Hired, Laid Off or Looking
Your Next Move in the Age of AI
By Andreas Zinonos · Founder of ZTG
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How to use this guide
AI is not taking your whole job tomorrow, but it is changing which parts of it people pay for, and this guide shows you where to move next.
It is written for people in tech, data, marketing, design, compliance, finance, accounting, legal, sales and operations roles. If you have ever thought "will my role still exist in three years?", this is for you.
I recruit for these departments, so I write from the hiring side. I see what employers ask for, what they quietly screen out, and what makes one candidate stand out from fifty. I am not an AI expert and I will not pretend to be one. I am the person who reads the job ads and sits in the interviews.
How to read it
- 1Read Part 1 (the big shift and the Cyprus picture). It takes ten minutes.
- 2Go to the page for your role in Part 2. Each page shows where you are now, where the market is heading, the three skills to add first, and how to show them.
- 3Finish with Part 3: CV and LinkedIn lines, interview answers, and a 30 day plan.
The big shift
AI is moving the value in most jobs from doing the task to owning the result, and the people who see that early get hired first.
Why this is changing: the data, the Cyprus picture and what recruiters sayOptional reading. Tap to open.
What the data says
The World Economic Forum surveyed over 1,000 employers for its 2025 outlook. The headline numbers for 2025 to 2030 (WEF):
Half of employers plan to move staff from shrinking roles into growing ones, and 85% plan to upskill their people. Around 40% also expect to reduce staff where AI can automate tasks.
The roles growing fastest include big data specialists, fintech engineers, AI and machine learning specialists and software developers. The fastest declines are in clerical work: data entry clerks, bank tellers and administrative assistants.
What AI takes first, and what stays yours
AI is strongest at work that is repetitive, rule based and easy to check: drafting, sorting, matching, first pass reviews, routine reports. It is weakest where the problem is unclear, the stakes are high, or people have to trust each other.
AI takes over first
- Repetitive checks and data entry
- First drafts of reports, emails, tests
- Matching and sorting information
- Routine status updates
Stays with you
- Judgment on messy, high risk cases
- Deciding what is good enough to ship
- Owning the outcome when something goes wrong
- Building trust with clients, teams and regulators
The WEF also lists analytical thinking, AI and big data skills, creative thinking, resilience and curiosity among the skills growing in importance.
Roles are merging
The job ads I read show a clear pattern: one person is now asked to do what used to take two or three. A quality assurance ad asks for automated testing, strategy and the final sign-off before release. A marketing technology ad asks for HTML and CSS, CRM knowledge, data skills and the ability to talk to IT, marketing and sales in the same meeting. Employers are not asking for fewer skills. They are asking for a wider mix in the same person.
What this means for you
- 1Do not defend the tasks AI is good at. Move up to the judgment around them.
- 2Add one adjacent skill, such as data, automation or a tool from the next role up.
- 3Be able to show it, in a project, a CV line or a story you can tell in an interview.
The rest of this guide shows you how, role by role.
The Cyprus picture
Cyprus is still hiring, and the strongest demand is in technology, financial services and sales, which are also the areas where AI is changing roles fastest.
What recruiters say is in demand
GRS Recruitment's 2026 list names these areas:
| IT and telecom | Developers, cloud and DevOps, cybersecurity, data engineers, AI specialists |
|---|---|
| Accounting and audit | Accountants, auditors, tax professionals (ACCA, ACA, CPA, IFRS) |
| Sales and marketing | Business development, multilingual sales, SEO, paid media, analytics |
| Financial and professional services | Compliance and AML officers, risk and governance, business analytics |
| Construction and engineering | Civil, mechanical and electrical engineers, project managers |
The same article says over 8% of the Cyprus workforce now works in ICT roles (GRS).
What the vacancy data says
Administrative and support services (3.0%) and telecom and IT (2.4%) were among the sectors with higher rates. Finance and insurance had a low rate of 0.9% (Cyprus Mail, from Cystat).
