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I keep hearing over and over that it is important in one's profession to have "AI skills". I understand how AI is going to change the work force in the future, but what particular AI skills are important to have?

So far I am a SME with AI computer hardware, networking and infrastructure. I have built an agent at home, used few shot tuning on a generic LLM, and did a resume builder and mock interview on it. I have a small prompt library that I keep at work and at home, slowly expanding to making skills and eventually larger tools. Is this enough to say "I have AI skills"? Is this going to become the same thing as saying "I have MS office and internet skills" in the future?
Should I be focused on training, prompting, building, RAG, embeddings, ethics, coding, APIs, genAI, model tuning, agentic workflows, and certifications before I say I have AI skills?
Basically which skills are desired or required in the future?

careerdevelopment ai artificial-intelligence machinelearning automation codingskills llm promptengineer promptengineering agentic-ai embeddings genai
Thank you comment icon If you’re a student, “AI skills” does not mean knowing everything about AI. It means understanding the basics, using AI tools well, and being able to build or improve something with them. Focus on AI literacy, prompting, data basics, simple building with tools or code, evaluation, and ethics. You do not need to master every topic at once. A better approach is to build one broad foundation, develop one or two deeper strengths, and complete a few small projects you can talk about. Basic AI use will likely become as common as Office or internet skills, but students who stand out will be the ones who can use AI to solve real problems. Kathy Klock
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Joseph’s Answer

I think your comparison with "MS Office and internet skills" is quite apt - both are a broad continuum of skills. At the bottom end of the range, a lot of people can claim on a resume or CV to have Office and internet skills, but what they often mean is a very basic "I can work a browser and handle simple documents" - really a default expectation in the modern world, and not skills that are making or breaking careers. However, as you increase through the scale into "'I'm an intermediate/advanced user who can build complex Excel formulae with XLOOKUP and INDIRECT; compose macros and VBA or a master of Power Automate", that's a different level of skills that does mean more.

AI skills are similar. Increasingly, "I have AI skills" is meaning at the bottom end - "I can get ChatGPT to write an email for me" or "I use Gemini for search" - which is increasingly becoming an expectation rather than a skill above the rest. However, what you've described is definitely more of the advanced skill set, certainly around the LLM side of AI. (you didn't list much around traditional ML "AI", but you're allowed to be a specialist in one form of AI and still claim "Advanced AI skills".)
I'd probably recommend you provide clarity in CVs/applications on what you mean by "AI skills" - rather than just "AI skills", say something like "Advanced AI skills including model tuning, agentic workflows and prompt engineering" - from what you've described, I think that's something you can legitimately claim.
Thank you comment icon Thank you for this was a very thorough answer! I will consider adding some ML skills to my skill set as well! L
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Kelly’s Answer

Given your answer here you are already in the top 10% of users who are actively working on AI skills. Great work!
The first step is awareness and understanding. It appears you know this well. On your resume, you should list the specific tools/models that you like to use and that you are familiar with prompting, etc.
Second (and this is where you can differentiate for now) is how are you using the tools to add value -- make money or save money. Even if you are able to streamline workflows, that results into time saved = dollars retained.
Once you are able to use your knowledge to create value from the tools you understand, you will be in the top 5% or 1% of AI users and this will become a key competitive advantage. When discussing this on your resume, be sure to lay out the quantifiable benefit (numbers) to showcase how these tools and skills have definitive value.
Thank you comment icon Thank you! This is a skill I am slowly building, this is the perfect direction to build this skill around! L
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Audrey’s Answer

Think about how you can utilise AI to optimise your work or personal life. Can you use ChatGPT or other resources to reduce the time you spend in planning i.e. writing a cover letter for a job, prepping for an interview, replying to a friend in a difficult conversation?
The most important thing is how willing you are to adopt AI and incorporating it into your life so that you can display this in an interview for a job. If used correctly you can minimise the time you spend on 'busy-work' and optimise the time spent on high level / priority tasks.

