Mobile gaming engagement is on the rise globally, with the latest industry data pointing to a maturing market where long-term player value is fast becoming the primary metric for success — and Asia-Pacific is leading the charge.

Measurement and analytics company Adjust released its Gaming App Insights Report: 2026 Edition today, revealing that global gaming app sessions have increased year-on-year, while the paid-to-organic ratio has climbed 61 per cent. The findings signal a broader strategic shift across the industry: studios are moving away from volume-driven acquisition models and towards precision-led growth built on retention, engagement, and monetisation data.

Across regions, APAC posted the largest increase in the paid-to-organic ratio, rising 45 per cent from 2.05 to 2.97. The surge reflects intensifying competition for users in a region that continues to draw significant investment from mobile gaming studios worldwide.

Engagement across APAC remained broadly stable, with sessions per user per day edging up from 1.69 to 1.70. Several markets, however, outperformed the regional average. Japan recorded a three per cent increase in sessions, rising from 1.76 to 1.81, while Singapore and Thailand each also posted three per cent growth. Indonesia, South Korea, and Vietnam each grew by two per cent.

On retention, APAC’s Day 1 rates held steady at 20 per cent, consistent with other global regions. Japan led the region at 25 per cent, followed by Singapore at 23 per cent, Thailand and Indonesia at 21 per cent, and South Korea at 20 per cent.

Also Read: Half of APAC consumers are tired of poor-quality AI content from brands: Report

At the subgenre level, strategy games recorded the strongest session growth of any category, climbing 57 per cent year-on-year. Casual and hyper-casual games also posted solid gains, rising 37 per cent and 31 per cent, respectively. On the install side, slots, casino, and casual games led growth, up 46 per cent, 22 per cent, and 19 per cent year-on-year. Hyper-casual, RPG, simulation, and word games also recorded growth across the same period.

The report’s findings reflect a structural evolution in how studios approach gaming app growth. Rather than prioritising install volumes alone, developers and marketers are placing greater emphasis on acquiring high-value players and sustaining their engagement over time.

Tiahn Wetzler, director of marketing at Adjust, said studios are increasingly focused on retaining high-value players, optimising creatives and channels, and building ad-to-experience flows that favour sustained play over fast turnover. “Understanding where true long-term value comes from, and the ability to connect acquisition, engagement, and monetisation data for fast decisions, is now a necessity,” Wetzler said.

April Tayson, Regional Vice President for INSEAU at Adjust, described the shift in APAC as indicative of growing market sophistication. “Studios are moving beyond pure install growth and focusing on building deeper player relationships through smarter acquisition, stronger retention strategies, and better measurement across the full player journey,” Tayson said, adding that markets across Southeast Asia in particular are showing strong engagement and retention potential.

Broader trends shaping mobile gaming in 2026

Beyond session and retention data, the report identifies several wider forces reshaping the gaming app landscape this year. Gaming App Tracking Transparency opt-in rates rose to 39 per cent in the first quarter of 2026, up from 38 per cent in the same period a year prior — a modest but continued improvement in signal quality for mobile marketers.

Also Read: Data trust remains AI’s biggest bottleneck as CIOs step into broader leadership roles: Report

The report also highlights the growing influence of direct-to-consumer strategies, AI-generated creative assets, live operations, reward-driven mechanics, and cross-platform approaches as studios look to diversify how they reach and retain players.

With competition accelerating across APAC and globally, the data suggests that the studios best positioned for sustainable growth in 2026 will be those that can connect the full arc from acquisition through to long-term player value — and act on that intelligence quickly.

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The rise of agentic AI presents a profound and dual-edged challenge to the traditional consulting industry, a sector that has long thrived now showing its age. While the technology offers the potential for unprecedented efficiency and new service lines, many consulting firms are grappling with an existential dilemma: their historical value proposition, built on the back of human labour and incremental digital solutions, is becoming obsolete.

Rather than leading clients into a new era of autonomous systems, these firms risk being left behind, still operating with a “digitalisation” mindset in an “AI-native” world. The industry’s failure to fully embrace the transformative power of agentic AI, both in its own operations and in its client services, is preventing it from reaping the true rewards of this technological revolution and even underdelivering on its promises to its clients.

