PROMPT This AI Challenge
Episode 4 – "The Autonomous Organization - Scaling with AI Teammates" featuring Gabe Larsen
Every episode of the PROMPT This podcast includes an AI Challenge for the audience. Follow the instructions below to complete this episode’s challenge.
Human-Like AI: Teammates or Just Tools?
AI is getting a lot of hype right now, especially around “agentic AI” and “human-like” digital coworkers. In this podcast episode, our guest Gabe Larsen cut through the hype and described what is real and what is not. In there, he painted a picture of the more sophisticated, "job oriented" AI agents that his company calls Cloud Employees. Whatever the label, the idea is the same: move beyond software tools and into AI that behaves more like a teammate.
But here’s the catch—most companies are still figuring out if this is practical progress or just a polished rebrand of old automation. The question leaders will start asking is not “can I buy this AI?” but “can I manage this AI like a human, and does it actually deliver outcomes?”
AI Agents vs. Cloud Employees
To cut through the jargon, let’s look at two common approaches that are being positioned as “human-like” coworkers.
AI Agents
- Task-oriented and narrow in scope
- Deterministic and rules-based (“if this, then that”)
- Often single-channel, like scraping a site or sending templated emails
- Best viewed as helpers for repetitive, transactional tasks
Cloud Employees (or AI Teammates)
- Job-oriented, closer to replacing a role than a task
- Work across multiple channels (phone, email, chat)
- Can be “coached” and updated like a real employee
- Faster to onboard—data can be uploaded in minutes instead of months
- Measured on performance KPIs, not just activity logs
The core distinction isn’t about the technology; it’s about expectation and accountability. Tools assist. Employees—human or AI—are expected to deliver results.
Selecting the right Cloud Employee
To get pragmatic, use a structured framework when testing or piloting AI coworkers.
1. Role Fit
- High-volume, structured roles are ideal: inbound lead response, outbound prospecting, candidate screening, ticket resolution.
- Avoid starting with high-stakes strategic roles like enterprise account management.
- Ask: Would I normally hire an entry-level rep for this work? If yes, it’s a good candidate for a Cloud Employee.
2. Onboarding & Ramp Time
- Test how quickly the AI can absorb company knowledge (e.g., product manuals, sales scripts, FAQs).
- Compare ramp time against human hires. Humans may take 90 days; AI can often ramp in hours.
- Measure: time to first productive output.
3. Performance Benchmarks
- Use the same KPIs as human peers: calls made, conversations started, meetings booked, tickets resolved.
- Track quality as well as quantity. Did the AI handle objections correctly? Was the resolution accurate?
- Run side-by-side comparisons in CRM reports: human SDRs vs AI Cloud Employee.
4. Cost Effectiveness
- Calculate cost per outcome (meetings, resolutions, opportunities).
- Cloud Employees typically cost 1/5 to 1/10 of human salaries, but the comparison must be outcome-based.
- Include management overhead in both calculations to keep it fair.
5. Customer Acceptance
- Segment audiences: SMB buyers may be indifferent to AI, while enterprise executives may resist.
- Track response rates, meeting acceptance rates, and NPS by audience.
- Ask: Would my customer prefer a fast response from AI over a delayed response from a human?
6. Scalability & Consistency
- Stress-test by simulating peak loads (e.g., 100,000 support tickets in a month).
- Check whether AI can sustain performance without sacrificing accuracy.
- Compare to human teams, who fatigue and vary in quality.
7. Manageability & Coaching
- Review the AI’s interactions regularly—calls, emails, chats.
- Provide coaching (e.g., tone adjustment, objection handling) and test whether improvements stick.
- Expect exponential learning: once corrected, AI doesn’t repeat mistakes.
8. ROI & Business Impact
- Combine all metrics into a simple ROI model:
- ROI = (Business Outcomes – TCO) ÷ TCO
- Outcomes = revenue generated, churn prevented, candidates placed
- TCO = annual subscription/salary equivalent + training + supervision
- Decide: Does this Cloud Employee outperform my lowest quartile human hire? If yes, scale.
Pragmatic Realities of “AI Teammates”
- They’re not universal replacements. AI struggles in strategic, nuanced, or relationship-heavy roles.
- They require oversight. Think of weekly one-on-ones, not set-and-forget.
- They’re best as force multipliers. Freeing humans from grunt work, not replacing them wholesale.
The AI Challenge: Test “Zoey”
Before we meet Zoey, let’s set the stage. Signals (www.getsignals.ai) is one of the companies leading the charge in defining what a Cloud Employee actually is. Instead of treating AI as just another software tool, Signals frames it as a teammate—something you onboard, coach, and measure against KPIs. They call this the building block of the “autonomous organization,” where growth scales on outcomes rather than headcount.
Signals focuses on three areas where Cloud Employees are proving most effective today:
- Recruiting – screening candidates at scale
- Customer Support – handling repetitive tickets 24/7
- Sales Development – qualifying leads, setting meetings, and managing early-stage outreach
And they are building more Cloud Employees to deploy across your business.
To make this concept tangible, Signals built a showcase Cloud Employee named Zoey. Zoey is designed to act as a sales development rep. She doesn’t just follow scripts—she learns fast, adapts with coaching, and works across multiple channels.
Go ahead and see for yourself: interact now with Zoey to test what “human-like AI teammates” look like in practice.
Your challenge is to put Zoey through the same evaluation you’d give a new SDR hire:
- Engage with her like a skeptical lead.
- Throw objections and edge cases at her.
- Use the Cloud Employee framework to score her performance and ROI.
The goal isn’t to decide if Zoey is flawless. It’s to decide whether Cloud Employees are ready to play a role on your team—and whether you’d “hire” one.
Final Thought
Agentic AI isn’t science fiction, but it’s also not magic. It’s a developing category of technology that demands pragmatic evaluation. The most useful way to think about it is as an experiment in shifting from tools you use to teammates you manage. The challenge for leaders is to test, measure, and decide where AI coworkers add value—and where human judgment still rules.
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