The Colleague Who Never Sleeps: What ChatGPT Agents Mean for CS and GTM Teams

This week, OpenAI released something that deserves more than a passing glance - ChatGPT Agents. Not another AI toy or chatbot gimmick, but a meaningful step forward in how professionals across Customer Success and Go-To-Market roles can work smarter, not just faster.

What makes this different isn’t the underlying technology on its own. It’s what that technology can now do autonomously, in the background, with little to no supervision. Completing entire workflows end-to-end. Researching prospects, updating CRM records, generating onboarding plans, drafting follow-up emails, even creating performance reports. All done by an AI system with its own browser, tools, and ability to choose how to act.

For many teams, this represents the first realistic opportunity to move from manual digital work to something far more scalable.

Now I'll be completely honest - I've spent quite a bit of time looking into the features, thinking about the use cases and where it adds value, however I haven't actually had the feature enabled on my own account yet. So I'm excited to jump in once I do and validate all my thinking. But in the meantime lets continue...

What Are ChatGPT Agents, in practical terms?

If you’ve used ChatGPT before, you’ll know it responds to prompts, which is a fairly simple you ask, it answers type of approach. An Agent, however, can take a goal and work out how to achieve it. It doesn’t just generate text; it navigates websites, interacts with tools like Gmail, Notion or Salesforce, fills out forms, runs code, and chains these actions together.

Think of it more like a digital operations assistant. One that:

• Pulls live customer data from various systems,

• Analyses usage metrics or support tickets,

• Generates a tailored onboarding journey,

• Writes a follow-up email based on that insight,

• Then logs the activity back into your CRM.

All without needing to be hand-held through each step.

Where it fits in a CS or GTM workflow

Some early use cases are already showing real, measurable value based on current data:

• Onboarding automation: The agent reviews customer details, identifies key segments, and creates personalised onboarding steps, monitoring progress along the way.

• Churn risk identification: By monitoring logins, feature adoption, and sentiment in support tickets, it can flag accounts needing attention and suggest next steps.

• Sales research and outreach: It can scan LinkedIn, company websites, press coverage, and CRM history, then produce an outreach plan with contextual messages, not just “Hi, saw your company raised funding” templates.

• Pipeline updates and forecasting: It can evaluate opportunity stages, prompt follow-ups, and even surface insights on deals going cold before they slip away.

• Reporting: It builds decks, compiles renewal or QBR prep material, and populates dashboards without you needing to chase data from four different places. (This is one I'm particularly excited for, have we finally killed off building powerpoint decks?!)

This isn’t just time saved. It’s time reallocated away from admin, toward higher-leverage work.

What This Doesn’t Mean

It’s important to be clear: this is not about replacing human professionals.

Agents are effective at execution, not judgement. They can pull data, synthesise it, and even act on defined triggers, but they don't replace strategic thinking or emotional intelligence.

A good CS manager isn’t defined by their ability to fill in spreadsheets or chase support tickets. It’s their ability to advise, influence, and build relationships that retain and grow accounts. Likewise, a strong AE knows when not to send that perfectly crafted sales email.

Used well, Agents free professionals from the repetitive work that often gets in the way of those more meaningful moments.

Getting started thoughtfully

Jumping in without preparation won’t yield the results the headlines promise. A few principles stand out from early adopters, and this is the approach I will personally be taking:

1. Don’t start with everything. Choose one or two repetitive workflows that are low risk and high volume. Onboarding email sequences, reporting templates, or pipeline hygiene are good candidates.

2. Make the process clear first. If the workflow is messy or inconsistent, automate it and you’ll just scale the mess. Spend time mapping out the process before handing it over.

3. Ensure visibility and control. Agents are powerful, but like any tool, they need oversight. Ensure there are checks and logs in place, especially when touching live systems or sending external comms.

4. Invest in prompt engineering skills. Understanding how to frame goals for agents is already emerging as a core skillset in CS and GTM teams. It’s less technical than it sounds, but very much essential. Think of it like giving an intern a project to work on - if you don't clearly articulate what you want, you'll likely get the wrong thing back.

5. Start the internal conversation now. Whether you're a CS Director or a frontline rep, this is the time to begin discussing how your team could use this capability. The sooner you begin, the less disruptive it will feel later.

The launch of ChatGPT Agents isn’t just another tool release. It signals a shift in how knowledge work can be structured, particularly in functions that rely on orchestration, judgement, and communication.

For Customer Success and GTM teams, the opportunity is not to become more robotic, but to become more human by offloading the rest.

We are still early in this journey, but for teams willing to explore, experiment, and evolve their processes, the advantages compound quickly.

If you’re working through what this might mean for your team or workflow, I’m always happy to compare notes.