The outdated model and the existential threat

For decades, the business of management consulting has relied on a well-established value proposition: it combines expert insight, proprietary frameworks, and a large human workforce to produce customised problem-solving and high-quality deliverables. This formula has been highly successful, scaling over time by hiring top university graduates and billing clients based on the scope and duration of projects.

At the core of this model were human-intensive tasks: gathering vast amounts of data, writing comprehensive reports, and creating polished, visually compelling PowerPoint decks. The value a client received was, in large part, the result of this labor-intensive process, culminating in a detailed and well-supported recommendation.

This traditional model, however, is a product of an earlier wave of digital transformation, and proving to be a poor fit for the age of agentic AI. The new generation of autonomous systems poses a direct and formidable threat to this long-standing approach. As the technology matures, it is directly capable of automating tasks that once justified significant teams and multi-month budgets. Some firms are still only in the nascent stages of this transition, with their internal AI efforts lagging behind a basic copilot subscription. Some of their so-called “AI agents” are, by some accounts, little more than simple models.

Also Read: From hype to harmony: Why agentic AI needs a platform-first mind-set to redefine CX

Preliminary strategy drafts, market scans, and benchmark reports, can now be generated in a matter of hours by advanced tools, directly compressing the value chain that traditional firms have long relied on. This commoditisation of a firm’s core output is forcing the industry to confront what has been described as an “existential” shift, yet many of their reactions are perceived as tame, defensive, and out of touch. The business is no longer about human labor as the primary means of production; it is about leveraging and orchestrating autonomous systems to achieve outcomes; a pivot many consulting firms are still struggling to make.

This resistance to change is also tied to a critical, unspoken element of the traditional consulting value proposition: providing “top cover” or a “seal of approval.” Clients would pay a premium to a well-known firm, not always to improve a solution, but to gain psychological and political leverage, a scapegoat if the plan failed. This dynamic is becoming obsolete. In an increasingly AI-driven world, it is plausible that AI itself will be viewed as a superior, more data-driven decision-maker, making the need for a human “seal of approval” from a consultant far less compelling.

Failing to grasp the true potential

The struggle of traditional consultants is not just about adapting to a new technology; it is about their fundamental failure to grasp what agentic AI truly is and the transformative potential it holds. Agentic AI represents a significant evolution beyond traditional AI systems and even the latest generation of Generative AI. At its core, an agentic AI is an autonomous system that can act independently to achieve a pre-determined goal.

Unlike traditional software, which follows a rigid set of rules, or a large language model (LLM) that is reactive to a prompt, agentic AI is proactive. It can break down a complex task into sub-tasks, plan its actions, execute them, and adapt to changing conditions with minimal human oversight. This inherent “agency” is the key distinguishing factor that empowers it to operate within dynamic, unstructured environments and orchestrate end-to-end processes.

This technological shift is not simply a matter of automating tasks; it is altering the nature of work itself. The future will not be a one-for-one replacement of human workers but the emergence of a fundamentally new organisational structure: the hybrid workforce. In this model, humans are not just supervisors but “coordinators,” “designers,” and “trainers” for AI agents.

Their roles are being redefined, and performance metrics are shifting from output quantity to more nuanced measures like innovation and strategic thinking. By remaining fixated on their old models, consultants are missing the opportunity to guide clients through this fundamental shift in organisational structure, leadership, and culture. They are still selling a product from the past, while the true market has already moved on to the future.

Also Read: Agentic AI, urban mobility & smart tourism: 2025’s travel investment hotspots

The new battlefield of cybersecurity they can’t protect

The most critical failure of the traditional consulting model lies in its inability to navigate the new Cybersecurity landscape created by agentic AI. The same autonomy and adaptability that make agentic AI so transformative for business also create a new and highly complex attack surface that shatters the static assumptions of most traditional security models.

An AI agent is not a static endpoint. It is a decentralised, adaptive entity that can operate across distributed systems, accessing multiple data sources and making independent decisions. The result is a dynamic, hard-to-predict security landscape that demands a completely new approach to defense.

Because many consulting firms are still “stuck in the old digitalisation,” they are not equipped to help their clients address these new and severe risks. The vulnerabilities are not confined to a single point but are embedded in the agent’s multi-layered architecture, leaving it susceptible to a range of sophisticated attacks.

