Five years ago, companies were looking for a strong candidate with deep specialisation and years of experience working within established systems. Today, especially in AI-adjacent policy, research, and innovation work, I find myself looking for a very different kind of person: someone who can learn in public, stay humble, adapt quickly, and think across disciplines without becoming intellectually shallow.

We no longer look only for specialists who know one chapter extremely well. We look for people who can read the whole book. In our space, that means navigating technology, policy, communication, ethics, and human behaviour simultaneously.

The shift became clear to us during a recent hiring discussion for a project involving AI governance and regional policy engagement. We discussed that if two candidates applied at the same time, who would we want to choose? One candidate had an exceptional résumé and prestigious credentials but struggled to adapt when project requirements changed continuously. Another candidate had fewer formal achievements but quickly integrated AI tools, synthesised policy information across disciplines, and independently proposed workable solutions. Increasingly, organisations, including us, are choosing the second profile. This isn’t an isolated hiring anomaly. It mirrors a massive global shift.

According to the 2026 PwC Global AI Jobs Barometer, skills required for AI-exposed roles are evolving 66 per cent faster than those in non-AI roles, pushing organisations to rethink hiring metrics beyond static credentials.

Today, we look for people with strong soft skills, consistent judgment, and the ability to operate in resource-constrained environments. Experience under pressure often reveals whether someone can adapt, prioritise, and continue functioning effectively in uncertainty. Experience in using AI or AI automation has also become important. Looking back, only three years ago, AI proficiency was barely discussed in hiring conversations, illustrating how rapidly organisational expectations have shifted.

Also Read: Generalist or specialist? Building future-proof skills in the age of AI

When access to information becomes increasingly universal through AI, competitive advantage shifts away from memorisation and toward judgement, adaptability, communication, and the ability to navigate uncertainty.

What is happening is not only the arrival of AI, but also the transformation of the working environment, which now requires people with diverse capabilities. Forward-thinking institutions need individuals who are well-rounded and understand how to continuously develop within the framework of their roles. Undoubtedly, deep expertise remains valuable, but agile teams must combine that specialised knowledge with speed, adaptability, and cross-domain collaboration.

Many outcome-oriented organisations have started asking four important questions in hiring:

Can the employee interpret problems rather than simply execute instructions? Can the employee collaborate with AI critically without losing independent judgment? Can the employee think creatively across disciplines? Can the employee operate independently under uncertainty?

Traditionally, these questions were often initially answered through résumés or CVs combined with HR interviews. In some cases, organisations also use standardised testing systems to measure capabilities numerically. Today, however, many organisations are beginning to realise that traditional hiring signals alone may no longer accurately predict long-term adaptability in AI-driven environments.

Also Read: AI’s tipping point: Why 2026 will separate the leaders from the laggards in financial services

My advice is to learn how to work effectively with AI and see it as a colleague whose capabilities can complement your own. Always prove that the information generated by AI is accurate and not misleading. Make AI part of your work and decision-making process because we place importance on evidence of real-world thinking through interdisciplinary collaboration, problem-solving principles, and the ability to manage uncertainty in AI-generated information. At the same time, organisations must be careful not to confuse AI-assisted speed with genuine understanding or good judgement.

All of this constantly makes me think that the concept of “great talent” is changing and spreading across industries. In other words, every industry increasingly agrees that people with great talent are those who possess fundamental qualities such as adaptability, learning ability, communication skills, and decision-making capability. In the future, each of these qualities will become separate skills that require even deeper mastery. More importantly, these skills must be visibly demonstrated during real work situations.

One challenge many organisations are currently facing is that many still prioritise stability and predictability, which conflicts with the rapidly changing nature of today’s world. At the same time, there is also a risk that organisations may begin undervaluing deep expertise in favour of constant adaptability. The challenge is not replacing expertise, but combining expertise with the ability to evolve continuously alongside AI.

Some employees who succeeded under older models of work may struggle to adapt if they rely solely on established expertise without integrating AI into their workflows. This contrasts with the new generation of great talent, who are able to adapt to changing environments by working alongside AI.

The future workforce may not be divided between technical and non-technical workers, but between those who can continuously learn alongside AI and those who cannot. In that environment, great talent is no longer defined only by what someone knows, but by how quickly they can reinterpret, apply, and evolve that knowledge in changing conditions.