What the job ads show
I read a sample of live ads on Cyprus job boards and company career pages. Three patterns repeat:
- Automation inside the role. Quality assurance ads from Limassol trading and fintech firms ask for automated testing, and one team lead ad says the role will push AI assisted testing (Cyprus Work).
- Data in everyday jobs. A junior data engineer ad asks for SQL, Python, ETL and dashboards, plus automating manual processes (Ergodotisi).
- Analyst roles near compliance and operations. SkillOnNet's careers page lists compliance, back office, CRM and profitability analyst roles (SkillOnNet).
If you have just lost your job
Losing a job after years of experience is a restructuring of your career, not a verdict on your worth, and your experience is the strongest card you hold.
I know this from the inside. After SkillOnNet I chose to pause on purpose, and I used the time to work out what I wanted next. You do not have to take six months, but you do deserve a plan and not a panic.
Your first 30 days
Check your notice, final pay and any redundancy entitlements with your former employer, and ask a lawyer or the relevant authority if anything is unclear. Tell your close circle. Do not send applications in a rush.
Write down 10 results from your last role: what you improved, built, saved or led. Numbers and names make them real.
Find your role in Part 2 and read where it is heading. Pick one skill from that page to start this week.
Update your CV and LinkedIn with the new direction, message 10 people you have worked with, and apply to a few roles that really fit.
How to explain the gap
Gaps worry people more than they worry employers. Be short, honest and forward looking.
On LinkedIn you can add a career break entry with a reason, such as personal development, so the gap reads as a chapter and not a hole.
Use experience as your edge
Junior candidates can learn the tools. They cannot copy your judgment, your network or the mistakes you have already made. When AI handles routine work, people who have seen real problems become more valuable. Say so. In interviews, tell one story where your experience saved time, money or a bad hire.
Show your years with confidence
Your next move
Find your role below. Each page shows where you are, where employers are heading and what to do first.
Tap your role to open it. Only the one you pick opens.
1QA tester to automation engineer, then AI evals engineer
From: testing software by hand and following test cases. To: designing automated tests, then testing AI systems.
QA tester to automation engineer, then AI evals engineer
From: testing software by hand and following test cases. To: designing automated tests, then testing AI systems.
Where the market is heading
One 2026 analysis argues QA is not dying but the manual only model is shrinking, with automation and quality engineering roles holding up better. It also says automation capable testers earn noticeably more. Its figures are the author's own estimates, so treat them as direction, not fact (SQA Experts). In Limassol, trading and fintech firms are advertising test automation roles, and one team lead ad says the role will push AI assisted testing (Cyprus Work). A new title is appearing abroad too: the AI evals engineer, who designs tests that catch errors, drift and bias in AI systems (Forbes).
Three skills to add first
- 1One programming language. Python, Java or Node.js. Job ads for automation roles ask for one of them.
- 2One automation tool. Playwright or Selenium, plus behaviour style testing such as Cucumber. Some automation ads name these tools, while others only say test automation, so read each ad.
- 3Testing strategy. Deciding what to automate, what to keep manual, and what a release really needs.
How to show it
- A small public project that tests a real website, with a short README.
- A CV line such as: "Built an automated test suite for login, checkout and API checks."
- Put your tool and language first in your LinkedIn skills, not "Testing".
Two interview questions
Next step into AI: learn to check an AI tool's answers against clear criteria. That is the heart of evals work.
2BI analyst to data engineer
From: building reports and dashboards from data someone else prepared. To: building the pipelines that prepare the data and automating the work.
BI analyst to data engineer
From: building reports and dashboards from data someone else prepared. To: building the pipelines that prepare the data and automating the work.
Where the market is heading
Data engineers are on GRS's 2026 list of in demand roles in Cyprus (GRS). A junior data engineer ad from a large Cyprus company asks for ETL, SQL, Python, dashboards in Tableau or Metabase, and automating manual processes (Ergodotisi). My own view: AI tools can now draft queries and simple dashboards in seconds, so the part of the job that stays valuable is making the data correct, consistent and automatic.