These include “poisoned sight,” where an agent ingests malicious data that skews all its decisions, and “hijacked execution,” where sophisticated prompt injection attacks trick agents into exfiltrating data. A successful attack on a single agent can persist indefinitely, quietly rewriting the agent’s “worldview” or leaking private chat history over time.

Beyond the technical vulnerabilities, the agentic AI revolution introduces specific, high-impact security challenges that business and security leaders must address, and which consultants are often unprepared to advise on:

• “Shadow AI agents”, the proliferation of unauthorised AI agents deployed autonomously by development teams or individual users without proper IT and security oversight. This creates a critical lack of visibility, making it impossible to enforce consistent security policies.
• “Black box” problem, where many agentic systems operate with decision-making processes that are not easily interpretable by humans. This creates a crisis of accountability, where organisations cannot explain why a specific action was taken, leaving them exposed to significant legal, regulatory, and reputational risks.
• The sheer volume and exponential increase in the number of AI agents pose a monumental challenge for managing and securing their unique, verifiable identities.
• The decentralised and autonomous nature of agentic AI makes traditional, perimeter-based security models obsolete. These models were built on the assumption that internal systems are inherently trustworthy, but a decentralised network of unpredictable, autonomous agents makes this assumption invalid. The absence of a Zero Trust architecture, where no agent or system is trusted by default, is not merely a best practice; it is a fundamental security imperative that many consulting firms are simply not helping their clients implement.

Also Read: Agentic AI: The next frontier in technology

Conclusion: The path not taken

The narrative that the AI boom is leaving consultants behind is not an oversimplification. It is a direct result of their own inabilities. The firms that are “left behind” are those that remain tethered to an antiquated business model focused on billing for human labor, creating commoditised deliverables, and offering superficial “top cover” to executives.

By failing to lead clients into the full scope of the agentic AI revolution, from its fundamental impact on the nature of work to its complex and dynamic security challenges, they are failing to reap its real rewards.

The future of professional services will not be defined by a choice between human and machine but by the strategic collaboration between them. The successful enterprise will be a hybrid entity where the speed, scale, and execution of agentic AI are perfectly complemented by the creativity, empathy, and strategic foresight of human leaders.

The only way for consultants to win in this new era is to move beyond the superficial and guide their clients through the full, multi-faceted revolution of agentic AI, a path many are still not on.

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Digital PR for businesses is no longer optional, it’s a strategic necessity. The internet has transformed how consumers interact with brands. Years ago, companies relied heavily on traditional channels like newspapers, magazines, and TV for exposure.

Now, the story has changed; consumers are online, attention spans are shorter, and competition for visibility is fierce. With information at their fingertips, most people now search for brands that connect with their values before making a purchase.

That’s where digital PR services and agencies come in. By leveraging online media intelligently, digital PR builds, manages, and protects a brand’s reputation while increasing visibility and credibility.

What is digital PR and why it matters

Digital PR (Public Relations) is the strategic use of online channels to promote a brand, improve its visibility, and strengthen relationships with customers and media outlets. It goes beyond press releases — encompassing content creation, influencer collaborations, SEO, and social media engagement.

Unlike traditional PR, digital PR integrates storytelling with search optimisation, helping brands reach the right audience, secure backlinks, and position themselves as industry authorities.

Benefits of digital PR for businesses

• Builds brand awareness and recognition: Digital PR campaigns help brands gain visibility across credible platforms. When your brand appears in top-tier publications, it strengthens consumer trust and establishes your authority in the industry.
• Improves brand reputation: Effective digital PR strategies help maintain a positive public image. By sharing authentic stories, responding to media coverage, and engaging audiences directly, your brand builds credibility and goodwill.
• Drives targeted traffic and leads: Securing backlinks from high-authority sites boosts your SEO performance and sends qualified traffic to your website — increasing your chances of generating leads and conversions.
• Strengthens SEO rankings: Backlinks are one of Google’s top ranking factors. Through strategic link building and media placements, digital PR enhances your search engine visibility and drives consistent organic traffic.
• Creates valuable partnerships: Collaborating with journalists, influencers, and industry leaders through PR outreach can lead to partnerships, sponsorships, and new business opportunities.

Also Read: How Category Design drives productivity and efficiency

How digital PR strengthens modern businesses

By combining brand storytelling, authority building, and digital communication, digital PR gives businesses the tools to compete in saturated markets. It not only raises awareness but also directly supports sales growth, recruitment, and investor relations.

