Business
Adrian’s Arena: Stop Collecting AI Tools and Start Building a Stack
How to transform scattered AI tools into a strategic stack that drives real business outcomes. Practical advice for startups and enterprises.
TL;DR — What You Need To Know
- Stop collecting random AI tools and start building an intentional “stack” – a connected system of tools that work together to solve your specific business problems.
- The best AI stacks aren’t complicated but intentional – they reduce friction, create clarity, and become second nature to your team’s workflow.
- For Southeast Asian businesses, successful AI stacks must address regional complexities like language diversity, mobile-first users, and local regulations.
Why Your AI Approach Needs a Rethink
Look around and you’ll see AI tools popping up everywhere – they’re like coffee shops in Singapore, one on every corner promising to give your business that perfect boost.
But here’s what I keep noticing in boardrooms and startup meetings: everyone’s got tools, but hardly anyone has a proper stack.
Most teams aren’t struggling to find AI tools. They’re drowning in disconnected tabs – ChatGPT open here, Perplexity bookmarked there, Canva floating around somewhere, and that Zapier automation you set up months ago but barely remember how to use.
They’ve got all the ingredients but no kitchen. No real system for turning all this potential into actual business results.
AI stack vs. tool collection
It’s so easy to jump on the latest shiny AI thing, isn’t it? The hard part is connecting these tools into something that actually moves your business forward.
When I talk to leaders about building real AI capability, I don’t start by asking what features they want. I ask what problems they’re trying to solve. What’s slowing their team down? Where are people burning valuable time on tasks that don’t deserve it?
That’s where stack thinking comes in. It’s not about collecting tools – it’s about designing a thoughtful, functional system that reflects how your business actually operates.
The best AI stacks I’ve seen aren’t complicated – they’re intentional. They remove friction. They create clarity. And most importantly, they become second nature to your team.
Building Intentional AI Workflows
For smaller teams and startups, an effective AI stack can be surprisingly simple. I often show founders how just four tools – something like ChatGPT, Perplexity, Ideogram, and Canva – can take you from initial concept to finished marketing asset in a single afternoon. It’s lean, fast, and totally doable for under $100 a month. For small businesses, this kind of setup becomes a secret weapon that levels the playing field without expanding headcount.
But once you’re in mid-sized or enterprise territory, things get more layered. You’re not just looking for speed – you’re managing complexity, accountability, and scale. Tools need to talk to each other, yes, but they also need to fit into approval workflows, compliance requirements, and multi-market realities.
That’s where most random collections of tools start to break down.
When Your AI Stack Actually Works
You know your AI stack is working when it feels like flow, not friction.
Your marketing team moves from insight to idea to finished asset in hours instead of weeks. Your sales team walks into meetings already knowing the context that matters. Your HR people personalise onboarding without rebuilding slides for every new hire.
This isn’t theoretical – I’ve watched it happen in real organisations across Southeast Asia, where tools aren’t just available, they’re aligned. When AI stacks are built thoughtfully around actual business needs, they deliver more than efficiency – they bring clarity, confidence, and control.
And again, this is exactly what we focus on at SQREEM. Our ONE platform isn’t designed to replace your stack – it’s built to expand its capabilities, delivering the intelligence layer that boosts performance, cuts waste, and turns behavioural signals into strategic advantage.
Because the best stacks don’t just work harder. They help your people think better and move faster.
The Southeast Asia Factor
If you’re building a business in Southeast Asia, the game is a little different.
Your AI stack needs to handle the region’s complexity – language diversity, mobile-first users, and regulatory differences. That means choosing tools that are multilingual, work well on phones, and respect local privacy laws like PDPA. There’s no point automating customer outreach if it gets flagged in Vietnam or launching a chatbot that can’t understand Bahasa Indonesia.
The smartest stacks I’ve seen in SEA are light, fast, and culturally aware. They don’t try to do everything. They focus on what matters locally – and they deliver results.
Why This Matters Right Now
If AI is the new electricity, then stacks are the wiring. They determine what gets powered, what stays dark, and what actually transforms your business.
