TL;DR — What You Need to Know:

• AI is replacing roles in moderation, customer service, writing, and warehousing—but it’s not all doom.
• In its place, AI created jobs paying £100k: prompt engineers, AI ethicists, machine learning leads, and more.
• The winners? Those who pivot now and get skilled, while others wait it out.

Let’s not sugar-coat it: AI has already taken your job.

Or if it hasn’t yet, it’s circling. Patiently. Quietly.

But here’s the twist: AI isn’t just wiping out roles — it’s creating some of the most lucrative career paths we’ve ever seen. The catch? You’ll need to move faster than the machines do.

The headlines love a doomsday spin — robots stealing jobs, mass layoffs, the end of work. But if you read past the fear, you’ll spot a very different story: one where new six-figure jobs are exploding in demand.

And they’re not just for coders or people with PhDs in quantum linguistics. Many of these jobs value soft skills, writing, ethics, even common sense — just with a new AI twist.

So here’s your clear-eyed guide:

• 8 jobs that AI is quietly (or not-so-quietly) killing
• 15 roles growing faster than a ChatGPT thread on Reddit — and paying very, very well.

8 Jobs AI Is Already Eliminating (or Shrinking Fast)

1. Social Media Content Moderators

Remember the armies of humans reviewing TikTok, Instagram, and Facebook posts for nudity or hate speech? Well, they’re disappearing. TikTok now uses AI to catch 80% of violations before humans ever see them. It’s faster, tireless, and cheaper.

Most social platforms are following suit. The remaining humans deal with edge cases or trauma-heavy content no one wants to automate… but the bulk of the work is now machine-led.

2. Customer Service Representatives

You’ve chatted with a bot recently. So has everyone.
Klarna’s AI assistant replaced 700 human agents in one swoop. IKEA has quietly shifted call centre support to fully automated systems. These AI tools handle everything from order tracking to password resets.

The result? Companies save money. Customers get 24/7 responses. And entry-level service jobs vanish.

3. Telemarketers and Call Centre Agents

Outbound sales? It’s been digitised. AI voice systems now make thousands of simultaneous calls, shift tone mid-sentence, and even spot emotional cues. They never need a lunch break — and they’re hard to distinguish from a real person.

Companies now use humans to plan campaigns, but the actual calls? Fully automated. If your job was cold-calling, it’s time to reskill — fast.

4. Data Entry Clerks

Manual input is gone. OCR + AI means documents are scanned, sorted, and uploaded instantly. IBM has paused hiring for 7,800 back-office jobs as automation takes over.

Across insurance, banking, healthcare — companies that once hired data entry clerks by the dozen now need just a few to manage exceptions.

5. Retail Cashiers

Self-checkout kiosks were just the start. Amazon Go stores use computer vision to eliminate the checkout experience altogether — just grab and go.

Walmart and Tesco are rolling out similar models. Even mid-sized retailers are using AI to reduce cashier shifts by 10–25%. Humans now restock and assist — not scan.

6. Warehouse & Fulfilment Staff

Amazon’s warehouses are a case study in automation. Autonomous robots pick, pack, and ship faster than any human.
The result? Fewer injuries, more efficiency… and fewer humans.

Even smaller logistics firms are adopting warehouse AI, as costs drop and robots become “as-a-service”.

7. Translators & Content Writers (Basic-Level)

Generative AI is fast, multilingual, and on-brand. Duolingo replaced much of its content writing team with GPT-driven systems.

Marketing teams now use AI for product descriptions, blogs, and ads. Humans still do strategy — but the daily word count? AI’s job now.

8. Entry-Level Graphic Designers

AI tools like Midjourney, Ideogram, and Adobe Firefly generate visuals from a sentence. Logos, pitch decks, ad banners — all created in seconds. The entry-level designer who used to churn out social graphics? No longer essential.

Top-tier creatives still thrive. But production design? That’s already AI’s turf.

Are you futureproofed—or just hoping you’re not next?