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Also Read: Oil crashed 5% but Bitcoin jumped US$4K, altcoins surged 2X harder: What’s driving this?

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There is a moment, somewhere in the lifecycle of every major consumer platform, when the marketing playbook breaks. Banner ads stop working. Sponsored posts lose their edge. The audience, which has grown up inside the platform, develops an immunity to anything that feels like a paid placement.

In Southeast Asian gaming, that moment has already passed, and most brands are still running the old plays.

Also Read: The mobile-first myth that is costing SEA’s gaming industry billions

A gaming report by Southeast Asian gaming marketing agency Ampverse puts the structural shift in unambiguous terms: “Creators are not media placements; they are gatekeepers of trust.” In Southeast Asia, the report notes, a single creator can define how a game is perceived, and long-term creator relationships consistently outperform short-term influencer buys.

More than 50 per cent of gamers in the region regularly watch gaming content, and discovery — the moment a potential player first encounters a new game — increasingly happens through creators rather than app store rankings or paid advertising.

That is not a marginal shift. It is a fundamental restructuring of the distribution stack.

Why the influencer playbook fails in gaming

To understand why most brand campaigns in Southeast Asian gaming underperform, it’s helpful to examine how gaming creators differ from conventional social media influencers.

A lifestyle influencer operates on reach and aesthetic. Their audience follows them for a curated version of a life — the products they use, the places they visit, and the image they project. The relationship between influencer and follower is aspirational but relatively thin. A sponsored post slots neatly into that framework because the influencer’s identity is already partly commercial.

Gaming creators operate on trust and competence. Their audience follows them because they are genuinely good at games, genuinely entertaining to watch, and genuinely part of the same community. When a gaming creator endorses a title, their credibility is on the line in a way that a lifestyle influencer’s rarely is. Gamers can tell immediately whether a creator has actually played a game or is simply reading a script. The community does not forgive inauthenticity, and it does not forget it.

The Ampverse report captures this dynamic precisely: “Gaming audiences reward brands that participate meaningfully.” The word “meaningfully” is doing significant work in that sentence. It is not enough to pay a creator to post. Brands that win in this environment are those that enter through creators and communities, build long-term presence, create value rather than noise, and respect gaming culture on its own terms.

The creator economy inside gaming is structurally different

Southeast Asia’s gaming creator ecosystem has several features that distinguish it from both Western gaming markets and the broader regional creator economy.

Also Read: Gaming in SEA: Understanding the growing opportunity for SMEs and payment providers

First, community density. Discord servers, Facebook Groups, in-game guilds, and live tournament formats form the connective tissue of gaming communities across the region. The Ampverse report describes these structures as “the backbone of long-term engagement” and argues that successful brands and publishers treat communities as assets rather than audiences. This is not a metaphor; it reflects the reality that in markets like the Philippines, where the report describes a “highly social gaming culture” with games spreading “virally through creators and peer networks,” community infrastructure is the actual distribution mechanism.

Second, the primacy of live formats. Creator-led tournaments and live events consistently outperform static campaigns in Southeast Asia, delivering high watch time, repeat engagement, and organic social amplification. The report’s summary is pithy and correct: “In Southeast Asia, participation beats exposure.” A campaign that invites players to do something — compete, collaborate, contribute — will always outperform one that asks them to watch and click.

Third, the speed of creator-to-commerce crossover. The Ampverse report identifies an emerging trend that has significant commercial implications: gaming creators are increasingly launching mainstream consumer products. This is not peripheral to the gaming economy; it is evidence of how deeply gaming creators are embedded in their communities’ consumption behaviour. A gaming creator who launches a beverage, a clothing line, or a peripheral product is not diversifying away from gaming; they are monetising the trust they have built inside it.

The startup opportunity hiding in plain sight

The gap between what brands need and what the current market provides is, in startup terms, a problem worth solving. Most brands entering Southeast Asian gaming markets lack three things: the contextual knowledge to identify which creators are genuinely influential versus merely large, the infrastructure to manage long-term creator relationships at scale, and the measurement frameworks to evaluate performance beyond impressions and reach.

All three are addressable by technology. Creator intelligence platforms that map gaming community influence rather than follower count, relationship management tools designed for the cadence and format of gaming partnerships, and attribution models that account for community-driven conversion rather than last-click metrics; these are the products that the next wave of gaming-adjacent startups in Southeast Asia will be built around.