Three skills to add first
- 1SQL at a deeper level. Joins, window functions, and writing queries others can reuse.
- 2Python for data. Cleaning data, calling APIs and scheduling scripts.
- 3Pipelines and ETL. Moving data from source systems to a clean reporting layer, and keeping it reliable.
How to show it
- One end to end project: pull data from a public source, clean it, load it into a database and build a dashboard on top.
- A CV line such as: "Automated a weekly report that used to take four hours."
- Name your tools: SQL, Python, Tableau, Power BI, Metabase, whatever you have used.
Two interview questions
Next step: learn how data is checked automatically, so wrong numbers are caught before anyone sees them.
3Developer to AI engineer
From: writing features and fixing bugs. To: building products and agents that run on AI models.
Developer to AI engineer
From: writing features and fixing bugs. To: building products and agents that run on AI models.
Where the market is heading
AI engineer sits at the top of LinkedIn's 2026 fastest growing roles list (Dice). The usual skills named are LangChain, retrieval augmented generation (RAG) and PyTorch. Software engineers and full stack developers are the most common people to move into the role, and a deployed project carries more weight than a certificate (Open Data Science). Most of that hiring is concentrated in large US cities, so in Cyprus the role often shows up as "AI specialist" or as an AI focused part of a developer job, and remote roles matter. Related titles abroad include AI agent architect and context engineer (Forbes).
Three skills to add first
- 1Working with model APIs. Calling a language model, handling its answers and its mistakes.
- 2RAG. Connecting a model to your own documents and data so it answers from facts.
- 3Agent workflows. Chaining steps and tools, with checks and fallbacks when the model is unsure.
How to show it
- One deployed project, such as a chatbot that answers from a real document set, with a short write up of what went wrong and how you fixed it.
- A CV line such as: "Built and deployed a RAG assistant used by [number] people."
- Keep your engineering strengths visible. Employers want AI builders who can also ship reliable software.
Two interview questions
Next step: add vector databases and basic evaluation, so you can prove your AI feature works.
4Email and HTML developer to marketing technologist
From: building email templates in HTML and CSS. To: connecting marketing systems, data and automation.
Email and HTML developer to marketing technologist
From: building email templates in HTML and CSS. To: connecting marketing systems, data and automation.
Where the market is heading
A marketing technology developer ad I reviewed asks for HTML and CSS, CRM systems, email and content platforms, mobile tracking, and the ability to connect systems so data flows between them, plus communication that works with IT, marketing and sales. A 2026 skills guide for marketing automation names the same mix: platforms such as HubSpot, Marketo and Salesforce, APIs to connect them, SQL for customer data, HTML and CSS for emails, and analytics. It also says AI is used for lead scoring, personalised content and send times, while people still set the strategy and check the results (Interview Kickstart).
Three skills to add first
- 1A CRM or automation platform. HubSpot, Salesforce or similar. Learn how data, segments and workflows fit together.
- 2APIs and basic SQL. Pull customer data, check it, and connect two tools.
- 3Reading the numbers. Funnel drop offs, A/B tests and what a result really tells you.
How to show it
- A portfolio of two or three responsive emails, plus one case that shows a result, such as better open or click rates.
- A CV line such as: "Built an automated email journey connecting [CRM] with [tool], saving [hours] per week."
- Show that you can talk to marketing and IT in plain words.
Two interview questions
Next step: learn how AI personalisation works, and how to check that it behaves.
5Graphic designer to digital or product designer
From: producing visuals and layouts. To: designing how digital products work, and how people and AI talk to each other.
Graphic designer to digital or product designer
From: producing visuals and layouts. To: designing how digital products work, and how people and AI talk to each other.
