Practical advice for tech professionals
Artificial intelligence is no longer a niche skill. It is becoming part of day-to-day work across engineering, product, design and data roles. As a result, employers are increasingly looking for candidates who can show not just awareness of AI, but real, practical experience using it effectively and responsibly.
Whether you are applying for a software engineering role and working directly with large language models, or using AI tools to improve how you work day-to-day, your CV should reflect how you actually use these tools in practice.
Here is how to do that clearly and credibly.
What Counts as AI Experience?
AI experience does not only mean building models from scratch. For many roles, it is about how intelligently you use the tools available to you.
Examples include:
Practical AI tool usage
Using tools such as ChatGPT, Claude, GitHub Copilot, Midjourney, Notion AI, or built in AI features within platforms like Figma or Photoshop to support your work.
Data and analytics
Applying AI for data cleaning, analysis, prediction, pattern detection or reporting to improve decision making.
Automation and workflow optimisation
Building prompts, scripts or lightweight automation to reduce manual effort, speed up delivery or improve consistency.
Technical AI capability
Hands on experience with machine learning, Python libraries, APIs, vector databases, model fine tuning or LLM integration.
AI governance and responsibility
Understanding ethical use, data privacy, bias, compliance and documentation around AI tools.
How to Add AI Skills to the Skills Section of Your CV
Rather than listing tools without context, show how you use them.
Instead of this:
- ChatGPT, Copilot, Midjourney
Try this:
- AI tools: ChatGPT for advanced prompting and research, GitHub Copilot for coding assistance, Midjourney for rapid visual ideation
- AI capabilities: Prompt engineering, workflow automation, AI supported data analysis
This gives employers a much clearer signal of your capability.
Show AI Impact in Your Work Experience
Using AI alone is not impressive. What matters is the outcome.
Strong examples focus on impact:
- Automated weekly reporting using AI driven scripts, reducing manual workload by 40 percent
- Implemented AI assisted content workflows, increasing output from four to ten articles per week
- Used AI analytics tools to identify customer behaviour trends, contributing to improved retention
If you have built something using AI, always call it out:
- Built an internal prompt library used across the team
- Designed an AI assisted onboarding guide for new hires
- Created a chatbot prototype using an LLM API to support customer queries
- Completed AI training and applied techniques directly to live projects
Specificity builds credibility.
Include AI Courses and Certifications
If you list cloud or platform certifications, your AI learning should sit alongside them.
This might include internal training, online courses, vendor led certifications or academic study. What matters most is showing that the learning has been applied in real work, not just completed in isolation.
Show That You Understand Responsible AI Use
Employers are increasingly cautious about how AI is used.
Demonstrating good judgement matters. For example, using AI as a sense check or accelerator rather than a replacement for original thinking. This is particularly relevant in areas like content, design and decision making, where human judgement and context are still critical.
Being able to articulate this balance shows maturity and professionalism.
Final Thought
AI is becoming part of how good teams work, not a bolt on skill. The strongest CVs show how candidates think, adapt and use tools thoughtfully to deliver better outcomes.
If you are exploring your next move in technology or want advice on how your experience translates in the current market, reach out to our tech expert Mitch Hooper at mitch@zebrapeople.com