The Ampverse report notes that many global strategies fail because they are “copied from Western playbooks.” That observation extends to the creator strategy. Western influencer marketing infrastructure, which was largely built for Instagram and YouTube at a time when reach was the dominant metric, is a poor fit for a region where participation, community, and long-term trust are the actual levers of commercial performance.

Also Read: SEA mobile gaming surges: 1.93B installs and growing global influence

Brands that are still buying short-term influencer slots in Southeast Asian gaming are not just leaving money on the table. They are actively building a reputation for inauthenticity in communities that have long memories and loud voices. The creators who matter in this market are not waiting for brands to catch up; they are already building the next generation of distribution infrastructure without them.

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Not long ago, turning an idea into something tangible required time, technical resources, and often a fair amount of patience.

Ideas waited.

They sat in notebooks, Slack threads, or development backlogs while founders debated feasibility, budgets, and timelines. Before anything could be tested, it typically needed approvals, specifications, and someone technical to bring it to life.

Recently, a late-night conversation reminded me just how much that assumption has changed.

The conversation that changed the question

It started with a WhatsApp exchange with entrepreneur and strategist Vicky Vaswani.

He had shared a book and pointed out something he found interesting – not the content itself, but a small interactive feature within the reading experience.

At first, I did not even understand what he meant.

I was looking at the landing page while he was referring to the book interface itself.

Then came the clarification.

The book was uploaded as a PDF, and almost jokingly, he mentioned that Seraphina – my AI twin – could probably summarise it.

Minutes later, the summary was done.

That was the easy part.

What caught my attention, however, was not the summary.

It was the interaction.

A linked chapter structure. A smoother mobile reading flow. Something that made static content feel more immersive and easier to navigate.

And almost instinctively, I replied: “I can take any PDF and make it into a digital flip page.”

Not as a polished offer.

Not as a planned product roadmap.

Just an observation.

Then came the question every founder eventually hears: “Do you have a sample?”

Historically, this is where momentum often slows.

You explain. You promise.

You say you will revert after checking with a developer or technical team.

You sell the idea through imagination.

Instead, I opened Lovable and started building.

Roughly 15 minutes later, there was a working proof of concept.

It was not formally launched. It was not meant to be perfect.

It was simply my interpretation of the idea Vicky had described – a digital reading experience paired with AI-generated summaries designed to make long-form content easier to consume.

His response was immediate.

And while the prototype itself was interesting, I quickly realised something more important:

The build was not the story.

What happened next was.

Also Read: Beyond the buzz: How AI and sustainability are reshaping design, manufacturing, and construction in APAC

The prototype that closed the deal

The following day, I was on a call with an existing client whose website I was helping develop.

During the conversation, I showed her the concept.

This was not something she had originally requested.

It was simply a proof of possibility.

She saw it. Liked it.

And chose to implement it immediately as an additional feature.

The top-up happened shortly after.

The commercial value itself is not the headline here. In fact, the amount reflected speed and optimisation more than the true value of the capability.

What mattered was the sequence.

A conversation sparked an idea.

An idea became a prototype.

The prototype changed the sales conversation.

And the sales conversation became revenue.

That progression would have looked very different even a few years ago.

AI is not changing how founders build, it is changing how founders sell

This is why I believe AI is not merely changing how founders build.

It is changing how founders sell.

Historically, entrepreneurs pitched possibilities.

They relied on decks, descriptions, and imagination.

Customers were often asked to visualise outcomes before they existed.

Today, AI-assisted tools are closing that gap.

Instead of saying, “Imagine if this worked like this.” Founders can increasingly say: “Here – try it.”

That shift matters.

Buyers rarely hesitate due to a lack of interest alone.

More often, they hesitate because of uncertainty.

They cannot visualise the outcome.

They fear making the wrong decision.

They struggle to bridge the gap between concept and lived experience.

A working prototype reduces that friction.

Not because it guarantees success, but because it transforms abstraction into something tangible.

Cheaper experimentation, compressed timelines

In many ways, AI has made experimentation dramatically cheaper.

And that changes the economics of entrepreneurship.

I have seen similar patterns emerging beyond my own projects.

In recent Money and AI Launchpad sessions, participants – many without traditional technical backgrounds – moved from ideas to live micro-SaaS applications within just 2.5 days. Alongside the build itself, they developed marketing visuals and promotional copy to support their launches.