Where the market is heading
A 2026 hiring guide says AI is taking over repetitive execution, such as generating layouts and first concepts, so designers are moving toward judgment, user research, systems thinking and leadership. It says employers care less about whether you use AI and more about how well you use it while keeping quality and usability high. The skills it lists include product thinking, user research, design systems, accessibility and clear communication with stakeholders. The tools it names include Figma AI, Adobe Firefly, Midjourney and Canva Magic Design (Mondo). A newer title abroad is conversation designer, who shapes how AI agents talk, including tone, flow and error handling (Forbes).
Three skills to add first
- 1Figma and design systems. Components, spacing rules and consistent screens.
- 2User research basics. Talk to five users, find the real problem, test your idea.
- 3AI tools with taste. Use AI to explore faster, then choose, edit and defend your decisions.
How to show it
- A portfolio of two or three case studies, each with the problem, your process, how you tested it and the result. Process matters more than pretty screens.
- A CV line such as: "Redesigned [flow] after user testing, reducing drop off by [number]."
- Show your Adobe Suite skills next to Figma, so employers see you can work across both.
Two interview questions
Next step: learn how to design the way an AI assistant speaks and responds. It is a growing niche.
6Project manager to AI enabled project leader
From: coordinating tasks, status and deadlines. To: owning outcomes and leading teams that use AI.
Project manager to AI enabled project leader
From: coordinating tasks, status and deadlines. To: owning outcomes and leading teams that use AI.
Where the market is heading
One 2026 analysis says AI will take over routine project work such as status reports, draft schedules, meeting notes and risk flags, while the core of the job stays human: owning the outcome, earning stakeholder trust, negotiating, and deciding when to trust AI output and when to challenge it. It argues the role is shifting from task coordination to outcome ownership, and that the bigger risk is falling behind peers who use AI well (Master of Project). New titles abroad include AI product manager, AI enablement lead and agent supervisor (Forbes).
Three skills to add first
- 1Hands on AI tools. Use them for reports, plans and risk checks, and learn where they go wrong.
- 2Judgment about AI output. Spot when a confident answer is wrong, and verify before you send it.
- 3Leading adoption. Run a small trial of a tool in your team, set simple rules, and track whether people actually use it.
How to show it
- One story where you introduced a tool or process and measured the result.
- A CV line such as: "Led a pilot of [AI tool] across a team of [number], saving [hours] per month."
- Keep your human strengths front and centre: negotiation, facilitation, hard conversations.
Two interview questions
Next step: a recognised credential such as the PMP, which the article above says now tests AI related judgment. Check the current exam outline before you rely on that.
7Compliance and AML analyst to technical compliance and risk
From: reviewing alerts and cases one by one. To: improving how the system finds risk, and handling the hardest cases.
Compliance and AML analyst to technical compliance and risk
From: reviewing alerts and cases one by one. To: improving how the system finds risk, and handling the hardest cases.
Where the market is heading
Compliance and AML officers are on GRS's 2026 list of in demand roles in Cyprus (GRS), and SkillOnNet's careers page lists compliance technical projects and AML analyst roles (SkillOnNet). A 2026 industry article says AI is taking over routine steps such as chasing missing documents, checking ownership structures and applying set policy to low risk cases, so analysts see mostly exceptions and higher risk cases. The skills that gain value are judgment on complex cases, handling situations no written rule covers, and feeding expert decisions back into the system to make it better (FinTech Global). A related new role abroad is the AI governance lead, who oversees accountability, regulation and fairness (Forbes).
Three skills to add first
- 1Deeper case judgment. Enhanced due diligence, escalations, and writing clear reasons for your decisions.
- 2Data and rules. Basic SQL, understanding how alert rules work, and spotting patterns in false positives.
- 3AI governance basics. How AI decisions are documented, tested and audited.
How to show it
- A story where you spotted something a rule missed, or reduced false alerts.
- A CV line such as: "Improved alert review process, reducing time per case by [number]."
