Originally published here: https://www.midwestquality.consulting/newsletters/ai-for-smbs-weekly/posts/intelligent-hiring

Your star employee just quit, and you need to hire their replacement fast. You dust off the job description from when you hired them two years ago, update the salary range, and post it online.

But here's the problem: that job description was written for a different world. A world where AI couldn't write code, analyze data, or generate marketing campaigns. A world where "prompt engineering" wasn't a skill and "AI workflow optimization" wasn't a responsibility.

Your old job descriptions aren't just outdated, they're actively working against you. They're attracting candidates who think like it's still 2022 while missing the people who understand how work actually gets done today.

This isn't about using AI to write better versions of your old job descriptions. It's about fundamentally rethinking what roles look like when humans and AI work together.

Here's how.

Step 1:
Assess Your Current Job Descriptions

Before you can build job descriptions for the AI era, you need to understand what you're working with today. Most SMBs have job descriptions that evolved organically: copied from templates, adapted from previous hires, or hastily written during urgent hiring pushes.

Start by gathering every job description you currently use and asking yourself (and generative AI) these questions:

Role Clarity Assessment:
- Can someone outside your company understand exactly what this person does day-to-day?
- Are responsibilities specific enough to guide performance evaluation?
- Do requirements actually predict job success, or are they just "nice to haves"?

AI Readiness Evaluation:
- Which responsibilities could be enhanced or transformed by AI?
- What new capabilities might be needed in an AI-augmented workplace?
- Are we describing the job as it exists today or as it should exist with AI?

Many organizations structure job descriptions using a proven framework: universal factors that apply across all roles at a given level, combined with role-specific core accountabilities. This approach ensures consistency while allowing for specialization.

For example, universal factors might include:
- Junior level: "Executes defined tasks with guidance; communicates progress clearly"
- Mid level: "Manages projects independently; collaborates effectively across functions"
- Senior level: "Drives strategic initiatives; mentors others and shapes team direction"

The beauty of this approach is that it scales. Whether you're a 5-person startup or a 50-person company, you can adapt these universal factors to your context while maintaining consistency across roles.

In larger organizations, this audit will likely involve HR, department heads, and other stakeholders. For SMBs, it might just be you and a few key team members—but the process remains the same.

Step 2:
Research How Others Are Evolving

Now comes the fun part: using AI's deep research capabilities to understand how job descriptions are evolving across industries. This isn't just about finding similar companies—it's about discovering innovative approaches you might never have considered.

Enable deep research in ChatGPT or Gemini, or turn on web search in Claude, and try this prompt:

I need to research how job descriptions are evolving for [your role type] positions in the AI era. Please analyze:

OBVIOUS COMPARISONS:
- Companies similar to ours in size, industry, and market
- Direct competitors in our space
- Fast-growing companies in adjacent industries

CREATIVE INSPIRATIONS:
- Tech companies known for innovative hiring (from mild to wild)
- Companies that have publicly discussed AI transformation
- Organizations in completely different industries that might offer unexpected insights

For each category, identify:
- How they're structuring role requirements differently
- What new skills or competencies they're emphasizing
- How they're describing human-AI collaboration
- What language they use to attract AI-literate candidates

Provide specific examples of innovative job description elements I could adapt.

Pay special attention to companies that have moved beyond traditional job descriptions. Some organizations now include sections like "AI Collaboration Expectations" or "Technology Partnership Responsibilities." Others have restructured entire role categories around human+AI workflows.

The goal isn't to copy what others are doing, but to expand your thinking about what's possible. As Ethan Mollick notes, we're all in R&D now when it comes to AI—and that includes rethinking fundamental workplace structures like job descriptions.

You may need to run multiple reports on different titles, levels, and tracks. Make sure to save things with clear names; you'll leverage this work in the next steps.

Step 3:
Rethink Roles for Human+AI Collaboration

This is where most companies get it wrong. They take their existing job descriptions and add "AI tools experience" as a nice-to-have skill. But generative AI isn't just another tool, it's a fundamental shift in how work gets done.

Instead of asking "How can AI help with this existing role?" ask "How should this role evolve when AI handles routine tasks?"

