The AI Move I Hope You’re All Using This Winter

My “pet play” if I were a CS leader heading into busy season

If I were running a Customer Success team right now, I’d be running one simple, high-leverage AI motion every week between now and Q1: Behavioral cohort analysis.

SBI found companies using cohort-based account management can predict renewal and expansion with 90% accuracy. I’ve never heard someone say that about their health score.

While I’d personally do it with an agent (better for reliable, repeatable processes and actually doing some of the work), many CS teams are still only using LLMs, their AI chat assistants.

So I thought I’d share how you can do it powered by ChatGPT, Claude, or Gemini.

No dashboards. No SQL. No new software. Just a basic export, a simple AI prompt, and a clear view of which customers are drifting, which are growing, and where to act first.

Why This Play Matters Right Now

Q4 and Q1 are high-stakes seasons for most CS teams. Renewals pile up. Expansions get squeezed. Everyone is busy, but a lot of risk stays hidden until it’s too late.

Behavioral cohort analysis is the fastest way to surface those risks and contextualize them in a human-centered, data-driven, actionable way — without setting up a new reporting system.

• It groups accounts based on what they actually do, not what your “health score” says.

• It shows early signals of churn and expansion.

• It lets you route action to the right people weeks earlier.

How to Run This Play

Step 1: Export Simple Usage Data

Pull a spreadsheet with:

• Logins or activity by week or month

• Feature usage trends

• Seat counts or licenses

• Support ticket volume

• Renewal or expansion dates

It doesn’t have to be perfect. AI can handle messy data.

There’s no reason to include customer emails, names, or any other private information — the system just has to look for patterns, it doesn’t need identifying or sensitive information. Use anonymized customer IDs and strip out sensitive information before Step 2, and follow your company’s policies.

Step 2: Drop It Into AI

Paste the finished spreadsheet into your AI assistant and use a prompt like:

Group these customers into behavioral cohorts based on how their usage changes over time.
For each group, summarize:
- When engagement drops, plateaus, or spikes
- Key behavior patterns
- Renewal or expansion likelihood
Flag any groups that show early risk or growth signals.

In seconds, you’ll see clear groups like:

• Never activated

• Plateau after onboarding

• Consistent power users

• Expansion spike

Step 3: Map Cohorts to Plays

Map your cohorts into distinct play and upload into your CRM.

For instance, Never Activated Cohort, who barely used after onboarded, can get customized personal outreach (ex. save as a sequence in your CRM for “Never Activated Cohort” tag) and re-onboarding.

Or the Plateaued After Onboarding, who stalled their usage, gets a re-engagement sequence with success planning.

Or the Expansion Spike, which gets put into a path for offering them other products that could better serve their needs at this time.

By grouping each cohort to a scaleable yet personalized action speaking to their exact behavior, you’re arming your CSMs with the right information, to the right people, at the right time.

To do this, update your anonymized or original sheet with the correct cohorts using your LLM. If you are using an anonymized sheet, make sure to order it the same as your other sheet, then copy and paste that column.

Step 4: Run Weekly

Cohorts shift quickly.

• Re-run the AI prompt ideally weekly with updated usage data.

• Flag changes in cohort sizes.

• Route “at risk” and “expansion” accounts to the right owners fast.

• Track movement over time to see where risk is building.

This takes less than an hour a week and gives you a live view of your book of business. If you are relying on this a lot and it becomes a pain point, then it’s time to make this into an agent that does the work for you.

Why AI Is Perfect for This

• Good with messy data — no need for clean dashboards.

• Fast at spotting patterns — drop-offs, spikes, plateaus jump out.

• No technical skills required — it explains the cohorts in plain language.

• Scales easily — works for 50 accounts or 5,000.

This is exactly the kind of AI work that pays off fast without needing a RevOps project.

Quick Start Checklist

• Export basic usage data (logins, features, tickets, renewals)

• Paste into ChatGPT, Claude, or Gemini

• Define 3–5 clear cohorts

• Map standard plays

• Run the analysis weekly

• Track changes and act early

Final Note

If I were leading CS through the busy season, this is the first AI play I’d set up. It’s smart, effective, and can yield big results at the best time.

If you want me to build you a custom agent for this workflow, contact me at kate@makrventuresllc.com.

