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:
List your workflows.
Capture where your team spends time: onboarding prep, renewal forecasting, reporting, data cleanup, etc.Highlight where humans add unique value.
Keep empathy, strategy, and customer judgment in human hands. Everything else is fair game for AI.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.”
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.
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