Here’s how digital PR for businesses plays a crucial role in sustainable growth:

• Builds positive business sentiment: Working with media professionals and influencers fosters positive perception — leading to trust, loyalty, and repeat customers.
• Boosts social media engagement: Digital PR campaigns often include social media promotion, helping you engage directly with your audience, spark conversations, and build long-term relationships.
• Improves search engine rankings: With backlinks from authoritative sites, your brand ranks higher on Google — increasing organic visibility and generating more traffic that converts.
• Enhances content reach and social proof: Digital PR amplifies your most valuable content, ensuring it reaches your target audience. This not only boosts engagement but also builds trust and social proof that reinforce your credibility.
• Encourages brand advocacy: Satisfied customers often become your strongest promoters. Through PR-driven engagement, you can turn loyal customers into advocates who spread your brand organically.
• Delivers long-term value: Unlike paid ads that disappear when budgets run out, digital PR content stays online — continuously driving backlinks, referral traffic, and brand exposure over time.

Also Read: What is digital PR, and how can you develop an effective strategy?

Digital PR vs traditional PR: What’s the difference?

Modern brands need the data-driven precision of digital PR to stay visible and credible in a competitive landscape.

Conclusion

Digital PR for businesses is no longer a “nice-to-have,” it’s a must-have strategy for brand growth and sustainability. It builds authority, drives organic traffic, and keeps your business top-of-mind in a crowded online space.

When done right, digital PR doesn’t just raise awareness — it fuels long-term success by blending storytelling, search visibility, and authentic engagement.

Whether you’re a startup or an established enterprise, investing in digital PR ensures your brand remains relevant, trusted, and influential in the years ahead.

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The layoff call came without warning, shattering more than 20 years of career building in one fell swoop. One moment, I was the Senior Vice President, planning and managing a team of 20 marketers for Singapore’s consumer banking business; the next, I was packing up my desk and receiving hushed, reluctant farewells from my colleagues.

I was just shy of reaching my fourth anniversary with the company. However, countless late nights and sacrificed weekends culminated in vain after that fateful call.

I wasn’t the newest hire, nor the highest-paid employee, and certainly not the least essential. Despite my experience and efforts, my value was reduced to a number that was eliminated to improve the company’s financial position.

The shock didn’t just affect me emotionally, but left me in a mid-career existential crisis with the realisation that no amount of corporate loyalty can keep you safe from the chopping block.

How Singapore’s younger workforce is fighting back

Ironically, Singapore’s been witnessing a slow uptrend in unemployment since 2023 despite its labour market growing. The tech sector, which once promised strong growth after the pandemic, is now one of the leading industries in terms of layoffs. As a result, a climate of uncertainty and anxiety is building among professionals at all career stages. 

Young Gen Z and millennial tech professionals are slowly realising that corporate loyalty does not equate to job security, and are taking control of their career trajectories. Now, they prioritise work-life balance and career flexibility, which manifests into embracing sabbaticals, “micro-retirement” and alternative working models to maximise their overall wellbeing.

For Singapore’s tech sector, the ripple effects are starting to take shape. Traditional tech careers usually followed linear, predictable trajectories: developer to tech lead to CTO, or marketing associate to marketing manager to CMO. Now, more tech professionals are intentionally carving out multiple career paths, developing their expertise across multiple projects simultaneously.

Younger workers are demanding for more meaningful work, to see the direct impact of their efforts and alignment between their values and what employees stand for. One of the fractional talents helping my company, Glenna Fong, is one such example.

With over 15 years of experience in media, business development, and content strategy, Glenna moved away from a stable traditional corporate role to become a Web3 token co-founder and digital pet care platform partner, while still offering fractional marketing services to young tech startups.

Flexibility is the future of work 

The unprecedented adoption of “working from home” a few years ago has fundamentally altered how we look at work. As a result, what were once considered niche like remote work, project-based contracts and freelancing have evolved from mere buzzwords into viable mainstream alternatives, allowing working professionals to reassess their definition of work.

Also Read: How founders can fund their startup without sacrificing ownership

After my retrenchment, I decided to turn towards fractional work and founded Mad About Marketing Consulting (MAMC), a fractional marketing consultancy that provides tech companies with highly experienced marketing teams on a part-time basis.

