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Can You Spot AI-Generated Content? Recognising Patterns and Making Your Content Sound More Human

Uncover the secrets of spotting AI-generated content. Learn strategies to keep your content fresh and engaging.

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Spotting AI-generated content

TL;DR

  • Spotting AI-generated content can be particularly straightforward when you know the common patterns to look for.
  • AI-generated content often relies on repetitive, formulaic phrases, making it easy to identify.
  • Buzzwords and filler language reduce engagement and can make content feel impersonal.
  • Using too many transitional and generic statements dilutes authenticity and trust.

Customising content with specific examples and avoiding overused phrases creates stronger connections.

Can You sSpot AI-generated Content?

Artificial intelligence is reshaping content creation, offering speed and scale but occasionally at the cost of authenticity. Recognising common AI language patterns is becoming essential, as formulaic phrases can make text sound generic. In this article, we’ll explore how to spot these patterns and share strategies to keep content fresh and engaging, giving it a truly human touch.

Why Recognising AI-Sounding Language Matters

For professionals in writing, marketing, and strategy, understanding these language patterns can transform how they engage audiences. The issue isn’t with AI itself but with how certain language choices create a “default” AI tone. This often gives readers a sense of being spoken at rather than being spoken to, which can erode connection and reduce engagement.

Identifying AI language Through Recognisable Patterns

AI writing tools often streamline content creation with structured language, yet this leads to certain words, phrases, and sentences that feel familiar—and not always in a good way. Here’s a breakdown of some of the most recognisable phrases and suggestions for making content more genuine.

1. Overused Buzzwords and Phrases

AI-generated content is often littered with impressive-sounding industry buzzwords that lack substance and sound repetitive. These include:

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  • “Revolutionise,” “Transform,” or “Next-generation”
  • “Cutting-edge” or “State-of-the-art”
  • “Leverage” and “Optimise”
  • “Game-changing”

Such words aim to be impactful but often feel empty. Replacing them with specific, concrete language improves readability and credibility, avoiding the impression of a polished but hollow message.

2. Vague or Redundant Expressions

Some AI phrases aim to create flow but can feel redundant and overly polished, including:

  • “Ultimately,” “All in all”
  • “It’s important to note”
  • “It is worth mentioning”

These expressions often pad out content without adding value, making readers feel as though they’re getting “filler” instead of real insight. Keeping sentences lean and purposeful can significantly improve the reader experience.

3. Overly Polished Transitional Phrases

AI tools often rely on polished transitional phrases, which link ideas but can feel formulaic. Phrases like:

  • “Consequently,” “Furthermore,” and “Additionally”

are useful in moderation but can quickly make content sound mechanical. Instead, try using informal links or even questions to guide readers naturally through ideas, enhancing engagement and making content flow more naturally.

4. Generic Sentence Starters

AI-generated content often begins sentences with broad statements that feel detached. Examples include:

  • “Many people believe…”
  • “There are many ways…”
  • “It is widely known that…”

These vague openers risk losing the reader’s attention. Human writers typically offer specific insights or intriguing details from the start, which readers find more engaging.

5. Impersonal General Statements

AI often uses broad phrases to create context but can come off as detached and impersonal. These include:

  • “Some would argue…”
  • “From a broader perspective…”
  • “It has been observed that…”

Personalising content with unique insights or actionable information creates a stronger sense of connection with the audience, keeping readers interested and engaged.

6. Repetitive Explanations

AI tends to repeat phrases to simplify content, but it often feels redundant. Examples include:

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  • “To put it simply…”
  • “This can be broken down into…”
  • “What this means is…”

These phrases become repetitive quickly, losing their intended clarifying effect. Instead, using precise language and avoiding unnecessary repetition ensures content stays engaging and valuable.

7. Common AI Phrasing in Descriptions or Analyses

When explaining ideas, AI often sticks to predictable phrases that sound clinical. These include:

  • “This has led to an increase in…”
  • “The primary benefit of this approach is…”
  • “There are several factors to consider”

Human writers can create more engaging analysis by using fresh phrasing or offering new perspectives on familiar topics.

