Business
How To Start Using AI Agents To Transform Your Business
AI agents can transform your business by automating tasks, streamlining operations, and boosting efficiency. Learn how to select, build, and deploy the perfect AI agent for your needs—and why human oversight remains vital.
Published
2 months agoon
By
AIinAsia
TL;DR – What You Need to Know in 30 Seconds
- Pinpoint your core pain points so you know exactly what you want your agent to solve.
- Choose the right tool: collaborative, automation, or social AI agents have distinct roles and strengths.
- Tread carefully: build your agent step-by-step and pilot it before a full-scale launch.
- Keep it human: no matter how advanced your agent is, you’ll need oversight and guidance to make it thrive.
Can AI Agents Can Really Transform Your Business?
Could 2025 Be the Year of AI Agents?
These brilliant, autonomous systems are set to take centre stage by making decisions and executing tasks without the need for constant human prodding. It’s no wonder that savvy business leaders across Asia (and beyond!) are champing at the bit to integrate them into their workflows. After all, who wouldn’t want to delegate time-consuming tasks to an unfailingly efficient digital helper?
But, as with any major tech leap, bringing AI agents into your business isn’t something you do at the drop of a hat. Trust me—my team and I recently built and launched our very own AI agent. The learning curve was steep, and along the way, I discovered a series of crucial steps you can’t afford to skip if you want to do it right. So, get comfy, and let’s explore how these pint-sized powerhouses can transform your operations.
Identify Your Business Needs
Let’s be honest: we’ve all been witnessing peak AI mania this past year. From wacky (and sometimes pointless) inventions like the $3,500 AI-enabled toaster to questionable ChatGPT-authored blog posts, it’s been a rollercoaster. Yet for all the hype, AI genuinely has reimagined the way we work. Its ability to process massive volumes of data and automate complex processes is beyond impressive. In fact, it can be downright revolutionary.
Still, in the rush to try every fancy new AI tool on the market, it’s easy to lose sight of what you actually need.
“Why should executives be the only people that have a ghost writer that writes their emails or does their slides? Imagine, now, all employees have that power?”
Phu Nguyen’s point perfectly illustrates the potential scope of AI empowerment in the workplace. But remember, before you throw an agent at every minor problem, sit down and identify the core challenges you face.
Whether you’re looking to speed up customer service response times, reduce operational bottlenecks, or optimise your supply chain, a clear understanding of the problem you need to solve is vital. Otherwise, you’ll risk investing in an agent that creates more headaches than it cures.
Pick Your AI Agent
Here’s the thing: not all AI agents are created equal. Much like your toolbox at home, you’ve got different gadgets for different jobs. You wouldn’t reach for a hammer if you needed to tighten a screw, would you? So, once you’ve outlined your business woes, it’s time to figure out which type of agent can best tackle them. Let’s take a look at three common flavours:
- Collaborative AI agents
Picture a small family of AI agents all pitching in to complete a task. AirOps is a great example: it’s a “content orchestration system” that taps into multiple tools and strategies to produce top-notch, SEO-friendly content—overseen by a real human to ensure quality. It’s like having a mini marketing team working 24/7! - Automation AI agents
These whizz-kids can handle entire tasks and processes with minimal (or sometimes zero) human input. Take Otter Pilot from Otter.ai: it automatically hops into virtual meetings, records and transcribes them, then fires off a tidy summary and action items to Slack or email. Essentially, it’s your personal meeting scribe—but one that’s never late, never tunes out, and never complains about taking notes. - Social AI agents
More people-focused, these are the chatty types. They excel at customer support, appointment scheduling, and giving you tailored information without forcing you to scour the web. If you’re dreaming of a kid-friendly, all-inclusive beach holiday that’s within driving distance and under a strict budget, a social AI agent can serve up your perfect itinerary—no more sifting through pages of reviews or questionable travel blogs.
Building And Releasing Your AI Agent
Now, if you’re a non-techie founder and the thought of building an AI agent makes you break out in a cold sweat, don’t worry. There are oodles of no-code resources out there that have your back. According to a white paper recently released by Google, two standout platforms are LangChain and Vertex AI.
LangChain, an open-source framework, is especially handy for connecting Large Language Models (LLMs) to external data sources. Meanwhile, Vertex AI lets you train, deploy, and customise AI models and applications in a snap—perfect for development teams that want to focus on finessing their agents instead of juggling the complexities of model building. You can read more about LangChain here, and visit the Vertex AI Studio here.
However you choose to build your AI agent, make sure you don’t leap straight into a massive, enterprise-wide rollout. Take it step-by-step. Begin with a small pilot, gather feedback, spot any bugs or bizarre behaviours, and fix them before letting your new digital assistant run riot across your entire organisation.
