Essential New Skills for Employees – How to Stay Competitive in the Age of AI (Reskilling / Upskilling Roadmap)

1. Why “not upgrading your skills” is the biggest risk

The latest Future of Jobs report from the World Economic Forum forecasts that by 2030, around 39% of the core skills needed for today’s jobs will undergo significant change, meaning many people’s current skill sets will become partially “out of date”.

The same report shows that over 80% of employers plan to increase investment in AI and digital technologies in the coming years, and place employee upskilling as a top priority to cope with transformation and automation.

In Hong Kong, research indicates that the share of AI‑related roles is steadily rising and more jobs require collaboration with AI tools, while roles based purely on repetitive tasks are gradually disappearing.​For employees, the real risk is not AI itself, but staying at a skill level that only performs work which can easily be automated.​

2. The three most valuable skill categories in the AI era

(a) Digital & data literacy

• Studies show that in Hong Kong, the number of positions requiring the use of AI or data tools has increased significantly, and the skill requirements of AI‑exposed jobs are changing about 60% faster than those of other roles.​

• Not everyone needs to become an engineer, but employees who can use Excel / BI / AI tools to read data, derive insights and support decision‑making usually enjoy better pay and promotion prospects.​

  
  

(b) Human skills that complement AI

• The WEF notes that the fastest‑growing skills over the next few years include creative thinking, complex problem‑solving, communication and collaboration, and technology literacy – capabilities that AI still struggles to fully replace.​

• Surveys in Hong Kong and across the region likewise find that companies most want “hybrid talents” who can combine AI tools with business judgement, rather than operators who only know how to use a single tool.

(c) Basic AI knowledge and automation mindset

• Multiple reports show that employees who understand AI concepts, prompt design and workflow automation enjoy significantly higher productivity and a clear wage premium.​

• In future, many job titles will not explicitly say “AI specialist”, but will expect you to break down workflows and rebuild them using AI plus automation tools – this is the golden direction for upskilling.

3. A reskilling / upskilling roadmap for employees

Path A: Non‑tech office workers (admin, operations, clerical)

Goal: Upgrade from “manual admin” to a “process‑ and data‑driven problem solver”.​

Suggested steps:

1. Spend 1–2 months building a solid digital foundation: strengthen your Excel / Google Sheets skills and learn basic automation such as formulas and pivot tables.​

2. Learn to use AI in daily work: for example, use generative AI to draft emails, prepare meeting notes and conduct basic analysis, while practising how to write high‑quality prompts.​

3. Pick one repetitive task and turn it into an “AI + template” workflow – such as reports or simple customer replies – then track the time saved and use it as evidence when asking for a raise or internal transfer.​

   
   

Path B: Professionals (marketing, HR, finance, etc.)

Goal: Upgrade from “professional executor” to a “strategy expert who leverages AI”.​

Suggested steps:

1. Keep up with AI use cases in your own field: for example, Gen AI advertising and content creation in marketing, AI recruitment and talent analytics in HR, or automated reporting and risk analysis in finance.

2. Each quarter, choose one AI / data tool to learn in depth and proactively propose a small pilot project in your current role – such as AI‑assisted segmented marketing or using data to predict customer churn.​

3. Join cross‑functional projects to build business negotiation and change‑management skills, which are often the key soft skills needed to move into manager / lead positions.​

   
   

Path C: Those considering a career change or worried about automation

Goal: Move from a “highly repetitive role” to a position that can coexist with AI or manage AI‑driven work.

Suggested steps:

1. First assess your industry’s automation risk and emerging roles, such as AI‑assisted customer service, data labelling, AI product operations, training and change management.​

2. Choose an “entry‑level skill bundle” for a new function and focus 3–6 months on it, for example basic data analysis (SQL + simple BI), UX / product operations, or AI‑enabled customer success.​

3. Build a portfolio through short internships, part‑time work or side projects, then use LinkedIn and your CV to package your “transferable skills + new skills” and gradually pivot into a new path.

4. Practical tips: how to upgrade yourself with limited time

• Break learning into small, job‑relevant goals: for example, “use AI to cut my weekly reporting time in half” is much easier to stick with – and to demonstrate to your manager – than the vague goal of “I want to learn AI”.​

• Leverage company resources for learning: many Hong Kong companies have started offering internal AI or digital training, and surveys show most local employees have been given some form of digital upskilling opportunity – making use of these is effectively getting your employer to “sponsor” your upgrade.​

• Track your growth with data: record how much working time AI helps you save, how much KPIs improve or how error rates drop – these metrics become powerful evidence when negotiating salary or changing jobs.

In the age of AI, the most valuable thing is not a job that “can never be replaced”, but employees who are willing to learn continuously and know how to collaborate with AI.​Many companies say their biggest barrier to adopting AI is the employee skills gap, which is why they are willing to invest in people who show strong learning motivation.​

If you want to design a personalised reskilling / upskilling plan for yourself or your team (for example HR, frontline operations or admin teams), you can start by assessing your current skills, then map out a 3–6 month learning path and practical projects to gradually turn AI into your competitive edge rather than a threat.