8. Filler Language and Informational Add-Ons

AI-generated text often includes filler language that, while aiming to create interest, tends to dilute the message:

  • “An interesting fact is…”
  • “Did you know that…”
  • “One thing to consider is…”

Readers value conciseness and relevance, so cutting filler phrases helps keep the focus on meaningful content that adds real value.

What Happens When You Use Words and Phrases Like This Already?

Using these patterns can have a noticeable impact on content effectiveness, sometimes negatively influencing reader perception, trust, and engagement.

1. Reduced Reader Engagement

Buzzwords and vague phrases may catch initial interest but can lead to disengagement. If content seems to lack depth, readers may stop reading before reaching the main message.

2. Loss of Trust and Authenticity

Readers value authenticity, and over-relying on generic phrases can make content feel detached or even inauthentic. This perceived lack of connection can lower reader trust and lessen the impact of your message.

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3. Diluted Brand Voice

Every brand has a unique voice, and AI-sounding language can drown it out, creating a message that feels like everyone else’s. Readers connect more deeply with distinctive, authentic voices that are not simply repeating industry-standard language.

4. Reduced SEO and Long-Term Impact

As search engines evolve, they prioritise content demonstrating “expertise, authoritativeness, and trustworthiness.” Formulaic language risks sounding less credible, which can reduce ranking effectiveness over time. Search engines reward high-quality, engaging content, and AI-sounding text can struggle to meet these standards.

Crafting Authentic, Human-Centred Content

Identifying and avoiding these common phrases lets brands and professionals focus on what matters—connecting with their audience through authenticity, relevance, and value. Here’s how to avoid the pitfalls of AI-sounding content:

Prioritise Specificity

Replacing generalities with examples or data points boosts credibility. Instead of “Data-driven insights drive growth,” say, “Brands using consumer-focused insights have seen a 30% boost in engagement.”

Vary Sentence Structure

AI often produces repetitive structures, making content feel monotonous. Varying sentence length and style keeps readers interested, creating a rhythm that feels human.

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Limit Transitional Phrases

Instead of stock transitions, experiment with questions or informal links to create natural flow, allowing ideas to connect without sounding forced.

Add Personal or Unique Insights

Adding original insights can elevate writing, making it relatable and distinct. Readers value authenticity, so expressing a unique perspective or anecdote adds value and fosters connection.

The Role of SEO in Human-Centred Writing

While AI-generated content may rely on keywords for SEO, a balanced approach keeps content engaging without compromising readability:

  • Relevance: Focus keywords on the reader’s search intent and integrate them naturally into the content flow.
  • Keyword Variation: Human writers can use keyword variations to avoid repetition, maintaining relevance while keeping the text fresh.
  • SEO in Headings: Using keywords naturally in descriptive headings improves readability and search ranking.

Final Thoughts

As AI technology advances, understanding language patterns helps professionals humanise content, avoid formulaic language, and keep audiences engaged. Recognising these patterns can guide content creators in connecting with readers in a memorable, relatable way.

Join the Conversation

Can you spot when a piece of content was generated by AI? What phrases make you immediately suspicious? Share your thoughts and join the discussion on how we can make content more human! And don’t forget to subscribe for updates on AI and AGI developments!

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Embrace AI or Face Replacement—Grab CEO Anthony Tan’s Stark Warning

ChatGPT now generates previously banned images of public figures and symbols. Is this freedom overdue or dangerously permissive?

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Anthony Tan AI Grab

TL;DR – What You Need to Know in 30 Seconds

  • Grab CEO Anthony Tan believes workers and companies that don’t embrace AI risk being replaced by those who do.
  • Grab paused normal operations for a nine-week generative AI sprint, significantly boosting innovation.
  • AI tools developed by Grab, such as driver and merchant assistants, are empowering everyday entrepreneurs.
  • Globally, many companies are downsizing due to AI, but Tan insists AI enhances human capabilities rather than replacing them.

Is Your Refusal to Embrace AI Secretly Sealing Your Fate?

Anthony Tan, co-founder and CEO of Grab—the Southeast Asian super-app that transformed regional transport, food delivery, and financial services—has made a bold and slightly unsettling prediction: “Humans who don’t embrace AI will be replaced by humans who embrace AI.”

