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Can AI Make Uber + Expedia the Next Big Super App

What would happen if Uber and Expedia merged? Explore the potential of AI-powered travel and mobility experiences and personalised super apps.

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Uber and Expedia Merger

TL;DR:

  • Uber’s potential acquisition of Expedia could transform Asia’s travel and mobility market, combining transportation, travel bookings, and lifestyle services into an AI-driven super app experience.
  • Leveraging AI for predictive analytics, personalised recommendations, and localised content, Uber could offer seamless, curated travel experiences, catering to Asia’s high demand for integrated digital platforms.
  • With a strong focus on data privacy and regional compliance, this merger positions Uber to compete with Asia’s established super apps by delivering a unique, AI-enhanced approach to travel and everyday mobility.

The latest news in the business world includes the much-anticipated Uber and Expedia Merger

If Uber and Expedia merge, this would mark a shift in digital platforms, opening doors to AI-powered travel and mobility experiences that Asia’s super app-savvy consumers might embrace. For Uber, it’s a chance to create a super app with unprecedented personalization and predictive analytics, blending ride-hailing, travel booking, and logistics—all driven by robust data integration and cutting-edge AI. In Asia, where consumers have long relied on super apps like Grab and Meituan, Uber’s approach could raise the bar by turning data into tailored, real-time experiences.

Redefining Personalisation with AI-Driven Insights

Personalisation is nothing new, but AI can take it to the next level, especially with the type of data this merger could bring. Imagine a scenario where Uber can map out an entire travel experience: transportation, accommodations, dining, and even recommended experiences. With Expedia’s booking data, Uber’s AI could recognize patterns in past behavior and predict preferences, creating custom travel plans that anticipate user needs. For example, Uber might suggest unique activities based on users’ prior interests, restaurant types they enjoy, or experiences they’ve previously rated highly.

In Asia, personalisation is increasingly expected; a McKinsey study shows 76% of consumers appreciate brands that personalise their experience. Leveraging AI to deliver on these preferences could set Uber apart in this market, appealing to users accustomed to seamless service across multiple verticals.

Predictive Analytics: Responding to Asia’s Dynamic Demand

Uber and Expedia would bring together transportation and travel data, allowing Uber to tap into AI-driven predictive analytics for market responsiveness. Travel demand in Asia, especially in dense urban areas and popular tourism spots, fluctuates dramatically. By analysing real-time location data alongside seasonal and historical travel insights, Uber could anticipate where and when users need transport most, optimising its fleet accordingly.

For instance, Uber could identify demand spikes tied to festivals, holidays, or even micro-weather changes, adjusting pricing or vehicle availability in response. According to BCG research, predictive analytics in mobility has the potential to reduce wait times by up to 30% during peak periods, a significant improvement for densely populated areas across Asia.

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The Super App Play: Competing in Asia’s App Ecosystem

Asia’s leading super apps—Grab, WeChat, and Meituan—each consolidate multiple services into a single platform. Uber, with Expedia’s data, could take a similarly ambitious approach. Integrating travel bookings, food delivery, and local transport with AI would enable Uber to deliver an all-in-one experience without users needing to switch apps, matching Asia’s demand for digital consolidation.

AI would be essential for managing this ecosystem, sorting through vast datasets to provide tailored recommendations and managing logistics efficiently. Uber’s competitive advantage could lie in AI-driven precision, creating experiences curated for individual tastes.

Statista reports that 83% of Asia’s online population already engages with super apps regularly, so Uber’s entrance into this space could appeal to a user base accustomed to convenience and personalisation.

Delivering Culturally-Relevant, AI-Powered Travel Experiences

Combining Uber and Expedia’s assets could bring new depth to localised travel recommendations. Using AI, Uber could deliver culturally specific content, suggesting relevant events, dining experiences, and activities based on a user’s location and interests. For example, a traveller to Japan might receive AI-curated tips on unique local festivals or dining experiences that align with their interests, building a bridge between AI and authentic cultural experiences.

This approach doesn’t just appeal to tourists but resonates with the growing trend in Asia for experiential, place-based travel. According to a Skift report, 78% of travelers across Asia-Pacific prioritise unique, locally relevant travel experiences. AI-powered insights into local attractions and events could give Uber’s platform an edge in delivering these experiences.