Stop Chasing Platforms: How to Build AI Agents That Actually Solve Customer Success Problems

There’s a lot of hype right now around AI agents — especially after Gemini Enterprise and OpenAI’s Agent Kit hit the scene.

Everyone’s eager to build “autonomous teams” and “AI copilots.” It’s exciting stuff — but for most companies, it’s also where chaos begins.

Because in the rush to do something with AI, many skip the most important step:
👉 It’s not about the platform. It’s about the problem you’re solving.

The Agent Gold Rush Problem

An abandoned Gold Rush town, dusty, lonely, and empty, which is exactly what your AI pilots will look like if you don’t spend time mapping them out.

Across industries, teams are spinning up agent projects because “we need to be doing something in AI.” They pick a platform first — Gemini, OpenAI, or Agent.ai — and then try to retrofit a use case.

That’s backwards.

When you start from the platform, your thinking anchors around features, not outcomes. You build impressive demos that don’t connect to your business reality or your customers.

And when the pilot fizzles, everyone assumes “AI doesn’t work here.”

The truth? You just started in the wrong place.
Without a clearly defined, measurable problem, any platform will fail you.

The Story of Two Pilots

Let me show you how this plays out in the real world.

(Names and details in the following examples have been changed or combined from real scenarios.)

A SaaS company wants to “do something with AI” in Customer Success.

Their first move was to build an internal “AI agent” that could answer CSM’s renewal questions to help them navigate renewal conversations better. It was created by a RevOps team member, who whipped it together in a week, then rolled out to a Mid-Market team.

The result?

• The agent only gave answers that were already in the CRM - nothing strategic or coachable to actually guide the CSM in the situation.

• CSM’s didn’t use it.

• The RevOps team member didn’t have the bandwidth to maintain it, which means it quickly deprecated.

In the end, it was a failure. The opportunity cost wasn’t just time and money — it was momentum. Now, leadership hesitates to greenlight future AI initiatives because of that early failure.

Compare that to another company, Northbeam Analytics. They had the same issue: we need to prepare CSMs for renewal conversations better. However, they carefully researched the problem, interviewing CSMs, looking at calls, and comparing analytics between renewals.

This led them to an important discovery: no CSMs went on the calls without renewal details, they all had their CRM and their CSP up, with plenty of details.

Where they struggled was adapting to each customer’s individual situation, especially when it came to complicated and ad hoc pricing. This led to CSMs spending at least 30 minutes of time post-call, on average, doing follow-ups, advocating internally, and coordinating across their internal team and the customer.

So they started small:
They created an agent that took the call transcript, the renewal contract information, and the playbooks, then sent the CSM a simple follow up guide every call with message templates and step by step customized for that particular customer.

The results?

• CSMs save ~5 hours a week.

• Managers and other renewal collaborators also save time, since the CSM has clarity and a pre-approved plan of action.

• There is also less back-and-forth messaging between the CSM and the customer, thanks to the templates and clear-cut, personalized guidelines.

The difference wasn’t talent or technology. It was focus.

Northbeam worked forwards from the problem, not backwards from the platform.

Work Forwards from the Problem

The most successful companies (especially in Customer Success) do what Northbeam did: they start by mapping opportunities, not choosing platforms.

This is exactly where the MAKR AI Transformation Staircase for Customer Success begins.

Stage 1: Map the AI Opportunity Landscape

Before you write a single line of code, identify where your CS team spends time on low-value, repetitive work. These are your entry points for AI automation and augmentation.

Here’s how to build your first AI Opportunity Map:

1. List your workflows.
   Capture where your team spends time: onboarding prep, renewal forecasting, reporting, data cleanup, etc.

2. Highlight where humans add unique value.
   Keep empathy, strategy, and customer judgment in human hands. Everything else is fair game for AI.

3. Define the problem narrowly.
   Replace “AI for customer success” with something precise:

   “An agent that prepares renewal briefs by pulling data from Salesforce and product usage reports.”

4. Rank by impact vs. effort.
   Plot your opportunities on a 2x2 grid. Start with high-impact, low-effort wins — the ones that show measurable ROI fast.

Once you have clarity on your biggest opportunities, then you move up the Staircase.

Stage 2: Activate the AI Pilots

Now that you know what to solve, you can experiment with how.
This is where platforms like OpenAI, Gemini, and Agent.ai come into play.