15 AI-Driven Jobs Now Paying £100k+

Now for the exciting bit. While AI clears out repetitive roles, it also opens new high-paying jobs that didn’t exist 3 years ago.

These aren’t sci-fi ideas. These are real jobs being filled today — many in Singapore, Australia, India, and Korea — with salaries to match.

1. Machine Learning Engineer

The architects of AI itself. They build the algorithms powering everything from fraud detection to self-driving cars.
Salary: £85k–£210k
Needed: Python, TensorFlow/PyTorch, strong maths. Highly sought after across finance, healthcare, and Big Tech.

2. Data Scientist

Translates oceans of data into actual insights. Think Netflix recommendations, pricing strategies, or disease forecasting.
Salary: £70k–£160k
Key skills: Python, SQL, R, storytelling. A killer combo of tech + communication.

3. Prompt Engineer

No code needed — just words.
They craft the perfect prompts to steer AI models like ChatGPT toward accurate, helpful results.
Salary: £110k–£200k+
Writers, marketers, and linguists are all pivoting into this role. It’s exploding.

4. AI Product Manager

You don’t build the AI — you make it useful.
This role bridges business needs and tech teams to launch products that solve real problems.
Salary: £120k–£170k
Ideal for ex-consultants, startup leads, or technical PMs with an eye for product-market fit.

5. AI Ethics / Governance Specialist

Someone has to keep the machines honest. These specialists ensure AI is fair, safe, and compliant.
Salary: £100k–£170k
Perfect for lawyers, philosophers, or policy pros who understand AI’s social impact.

6. AI Compliance / Audit Specialist

GDPR. HIPAA. The EU AI Act.
These specialists check that AI systems follow legal rules and ethical standards.
Salary: £90k–£150k
Especially hot in finance, healthcare, and enterprise tech.

7. Data Engineer / MLOps Engineer

Behind every smart model is a ton of infrastructure.
Data Engineers build it. MLOps Engineers keep it running.
Salary: £90k–£140k
You’ll need DevOps, cloud computing, and Python chops.

8. AI Solutions Architect

The big-picture thinker. Designs AI systems that actually work at scale.
Salary: £110k–£160k
In demand in cloud, consulting, and enterprise IT.

9. Computer Vision Engineer

They teach machines to see.
From autonomous cars to medical scans to supermarket cameras — it’s all vision.
Salary: £120k+
Strong Python + OpenCV/TensorFlow is a must.

10. Robotics Engineer (AI + Machines)

Think factory bots, surgical arms, or drone fleets.
You’ll need both hardware knowledge and machine learning skills.
Salary: £100k–£150k+
A rare mix = big pay.

11. Autonomous Vehicle Engineer

Still one of AI’s toughest challenges — and best-paid verticals.
Salary: £120k+
Roles in perception, planning, and safety. Tesla, Waymo, and China’s Didi all hiring like mad.

12. AI Cybersecurity Specialist

Protect AI… with AI.
This job prevents attacks on models and builds AI-powered threat detection.
Salary: £120k+
Perfect for seasoned security pros looking to specialise.

13. Human–AI Interaction Designer (UX for AI)

Humans don’t trust what they don’t understand.
These designers make AI usable, friendly, and ethical.
Salary: £100k–£135k
Great path for UXers who want to go deep into AI systems.

14. LLM Trainer / Model Fine-tuner

You teach ChatGPT how to behave. Literally.
Using reinforcement learning, you align models with human values.
Salary: £100k–£180k
Ideal for teachers, researchers, or anyone great at structured thinking.

15. AI Consultant / Solutions Specialist

Advises companies on where and how to use AI.
Part analyst, part strategist, part translator.
Salary: £120k+
Management consultants and ex-founders thrive here.

The Bottom Line: You Don’t Need to Fear AI. You Need to Work With It.

If AI is your competition, you’re already behind. But if it’s your co-pilot, you’re ahead of 90% of the workforce.

This isn’t just about learning to code. It’s about learning to think differently.
To communicate with machines.
To spot where humans still matter — and amplify that with tech.