Balancing Innovation with Data Privacy and Compliance

One of the biggest hurdles will be managing data privacy. In Asia, where data privacy laws vary widely, Uber will need to ensure its AI respects these regulations across multiple markets. This integration would require Uber to adopt AI solutions that automate compliance checks, anonymize data, and create frameworks for region-specific data management. AI could streamline compliance by ensuring data is processed in a way that meets each country’s standards, maintaining trust and respecting privacy.

This focus on responsible AI aligns with rising data sensitivity in markets like Singapore, Japan, and South Korea, where recent laws mandate strict data handling and protection.

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Navigating these regulations thoughtfully is critical to building trust with users, especially in a region where over 65% of consumers worry about data security, according to a KPMG survey.

Moving Beyond Mobility: AI-Driven Content for Engagement

Uber’s move into the travel content space could differentiate its platform even further. With access to Expedia’s travel insights, Uber could provide users with valuable, AI-curated content, such as destination guides, local activities, and exclusive events. This expansion into travel content could enhance user engagement, offering more than a transactional experience by providing travelers with useful, relevant information tailored to their journeys.

For Asian consumers, who respond well to content-rich platforms, this feature could increase user stickiness. By aligning Uber’s offerings with visual and narrative-driven recommendations, the brand could offer an experience more akin to a digital concierge than a standard service provider.

How the Merger Could Transform Asia’s Travel and Mobility Landscape

Uber’s potential acquisition of Expedia represents more than a merger; it’s a strategic move towards AI-enhanced, multi-service convenience that could redefine the travel and mobility landscape, especially in Asia. By leveraging AI for real-time data insights, predictive capabilities, and localised personalisation, Uber could emerge as a strong contender in Asia’s super app market, catering to the region’s demand for seamless, customised, and secure digital experiences.

In a competitive space dominated by incumbents like Grab, Uber has the chance to use AI in ways that go beyond functionality, delivering experiences that are contextually rich, personal, and more in tune with user needs. This acquisition could set the stage for a new era of travel and mobility—one where AI doesn’t just support operations but drives an entirely new way to connect, travel, and engage.

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Is Uber ready to challenge Asia’s super apps and redefine the future of travel, or is this ambitious merger too much, too soon? Please share your thoughts and don’t forget to subscribe for updates on AI and AGI developments.

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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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Why are CMOs Still Holding Back on AI Marketing?

The New York Times embraces generative AI for headlines and summaries, sparking staff worries and a looming legal clash over AI’s role in modern journalism.

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generative AI marketing

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

  1. Reluctant 27%: A significant chunk of CMOs have minimal or zero use of AI marketing, citing costs and ethical concerns.
  2. High Performers: Businesses that exceed profit goals are widely using generative AI for both creative work and strategy.
  3. Cautious Optimism: While some see major wins in campaign analytics, others struggle to find benefits in cost reduction and customer service.
  4. Risk of Lagging: Experts warn that slow adoption could leave traditional marketers scrambling to catch up in a rapidly evolving field.

Why Are CMOs Still Holding Back on Generative AI Marketing?

In a world where even your local bakery is dabbling with AI-driven marketing campaigns, it seems a little baffling that some Chief Marketing Officers (CMOs) are still on the fence about generative artificial intelligence (AI). The hype machine is running full tilt, with countless headlines promising a revolution in how we strategise and create marketing materials. And yet, according to Gartner’s latest research, 27% of CMOs report no or limited adoption of generative AI in their teams. What’s going on, and should these marketing chiefs be worried? Let’s explore.

The Reluctant Third

Let’s start with the eye-catching number that’s got tongues wagging across the marketing landscape: 27% of CMOs either aren’t using generative AI at all or are only dabbling on the periphery. Considering we’re several years into the generative AI hype wave, you’d think that figure would be lower. After all, we hear success stories about AI-generated ad campaigns or chatbots that transform customer service on a nearly daily basis. So why the reluctance?