Your goal in Stage 2 is not perfection — it’s validation.
Can an AI agent solve the problem faster, better, or more accurately than your current workflow?

When you define the problem well, you can measure success well.
And that’s what earns you permission to scale.

Stage 3: Key Up AI Operations (KeyOps)

Once you’ve proven value, scale it.
Stage 3 is all about operationalizing your agents:

• Define ownership and governance.

• Build repeatable playbooks.

• Monitor usage, feedback, and drift.

This is where teams start building durable AI capabilities — not one-off experiments.

Stage 4: Redefine Success Criteria

Finally, move from “AI in Customer Success” to AI-native Customer Success.

Your success metrics evolve. Your people evolve.
Agents handle operational load, while humans focus on trust, strategy, and growth.

That’s what the top of the MAKR AI Transformation Staircase looks like: Customer Success teams that drive revenue, not just retention.

The Power of a Narrow Use Case

The first successful AI agent rarely does something dramatic.
It might just:

• Prep a customer call.

• Summarize a success plan.

• Tag tickets or sentiment.

• Give post-call analysis and guardrails for a CSM to conduct a save.

But those small, useful wins build trust.
And trust builds momentum.
That’s how you climb the Staircase — one solid step at a time.

Map Before You Build

So before you spin up your next “AI agent initiative,” pause.

If you don’t have a list of clearly defined problems and solutions, your best next step isn’t another prototype — it’s opportunity mapping.

Because when you start from clarity, not hype, the rest of your AI transformation becomes obvious.

🧠 Stay Ahead of the Noise

If you’re trying to make sense of everything happening in AI right now — from Gemini Enterprise to ChatGPT’s new Agent Kit and Builders — I’m launching a weekly newsletter that will break it all down.

Every issue cuts through the noise with:

• Smart analysis of the biggest AI updates for Customer Success and GTM teams

• Real-world examples of how companies are using AI agents effectively

• Strategic frameworks (like the AI Transformation Staircase) to help you plan your next move

💌 Subscribe here → Kate’s Cut
Get one thoughtful, human take on AI for Customer Success — every week.

Read This Before Scaling: Why 95% of AI Efforts Fail After Pilot (and How to Avoid Being One of Them)

Step 3 of the 🪜MAKR AI Transformation Staircase 🪜

You’ve mapped the opportunities. You’ve run the pilots. You’ve got at least one win (and hopefully more).

MIT’s latest research is clear: 95% of enterprise AI pilots fail to deliver measurable business outcomes.

Not because the tech doesn’t work. But because teams never turn the wins from pilots into repeatable, measurable operations.

If you’re a CCO or CRO, this is the moment where AI stops being a science experiment and starts being a line item with ROI expectations. And that’s where MAKR AI Transformation Staircase’s Stage 3, Keying Up AI Operations, comes in.

Why Many Fail at Stage 3

Based on the MIT study + other research, here are the most common breakdowns, with specifics:

1. Workflow Misalignment
   MIT found many failed pilots were using generic AI tools without tailoring to existing workflows. The tools didn’t connect to how teams already work, so adoption dropped off.

2. Poor Governance & Data Inputs
   Without guardrails or defined data sources, AI outputs are inconsistent. This leads to trust issues. MIT notes that in-house builds often underperform compared to vendor-led ones (roughly 33% vs ~67% success rate).

3. Lack of Meaningful Metrics
   Many pilots are judged on “adoption” or “usage” rather than on impact. That’s a red flag. As one case study in “AI for Customer Success Automations” showed, only when they tracked reduction in operational costs and customer retention did the business case sustain.

4. Scaling Without Standardization
   Success in one pod or team doesn’t automatically generalize. When there are no standardized playbooks or shared processes, each team reinvents, which undermines efficiency gains. MIT found cross-organization variance in success was large.

That’s where keying up operations comes in.