I’ve been able to step in as a part-time CMO for startups, challenging my skillsets in supporting each business’ own goals while also ensuring that these businesses receive the qualified expertise needed in order to grow, scale and compete in Singapore’s increasingly competitive landscape. 

What started as a response to my personal setback has now evolved into a model that can benefit both employers and employees that seek autonomy and impact. For startups, this immensely helps keep business costs lean, tapping into strategic expertise without incurring any executive overheads. For fractional professionals, this increases the learning velocity while reducing the risk of becoming obsolete.

A need to adapt and re-assess hiring processes

So, what does this mean for tech startups who are on the hunt for tech talent? They need to re-examine how they hire and retain these workers that look for flexibility and meaning in their professional and personal lives. Startups that can redesign their organisational structure would be able to thrive in the long-run.

Blended teams represent a promising model for startups. These hybrid structures allow permanent employees who know the ins and outs of the startup to collaborate effectively with fractional specialists. Successful clients that we’ve worked with focus on communication and cross-functional teamwork, ensuring that neither team or any department in the startup is functioning in silo.

Additionally, I strongly advocate for building flexibility and autonomy into the startup’s organisational DNA from day one. Those who democratise information access and focus on making communication dynamic result in working environments that value every individual contribution regardless of hierarchy, seniority or experience. 

Also Read: Southeast Asia’s travel tech boom: The startups powering a US$73B industry

Finding security and value in a world without guarantees

My last day in my corporate job seemed like an abrupt ending, when in truth marked the beginning of something more resilient and fulfilling than what any corporate role can offer.

Building a personal brand and robust professional network is your true safety net when building your fractional career. In the fractional economy, visibility creates opportunity. My first few clients at MAMC came through friends and relationships that I have cultivated in my over 20 years in marketing. Today, MAMC successfully serves multiple startups that value our team’s expertise while fuelling our growth.

For Singapore’s tech ecosystem to thrive amid global competition, both startups and professionals must embrace this shifting reality. The question isn’t wondering if alternative work models will be the future, but on how quickly the workforce can adapt to new norms of work where meaning and flexibility are valued.

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In this interview, e27 speaks with Ian about AIBYML SG’s approach to designing and deploying custom AI systems for enterprise environments. As organisations move beyond experimentation toward operational AI adoption, consulting partners increasingly play a role in bridging technology capability with governance, cost discipline, and measurable business outcomes.

This conversation forms part of e27’s broader AI Pulse coverage, which examines how organisations across the region are building, deploying, and governing AI in real-world settings.

Custom AI systems for operational workflows

e27: Briefly describe what your organization does, and where AI plays a meaningful role in your work or offering.

Ian: AIBYML SG is an AI consulting and solution firm focused on designing, building and deploying custom AI systems tailored to real operational needs, from AI-native assistants and workflow automation to intelligent customer engagement and analytics.

Transforming workflows with end-to-end AI systems

e27: What is one concrete way AI is currently creating value within your organisation or for your users or customers?

Ian: One concrete way AI is creating value for our clients is through end-to-end process transformation powered by custom AI systems, rather than standalone tools. At AIBYML SG, we begin by working closely with clients to analyse their existing “as-is” workflows – identifying operational bottlenecks, manual handoffs, and hidden cost drivers.

leverage human intelligence and LLM AI

From there, we design “to-be” processes where AI is embedded in a targeted and measurable way.

A key part of our approach is selecting the most cost-effective model and architecture for each use case pairing it with clear performance metrics, investment estimates and usage projections. This allows clients to evaluate AI initiatives like any other business project. This discipline is what turns AI from an experiment into a scalable capability with positive ROI.

Also read: AI Pulse Exclusive: How Asia AI Association is advancing human-centred AI across the region

Balancing advanced models with operational sustainability

e27: What was a key decision or trade-off you had to make when adopting, building, or scaling AI?

Ian: One recurring trade-off we help clients navigate is between using the most advanced AI models available and building solutions that are more economically and operationally sustainable at scale with open source foundation models. In many early discussions, stakeholders are understandably thrilled by the latest models because of their impressive capabilities. However, in production environments, model performance is only one part of the equation – cost, latency, reliability, data governance, and integration complexity.

