I've been writing PDPs for over 15 years. They're one of my favorite parts of the job (an excuse to sit down with someone, get to know them properly, and build something that actually helps them grow).
The problem? PDPs don't scale. They take time, conversation, and reflection (all the things that make them worthwhile) but also limit how many you can meaningfully create.
This year I've been experimenting with AI to help with that. First by transcribing PDP meetings (by uploading my hand written notes with my phone camera) and then using ChatGPT to structure them into plans. Then I wanted to push further: Could ChatGPT actually build a better PDP than I could?
The results were surprisingly strong if we measure on speed, structure, and range of ideas. So I decided to test it properly: building a full 6-month PDP for myself from scratch.
But here's what ChatGPT can't do alone: it doesn't know what you're avoiding.
A good PDP isn't just about listing strengths and development goals. It's about surfacing blind spots, the patterns you repeat, the gap between what you say matters and what you actually prioritise. That's the human edge: accountability, intuition, and the ability to call bullshit (with love, of course).
So yes, ChatGPT can build a strong PDP. But to make it a real one, you still need to do the hard part: be honest with yourself.
Here's how I did it and how you can replicate it step-by-step.
1. Start with Evidence, Not Guesswork
Most development plans begin with a vague sense of what we "should" work on. Instead, I started with the job I actually wanted: AI Adoption Consultant.
Using ChatGPT, I created a market report from live UK job listings, identifying essential vs. desirable skills, tools, and experience.
Pro Tip: Use the deep research feature (available in ChatGPT with web access, or any model with browsing).
Now copy paste everything between Start Prompt and End Prompt, and don't forget to add your own relevant details.
Start Prompt
Prompt title: "Understand any job market in depth (roles, skills, and hiring signals)."
Role: You are a meticulous labour-market researcher and analyst.
Goal: Build an evidence-based snapshot of the current job market for a specific role or field that I'll define below. Identify what employers are actually looking for (the skills, experience, and signals that drive hiring today).
Step 1: Define the Target Role
Before you begin, ask me to specify:
- Target Role / Title(s): (e.g. "AI Adoption Consultant", "Learning Designer", "Data Governance Lead", "Sustainability Analyst")
- Geography: (e.g. "United Kingdom", "US West Coast", "Europe", "remote")
- Time window: (default: last 60-90 days)
- Seniority: (optional -- entry, mid, senior, director, exec)
Once I provide these, continue.
Step 2: Scope and Sources
Search current listings from:
- Major job boards (LinkedIn Jobs, Indeed, Glassdoor, Totaljobs, Reed, CWJobs, or region-specific equivalents)
- Specialist portals (sector-specific boards, Civil Service Jobs, academic, or startup sites)
- Company career pages, employer blogs, or RFPs mentioning the target capability.
Step 3: Extract for Every Posting
Return one row per posting using this structure:
{
"job_title": "",
"employer": "",
"sector": "",
"location_mode": "Remote | Hybrid | On-site",
"city_region": "",
"posted_date": "",
"salary_currency": "",
"salary_range_annual": "",
"contract_type": "Permanent | FTC | Contract",
"seniority": "",
"core_responsibilities": [],
"essential_skills": [],
"desirable_skills": [],
"technical_or_tool_keywords": [],
"governance_or_regulatory_terms": [],
"change_or_methodology_terms": [],
"experience_requirements": {
"years_overall": "",
"years_specialist": ""
},
"education_quals": "",
"certifications": [],
"metrics_kpis": [],
"nice_to_have": [],
"application_link": "",
"source": "",
"notes": ""
}
Step 4: Synthesise and Analyse
Create a 5-part summary:
Part A -- Evidence Table
All collected data (deduplicated) in a clean table.
Part B -- Skill Heatmap
Rank the most frequent essential and desirable skills and tools. Show frequency counts and representative quotes.
Part C -- Patterns and Insights
Summarise:
- Top 10 essential vs. desirable skills
- Common backgrounds and experience routes
- Sector-specific language or compliance patterns
- Typical KPIs, deliverables, and success metrics
- Emerging gaps or unmet needs (where demand exceeds supply)
Part D -- Salary and Seniority Snapshot
Show salary bands (or contract rates) by seniority and sector.
Part E -- Readiness Checklist
Convert insights into a checklist for someone entering this field:
- Evidence to build (case studies, portfolio pieces)
- Capabilities to strengthen
- Tools or platforms to learn
- Certifications or micro-credentials to pursue
- Typical "day-1 deliverables" expected
Step 5: Apply Quality Filters
- Only include listings clearly tied to the defined role or domain.
- De-duplicate identical cross-posts.
- Exclude irrelevant jobs (e.g., data scientist if researching adoption consultant).
- Quote short snippets to justify classifications.
- Flag ambiguous roles.
Step 6: Deliverables
Return:
- A clean table + JSON export of all roles found.
- A written synthesis (800 words or fewer) of the findings.
- A 10-15 item portfolio/readiness checklist aligned to the essential skills.
End Prompt
2. Turn Research Into a Coaching Session
Next, I gave ChatGPT both the report and my CV and asked it to act as a career strategist (the full prompt can be seen here):
Prompt:
"Compare my current skills and experience with this AI Adoption Consultant report. Identify critical, differentiating, and optional gaps. Then create a 6-month professional development plan."
It returned a structured plan covering:
- Technical / AI Tools
- Governance and Risk
- Change and Enablement
- Consulting and Commercial
- Thought Leadership and Credibility
Each action was concrete and time-bound -- e.g. "Complete Microsoft Copilot learning path by Month 2" or "Draft Governance-to-Practice Map by Month 4."
3. Visualise It in a Simple Tracker
At this stage, I exported everything into Excel. One row per week, grouped by focus area.
That alone gave me a real, structured PDP (not a wish list):
- Time-bound
- Market-aligned
- Easy to measure
For most people, you could stop right here. You'd already have a clear six-month plan you can actually follow.
4. Make It Dynamic (Optional but Powerful)
I wanted to take it further to see progress, not just record it.
So I built a simple dashboard in Firebase Studio (Google's no-code web app builder). It tracks tasks, visualises completion by category, and even lets me self-assess my readiness each month.
Features included:
- Progress bar by category (Technical, Governance, Change, etc.)
- Line chart showing progress over time
- Radar chart comparing perceived vs actual readiness
- CSV import/export for updates
- Works on phone, laptop, or desktop
If you'd like to try it let me know and I'll send it directly.
5. Why This Approach Works
Traditional PDPs are static (they don't evolve). This one does.
- Evidence-based -- built from real job data
- Action-oriented -- every step linked to a career goal
- Visible progress -- charts keep you honest
- Adaptive -- easy to extend or repurpose
It's not just a document. It's an ecosystem you can grow into.
6. Want to Try It Yourself?
Here's the full process in one view:
- Research your next role. Use ChatGPT to summarise real-world requirements.
- Analyse the gap. Feed your CV and the report into a coaching prompt.
- Build your 6-month plan. Export to Excel or Notion.
- Track it. Use the spreadsheet (or level up and create a dynamic version in Firebase Studio).
That's it. You've just built a personal career system that's data-driven, personalised, and alive.
If you'd like a copy of the prompts I used for each stage please go ahead.
And if you do build your own version, let me know (I'd love to see how you adapt it for your own field).