- Name the regulations and frameworks you know, and any certification you hold.
Two interview questions
Next step: move toward technical projects or second line roles, where you help define the rules and the risk levels.
8CRM and back office analyst to data driven operations analyst
From: keeping systems updated and producing routine reports. To: finding patterns in the data and improving how the business runs.
CRM and back office analyst to data driven operations analyst
From: keeping systems updated and producing routine reports. To: finding patterns in the data and improving how the business runs.
Where the market is heading
SkillOnNet's careers page lists a junior CRM and profitability analyst and a back office analyst, which shows how these roles sit between operations and data (SkillOnNet). The World Economic Forum expects clerical and data entry work to decline fastest in percentage terms, while analytical thinking is the skill employers rank highest (WEF). My own view: the routine updates and standard reports will be automated, so the analysts who stay valuable are the ones who explain what the numbers mean and suggest what to change.
Three skills to add first
- 1Spreadsheet and SQL fluency. Move beyond copy and paste to formulas, pivot tables and simple queries.
- 2Business thinking. Link numbers to money: profit by customer, cost per case, time saved.
- 3Automation basics. Let tools handle repetitive updates so you spend time on analysis.
How to show it
- One example where you found a pattern and the business changed something because of it.
- A CV line such as: "Identified [issue] in customer data, leading to [saving or improvement]."
- List the systems you know by name.
Two interview questions
Next step: move toward a data analyst or operations analyst role by learning one reporting tool in depth.
9Business development and sales to AI augmented sales
From: finding leads and sending follow ups yourself. To: selling alongside tools that handle research and follow up, while you build the relationship.
Business development and sales to AI augmented sales
From: finding leads and sending follow ups yourself. To: selling alongside tools that handle research and follow up, while you build the relationship.
Where the market is heading
Business development managers, multilingual sales executives and account managers are on GRS's 2026 list for Cyprus (GRS). Abroad, new titles include the AI augmented sales specialist, who sells alongside agents that handle research and follow up, and the AI customer success manager, who helps customers adopt AI and shows the results (Forbes). My own view: AI will draft the outreach, so the people who win are the ones whose clients trust them and who understand the client's business.
Three skills to add first
- 1Using AI for research and follow up. Prepare for a client in minutes, then personalise it by hand.
- 2Consultative selling. Ask better questions, understand the real problem, and propose what actually helps.
- 3Reading your pipeline data. Know which leads convert and why.
How to show it
- Results with numbers: deals won, revenue, accounts retained.
- A CV line such as: "Used AI tools to cut research time per prospect by [number], raising meetings booked by [number]."
- A short story about a client who stayed because of how you worked with them.
Two interview questions
Next step: move into account management or customer success, where trust matters more than volume.
10Office admin to operations and automation coordinator
From: handling paperwork, scheduling and routine office tasks. To: running and improving the processes the office depends on.
Office admin to operations and automation coordinator
From: handling paperwork, scheduling and routine office tasks. To: running and improving the processes the office depends on.
Where the market is heading
The World Economic Forum names administrative assistants and clerical roles among those expected to decline most by volume by 2030, while flexibility, curiosity, analytical thinking and technology literacy grow in importance (WEF). That is not a reason to panic. It is a reason to move early. My own view: the person who learns the tools and who can fix the process becomes the one the office cannot do without.
Three skills to add first
- 1Spreadsheets and basic automation. Formulas, templates, and simple automations that connect the tools you already use.
- 2AI assistants. Draft emails, summaries and documents faster, and always check the result.
- 3Process thinking. Write down how a task works, find the slow step, and propose a better way.
How to show it
- One process you improved, with the time or errors saved.
- A CV line such as: "Created a template and checklist that reduced invoice errors by [number]."
- Add accounting or finance tools you know by name. In accounting support, this matters a lot.
Two interview questions
Next step: grow toward operations coordinator, finance operations or process improvement roles.