Hope that helps!
Thank you comment icon I am all about taking boring tasks and automating them with AI! Thank you for your response! L
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Hemant’s Answer

Hi,

- Show value, not just knowledge — the real differentiator is "I used AI to save X hours or automate Y task" not just "I know how to prompt"
- Put specific tools on your resume — LangChain, specific LLMs, vector DBs, whatever you've touched — name them explicitly
- Add RAG next — it's the #1 skill companies need right now for working with their own internal data
Learn MCP (Model Context Protocol) — Anthropic's open standard for connecting AI agents to real tools, files, APIs — this is becoming the backbone of agentic systems, learn it now before everyone else does
- Look into ACP (Agent Communication Protocol) — emerging standard for how agents talk to each other in multi-agent systems, very early but very important
- Sandboxes & safe execution — knowing how to run AI-generated code safely (E2B, Docker sandboxes, isolated environments) is a real skill as agentic systems grow
- API + workflow integration — connecting AI to real systems (Slack, databases, internal tools) is where the money is
- Even basic Python — you don't need to be a dev, just enough to read and tweak scripts
- Quantify everything — "reduced X process from 2 hours to 10 minutes using an agent I built" beats any certification
On the MS Office comparison — you're right, it's coming:

- Basic AI use will be table stakes soon
- Building and integrating AI systems will stay rare and valuable for a long time
Your hardware + infrastructure background is a secret weapon — most AI people can't do what you do on the infrastructure side. That combo is genuinely hard to find.

Keep building real projects. Real things you've built will always speak louder than any certification. You're on the right track — just keep going.
Thank you comment icon ACP is a new term to me! I'll dig into that one and brush up or learn everything else you listed here! Thank you! L
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Eric’s Answer

I think building an AI agent, doing few-shot tuning, and maintaining a prompt library is fantastic/are fantastic use cases and puts you ahead of a LOT of people.

A few thoughts:

AI Fluency will definitely become table stakes. Prompting, being a prompt engineer, and knowledge of GenAI will become like Microsoft Office skills in my opinion (i.e., everyone needs them).

Human judgment is a huge skill - not just always automating/using AI in all circumstances, but having the discipline to know when AI is a good fit to solve a given, real problem. People will always run to say AI is the solution, but taking a step back, considering the technical or business problem at-hand, and THEN asking yourself if AI is the answer, is good, necessary reflection.

Build, experiment, keep playing around! Lean into building and agentic workflows, you're already doing a lot of this, but there's a lot of value in agent orchestration and application.

Be that person that can bridge technical and business capabilities - i.e., connecting AI capabilities to real business outcomes AND being able to explain that to others. E.g., "here's what AI is solving in this case, in simple terms." That's a huge differentiator.

Certifications are good, but experience is huge in the field of AI. What you built/the portfolio of agents and other things you've put together is amazing, hands-on experience that employers are looking for.

Take with a grain of salt, but hope those tidbits help!
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Eashan’s Answer

Your hands-on experience with building a local agent, using few-shot tuning on a generic LLM, creating mock-interview apps, and maintaining prompt libraries shows you have strong foundational AI and prompting skills. However, prompting is quickly becoming a basic skill, like using MS Office. Just as typing and searching online became essential skills, basic prompting and using LLMs are becoming standard for knowledge workers.

With your technical background, claiming "AI skills" in a tech environment will soon require more than basic prompting. Aim to build, secure, scale, and manage AI systems. Don't just focus on prompting. Position yourself as an AI Platform & Systems Architect. Use your hardware and networking skills as a base, and add expertise in Agentic Workflows, RAG/Embeddings, and MLOps deployment. This will help you build a career that is secure against future automation.
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Supreeti’s Answer

You are definitely ahead in the game. The differentiator will be depth + integration + outcomes. There are many different layers to AI:-
1. Layer 1 : Foundations (you already have most of this) - Prompting (beyond basics), understanding LLM behavior (hallucinations, context limits), basic agent workflows
2. Layer 2: Systems thinking (RAG, database etc)
3. Layer 3: Integration -APIs (must-have), Connecting AI to real tools (Slack, CRM, databases), Automation pipelines, Backend logic
Push yourself and build on what you have already learned and focus on orchestrating workflows based on efficiency and tool usage. Pick a layer and integrate systems that will help you or the organization reduce manual effort while utilizing minimum tokens. In the process you are designing, validating implementing, validating and debugging the system from end to end. Showing impact that will stand you out. If you are interested in security or specialization in a field then add certifications.
Thank you comment icon Thank you so much! Thank you for breaking this question down and responding to its parts as well!! L
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Ethan’s Answer

