Most organizations don’t have an AI problem, their Post-Sale Leaders just aren’t asking the right questions.
Good Decisions Require Questions From Post-Sale Leaders
Over the last several months, I’ve spoken with Customer Success leaders, Chief Customer Officers, and CS Operations teams who are rapidly adopting AI. What I’ve noticed is that the challenge isn’t poor decision-making. It’s the accumulation of many good decisions.
A Customer Success Manager creates a custom GPT for QBR preparation. An implementation team builds an onboarding workflow. Support introduces AI-powered case triage. Operations connects AI into Salesforce, Zendesk, Gong, or Gainsight.
Each initiative makes perfect sense on its own.
The problem is that very few organizations stop to evaluate what all of those initiatives add up to collectively. As I discussed recently in AI Workflow Management, the future of post-sale leadership is becoming increasingly operational. Leaders are no longer managing only people and processes. They’re also managing a growing ecosystem of AI-powered workflows.
Start With These 7 Questions
If you’re wondering whether this applies to your organization, try answering these seven questions.
1. Do You Have a Complete Inventory of AI Workflows?
Could you produce a list of every AI agent, custom GPT, automation, and workflow being used across your post-sale organization?
2. Do You Know Who Owns Each One?
If something breaks tomorrow, is it clear who is responsible for fixing it?
3. Is Every Workflow Tied to a Business Outcome?
Can you explain what each workflow is intended to improve and how success is measured?
4. Do You Know What People Actually Use?
Many AI projects launch successfully but quietly fall out of use months later. Do you know which workflows are still creating value?
5. Do You Understand Your AI Spend?
Between subscriptions, model usage, token consumption, and embedded AI tools, costs can grow quickly. Can you quantify what you’re spending?
6. Have Multiple Teams Built the Same Thing?
It’s increasingly common for different groups to create slightly different versions of similar workflows. Do you know where duplication exists?
7. What Happens If the Creator Leaves?
If a critical workflow was built by one employee, would anyone else understand how it works or whether it should continue running?
This Is What AI Agent Debt Looks Like
If those questions are difficult to answer, you’re not alone. What you’re seeing is a natural byproduct of AI adoption moving faster than the operating model designed to support it.
Many organizations are still progressing through different stages of AI adoption. The challenge is that the capabilities required for experimentation are very different from those required for scale. That’s a theme I explored in AI Maturity Model, where I discuss the transition from isolated experiments to repeatable operational practices.
The First Step Is an Agent Registry
That’s why one of the first recommendations I make to leaders is surprisingly simple. Create an Agent Registry. You don’t need specialized software, a spreadsheet is enough. Document what exists, who owns it, what it costs, what systems it touches, and what business outcome it’s intended to improve.
Visibility Comes Before Optimization
Visibility creates the foundation for every governance decision that follows. Before organizations can optimize AI, they first need to understand what they’re running. And before leaders can demonstrate ROI, they need visibility into where AI is creating value in the first place. That’s one reason the business impact of AI often feels difficult to measure despite being very real.
The organizations that get ahead of this challenge won’t necessarily be the ones adopting the most AI. They’ll be the ones creating the visibility, governance, and operational discipline needed to manage it effectively.