11Accountant and finance professional to finance business partner
From: preparing entries, reconciliations and month end reports by hand. To: checking what AI produces, explaining the numbers and advising on decisions.
Accountant and finance professional to finance business partner
From: preparing entries, reconciliations and month end reports by hand. To: checking what AI produces, explaining the numbers and advising on decisions.
Where the market is heading
GRS Recruitment's 2026 list names accountants, auditors and tax professionals among the in demand roles in Cyprus (GRS). ACCA's 2026 survey of 11,389 finance professionals found that 52% use AI tools regularly and 51% worry about AI's effect on their jobs, with younger respondents the most concerned (ACCA). A vendor survey of UK finance leaders found that they rate AI and automation skills (41%) well above traditional accounting skills (14%) for the next generation (HRreview). My own view: AI will do the matching, coding and first drafts, so the accountants who stay valuable check the output, explain the numbers and advise on the decision.
Three skills to add first
- 1Excel and SQL at a deeper level. Models, pivot tables and simple queries.
- 2Automation and AI with controls. Reconciliations and reporting, always with a human check.
- 3Business partnering. Turn numbers into a clear recommendation that a manager can act on.
How to show it
- A CV line such as: "Automated the monthly reconciliation, cutting close time from [number] days to [number]."
- One story where your analysis changed a decision.
- Name your systems and your qualifications: ACCA, ACA, CPA, IFRS.
Two interview questions
Next step: move toward finance business partner, FP&A or finance operations roles.
Where are you in your career?
If you are just starting
AI now supports or replaces some of the routine work juniors used to learn on, such as data gathering and first drafts, and firms may hire fewer juniors who are expected to perform at a higher level from day one (Accounting Today, June 2026). So: learn the fundamentals properly, because you need them to catch AI mistakes. Do a task yourself first, then compare it with the AI answer and note the differences. Keep a short log of what you automated or improved, and ask early for review and client exposure.
If you are experienced
Your edge is judgment and professional scepticism built over years of seeing what goes wrong (Accounting Today, May 2025). So: write down the rules of thumb you use, so you can check AI against them and teach them. Learn to direct and review AI work the way you review a junior's. Move toward advisory: pick one decision your team makes every month and own the analysis behind it.
Career guidance, not financial advice.
12Lawyer and legal professional to AI savvy counsel
From: reviewing contracts, researching and drafting mostly by hand. To: using AI for first drafts and review, and owning the judgment, the risk and the client's trust.
Lawyer and legal professional to AI savvy counsel
From: reviewing contracts, researching and drafting mostly by hand. To: using AI for first drafts and review, and owning the judgment, the risk and the client's trust.
Where the market is heading
AI can quickly scan documents and flag missing clauses, but a Cyprus law firm notes that it cannot judge whether a clause is enforceable under Cyprus law, and that only qualified lawyers may give legal advice and carry the responsibility for it (Kyprianou). The risk of unchecked AI is real: in June 2026 a US judge cancelled a trial and sanctioned four lawyers after both sides filed briefs citing AI invented cases (Legal Cheek). Regulation is creating work too: the EU AI Act applies in Cyprus alongside GDPR, so companies need people to document and oversee their AI use (Mondaq). Check the current deadlines before you advise anyone. My own view: AI will do the first pass, and the lawyers who stay valuable verify, advise and take responsibility.
Three skills to add first
- 1AI assisted review with verification. Use AI for a first pass, then check every source and clause yourself.
- 2Data protection and AI regulation basics. GDPR, the EU AI Act, and how companies document and oversee their AI use.
- 3Plain language advice. Turn a legal risk into a clear recommendation for someone who is not a lawyer.
How to show it
- A CV line such as: "Cut first pass contract review time by [number] using [tool], with full human verification."
- One story where you caught an error or a risk that the tool missed.
- Name the areas of law and the regulations you work with.
Two interview questions
Next step: move toward data protection, compliance or in house counsel roles, where AI regulation work is growing.