Too many teams are stuck in the “tool hoarding” phase – downloading, demoing, trying things out. But that’s not transformation. That’s just tinkering.
The real shift happens when teams design their workflows with AI at the centre. When they align their stack with their business strategy – and build in engines like SQREEM that drive real-world precision from day one.
That’s when AI stops being a novelty and starts being your competitive edge.
It’s the same shift we see in startups that go from idea to execution in a weekend. It’s the same shift large companies make when they finally move from small pilots to company-wide impact.
And it’s available to any team willing to think system-first.
A Simple Test
Here’s a quick way to check where you stand: If every AI tool you use disappeared overnight… what part of your workflow would actually break?
If the answer is “nothing much,” you don’t have a stack. You have some clever toys.
But if the answer is “everything would grind to a halt” – good. That means you’re not just playing with AI. You’ve made it essential to how you operate.
And here’s the harder question: Is your AI stack simply helping you move faster – or is it actually helping you compete smarter?
If you’re serious about building the kind of AI stack that drives real outcomes – not just activity – I’d love to hear how you’re approaching it. What’s in your stack today? Where are you seeing gaps? Drop a comment below and let’s swap ideas.
Thanks for reading!
Adrian 🙂
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Apple’s China AI pivot puts Washington on edge
Apple’s partnership with Alibaba to deliver AI services in China has sparked concern among U.S. lawmakers and security experts, highlighting growing tensions in global technology markets.
As Apple courts Alibaba for its iPhone AI partnership in China, U.S. lawmakers see more than just a tech deal taking shape.
TL;DR — What You Need To Know
- Apple has reportedly selected Alibaba’s Qwen AI model to power its iPhone features in China
- U.S. lawmakers and security officials are alarmed over data access and strategic implications
- The deal has not been officially confirmed by Apple, but Alibaba’s chairman has acknowledged it
- China remains a critical market for Apple amid declining iPhone sales
- The partnership highlights the growing difficulty of operating across rival tech spheres
Apple Intelligence meets the Great Firewall
Apple’s strategic pivot to partner with Chinese tech giant Alibaba for delivering AI services in China has triggered intense scrutiny in Washington. The collaboration, necessitated by China’s blocking of OpenAI services, raises profound questions about data security, technological sovereignty, and the intensifying tech rivalry between the United States and China. As Apple navigates declining iPhone sales in the crucial Chinese market, this partnership underscores the increasing difficulty for multinational tech companies to operate seamlessly across divergent technological and regulatory environments.
Apple Intelligence Meets Chinese Regulations
When Apple unveiled its ambitious “Apple Intelligence” system in June, it marked the company’s most significant push into AI-enhanced services. For Western markets, Apple seamlessly integrated OpenAI’s ChatGPT as a cornerstone partner for English-language capabilities. However, this implementation strategy hit an immediate roadblock in China, where OpenAI’s services remain effectively banned under the country’s stringent digital regulations.
Faced with this market-specific challenge, Apple initiated discussions with several Chinese AI leaders to identify a compliant local partner capable of delivering comparable functionality to Chinese consumers. The shortlist reportedly included major players in China’s burgeoning AI sector:
- Baidu, known for its Ernie Bot AI system
- DeepSeek, an emerging player in foundation models
- Tencent, the social media and gaming powerhouse
- Alibaba, whose open-source Qwen model has gained significant attention
While Apple has maintained its characteristic silence regarding partnership details, recent developments strongly suggest that Alibaba’s Qwen model has emerged as the chosen solution. The arrangement was seemingly confirmed when Alibaba’s chairman made an unplanned reference to the collaboration during a public appearance.
Washington’s Mounting Concerns
The revelation of Apple’s China-specific AI strategy has elicited swift and pronounced reactions from U.S. policymakers. Members of the House Select Committee on China have raised alarms about the potential implications, with some reports indicating that White House officials have directly engaged with Apple executives on the matter.