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The real shift was not simply faster development.

It was compressed experimentation.

Ideas no longer needed months of commitment before validation could begin.

They could be tested while momentum was still alive.

Not replacement, leverage

This is perhaps where AI discussions often become misunderstood.

Much of the public conversation still revolves around replacement.

Will AI take jobs? Will it remove the need for people?

My experience has been different.

AI has not removed the need for judgment, taste, or strategy.

If anything, those skills matter more.

Execution, however, has become significantly cheaper.

Through my own workflows, supported by Seraphina and a growing ecosystem of AI tools alongside platforms like Lovable and systems we have long explored through People’s Inc. 360, I increasingly see AI functioning less as novelty and more as infrastructure.

And honestly?

I would have burned out doing this manually.

Not just the thinking. The execution.

Managing multiple ideas, testing concepts, supporting communities, refining workflows, and building across several initiatives simultaneously would have been unsustainable without AI-assisted execution.

This is why I often describe AI not as a replacement, but as leverage.

The founders benefiting most from this shift may not necessarily be those with the largest teams or deepest technical expertise.

Increasingly, they may be the ones who learn how to prototype quickly enough to test ideas before momentum fades.

AI did not eliminate the importance of good ideas.

Nor did it eliminate the need for human insight.

But it did make prototyping cheaper.

And in doing so, it may have quietly changed how modern entrepreneurship works.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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Most hyper-growth businesses are not solving a marketing problem. They are wrestling with scaling pains masquerading as one.

Here is the truth: the playbook that powered your first million in sales will sabotage you at ten million. What flourished amid disorder now overwhelms. Once agile teams falter in the tumult, while executives, previously decisive, become mired in deliberations over processes, coordination, and infrastructure, imperatives that emerged only with scale.

This reflects not a marketing deficit, but a transitional oversight.

Two marketing realms collide

Having led corporate marketing campaigns and mentored early-stage startups, I have witnessed two starkly opposing playbooks.

Enterprise marketing prioritises methodology, alignment, and scalability. It is rigorous, data-centric, and optimised for sustained expansion. Ample budgets support specialised teams, with every initiative rigorously evaluated for reproducibility. The emphasis extends beyond outcomes to their consistent replication.

In contrast, startup marketing embodies resourcefulness and velocity, fixated on proximate impact. Perfection yields to pragmatism; traction reigns supreme.

The critical error lies in presuming seamless transferability between these domains.

A fast-scaling firm transcends startup volatility yet falls short of enterprise maturity. It inhabits an interstitial phase where legacy approaches obsolesce, and nascent frameworks remain undefined.

The scaling pitfall: Where growth grinds to a halt

Misaligning marketing orientation yields dual pitfalls. Prematurely appointing an enterprise marketer begets premature systematisation for nonexistent challenges. Meetings proliferate; velocity diminishes. The nimble operation transforms into a ponderous vessel.

Also Read: The secret weapon of marketing? Why every business needs a CDP

Conversely, retaining a startup-oriented marketer indefinitely erodes efficacy. Proven campaigns wane; redundancy mounts. Growth plateaus, not from market exhaustion, but methodological limits.

Tactics themselves are not at fault.

Unlocking marketing at scale

The ideal marketing executive possesses these attributes:

• Constructs scalable processes devoid of bureaucratic excess, with discretion to adapt.
• Advances expeditiously while anchoring to strategic imperatives.
• Validates hypotheses through empirical analysis, adept in quantitative and qualitative realms.
• Excels amid constraints, leveraging them for ingenuity.

Such leaders are rare, yet instrumental in distinguishing plateaued companies from those achieving breakout velocity.

Spotting if you are snagged

Thriving organisations discern this inflection and recalibrate their marketing apparatus accordingly. I have watched too many high-potential ventures sputter by ignoring this evolution.

If your marketing efforts are falling short, use these simple checks to pinpoint the issue:

• Are you stuck in startup mode? Look for one-off campaigns with no repeatable blueprint, a knee-jerk rejection of any process, and constant crisis firefighting. These signs show you are clinging to early-stage habits that no longer fit your scale.
• Are you acting too corporate too soon? Watch for endless meetings, decisions stalled by alignment talks, and complex systems built for problems that have not arrived yet. This flips the script, putting structure ahead of speed.

These questions reveal exactly what to adjust.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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