Where are you in your career?
If you are just starting
A legal recruitment firm warns that leaning on AI too early can weaken your judgment, your issue spotting and your own drafting voice (Laurence Simons). So: read and analyse a document yourself before using AI, research manually before you ask a tool, and draft before you let AI revise. Join live client meetings, even in a supporting role. Specialise early in an area that is growing, such as data privacy or AI regulation.
If you are experienced
Clients pay for your judgment, your accountability and their trust in you. The June 2026 case shows where blame lands: the judge also sanctioned the lawyers who only signed as local counsel, and called it rubber stamping (Legal Cheek). So: set a firm rule that a person checks every citation before filing, write a short AI use policy for your team that covers confidentiality, and offer AI regulation advice to your clients as a service.
Career guidance, not legal advice.
Make your move visible
Employers hire what they can see, so every new skill needs a line on your CV, a line on LinkedIn and one story you can tell.
The CV line formula
Write each line as: action, tool, result.
If you do not have a number yet, use a clear before and after. Do not invent figures. A recruiter can tell, and an interviewer will ask.
Your LinkedIn in four lines
- 1Headline: your target role, your strongest skill and one proof. For example: "Test Automation Engineer | Python and Playwright | Moving from manual QA".
- 2About: three short paragraphs. Who you are, what you are good at, what you want next.
- 3Skills: put your top five first. They are what recruiters search for.
- 4Featured: link one project, one article or one case study.
If you are between jobs or changing direction, add the new direction to your headline now. Do not wait until you feel ready.
Your proof of work
One small public project beats ten certificates. Pick the one that matches your pathway: a test suite, a dashboard, a deployed assistant, a case study, a one page process. Write a short README or description that answers three questions: what problem it solves, what you built, what you learned.
A short checklist
- CV is two pages or less, with results first
- Headline shows the next role, not only the current one
- Top five skills match the roles you want
- One project or case study is public and linked
- You can explain any gap in one sentence
Interviews and salary conversations
Interviewers are testing three things: can you do the work, can you learn, and can they trust you, so prepare a story for each.
The three stories to prepare
- 1A result. Something you improved, with a number or a clear before and after.
- 2A mistake. Something that went wrong, what you did about it and what you changed. Employers trust people who can say this calmly.
- 3A learning moment. A time you picked up a new tool or skill quickly. This is the proof that you can adapt to AI.
Tell each one in four parts: the situation, what you did, the result, and what you learned. Keep it under two minutes.
Questions about AI you should expect
Questions you should ask them
- "How does your team use AI today, and what is planned next?"
- "What would success look like in the first six months?"
Talking about salary
Salary is easier when you have done the homework.
- 1Know your range. Check the market for your role and level, and decide your minimum and your target.
- 2Let them speak first when you can. If asked first, give a range, not a single number.
- 3Anchor to value. Link your number to results and skills: "Given what I would take on and my experience with [skill], I am looking for [range]."
- 4Look at the whole package. Bonus, training budget, flexibility and growth can matter as much as the base.
- 5Stay calm and polite. If the number is too low, ask what is possible, and whether it can be reviewed after six months.
Using AI at work without losing your edge
Use AI to do the first draft faster, and keep for yourself the decisions, the checking and the relationships.
Five rules that keep you valuable
- 1Verify before you send. AI can sound sure and be wrong. Check facts, numbers and names every time. The articles behind this guide agree on this point: the person who decides when to trust the output and when to challenge it is the one who stays valuable.
- 2Own the decision. If something goes wrong, the responsibility is yours, not the tool's. Make the final call yourself.
- 3Protect confidential data. Do not paste customer details, contracts or internal numbers into a public AI tool unless your company allows it. Ask your manager or IT what is approved.
- 4Keep your own voice. Edit what AI writes so it sounds like you. Clients and colleagues notice the difference.
- 5Show your work. Tell your team how you used AI and what you checked. People trust the people who are open about it.