In other words, whether you’re a company or an individual, ignoring AI isn’t merely shortsighted—it’s career suicide.

But before we panic, what exactly does Tan mean?

Making Humans ‘Superhuman’

Speaking at Converge Live in Singapore, Tan explained to CNBC’s Christine Tan that AI isn’t just a fancy tech upgrade. Instead, it’s a crucial tool to “make you superhuman” by significantly boosting productivity and freeing up valuable time.

Tan himself isn’t just preaching—he’s practising. Despite not being a coder, he’s enthusiastically using AI coding assistants for personal and professional projects. He claims AI has radically changed his productivity, helping him accomplish things previously impossible.

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I can’t code myself, but I use AI to build my own projects, for research, for Grab,” Tan explained. “It totally changes how you spend your time.
Anthony Tan, Grab CEO
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Grab’s Radical AI Experiment

Grab didn’t stop at encouraging individual AI adoption. Instead, the company took it to a whole new level, implementing an ambitious, company-wide nine-week “generative AI sprint”.

This meant putting all regular business on pause to explore AI-driven solutions across the entire company. As Tan humorously admitted:

People thought I was crazy—maybe I am—but it really moved the needle.
Anthony Tan, Grab CEO
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During this sprint, Grab developed powerful AI tools, including:

  • Driver Co-pilot: An AI assistant reducing wait times and boosting job opportunities for drivers.
  • Merchant AI Assistant: Imagine a single mother in Jakarta now equipped with an AI-driven sous chef, packaging expert, and even a chief revenue officer—all in one assistant. This innovation isn’t just about efficiency; it’s empowerment, reshaping the livelihoods of Grab’s vast network of entrepreneurs.

The Wider Implications for Asia

This isn’t just a Grab-specific phenomenon. According to the World Economic Forum’s 2025 Future of Jobs Report, 40% of employers globally plan to downsize due to AI, and a whopping 86% anticipate AI reshaping their businesses by 2030.

Asia, in particular, with its digitally fluent workforce and vibrant entrepreneurial scene, stands uniquely poised to lead this transition. Grab’s aggressive AI strategy under Tan’s leadership could become a model for businesses across Southeast Asia, showcasing how AI can be harnessed responsibly and productively.

Human vs AI: Not a Zero-Sum Game

Tan stresses AI shouldn’t evoke fear—it should inspire excitement. AI adoption isn’t about machines replacing humans. It’s about humans becoming irreplaceable by effectively harnessing these tools.

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If you’re reluctant or sceptical, Anthony Tan’s message is clear: embrace AI now, or watch as those who do leave you behind.

Hot Take

Anthony Tan might sound dramatic—but he has a point. If you’re not actively exploring AI, you’re preparing yourself (and your company) to become obsolete. The clock is ticking: Will you adapt, or will you become the adaptation?

What do you think?

Are you inspired or intimidated by Anthony Tan’s AI-driven future? Drop your thoughts below!

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Can PwC’s new Agent OS Really Make AI Workflows 10x Faster?

PwC’s Agent OS seamlessly connects and orchestrates AI agents into scalable enterprise workflows, promising 10x faster AI deployment and real-world productivity gains.

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PwC Agent OS

TL;DR – What You Need to Know in 30 Seconds

  • PwC’s Agent OS orchestrates diverse AI agents into unified, scalable workflows, promising deployment up to 10x faster.
  • Real-world cases already show efficiency boosts: 40% faster supply chains, 70% reduction in compliance tasks, and 30% quicker marketing launches.
  • Designed for complex enterprise environments, it’s cloud-agnostic, multilingual, and actively deployed in leading global businesses, including PwC itself.

AI Agents are everywhere—but can they talk to each other?

PwC has unveiled its ambitious “Agent OS,” aiming to streamline AI orchestration at enterprise scale—promising workflows built and deployed 10 times faster. But is this platform truly the missing link enterprises need for their AI strategy?

Let’s dig in.

Enterprise AI sounds fantastic until you realise it often means managing a tangled web of different tools, platforms, and “intelligent” agents, each stubbornly refusing to play nice with each other. Companies regularly find themselves stuck between AI experiments and true enterprise-scale AI adoption because—ironically—these clever AI agents simply can’t collaborate.