Because while AI might be killing off 8 jobs…

It’s creating 15 new ones that pay double — and need smart, curious, adaptable people.

So—

Will you let AI automate you… or will you get paid to run it?

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TL;DR — What You Need to Know About Agentic AI

• Anyone can now build a powerful AI agent using ChatGPT — no technical skills needed.
• Tools like Custom GPTs and Make.com make it easy to create agents that do more than chat — they take action.
• The key is to start with a clear purpose, test it in real-world conditions, and expand as your needs grow.

Anyone Can Build One — And That Includes You

Not too long ago, building a truly capable AI agent felt like something only Silicon Valley engineers could pull off. But the landscape has changed. You don’t need a background in programming or data science anymore — you just need a clear idea of what you want your AI to do, and access to a few easy-to-use tools.

Whether you’re a startup founder looking to automate support, a marketer wanting to build a digital assistant, or simply someone curious about AI, creating your own agent is now well within reach.

What Does ‘Agentic’ Mean, Exactly?

Think of an agentic AI as something far more capable than a standard chatbot. It’s an AI that doesn’t just reply to questions — it can actually do things. That might mean sending emails, pulling information from the web, updating spreadsheets, or interacting with third-party tools and systems.

The difference lies in autonomy. A typical chatbot might respond with a script or FAQ-style answer. An agentic AI, on the other hand, understands the user’s intent, takes appropriate action, and adapts based on ongoing feedback and instructions. It behaves more like a digital team member than a digital toy.

Step 1: Define What You Want It to Do

Before you dive into building anything, it’s important to get crystal clear on what role your agent will play.

Ask yourself:

• Who is going to use this agent?
• What specific tasks should it be responsible for?
• Are there repetitive processes it can take off your plate?

For instance, if you run an online business, you might want an agent that handles frequently asked questions, helps users track their orders, and flags complex queries for human follow-up. If you’re in consulting, your agent could be designed to book meetings, answer basic service questions, or even pre-qualify leads.

Be practical. Focus on solving one or two real problems. You can always expand its capabilities later.

Step 2: Pick a No-Code Platform to Build On

Now comes the fun part: choosing the right platform. If you’re new to this, I recommend starting with OpenAI’s Custom GPTs — it’s the most accessible option and designed for non-coders.

Custom GPTs allow you to build your own version of ChatGPT by simply describing what you want it to do. No technical setup required. You’ll need a ChatGPT Plus or Team subscription to access this feature, but once inside, the process is remarkably straightforward.

If you’re aiming for more complex automation — such as integrating your agent with email systems, customer databases, or CRMs — you may want to explore other no-code platforms like Make.com (formerly Integromat), Dialogflow, or Bubble.io. These offer visual builders where you can map out flows, connect apps, and define logic — all without needing to write a single line of code.

Step 3: Use ChatGPT’s Custom GPT Builder

Let’s say you’ve opted for the Custom GPT route — here’s how to get started.

First, log in to your ChatGPT account and select “Explore GPTs” from the sidebar. Click on “Create,” and you’ll be prompted to describe your agent in natural language. That’s it — just describe what the agent should do, how it should behave, and what tone it should take. For example:

“You are a friendly and professional assistant for my online skincare shop. You help customers with questions about product ingredients, delivery options, and how to track their order status.”

Once you’ve set the description, you can go further by uploading reference materials such as product catalogues, FAQs, or policies. These will give your agent deeper knowledge to draw from. You can also choose to enable additional tools like web browsing or code interpretation, depending on your needs.

Then, test it. Interact with your agent just like a customer would. If it stumbles, refine your instructions. Think of it like coaching — the more clearly you guide it, the better the output becomes.

Step 4: Go Further with Visual Builders

If you’re looking to connect your agent to the outside world — such as pulling data from a spreadsheet, triggering a workflow in your CRM, or sending a Slack message — that’s where tools like Make.com come in.