Where AI helps most
- First drafts of emails, reports and summaries
- Explaining unfamiliar concepts quickly
- Brainstorming options before you choose
- Checking your work for gaps or errors
- Turning rough notes into clear structure
Where you must stay in charge
- Anything with legal, financial or safety impact
- Decisions about people
- Messages that need empathy or trust
- Final numbers that others will rely on
How to practise
Pick one task you do every week and do it with AI for a month. Keep a short note of what saved time and what you had to fix. At the end you will have your own proof, and a story for your next interview.
Your 30 day plan
Thirty minutes a day for 30 days is enough to move from worried to prepared, as long as you finish with something you can show.
Read your pathway page. Pick one target role and one first skill. Write down your 10 best work results.
Spend 30 minutes a day on your first skill. Build the first small piece of your project.
Finish the project. Write a short description of it. Add it to LinkedIn and your CV.
Message 10 people in your target field. Apply to 3 roles that really fit. Prepare your three interview stories.
Daily habit
Pick a fixed time, keep it short and track it. A simple tick box on paper is enough. Missing one day is fine, missing a week is the thing to avoid.
Your one page checklist
- I chose my target role from the pathways
- I picked one skill to learn first
- I wrote down my 10 best results
- I started one small public project
- My CV lines use action, tool and result
- My LinkedIn headline shows my next role
- I have three interview stories ready
- I know my salary range
- I messaged 10 people in my field
- I applied to 3 roles that really fit
If you do only half of this, you are already ahead of most people who just read about AI and worry.
Ready to make the move?
If you want a second pair of eyes on your next step, I am happy to help.
See open roles on the ZTG pagelinkedin.com/company/ztg-zinonthegreat Follow ZTG on Instagram for new roles and career ideas@ztg.roles Message me directlylinkedin.com/in/zinonthegreatAbout the author
About the author
I am Andreas Zinonos, founder of ZTG (Zinon The Great). I have spent over seven years in recruitment across agency, consulting and in house roles, most recently in iGaming at SkillOnNet, working with IT, BI, marketing, design and project teams. I studied Psychology at the University of Huddersfield and Applied Positive Psychology and Coaching at the University of East London. ZTG exists to connect people with great opportunities, and businesses with the right businesses.
Sources and checks
Sources
Checked on 22 October 2026, and on 6 November 2026 for the accounting and legal pages. Job ads and market data change quickly, so check the live pages before you decide.
- World Economic Forum, Future of Jobs Report 2025 digest
- GRS Recruitment, Top in demand jobs in Cyprus for 2026
- Cyprus Mail, job vacancies Q1 2026 (Cystat figures)
- Cyprus Work, QA automation engineer listings
- Carierista, Quality Assurance Engineer ad
- Ergodotisi, Junior Data Engineer ad
- SkillOnNet careers page
- SQA Experts, Is QA a dying career?
- Dice, LinkedIn's fastest growing roles for 2026
- Open Data Science, in demand AI job roles 2026
- Forbes, the 20 new agentic AI jobs
- Interview Kickstart, marketing automation specialist skills
- Mondo, AI design tools for 2026
- Master of Project, AI in project management
- FinTech Global, RegTech's AI shift
- ACCA, Global Talent Trends 2026 (executive summary)
- HRreview, AI reshapes finance jobs as entry-level roles come under pressure (4 March 2026)
- Accounting Today, How AI will reshape talent training and pipelines in accounting (June 2026)
- Accounting Today, AI will replace (some) accountants using AI (May 2025)
- Kyprianou, Can AI replace lawyers in reviewing legal agreements in Cyprus?
- Legal Cheek, US judge stops case after lawyers on both sides cite AI fabricated cases (June 2026)
- Mondaq, Navigating the EU AI Act in Cyprus
- Laurence Simons, The impact of AI and legal tech on junior lawyers
ZTG, Zinon The Great. Connecting people with great opportunities.