Not only do I recommend getting comfortable with AI for young professionals' and college students, but people of any age group. Most people are worried about the use of AI in the workforce replacing jobs. I think quite the contrary, I look at this as an opportunity to become better at your job or profession than the next person. Even at a simple level, if you can practice prompting AI and getting comfortable with its different uses your able to complete your tasks at a quicker and more efficient pace, which will set you apart from other candidates.
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Mike’s Answer

I think there is a difference between using AI for "fun things" and understanding how this tech works and what it is good at. Although I have seen some colleagues who have done some fun things by creating marketing videos, etc., my job is not marketing - so I need to focus on "how does this technology work, what is the next generation of this technology going to be able to do, and how does these capabilities better help me solve my clients' business problems." At the end of the day we are all expected to understand complex business problems and figure out how to solve for them - this technology is one (albeit powerful) tool for us to use.
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Vincent’s Answer

You already have AI skills, which is fantastic! The key now is to clearly describe those skills and show how they add value. As AI knowledge becomes as common as basic software skills, what will really make you stand out is your ability to design effective AI workflows, connect AI with real data and systems, and use it responsibly to achieve better results.

To present yourself more effectively, you might say: I have practical AI experience in infrastructure, building agents, designing prompts and workflows, and applying LLMs in real-world situations. I focus on deploying AI systems, integrating them with tools and knowledge, and using them effectively.

I suggest you claim your skills now, but be specific about them. Then, focus on enhancing your expertise in three key areas:

1. RAG and embeddings
2. Agentic workflows and tool use
3. Governance, security, and evaluation

This approach aligns more with the current needs of businesses than trying to keep up with every new trend. Keep learning and growing, and you'll be well-prepared for the future!
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Sharadha’s Answer

To build a strong future in today’s workforce, it is important to develop AI skills such as knowing how to use AI tools effectively, writing clear and smart prompts, understanding data, checking AI answers for mistakes or bias, using AI to save time on repetitive work, and applying it responsibly so that you can work faster, make better decisions, and stay valuable in almost any profession.
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SeanRobert’s Answer

Going forward with "AI Skills" Is more about your ability to learn and adapt to new and changing fields. Prompt engineering is a term that was huge 8 months ago and has no become a nice skill to learn but not the must have it was as the models get smarter. The understanding that is going to be really important in the next year or two will be Token management. The era of subsidized token use on frontier models will evaporate at some point until the hardware can keep up with the speed of models and economy of scale spins up. Minimizing the over all token use and making sure each ask is hitting efficiently will save thousands of dollars.
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Paras’s Answer

You're doing great by using AI tools and starting to explore prompts and build basic agents. This area is growing fast, so it's a good idea to keep up with new updates from companies like Claude or Google. It's also helpful to understand productivity tools like Copilot or specialized agents on platforms like agentforce. Learning how to use AI agents, create simple apps with coder agents, and pick the right LLM for your needs are valuable skills. These will prepare you well for applying AI in any specific field or industry. Keep going, and you'll achieve amazing things!
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Adam’s Answer

I think you are on the right track. Yes, AI skills will be like saying you know how to use the Internet.

However, there is much greater value in saying I have implemented AI to perform this task and this is how I solved this problem with AI tools. You are on the right track with certifications and additional professional development. Never stop learning new things because this will ultimately set you up for success when you become the subject matter expert for a new tool just because you were curious and played around with it.

Continuous improvement in all things you do will open doors and you'll be prepared to seize opportunity when its presented.
Thank you comment icon Thank you! Never stop learning will continue to be my mantra! L
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Sunil’s Answer

Great job! You're doing amazing, and it's exciting to see how much you've already accomplished in AI.

Now, the real opportunity is to use your skills to solve real-world problems.

With your engineering background, think about picking an industry you like and start creating practical solutions there. For instance, if you're interested in financial services, you could develop an AI tool for stock research or market analysis. This kind of work will help you grow both technically and professionally by enhancing your AI skills and gaining industry knowledge.

At this point, progress comes from being curious, consistent, and practicing regularly. Keep asking good questions, learning from trustworthy sources, and finding ways to turn ideas into useful results. These habits will boost your confidence, help you adapt to changes, and set you up for long-term success.