However, when you successfully scale Customer Success with AI, the outcomes stop being about “adoption” and start showing up in retention curves, save rates, and dollar math. Stage 3 of the MAKR AI Transformation Staircase, Keying Up Operations, is where you build repeatability, discipline, and measurable business returns. Just look at these successful Customer Success examples:

• PayPal + H2O.ai: Machine-learning churn prediction models cut model runtime from 6+ hours to minutes, enabling near-real-time churn scoring and proactive save motions. Case study

• Audiobooks.com + Provectus: AI-driven churn prediction on AWS pipelines segmented customers into risk cohorts with high accuracy, powering proactive CS outreach and boosting retention in premium cohorts. Case study

• CallHippo + Enthu.AI: Conversation AI flagged churn-risk signals in customer calls, enabling CSMs to act early. Results: 20% reduction in revenue churn and 13% increase in new revenue. Case study

• Sigmoid ML Retention Campaigns: Predictive churn models across 15+ data sources powered targeted CS-led campaigns that improved customer retention by 70% vs. baseline. Case study

These are the kinds of curves Keying Up Operations is about: churn decreasing in double-digits, intervention speed coming in minutes not quarters, and save plays that move millions in ARR.

Three Critical Metrics to Embed in Stage 3

Notice the pattern: every example zeroes in on churn. That’s not a coincidence. Churn is one of the most P&L-sensitive levers in Customer Success—and one of the first places AI delivers fast, measurable wins. Think predictive saves, live risk scoring, and interventions that bend the ARR curve, not just more dashboards.

In the MAKR Transformation Staircase, Stage 3 is where AI has to graduate from “interesting activity” to hard financial outcomes. That means tying every pilot, model, and playbook back to the system metrics your board already watches:

• NRR: Don’t just report adoption. Show the delta between predicted and actual renewal/expansion, and quantify how AI closed the gap.

• GRR: Track lift in save rates—e.g., percentage of “at risk” accounts successfully retained after AI-triggered interventions.

• Cost-to-Serve: Measure AI’s impact on marginal efficiency: time per CSM, cost per retained dollar, scale of accounts handled without headcount.

The metrics themselves aren’t new. What’s new, and what proves operational success, is demonstrating how AI changes their slope.

What You Can Do: A Specific Operating Plan

Here’s how you make Stage 3 real, with specificity and numbers:

1. Pick 2 High-Impact Pilots That Show Clear Metrics
   E.g.

   • An AI risk-alert pilot in renewals aimed to reduce time to risk detection by 40%.

   • A customer feedback summarization pilot to reduce manual summary time by 80%.

2. Set Up a Unified Dashboards with Leading Indicators
   Example KPIs (world-class):

   • Hours saved / CSM / month

   • Save rate for at-risk accounts (compare pilot vs control)

   • Time to detection/resolution

   • Retention lift (NRR or churn rate variance) + cost savings

3. Establish AI Governance & Playbook

   • Define allowed data sources, human oversight, tone/brand guardrails.

   • Build playbooks that live in tools your teams use already (Slack, Salesforce, Gainsight).

   • Use pre-mortems: “What could go wrong if we scale this without cleaning the data or standardizing the model?”

4. Measure & Communicate “Dollar Value” Wins Quarterly

   • Example: If a pilot yields 50 hours saved / month across 20 CSMs and those 50 hours are used to handle 10 more accounts, translate that into the revenue those 10 accounts bring.

   • Report savings or revenue impact in actual dollars in leadership meetings (not just % improvements).

5. Ensure Cross-Org Alignment
   Because scaling often fails when CS AI moves ahead in isolation. Pull in Revenue Operations, Product, Support so workflows, data pipelines, customer feedback loop across teams are all integrated

The Bottom Line

Public perception, MIT reports, case study after case study: AI doesn’t fail because the models are bad. It fails because organizations scale mess, not method.

If you’re scaling before fixing process, governance, metrics, and workflow alignment, you’re handing your team a liability, not a growth lever.

Codify. Measure. Govern. Align. That’s the difference between an AI pilot that gets a headline and an AI transformation that delivers millions on the bottom line.

From Messy Data to Meaningful AI: The Two-Step That Starts the Journey

“My leadership says we must start using AI, but our data is such a mess I don’t even know where to start.”

The friction is real, and this comes up in almost every conversation I have with CS leaders.

True. Your data is messy. Executives want world-class AI, yesterday. To CS leaders, this feels like being stuck between a rock and a hard place. After all, nobody wants to be the person that just released a bot that scaled bad customer data across millions of customer profiles (please, please don’t be that person).

And here’s the crisp, actionable truth: AI isn’t waiting for perfection. It thrives on intentional progress.

That’s why I call it the Two-Step.