Enter PwC’s new Agent OS, positioned as a kind of universal translator and orchestration conductor rolled into one. Imagine a central nervous system for enterprise AI, capable of seamlessly linking different agents and platforms into coherent workflows—no matter where the agents were developed or what tech stack they’re built on.

But is it all hype, or can PwC’s Agent OS genuinely unlock seamless, scalable enterprise AI?

What exactly is PwC’s Agent OS?

PwC’s Agent OS acts as a unified command centre, orchestrating a multitude of AI agents across popular enterprise platforms, including Anthropic, AWS, GitHub, Google Cloud, Microsoft Azure, OpenAI, Oracle, Salesforce, SAP, and Workday, to name just a few. It connects, coordinates, and scales AI agents—whether they’re custom-built, developed via third-party SDKs, or fine-tuned with proprietary data.

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Think of it as the ultimate workflow builder, letting users—from AI specialists to your average non-tech-savvy manager—design sophisticated AI processes using intuitive drag-and-drop tools, natural language interfaces, and visual data-flow management.

Better yet, it’s cloud-agnostic, deploying effortlessly across AWS, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure, Salesforce, and even on-premises solutions.

Real-World Impact (not just theory)

Sceptical about fancy AI promises? Let’s look at some concrete use-cases PwC already claims are working in practice:

  • Supply Chain: Imagine reducing your manufacturing firm’s supply chain delays by up to 40% through seamless integration of forecasting, procurement, and real-time logistics tracking agents from SAP, Oracle, and AWS, topped with PwC’s custom disruption detection agents.
  • Marketing Operations: What if your retail marketing campaigns could launch twice as fast, with 30% higher conversion rates by orchestrating agents from OpenAI, Google Cloud, Salesforce, and Workday—all talking together in harmony?
  • Compliance Automation: Picture a multinational bank automating regulatory workflows, drastically reducing manual reviews by 70%, thanks to agents seamlessly interpreting and aligning evolving regulatory policies via Anthropic and Microsoft Azure.

Who’s Already Benefiting?

PwC’s Agent OS isn’t just theoretical—real companies are already seeing transformative results:

  • A tech company revamped its customer contact centre, reducing average call times by nearly 25%, slashing call transfers by 60%, and boosting customer satisfaction.
  • A global hospitality firm automated brand standards management, achieving up to 94% reduction in manual review times.
  • A healthcare giant applied AI agents to oncology workflows, streamlining clinical document processing to unlock actionable insights 50% faster, while simultaneously reducing administrative burdens by 30%.

And PwC themselves aren’t sitting idle: They’ve deployed over 250 internal AI agents, turbocharging productivity across tax, assurance, and advisory divisions—proving they’re ready to eat their own AI cooking.

Why PwC’s Agent OS Matters to Asia

In Asia, where enterprises are rapidly adopting AI to stay competitive (especially in dynamic markets like Singapore, India, and Indonesia), PwC’s Agent OS could offer a real edge. Asian enterprises grappling with complex multilingual data streams and diverse regional platforms may find a solution in the adaptive, multilingual capabilities of this system.

But it’s not just about tech. It’s about helping Asia’s leading enterprises quickly build, adapt, and scale AI-driven workflows to compete globally—accelerating innovation at a pace that keeps them ahead.

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Could PwC’s Agent OS finally mean enterprises spend less time wrestling with AI tech—and more time reaping its benefits?

We’d love your take. Let us know in the comments below.

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Forget the panic: AI Isn’t Here to Replace Us—It’s Here to Elevate Our Roles

Learn why managing AI agents—not fearing them—is key to thriving in the workforce of tomorrow. Discover how to become an effective AI manager today.

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AI Manager

TL;DR – What You Need to Know in 30 Seconds About the Rise of the AI Manager

  • AI creates new leadership roles, not job losses.
  • Successful AI managers combine tech knowledge with clear communication.
  • AI boosts productivity, creating more jobs and opportunities.
  • Invest early in AI literacy and critical thinking to thrive.

Professionals who master the art of managing AI agents are set to define the next era of work.