One big reason often cited is the cost. While there are open-source options, enterprise-level tools (complete with robust support and advanced data security features) don’t come cheap. Then, there’s also the legal and ethical minefield: some executives worry about brand risk or data security concerns. If your marketing AI is scraping questionable sources for content, or if it accidentally pinches trademarked materials, the cost could be more than just monetary—it might damage your brand’s reputation.

High Performers Blaze the Trail

If you think generative AI is all hype, you might want to pay attention to the marketing teams who are actually succeeding with it. According to Gartner’s findings, 84% of high performers—businesses that exceed their annual profit growth and marketing goals—are leveraging generative AI for creative development, and 52% are putting it towards strategy development.

These stats matter because they highlight a gap between those who’ve embraced the AI revolution and those who are dragging their feet. High-performing organisations see “creative development” as the perfect playground for generative AI: from drafting copy to brainstorming design ideas, the tech is boosting the volume and diversity of creative work. Strategy development is also getting an AI-powered makeover, with marketers crunching campaign data in record time to find winning formulas.

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As Gartner notes, CMOs who ignore the technology “are in a position of greater risk.” It’s not just about keeping up with the Joneses—it’s about leveraging a tool that can genuinely make marketing campaigns more efficient, more targeted, and possibly even more profitable.

Not Everyone Sees the Glitter of AI Marketing

Interestingly, the Gartner research also shows that generative AI’s benefits aren’t universally acknowledged. Over a quarter of CMOs surveyed reported little to no benefit in areas like cost reduction, customer service, and scalability. That’s a bit of a head-scratcher when many of us have been sold the dream that AI would turn marketing teams into lean, mean campaigning machines.

Part of the mismatch might come from inflated expectations. Some CMOs might have imagined generative AI swooping in like a marketing superhero, solving every challenge overnight. As a result, when the reality—training, experimenting, refining—sets in, disappointment can ensue.

Many believe GenAI will transform marketing, but despite the hype, many CMOs feel that their GenAI investments have yet to pay off.
Suzanne Schwartz, Senior Director Analyst at the Gartner Marketing Practice
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It’s also worth noting that 6% of CMOs have no usage of generative AI at all, whereas 21% have only waded into the shallow end. Yet on the other side of the spectrum, around 15% see extremely broad use among their teams. That discrepancy screams caution from some corners and gung-ho enthusiasm from others.

Disruptors and Doubts

Remember those corporate AI solutions that come with hefty price tags? Well, the pace of AI evolution has accelerated massively, especially in Asia. Enter disruptive companies like China’s DeepSeek, which have introduced more affordable—or at least more flexible—versions of AI. They’ve changed the conversation around pricing, data security, and the potential of open-source models.

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But not everyone is convinced. A survey by The Wall Street Journal found 21% of IT leaders aren’t currently using AI agents, with reliability being a major sticking point. While that might sound like a small number, keep in mind that these are the folks who sign off on the tech stack. If they harbour doubts, the marketing team’s AI ambitions could remain tethered to a cautionary anchor.

Where Are the Gains?

Despite the reluctance from some, 47% of those who have embraced generative AI are seeing a large benefit in tasks such as campaign evaluation and reporting. This indicates that when deployed properly, AI can absolutely streamline some parts of the marketing machine. Whether it’s quicker insight generation from data analytics or more accurate audience segmentation for targeted campaigns, the gains can’t be ignored.

So, if you find yourself in that 27% who are holding out, consider this: the competitive edge might be slipping away to those high performers who are pairing human creativity with AI efficiency.

Balancing Caution and Curiosity

Let’s be honest: any new technology comes with risks. Data security, ethical boundaries, and steep pricing are real concerns. The key might lie in adopting a balanced approach: start with smaller, safer implementations—like using AI for ad copy testing or initial design mock-ups—before rolling it out to high-stakes areas.

It’s a bit like learning to swim: you wouldn’t jump off the high dive if you’ve never been in the pool before, but you wouldn’t stand on the edge of the pool forever, either.

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The Final Word: Ready to Jump In or Watch from the Sidelines?

So, is generative AI in marketing a passing fad or the future of the industry? The data suggests it’s much more than a flash in the pan. High performers are already capitalising on AI’s creative and strategic potential.

The question is: will the sceptics catch up before they’re left behind entirely?

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