Why bother with this slow-and-steady approach? Well, as Google’s white paper notes, “no two agents are created alike due to the generative nature of the foundational models that underpin their architecture” 2023 Google White Paper on Generative AI. In plain English: your AI agent is going to behave uniquely, and the only way to refine it is through experimentation, feedback, and ongoing tweaking. That’s how you’ll strike gold (or at least avoid a meltdown).
The Human Touch Remains Essential
With all the hype and glittering potential of AI agents, it’s tempting to think: Set it and forget it. But let’s pump the brakes there.
“Just as in traditional, human workforce settings, managers must still pay heed to issues of team composition and role selection, and they must set the right overall goals to ensure that agentic AI or hybrid teams can be successful.”
In other words, your AI agent is not a magic wand. Yes, it can supercharge productivity and slash tedious busywork, but it still needs oversight, guidance, and purpose. Think of AI agents like members of your team: train them, guide them, set the right objectives, and you’ll unlock dazzling new levels of efficiency and creativity. Let them run rampant without proper guardrails, and, well, don’t be surprised if something goes awry.
Final Thoughts
AI agents have already begun to reshape the global business landscape. From automating everyday tasks to orchestrating more complex, multi-step projects, they’ve become indispensable for companies looking to stay ahead. But success depends on identifying why you need them in the first place, choosing the right one for the job, rolling it out carefully, and giving it ongoing human supervision.
Will 2025 truly be the year of the AI agent? If current trends are anything to go by, absolutely. Businesses that embrace AI agents with strategy, foresight, and a healthy dose of realism stand to gain a competitive edge in the coming years. Will you be one of them?
You may also like:
- Microsoft’s AI Agents Set to Transform Asian Workplaces
- How Digital Agents Will Transform the Future of Work
- Unleashing the Power of AI Agents
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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.
Published
2 days agoon
May 9, 2025By
AIinAsia
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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- Or tap here to try this out now at ChatGPT by tapping here.
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Business
Is AI Really Paying Off? CFOs Say ‘Not Yet’
CFOs are struggling with AI monetisation, with many failing to capture its financial value, essential for AI’s success in the boardroom.
Published
3 days agoon
May 8, 2025By
AIinAsia
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.
But here’s the kicker: only 29% of companies have a working AI monetisation model. The rest? They’re either experimenting or flying blind.
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?
“Every prompt is now a revenue event.”
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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Anthropic’s CEO Just Said the Quiet Part Out Loud — We Don’t Understand How AI Works
Anthropic’s CEO admits we don’t fully understand how AI works — and he wants to build an “MRI for AI” to change that. Here’s what it means for the future of artificial intelligence.
Published
4 days agoon
May 7, 2025By
AIinAsia
TL;DR — What You Need to Know
- Anthropic CEO Dario Amodei says AI’s decision-making is still largely a mystery — even to the people building it.
- His new goal? Create an “MRI for AI” to decode what’s going on inside these models.
- The admission marks a rare moment of transparency from a major AI lab about the risks of unchecked progress.
Does Anyone Really Know How AI Works?
It’s not often that the head of one of the most important AI companies on the planet openly admits… they don’t know how their technology works. But that’s exactly what Dario Amodei — CEO of Anthropic and former VP of research at OpenAI — just did in a candid and quietly explosive essay.
In it, Amodei lays out the truth: when an AI model makes decisions — say, summarising a financial report or answering a question — we genuinely don’t know why it picks one word over another, or how it decides which facts to include. It’s not that no one’s asking. It’s that no one has cracked it yet.
“This lack of understanding”, he writes, “is essentially unprecedented in the history of technology.”
Unprecedented and kind of terrifying.
To address it, Amodei has a plan: build a metaphorical “MRI machine” for AI. A way to see what’s happening inside the model as it makes decisions — and ideally, stop anything dangerous before it spirals out of control. Think of it as an AI brain scanner, minus the wires and with a lot more math.
Anthropic’s interest in this isn’t new. The company was born in rebellion — founded in 2021 after Amodei and his sister Daniela left OpenAI over concerns that safety was taking a backseat to profit. Since then, they’ve been championing a more responsible path forward, one that includes not just steering the development of AI but decoding its mysterious inner workings.
In fact, Anthropic recently ran an internal “red team” challenge — planting a fault in a model and asking others to uncover it. Some teams succeeded, and crucially, some did so using early interpretability tools. That might sound dry, but it’s the AI equivalent of a spy thriller: sabotage, detection, and decoding a black box.
Amodei is clearly betting that the race to smarter AI needs to be matched with a race to understand it — before it gets too far ahead of us. And with artificial general intelligence (AGI) looming on the horizon, this isn’t just a research challenge. It’s a moral one.
Because if powerful AI is going to help shape society, steer economies, and redefine the workplace, shouldn’t we at least understand the thing before we let it drive?
What happens when we unleash tools we barely understand into a world that’s not ready for them?
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