Traditional thinking:
"Marketing Manager should know how to use AI writing tools"

AI-era thinking:
"Marketing Manager orchestrates AI systems to generate content at scale while focusing on strategy, brand voice, and campaign optimization"

Consider these role transformations:

• Customer Service Representative -> Customer Experience Orchestrator
  - Old focus: Responding to individual inquiries
  - New focus: Training AI systems, handling complex escalations, identifying systemic issues from AI interactions
  
  
• Financial Analyst -> Financial Intelligence Manager
  - Old focus: Creating reports and basic analysis
  - New focus: Designing AI-powered dashboards, interpreting complex patterns, strategic scenario planning
  
  
• Operations Manager -> Workflow Automation Strategist
  - Old focus: Managing manual processes
  - New focus: Designing AI-enhanced workflows, optimizing human-AI handoffs, continuous process improvement
  
  

Your employees aren't just using AI—they're conducting ongoing R&D to discover what AI can do in their specific context. This means job descriptions need to include:

• AI Fluency: Ability to effectively prompt, evaluate, and iterate with AI systems
• Quality Assessment: Skills to judge AI output and identify errors or hallucinations
• Workflow Design: Capability to structure tasks for optimal human-AI collaboration
• Continuous Learning: Comfort with rapidly evolving tools and techniques

Try pasting this whole section into you jig and  

Step 4:
Build Your New Framework

Now you're ready to construct job descriptions that work for the AI era. Use the universal factors + core accountabilities approach, but design both components with AI collaboration in mind.

Create a simple jig to help with this process. Build a new GPT (ChatGPT), Project (Claude), or Gem (Gemini) with these instructions:

You are my AI-Era Job Description Architect. Your purpose is to help me create job descriptions that reflect how work actually gets done when humans and AI collaborate effectively.

When building job descriptions:
1. Structure them as Universal Factors (level-appropriate) + Core Accountabilities (role-specific)
2. Emphasize human+AI collaboration rather than traditional solo work
3. Focus on outcomes and value creation, not just task completion
4. Include specific AI-related competencies where relevant
5. Maintain clarity and specificity—no one has ever complained their role was "too clear"

For Universal Factors, consider these levels:
- Individual Contributor: Executes with AI assistance, learns tool capabilities
- Team Lead: Designs AI-enhanced workflows, mentors others on AI use
- Manager: Drives AI adoption strategy, measures human+AI performance
- Senior Leader: Sets AI vision, allocates resources for transformation

For Core Accountabilities:
- Focus on what humans uniquely contribute in an AI-augmented role
- Specify how AI amplifies rather than replaces human capabilities
- Include responsibility for continuous learning and adaptation
- Emphasize judgment, creativity, and strategic thinking

Always ask clarifying questions about the role, team context, and business needs before generating content.

Upload your research from Step 2 and your current job descriptions from Step 1 to give your jig context.

Start with a prompt like:

Help me redesign the job description for [role title] at our [company size/type]. Based on my research and current JD, create:

1. Universal factors appropriate for this level
2. Core accountabilities that reflect AI-era work
3. Required competencies including AI collaboration skills
4. Success metrics that emphasize outcomes over activities

Focus on how this role creates value when AI handles routine tasks, and what unique human contribution we need.

Collaborate with your jig until you've got your JD's finalized. Drop them into a well organized folder on your shared drive; you'll be revisiting them often.

Step 5:
Iterate

I've you've been paying attention, the final step in AI for SMBs Weekly follows a pattern. One of gen AI's many superpowers it its ability to contract the time you spend analyzing and more time executing, which means you should contract the cycles of iteration.  

In The On-Ramp (follow this link to take the first chapter for free), one of the leadership mindsets that you'll learn is the importance of a "directional - not static - understanding of generative AI." The pace of change is accelerating faster over time, which upends much of the prior 'math' of leadership when weighin the time-to-ROI for any kind of investment against the cost in dollars and attention.

Job descriptions in the AI era require ongoing refinement. What works in theory might not work in practice, and AI capabilities are evolving rapidly.

Build feedback loops into your process:

• During hiring: Track which descriptions attract the right candidates and which create confusion.
• After hiring: Ask new employees what was unclear or misleading in the job description.
• Quarterly reviews: Update job descriptions based on how roles are actually evolving with AI adoption.
• Tool evolution: As new AI capabilities emerge, consider how they might change role requirements.
  
  

This iterative approach acknowledges a fundamental truth: we're all figuring this out together. The businesses that succeed will be those that treat job descriptions as living documents that evolve with their understanding of how humans and AI work best together.

Clarity is Kindness

I'm fond of saying, "No one has ever said 'my role was too clear'."

In a world where work is changing rapidly, role clarity becomes even more critical. Employees need to understand not just what they're responsible for, but how their work connects to AI systems, what decisions they can make independently, and when they should collaborate with or override AI recommendations.

Clear job descriptions also help you hire people who are excited about human+AI collaboration rather than threatened by it. They signal that you're thinking strategically about the future of work, not just trying to cut costs with AI.

Start with one role. Pick something important but not mission-critical. Go through the full process. See what you learn. Then scale the approach across your organization.

Your next hire could be the difference between a team member who views AI as a threat and one who sees it as their superpower. The job description you write will determine which one you attract.

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