I also call it the Two-Step because I lived in Louisiana for 14 years, and really wish I could dance the real two-step as well as we can do this one together, but I digress. \

Let’s dive in.

Step One: Map the AI Opportunity as It Exists Today

You don’t need a pristine data warehouse to start seeing value. Just look at your friction points: meeting prep chaos, renewal lag, disjointed handoffs.

Zero in on the friction until you have a narrowly defined situation with concrete, accurate information. Not the entire customer record, or every engagement you’ve ever had with this book of business. Look at narrow instances where you have concrete information.

Real examples of narrowly defined opportunities:

• Keyword Risk Alerts: AI scans only the latest customer email or call transcript for phrases like “budget cut,” “renewal,” or “looking at alternatives,” and pings the CSM with an alert.

• QBR Slide Drafting: AI auto-generates just three slides (“Usage Highlights, Top Issues, Next Steps”) directly from product usage data—no narrative or full deck creation.

• Support Case Digest: AI produces a one-paragraph summary of the most recent closed support case, highlighting “Problem, Resolution, and Next Risk” for the CSM.

• Sales Engagement Nudge: AI notifies the CSM only when Sales reaches out to their customer, including a two-sentence summary of the outreach and a suggested next action.

• Renewal Prep Coach: AI guides the CSM with a simple renewal prep checklist (questions to ask, objections to expect, messaging tips), without requiring any customer or product data.

That’s Step One: spot where AI can help now.

Step Two: Activate a Narrowly Scoped Pilot

Once you’ve mapped a friction point, the next move is to design a pilot that proves value fast, without relying on perfect data.

Take the Renewal Prep Coach example.

This company has mostly junior CSMs. They don’t need a predictive churn model, they need practice. Instead of waiting on customer data that’s too messy to use, the AI agent creates a custom roleplay for renewal conversations based on a few form-field inputs such as:

• Customer size (Enterprise, Mid-Market, SMB)

• Renewal amount (small, medium, large)

• Primary objection (budget, product fit, competitor, internal change)

The AI then generates a short simulation: a customer persona, likely objections, and suggested talking points. The CSM can “practice” the conversation in a safe space before the real call.

This pilot doesn’t touch the messy CRM. It doesn’t pretend to solve all of renewals. It targets one concrete friction point (junior CSMs lacking confidence in renewals) and provides immediate, measurable value.

That’s the essence of Step Two: move from spotting opportunity to running an intentional pilot in a narrowly defined slice of the workflow.

Managing Up While Managing Reality

This approach gives you narrative not paralysis:

• Show leadership real AI impact—today.

• Buy time to clean data and refine processes behind the scenes.

• Shift from “waiting for perfect” to “launching intent-led progress.”

The Real Opportunity

No CS team has perfect data or processes. That’s not the blocker, it’s the context.

The opportunity? Start smart:

1. Map what’s real.

2. Activate what works.

Ideal isn’t necessary. Intention is.

Activate the AI Pilots 🪜 (for Customer Success)

Step 2 of the 🪜MAKR AI Transformation Staircase 🪜

Once you’ve mapped your AI opportunities, the next move isn’t to buy a platform, roll it out everywhere, and hope for magic. That’s the fastest way to burn credibility.

The next move is to activate the pilots.

Think of this step as your first real “hire.” You’ve scoped the job in Step 1 of the staircase. Now you’re running a 90-day trial to see if AI can actually perform it.

What “Activate the Pilots” Means

A pilot isn’t “let’s play with a shiny tool.”
A pilot is a structured experiment:

• 🎯 Defined Workflow: One workflow, not the whole org. (Onboarding nudges, renewal briefs, escalation summaries, etc.) ONE PILOT, ONE WORKFLOW. Remember, in experiments, it’s important to isolate a single variable.

• 📊 Clear Success Metric: Time saved, errors reduced, customer outcomes improved. Not “AI adoption.”

• 🧑‍🤝‍🧑 Real Users: CSMs or Onboarding Specialists actually use the AI in their day-to-day, not just a sandbox demo.

• 🗓 Short Timeline: 30–90 days. Enough to measure, not so long it drags.

Three Tips for Running AI Pilots in CS

1. Scope Small, Measure Big

   • Pilot one discrete job (e.g. drafting renewal briefs).