AI is everywhere right now—and so are fears about job displacement. But take a deep breath; there’s good news! Rather than making human skills obsolete, artificial intelligence is actually paving the way for a new, exciting role: the AI manager.

As AI agents evolve into reliable digital teammates capable of handling complex tasks, the spotlight shifts onto the people who manage them. In fact, the most successful professionals of the future won’t just understand how AI works—they’ll know exactly how to lead, direct, and collaborate effectively with their digital colleagues.

AI as High-Performing Team Members

Today’s AI isn’t just impressive—it’s genuinely useful. In the past few years alone, we’ve witnessed remarkable leaps in capabilities, especially with generative AI. These digital teammates are now expertly handling everything from financial analysis and legal research to content creation and data-driven decision-making.

The next big thing is ‘agentic AI’—digital agents that don’t just assist humans but actively work alongside them with a level of independence. Think about it: consistent, reliable, and tireless digital employees who never need a coffee break. Of course, that might make some of us nervous—who wouldn’t worry about a colleague who can work 24/7 at lightning speed?

But here’s the key: even the best talent needs effective management. AI might be powerful, but it still needs direction, oversight, and human judgement. The professionals who thrive won’t be replaced by AI—they’ll manage teams of digital talent to deliver results greater than anything achievable alone.

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What Does It Mean to Manage AI?

Being an AI manager doesn’t mean abandoning traditional leadership skills; it means expanding them. Great managers have always needed two core competencies:

  • People management: motivating, inspiring, and guiding human teams. While AI lacks emotions, clear communication and setting precise expectations are still vital.
  • Technical management: structuring workflows, delegating tasks strategically, and ensuring alignment towards organisational goals.

Both skill sets are critical when managing AI. A manager of digital agents must understand the nuances of the technology—its strengths, weaknesses, and quirks—while also working effectively with their human counterparts. Just as a great sales manager might stumble managing engineers without understanding their workflows, managing AI requires hands-on technical knowledge combined with clear strategic vision.

Ultimately, being disconnected from practical realities won’t cut it. Leaders in an AI-driven environment must be equally comfortable engaging with technology as they are with strategy and collaboration.

Re-examining the Job Displacement Myth

Fears around AI’s impact often overlook one important economic principle: Jevons paradox. Simply put, when efficiency improves, overall demand frequently increases too. Yes, AI might automate tasks currently performed by humans—but that same efficiency boost can open doors we can’t yet imagine.

Think of the industrial revolution: automation displaced manual labour, but it simultaneously created unprecedented wealth, innovation, and new kinds of employment. Similarly, AI’s efficiency will likely spawn entirely new markets, industries, and roles—like AI agent managers—ensuring that human creativity and insight remain irreplaceable.

How Can We Prepare for This Shift?

Change can be uncomfortable, and the rise of AI is no exception. But the transition doesn’t have to be painful. Here’s how we can adapt:

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1. Prioritise Practical Skills in Education

Universities excel at theory but often overlook practical skills that the workplace demands. It’s time to elevate vocational and professional training, the kind traditionally offered by polytechnics or community colleges, to build job-ready skill sets.

2. Embrace AI Literacy in the Workplace

Companies should embed AI literacy into their core training, ensuring everyone—from new hires to senior executives—is comfortable using and collaborating with AI tools. Businesses that invest early in AI literacy will hold a powerful competitive advantage.

3. Take Personal Responsibility for Learning

Individuals, especially those in roles susceptible to automation, need to proactively upgrade their skillsets. This doesn’t mean everyone should become a developer, but learning to confidently use AI, understand digital workflows, and develop critical thinking around tech are essential.

Crucially, becoming AI-literate doesn’t mean blindly trusting technology; it means being savvy enough to challenge it. An effective AI manager must know when to push back against the recommendations of digital teammates, recognising that AI isn’t perfect—it’s only as good as the people who oversee it.

Luckily, resources to build these skills abound: free online courses, corporate training, AI boot camps, and independent learning opportunities are readily available. Your job is to start learning—and keep asking smart questions.

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Are YOU ready?

The future belongs to those who adapt, question, and lead the digital workforce. Are you ready to become an AI manager?

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