Representative Raja Krishnamoorthi of the House Intelligence Committee didn’t mince words, describing the development as “extremely disturbing.” His reaction encapsulates broader concerns about American technological advantages potentially benefiting Chinese competitors through such partnerships.
Greg Allen, Director of the Wadhwani A.I. Centre at CSIS, framed the situation in competitive terms:
“The United States is in an AI race with China, and we just don’t want American companies helping Chinese companies run faster.”
The concerns expressed by Washington officials and security experts include:
- Data Sovereignty Issues: Questions about where and how user data from AI interactions would be stored, processed, and potentially accessed
- Model Training Advantages: Concerns that the vast user interactions from Apple devices could help improve Alibaba’s foundational AI models
- National Security Implications: Worries about whether sensitive information could inadvertently flow through Chinese servers
- Regulatory Compliance: Questions about how Apple will navigate China’s content restrictions and censorship requirements
In response to these growing concerns, U.S. agencies are reportedly discussing whether to place Alibaba and other Chinese AI companies on a restricted entity list. Such a designation would formally limit collaboration between American and Chinese AI firms, potentially derailing arrangements like Apple’s reported partnership.
Commercial Necessities vs. Strategic Considerations
Apple’s motivation for pursuing a China-specific AI solution is straightforward from a business perspective. China remains one of the company’s largest and most important markets, despite recent challenges. Earlier this spring, iPhone sales in China declined by 24% year over year, highlighting the company’s vulnerability in this critical market.
Without a viable AI strategy for Chinese users, Apple risks further erosion of its market position at precisely the moment when AI features are becoming central to consumer technology choices. Chinese competitors like Huawei have already launched their own AI-enhanced smartphones, increasing pressure on Apple to respond.
The situation is further complicated by China’s own regulatory environment, which requires foreign technology companies to comply with data localisation rules and content restrictions. These requirements effectively necessitate some form of local partnership for AI services.
A Blueprint for the Decoupled Future?
Whether Apple’s partnership with Alibaba proceeds as reported or undergoes modifications in response to political pressure, the episode provides a revealing glimpse into the fragmenting global technology landscape.
As digital ecosystems increasingly align with geopolitical boundaries, multinational technology firms face increasingly complex strategic decisions:
- Regionalised Technology Stacks: Companies may need to develop and maintain separate technological implementations for different markets
- Partnership Dilemmas: Collaborations beneficial in one market may create political liabilities in others
- Regulatory Navigation: Operating across divergent regulatory environments requires sophisticated compliance strategies
- Resource Allocation: Developing market-specific solutions increases costs and complexity
Looking Forward
For now, Apple Intelligence has no confirmed launch date for the Chinese market. However, with new iPhone models traditionally released in autumn, Apple faces mounting time pressure to finalise its AI strategy.
The company’s eventual approach could signal broader trends in how global technology firms navigate an increasingly bifurcated digital landscape. Will companies maintain unified global platforms with minimal adaptations, or will we see the emergence of fundamentally different technological experiences across major markets?
As this situation evolves, it highlights a critical reality for the technology sector: in an era of intensifying great power competition, even seemingly routine business decisions can quickly acquire strategic significance.
You May Also Like:
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- Apple Unleashes AI Revolution with Apple Intelligence: A Game Changer in Asia’s Tech Landscape
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AI Just Killed 8 Jobs… But Created 15 New Ones Paying £100k+
AI is eliminating roles — but creating new ones that pay £100k+. Here are 15 fast-growing jobs in AI and how to prepare for them in Asia.
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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Build Your Own Agentic AI — No Coding Required
Want to build a smart AI agent without coding? Here’s how to use ChatGPT and no-code tools to create your own agentic AI — step by step.
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!
You may also like:
- How To Start Using AI Agents To Transform Your Business
- Revolution Ahead: Microsoft’s AI Agents Set to Transform Asian Workplaces
- AI Chatbots: 10 Best ChatGPTs in the ChatGPT Store
- Or tap here to try this out now at ChatGPT by tapping here.
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