   • Define the metric that matters (e.g. prep time cut from 3 hours to 30 minutes).

2. Co-Pilot, Don’t Auto-Pilot

   • Let AI draft or suggest, but keep the human in the loop.

   • Adoption skyrockets when AI enhances rather than replaces CSM workflows. It should act as a force multiplier rather than a replacement.

3. Capture Stories, Not Just Stats

   • Document where the pilot made a CSM’s day easier or impressed a customer.

   • Execs want numbers and narratives.

Why Pilots Fail

🚩 Success metric = “adoption.” If the goal is just “did people use it?” you’ll end up with usage, not impact. And you will never be able to get to Stage 3 if you don’t come up with meaningful goals and impact.

🚩 The scope was too vague. “Fix renewals” is too broad. “Cut prep time for renewal briefs by 75%” is testable.

🚩 The team didn’t know the “why.” If CSMs see it as a top-down science project, they won’t care.

🚩 No alignment with the company’s AI direction. If CS is running its own AI experiments in a silo, you risk rowing against the org’s strategy instead of amplifying it.

Where This Fits on the AI Staircase

“Activate the Pilots” is Step 2 of the MAKR AI Transformation Staircase:

1. Map Opportunities: Define jobs AI could realistically do.

2. Activate Pilots: Test AI in one workflow with real metrics.

3. Key Up Operations: Bake proven AI use into CS processes.

4. Redefine CS: Reimagine CS roles and models with AI at the core.

If you skip Step 2, you risk scaling hype instead of results.

The Bottom Line

A pilot isn’t proof that AI works in general.
It’s proof that AI works here, for this job, in this workflow.

Get one win. Show the impact. Build credibility.

Then climb to Step 3. 🪜

Map the AI Opportunity (for Customer Success)

Step 1 of the 🪜MAKR AI Transformation Staircase 🪜

Map the AI Opportunity: The first stage of AI transformation in the MAKR AI Transformation Staircase

When you hire a new CSM, you don’t just say “We need a CSM.”

You define the role: the tasks they have, the workflows they use, the segment they work with, how you’ll measure success, and how they’ll integrate with the team.

And, as every hiring manager can attest, the better you define the role, the better you can find a candidate that 1) fits what you’re looking for and 2) has the right expectations for the job.

It’s the exact same thing when it comes to introducing AI to your team’s workflows.

Before you “hire” AI into your org, you need to know what that AI will be doing.

That’s where Mapping the AI Opportunity comes in. It’s the work you do that helps you avoid pilots that burn cycles without proving value, and find AI opportunities that are low risk, high impact.

This mapping exercise is your first step onto the AI in Customer Success Staircase. It’s the path from experimentation to transformation.

Three Ways to Map Your AI Opportunity

1. Workflow Audit (The Obvious One)

   • Pull up 5 recurring CS workflows (onboarding, renewals, adoption campaigns, escalations, etc.).

   • For each, map:

     • Current Actions: what humans are doing today.

     • AI Candidate Job: what part could AI reasonably do.

     • Data Inputs: what data AI would need to succeed.

   • Look for overlap between high manual effort and repeatability. That’s usually your lowest-risk, highest-value AI entry point. It’s also where your CSMs often get bored or frustrated, so it’s a win-win when you find it.

2. Friction Mapping (Kate’s Favorite)

   • Go straight to your CSMs: “What’s the work you dread or repeat the most?”

   • What really shines about this tactic is that it’s human-led and gets your team involved early, which means better adoption later when it’s seen as a bottoms-up initiative rather than top-down.

   • Document the pain points by workflow (e.g. chasing onboarding forms, manually prepping customer decks, summarizing escalations).

   • Score each by:

     • Customer impact (does it directly improve CX?)

     • Internal impact (does it save real time?)

   • The sweet spot is where high-friction tasks align with measurable outcomes.

3. Cross-Functional Opportunities (Great for Showing Impact & Getting Adoption Later)

   • Sit down with cross-functional stakeholders (Sales, Product, Support) who often interact with CS. Your goal is to find a workflow that AI can help increase cross-functional alignment, which ultimately means a better experience for your customers, your team, and your colleagues.

   • What I love about this approach is that it breaks down silos.

   • Ask three simple questions:

     • What does “good” look like in this workflow?

     • Do we have the data to prove it?

     • Would AI save us time or improve customer outcomes here?

