other times leadership teams bring in external experience to help diagnose the challenges and reshape the operating model.

Either way, when the post-sales engine is designed deliberately, customer success stops being reactive and becomes one of the most powerful drivers of long-term SaaS growth.

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.

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.