How AI Is Changing Talent: Finding Workforce and Skills Gaps That Matter
AI is a practical way to analyze the mountains of HR data sitting in your systems. Use it to spot patterns, surface gaps, and benchmark current skills against what the business will need next.
As roles shift with technology and market demand, your team's skills must shift too. HR leaders are turning to AI to get in front of those changes with faster, clearer analysis.
1) Organize your data
Your organization already has the raw material: job ads, job architectures, performance reviews, LMS history, certifications, internal mobility moves, and more. The problem is quality and completeness.
Before plugging in any tool, commit to data hygiene. Build a consistent process for collecting, maintaining, and updating workforce data. Standardize job descriptions and include the skills, knowledge, and key activities for each role so AI can make real comparisons. As one expert put it, "garbage in, garbage out."
- Define a canonical skills list and map it to roles.
- Enforce consistent titles and levels across departments.
- Normalize training records and performance metrics.
- Set update cadences (monthly/quarterly) for each dataset owner.
2) Analyze the insights
Large language models like ChatGPT and Microsoft Copilot can summarize, cluster, and draft reports from your data quickly. For deeper workforce planning, specialized HR analytics platforms (e.g., Workday, Disco) can model supply vs. demand, skill adjacency, and internal mobility paths.
With project performance data and sales forecasts, AI can estimate the skills and roles you'll need to hit next-quarter targets. Looking at an employee's job history and training, it can also gauge their capacity to upskill or reskill into high-demand roles.
IBM reports it uses an internal AI system to infer employees' skills from their digital activity, predict proficiency levels, and then recommend training and mentors. The result: a 20% boost in employee engagement in 2024, tied to clearer growth paths and targeted learning.
3) Respect AI's limits and build trust
AI won't catch every nuance that makes someone effective: soft skills, small but critical tasks that never make it into a job description, or the extra effort behind the scenes. Keep a human in the loop to validate skills signals and reality-check the outputs.
Privacy and buy-in matter. Be transparent about what data you'll use, how you'll use it, and why. Align with internal policies and external guidance to reduce risk and strengthen trust.
Data literacy is the other hurdle. Even the best system needs HR pros who can interpret results, translate them for the business, and turn them into action. Skills analysis isn't a one-time audit-make it a continuous process.
4) Turn insights into action
AI is not a shortcut or a silver bullet. It accelerates good process; it doesn't replace it. Use the findings to make clear decisions and keep score.
- Prioritize the top skills gaps by business impact and time-to-fill or time-to-train.
- Decide build vs. buy: upskill/reskill paths for internal talent vs. targeted hiring.
- Refresh job architecture and descriptions with explicit skills and proficiencies.
- Stand up skills-based learning plans and internal mobility campaigns.
- Track leading indicators monthly: training completion, skill proficiency gains, internal fill rates, and time-to-productivity for reskilled roles.
Quick starting checklist
- Inventory your data sources and fix the basics (duplication, missing fields, outdated roles).
- Choose a pilot area with clear business demand (e.g., sales engineering, data analytics, customer success).
- Run an AI-driven skills benchmark vs. upcoming projects and targets.
- Validate with managers and employees; adjust for soft skills and hidden work.
- Launch a 90-day upskilling sprint and measure movement on the top 3 gaps.
Tools and training for HR teams
If your team needs to build confidence with AI tools and data analysis, consider structured upskilling. A focused curriculum helps you interpret results, communicate tradeoffs, and act on insights faster.
Explore AI courses by job role at Complete AI Training
Bottom line: clean data, clear process, and consistent action. With that foundation, AI becomes the accelerant that takes workforce planning and skills analysis from guesswork to repeatable practice.
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