   • You’ll quickly uncover which workflows have enough clarity + data + impact to be realistic AI candidates.

Another critical point is to search for opportunities for transformation that are aligned with your org’s overall vision for AI transformation. For instance, if the emphasis is on breaking down silos, #3 is a great place to start. You don’t want CS to be rowing in the opposite direction of the rest of the org; instead, you want your work to reinforce and expand it.

Mapping Your Opportunity: 5 Core CS Workflows

While every org is unique, most CS motions are pretty common. Here’s a few common opportunities:

1. Onboarding: Customize coaching, automate reminders, draft personalized plans, flag setup risks.

   🚩 If onboarding lives in tribal knowledge, AI just automates confusion.

2. Adoption: Identify gaps, generate tailored success plans.

   🚩 If adoption = “logging in,” AI can’t prove impact.

3. Renewal & Expansion: Forecast risk, flag upsell signals, generate briefs.

   🚩 If renewal is a fire drill, AI only speeds up the chaos (or makes it sound even less sincere).

4. Escalations: Summarize incidents, suggest resolution steps.

   🚩 If you rely on memory, AI has no foundation.

5. Voice of Customer: Aggregate feedback, surface patterns.

   🚩 If VoC = “what we remember,” AI insights will be shallow.

Where This Fits on the MAKR AI Transformation Staircase 🪜

Once you’ve mapped your opportunities, you’re ready to climb the next steps of the MAKR AI Transformation Staircase:

1. Activate pilots: Test AI in narrow workflows with measurable outcomes and defined impact.

2. Key up operations: Bake AI into processes and data flows across the org.

3. Redefine CS: Redesign CS around new AI-driven capabilities.

Most teams want to jump to “operationalize.” Mapping opportunities helps you start where the risk is low and the impact is high—so every step you take builds credibility.

The Bottom Line

AI doesn’t succeed in CS because it’s trendy.

It succeeds when you map the opportunity, define the job, and measure the outcome.

Start with one workflow. Give AI a real job to do. That’s your foothold on the staircase.

From there, the climb begins. 🪜

The MAKR AI Transformation Staircase

A new study from MIT’s Center for Information Systems Research (CISR) has put numbers to what many B2B SaaS leaders already feel in their gut: 95% of generative AI pilots are failing to deliver measurable business outcomes.

The findings are based on interviews with 150 executives, surveys of 350 employees, and reviews of 300 publicly disclosed AI projects. The verdict is sobering: most AI onboarding efforts sputter out, stall in pilots, or never scale beyond experiments.

If you’re in Customer Success, this probably sounds familiar.

• One of your team members is a self-appointed “agent builder,” creating automations without safeguards.

• Another has flat-out refused to use the new AI copilot because hallucinations made it harder, not easier, to do their job.

• Meanwhile, your CEO is forwarding you every AI think-piece they find, with vague directives to “just use AI.”

And then there’s you, the CS leader. You’re stuck between pressure from above, experimentation from below, and your own bandwidth and skills gap in making sense of AI’s chaotic landscape. All while trying to hit your number.

The potential of AI in CS is real: it can streamline workflows, coach teams, and scale engagement. But the MIT data proves what many have suspected: without structure, most AI adoption fails.

That’s why I created the MAKR AI Transformation Staircase: a four-stage model, adapted from the MIT CISR framework, tailored specifically to the realities of Customer Success.

It’s a playbook for moving from scattered, high-risk experiments to intentional, outcome-driven AI adoption.

The four stages of the MAKR AI Transformation Staircase, which guide CS leaders from initial assessment through successful transformation with AI in Customer Success.

The 4 Stages of the MAKR AI Transformation Staircase

🗺️ Stage 1: Map the AI Opportunity Landscape

📍 How do you know you’re here?

• Each team (or individual) has their own process

• Leaders don’t know where to start

• Everyone is either throwing random AI tools at the wall or ignoring them completely

• Customers receive uneven experiences

This is what the MIT CISR framework calls “silos and spaghetti.” Even if individuals succeed, the org as a whole doesn’t get more efficient. If you stay here too long, CSMs will begin to distrust AI entirely.

