Use ChatGPT to Predict and Prevent Workforce Skill Obsolescence

Use ChatGPT to Map Emerging vs. Declining Skills per Role

Start by giving ChatGPT structured information about your current roles and responsibilities. This can be as simple as pasting in job descriptions, role profiles or your existing competency framework. Ask it to identify which required skills are likely to decline and which new ones will become critical over the next 12–24 months, based on technology and regulatory trends.

Here is a practical prompt structure you can adapt:

You are an HR workforce planning analyst.
I will provide you with:
1) A role profile including responsibilities and current required skills
2) Our industry and main technologies we use

Tasks:
- Classify the required skills into: core, supporting, and at-risk (potentially obsolete in 12-24 months)
- Propose 10 emerging or adjacent skills that will likely grow in importance for this role
- Explain briefly why each skill is at-risk or emerging, considering technology, automation and regulation trends
- Output as a table: Skill | Category (Core/Supporting/At-risk/Emerging) | Rationale | Suggested HR action (upskill/reskill/redeploy/hire)

Context:
[Paste role profile]
[Describe your industry and tech stack in 3-4 sentences]

This gives HR a clear, action‑oriented view per role: which skills to maintain, which to phase out, and where to invest in upskilling or hiring.

Cluster Roles into Capability Families Using ChatGPT

Skill risk rarely sits in a single job. It spreads across related roles – for example, all legacy system specialists or all frontline roles in a changing regulatory environment. Use ChatGPT to cluster your roles into capability families based on shared skills and responsibilities, then analyse obsolescence risk at the family level.

You can feed ChatGPT a list of roles and ask it to group them:

You are helping an HR team create capability families.

Input:
- A list of job titles and short descriptions from our organisation.

Tasks:
- Group these roles into capability families based on overlapping skills and responsibilities.
- For each capability family, list the 10 most common skills.
- Flag capability families that are likely to have high skill obsolescence risk in the next 12-24 months, and explain why.
- Suggest 3-5 strategic HR actions for each high-risk capability family (e.g., redesign roles, targeted upskilling, internal mobility paths).

Here are the roles:
[Paste job titles & 2-3 line descriptions]

This clustering helps HR focus on the few capability areas where skill obsolescence risk could impact many people and critical business processes at once.

Generate Future-Ready Role Profiles and Capability Frameworks

Once you understand which skills are at risk, use ChatGPT to co‑create updated role profiles and capability frameworks that reflect the future state. Start from your current descriptions and ask ChatGPT to rewrite them to emphasise emerging skills, de‑emphasise outdated ones, and include clear proficiency levels.

Example prompt:

You are an HR role design expert.

I will provide:
- Our current role profile
- A list of emerging skills we want to add
- A list of at-risk skills we want to phase out in the next 12-24 months

Tasks:
- Rewrite the role profile to be future-ready, highlighting emerging skills and reducing emphasis on at-risk skills.
- Propose a simple capability framework for this role with 4-6 capabilities and 4 proficiency levels (Foundation, Proficient, Advanced, Expert).
- For each capability, define what performance looks like at each level.

Input:
[Current role profile]
[Emerging skills]
[At-risk skills]

This allows HR to quickly produce consistent, future‑oriented documentation that can feed into recruiting, performance management and learning design.

Design Targeted Learning Journeys to Mitigate Skill Obsolescence

With future‑ready capability frameworks in place, use ChatGPT for learning design to create practical upskilling and reskilling paths. Supply details about your existing learning ecosystem (LMS, external providers, on‑the‑job opportunities) and ask ChatGPT to map concrete learning journeys for employees moving from at‑risk skills to emerging ones.

A concrete prompt pattern could be:

You are an L&D designer working with HR on skill obsolescence.

I will provide:
- A capability framework for a role
- The employee's current skill profile (self-assessment or manager assessment)
- Our learning constraints (time availability, budget, preferred formats)

Tasks:
- Identify the top 3 skill gaps that put the employee at risk in the next 12-24 months.
- Design a 6-month learning journey to close these gaps, combining:
  - Existing internal courses
  - External resources (MOOCs, certifications, communities)
  - On-the-job learning and stretch assignments
- Structure the journey by month and estimate time per week.

Input:
[Capability framework]
[Current skill profile]
[Learning constraints]
[Brief description of available internal/external learning resources]

The result is a concrete plan managers and employees can use, rather than vague advice to "develop digital skills".

Connect ChatGPT Insights with Your HR Data for Prioritisation

To make AI outputs operational, HR needs to link them to actual people and teams. Even without full technical integration, you can combine ChatGPT skill forecasts with HRIS exports in a semi‑manual way. For example, export headcount by role, location and business unit, then ask ChatGPT to help you prioritise where to act first.

Example workflow:

Step 1: Export from HRIS
- Fields: Role title, department, location, headcount, average age/tenure.

Step 2: Summarise for ChatGPT
- Group similar roles together and calculate headcount per group.

Step 3: Prompt ChatGPT
"You are a workforce planner. Here is a summary of our workforce by role family, with headcount.
Here are the role families flagged as high skill obsolescence risk and why: [paste from previous analyses].

Tasks:
- Rank the role families by combined risk AND impact (headcount, business criticality).
- Suggest where HR should focus first in the next 6-12 months.
- For the top 3 role families, propose key metrics we should track to monitor risk reduction (e.g., % employees with future-ready certification, internal mobility rate)."

This creates a bridge between descriptive analytics and concrete HR portfolio decisions.

Standardise Prompts and Document Workflows for HR Teams

To scale ChatGPT in HR, you need consistency. Create a simple internal playbook of standard prompts for common tasks: analysing a role for obsolescence risk, generating updated profiles, designing learning journeys, and prioritising interventions. Store these in your HR knowledge base or as templates in your ChatGPT environment.

Document, step by step, how an HRBP or L&D manager should run these analyses: what data to export, which prompts to use, how to review and validate outputs, and how to present results to business leaders. Reruption often packages these workflows into lightweight internal tools so that even non‑technical HR colleagues can run advanced AI skill analyses without touching code.

Implemented well, these practices can realistically reduce manual analysis time by 40–60%, cut external consulting spend on skills frameworks, and, more importantly, shift a significant portion of your workforce from “at risk” to “future‑ready” within 12–24 months through targeted upskilling and role redesign rather than reactive hiring and layoffs.