Another important trade-off is between speed and organisation’s readiness. Moving quickly with a proof of concept demonstrates value, while scaling too fast without process redesign, user training, and clear benefits often lead to underutilised systems.

One thing we learned is that sustainable AI adoption requires balancing technical ambition with operational maturity. The right decision is rarely about maximising model capability – it is about maximising long-term business impact.

Adoption momentum and operational uncertainties

e27: Looking back, what has worked better than expected, and what proved more challenging than anticipated?

Ian: Looking back, what has worked better than expected is how rapidly our AI engineering team pivot suitable AI models embedded directly into their workflow design and tied to clear business outcomes. When we redesign the “to-be” process properly and define practical metrics — such as turnaround time, cost per case, or productivity uplift — adoption tends to accelerate.

What proved more challenging was managing uncertainty across cost, governance, and technology evolution. Clients understandably want clarity on short- and long-term AI investment, from model usage and infrastructure to maintenance and scaling. In practice, variable demand patterns and shifting pricing models make precise forecasting difficult, requiring scenario-based planning rather than fixed projections.

We also regularly navigate trade-offs between cost-efficiency and data governance. Stronger controls — private deployments, access management, auditability — reduce risk but increase operational overhead. At the same time, fast-moving advances in AI models make interoperability critical. Designing modular architectures adds upfront complexity, but protects long-term flexibility.

Also read: AI Pulse Exclusive: How CAWIL.AI is building industry-focused AI solutions across specialised sectors

AI exposing broken processes

e27: What is one lesson about applying AI in real-world settings that leaders or founders often underestimate?

Ian: One lesson from our client engagements is that AI does not fix broken processes – it exposes them.  In many engagements, we are initially asked to “add AI” to improve speed and reduce cost. However, once we analyse the existing workflow, we often discover redundancy, inconsistent data, unclear objective and undocumented exceptions. If AI is layered on these inefficiencies, it only automates complexity rather than solving it.

Another challenge is that stakeholders start by asking for a single AI feature. But once users test a prototype in their real workflow, they quickly form an “I know it when I see it” understanding and they start uncovering latent needs they couldn’t articulate upfront. Prototyping is powerful precisely because it reveals these hidden requirements through hands-on use.

Treating AI as structured transformation

e27: Based on your experience, what is one practical recommendation you would give to organisations that are just starting to explore or scale AI?

Ian: “AI doesn’t fail because models aren’t smart enough — it fails because organisations aren’t ready enough. Teams that treat AI as a structured transformation, with clear metrics, cost discipline, and room for iteration, are the ones that turn experimentation into lasting ROI.”

From AI features to operating models

e27: Over the next 12 months, how do you expect your organisation’s use of AI, or the role of AI in your industry, to evolve?

Ian: As AI tools become more powerful and no-code platforms lower the barrier to building applications, our strategic focus has shifted from “building AI features” to enabling sustainable enterprise capability. Basic chatbots and workflows will continue to be commoditised. Our long-term relevance depends on helping clients solve the harder problems around governance, integration, data, cost control, and operating ownership.

As part of our next strategic initiative, we will be working with regional enterprises that have experimented with multiple off-the-shelf AI tools but now face rising usage costs, inconsistent outputs, and growing compliance concerns. Our approach is to redesign their AI architecture to be model-agnostic, introduce structured cost monitoring and governance controls, and embed AI more deeply into core workflows.

Our key pivot is towards becoming an “AI operating model” partner — combining process redesign, modular architecture, and ongoing optimisation. The takeaway is simple: in the AI era, tools will keep changing, but organisations will always need partners who can turn fast-moving technology into reliable, governable, and scalable business capability.

Also read: AI Pulse Exclusive: How GenAI Fund is accelerating enterprise AI adoption across Southeast Asia

Operationalising AI beyond experimentation

This conversation highlights a growing shift from experimenting with AI tools to building sustainable operational capability. As enterprises face rising costs, governance considerations, and integration complexity, the focus increasingly turns toward process redesign, architecture flexibility, and measurable business outcomes. Organisations that successfully operationalise AI may find that long-term advantage lies less in the models themselves and more in how effectively they embed AI into everyday workflows.

For more interviews, analysis, and real-world perspectives on how organisations across the region are applying AI in practice, explore more stories here.

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