You're on a great path—keep it up!
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Angel’s Answer

I definitely think you are on the right track! Maintain your curiosity and understand that the technology is changing rapidly. I would highlight a focus on AI governance and ethics- how do we as technologists embrace how AI helps us while at the same time understand our part in how data centers impact our resources like water, are we working with the communities surrounding the data centers to understand impact to water and power infrastructure. Also, I think we need to embrace that using AI in our work is becoming as common as using MS Word, Excel ,and PowerPoint and we want to make sure that we disclose our use of AI in our work.
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Natig’s Answer

The work you are doing now — building an agent, tuning it, and creating a custom prompt library — elevates you from a mere user to an "AI implementer." Yes, these are pretty serious skills for today. But you're right, in the near future, saying "I know AI" will be as commonplace as saying "I can use the Internet" today. What will make the difference is not how, but to what depth you use the tool.

​The most sought-after skills in the future will not just be the ability to ask questions, but the ability to build systems. You should pay special attention to RAG (Retrieval-Augmented Generation) technology; because the most important thing for companies is to get AI to work properly with their internal, confidential data. In addition, learning Agent workflows (the coordinated work of several bots) and API integrations will greatly strengthen you from a technical perspective.
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As for coding, even if you are not a professional programmer, understanding scripts written in Python and modifying them according to your needs will put you one step ahead of others. Ethics and security are the "responsibility" side of things, which are absolutely required in large projects.

​In short, you've already laid the foundation. Now, if you focus on connecting these skills (deployment and workflow), you'll have solidified your place as a future expert. Certifications are great, but real projects you've built and agents working effectively will always speak louder.
Thank you comment icon Thank you! I think I am going to find some more projects to do! L
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Sandeep’s Answer

Hello,

You already have more AI experience than many people who simply list “AI skills” on a resume. The important part is not just using AI tools, but understanding how to apply them to solve real problems.

In the future, valuable AI skills will likely include:

* Working with APIs and automation
* Prompting and workflow design
* Understanding data and model limitations
* Building AI-assisted applications
Thank you comment icon Thank you! I have been looking at MCP for a while so I will definitely be following your advice! L
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Jules’s Answer

You already have valuable AI skills. Your experience with LLM prompting, few-shot tuning, agent building, and AI infrastructure is impressive and shows real expertise, not just interest.

For most jobs, the key skills are:

- Understanding what AI can and cannot do
- Designing effective prompts
- Using RAG, embeddings, and retrieval
- Integrating APIs and tools
- Evaluating and testing AI systems
- Ensuring governance, privacy, and safety
- Creating automation and agent workflows
- Knowing Python/SQL if you want to build, not just use

Focus on building useful AI tools, connecting them to systems, and assessing their quality. These are the core skills employers look for. While certifications are nice, a solid portfolio is even better.

You can confidently say, "I have hands-on experience with GenAI prototyping, prompting, tuning, and AI infrastructure."

To strengthen your skills, work on 2-3 strong portfolio projects. Create one RAG app, one agent workflow, and one integrated tool. These will showcase your abilities more effectively than any certificate.
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Cameron’s Answer

Hi - I work in AI consulting at Deloitte as an AI strategist for finance & accounting teams. What you're describing already puts you qualified to highlight AI skills on your resume. That said, push back on the question itself. 'AI skills' is too broad to be useful until you answer what role you want to play. I think about it in three tracks.

First - go deep as a developer. If that's the path, stop dabbling. Pick one platform (Claude, Gemini, OpenAI, Bedrock) and become the person who can build the hardest things on it. Bouncing between four platforms means you're a 2/10 on each instead of a 9/10 on one. Deep builders get hired to do the hard work.

Second - become a domain expert who knows AI. AI for Finance, AI for Manufacturing, AI for Legal, whatever you care about. Real example: I'm on calls constantly where a CFO says something like 'I need to capture remittance advices to improve cash application for large unapplied balances.' The AWS or Azure folks on the line are technically brilliant and have no idea what that means. The gap between the technology and the business problem is enormous. That's where careers get built right now.

Third - be a strategist. Vendor assessment, build vs buy, where AI fits in the operating model. Works best paired with a domain (that's what I do - AI strategist for finance), but it can stand alone if you'd rather be a generalist.

People who go deep in one of those lanes will outpace generalists who stay shallow forever. What separates people is being able to look at a real problem and say 'I know how to solve that with AI, and I can explain it to both a developer and a CFO.'

My advice - think about the area where AI intersects with your passion, that will help you narrow down on tech, domain or strategy. Hope this helps.
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