These platforms allow you to visually design workflows by dragging and dropping different actions and services into a flowchart-style builder. You can set up scenarios like:

• A user asks the agent, “Where’s my order?”
• The agent extracts key info (e.g. email or order number)
• It looks up the order via an API or database
• It responds with the latest shipping status, all in real time

The experience feels a bit like setting up rules in Zapier, but with more control over logic and branching paths. These platforms open up serious possibilities without requiring a developer on your team.

Step 5: Train It, Test It, Then Launch

Once your agent is built, don’t stop there. Test it with real people — ideally your target users. Watch how they interact with it. Are there questions it can’t answer? Instructions it misinterprets? Fix those, and iterate as you go.

Training doesn’t mean coding — it just means improving the agent’s understanding and behaviour by updating your descriptions, feeding it more examples, or adjusting its structure in the visual builder.

Over time, your agent will become more capable, confident, and useful. Think of it as a digital intern that never sleeps — but needs a bit of initial training to perform well.

Why Build One?

The most obvious reason is time. An AI agent can handle repetitive questions, assist users around the clock, and reduce the strain on your support or operations team.

But there’s also the strategic edge. As more companies move towards automation and AI-led support, offering a smart, responsive agent isn’t just a nice-to-have — it’s quickly becoming an expectation.

And here’s the kicker: you don’t need a big team or budget to get started. You just need clarity, curiosity, and a bit of time to explore.

Where to Begin

If you’ve got a ChatGPT Plus account, start by building a Custom GPT. You’ll get an immediate sense of what’s possible. Then, if you need more, look at integrating Make.com or another builder that fits your workflow.

The world of agentic AI is no longer reserved for the technically gifted. It’s now open to creators, business owners, educators, and anyone else with a problem to solve and a bit of imagination.

What kind of AI agent would you build — and what would you have it do for you first? Let us know in the comments below!

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TL;DR — What You Need to Know:

• AI monetisation is a priority: Despite AI’s transformative potential, 71% of CFOs say they’re still struggling to make money from it.
• Traditional pricing is outdated: 68% of tech firms find their legacy pricing models don’t work for AI-driven economies.
• Boardrooms are getting serious: AI monetisation is now a formal boardroom priority, but the tools to track usage and profitability remain limited.

Global Bean Counters are Struggling to Unlock AI Monetisation, and That’s a Huge Issue

AI is being hailed as the next big thing in business transformation, yet many companies are still struggling to capture its financial value.

A new global study of 614 CFOs conducted by DigitalRoute reveals that nearly three-quarters (71%) of these executives say they are struggling to monetise AI effectively, despite nearly 90% naming it a mission-critical priority for the next five years.

So, what’s the hold-up? Well, it’s clear: traditional pricing strategies just don’t fit the bill in an AI-driven economy. Over two-thirds (68%) of tech firms say their legacy pricing models are no longer applicable when it comes to AI. And even though AI has moved to the boardroom’s priority list — 64% of CFOs say it’s now a formal focus — many are still unable to track individual AI consumption, making accurate billing, forecasting, and margin analysis a serious challenge.

The concept of an AI “second digital gold rush” has been floating around, with experts like Ari Vanttinen, CMO at DigitalRoute, pointing out that companies are gambling with pricing and profitability without real-time metering and revenue management systems.

This is where the real opportunities lie. Vanttinen’s insight?

So, businesses that can meter AI consumption at the feature level and align their finance and product teams around shared data will unlock the margins the market expects.

Regional differences are also apparent in the study. Nordic countries are leading in AI implementation but are struggling with profitability. Meanwhile, France and the UK are showing stronger early commercial returns. The US, while leading in AI development, is more cautious when it comes to monetisation at the organisational level.

Here’s the key takeaway for CFOs: AI is a long-term play, but to scale successfully, businesses need to align their product, finance, and revenue teams around usage-based pricing, invest in new revenue management infrastructure, and begin tracking consumption at the feature level from day one.

The clock is ticking — CFOs need to stop treating AI as a cost line and start seeing it as a genuine profit engine.

So, what’s holding your company back from capturing AI’s full value?

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