🚚 The Solution: Map your AI opportunities through a structured assessment.
This might include:

• Running an AI readiness check across people, processes, and data

• Identifying high-friction workflows and the easiest, highest-impact places to start (capturing low hanging fruits to get started to demonstrate viability and path to success)

🎯 The goal: Find low-risk, high-impact starting points that directly improve efficiency and customer experience. Done right, you’ll move from overwhelm to clarity with a structured roadmap that creates alignment and trust.

✈️ Stage 2: Activate the AI Pilots

📍 How do you know you’re here?

• You’ve identified opportunities but stall over cost, compliance, or ROI

• Struggling to get buy-in across teams

• Pilots are random tool trials with no measurement

• Initiatives die in procurement

The result: your team doesn’t trust AI, customers see no improvement, and the CEO keeps demanding “AI literacy” while competitors pull ahead.

🚚 The Solution: Launch narrow, intentional pilots tied to strategy and measurable outcomes.
Examples include:

• AI playbook recommendations for a specific pod

• Agents that coach CSMs through saves, measuring impact on churn

• Standardizing LLMs for call planning and follow-up

• Custom health score agents surfacing risks and opportunities

🎯 The goal: Validate AI’s impact with measurable business outcomes and continuously iterate to find the best use cases. Early wins build trust, secure budget, and position CS as the model for AI transformation.

⚙️ Stage 3: Key Up AI Operations (KeyOps)

📍 How do you know you’re here?

• Pilots succeed but remain in silos

• No governance, no scaling plan, adoption fizzles

• CSM experience with AI varies drastically across teams

• Customers still see inconsistencies

You know AI can unlock value, but without scaling, the wins evaporate.

🚚 The Solution: Codify and operationalize what works.
That means:

• Embedding AI-driven actions into every workflow

• Setting governance policies that are actually adopted

• Creating standardized AI playbooks across the org

• Deploying dashboards to track adoption and performance

🎯 The goal: AI becomes part of the CS operating system. Teams work consistently, customers see predictable value, and CSMs get to focus more on being proactive and strategic.

🚀 Stage 4: Redefine Customer Success Criteria

📍 How do you know you’re here?

• AI is embedded and makes teams efficient

• CSMs use predictive health scores, automated prep, proactive nudges

• But CS is still measured only on traditional metrics (renewals, churn, growth)

This is not future-ready. Efficiency matters, but AI’s true potential lies in reshaping CS into a strategic growth function.

🚚 The Solution: Redefine CS around AI-native capabilities.
Examples:

• Deliver predictive benchmarks back to customers

• Use CS intelligence to shape product and go-to-market strategy

• Position CS as a revenue-generating, strategy-defining function

• Create AI-driven value streams that didn’t exist before

🎯 The goal: Future-proof CS. With AI as the foundation, CS becomes the engine of growth, competitive advantage, and strategic customer value.

How to Get There

AI transformation doesn’t happen by accident. It requires structure, momentum, and a clear path forward.

The MAKR AI Transformation Staircase gives Customer Success leaders a way to move from scattered experiments to sustainable, measurable impact. By mapping opportunities, running focused pilots, scaling what works, and redefining the role of CS, you can future-proof your team and turn AI into a growth driver (rather than a shiny new tool).

✨ Next Step for You: Where is your team on the AI Transformation Staircase?

If you want help accelerating the journey, you can check out how MAKR works with CS leaders on AI transformation.

Further Reading

Here are some of the best starting points if you want to go deeper into AI and Customer Success:

• MIT Report: 95% of Generative AI pilots at Companies Are Failing — MIT’s new GenAI Divide report finds that 95% of enterprise generative AI pilots fail to deliver measurable business impact due to poor integration and misaligned investment, while the small minority that succeed focus narrowly, partner strategically, and drive operational rather than marketing gains.

• MIT AI Maturity Model — The MIT CISR Enterprise AI Maturity Model describes four stages of enterprise AI maturity, and identifies capabilities an enterprise needs as it progresses through the four stages.

• MIT CISR AI & Digital Transformation Research – The framework this staircase adapts from.

• OpenAI: Understanding GPTs in the Enterprise – Guidance on responsible and effective AI adoption.

• HubSpot: How AI Is Changing Customer Success – Accessible overview with examples.

• Harvard Business Review: Getting AI Right in Business – A strategic take on avoiding hype and focusing on outcomes.

• MAKR Blog – Ongoing insights, frameworks, and case studies from me.