Day: October 13, 2026

Artificial intelligence is increasingly moving from an experimental technology to an operational tool for small and medium-sized enterprises across Southeast Asia. Yet the more important question is not whether SMEs will use AI, but where AI can create measurable business value without adding disproportionate complexity, cost, or risk.

This distinction matters because Southeast Asia’s SME landscape is unusually diverse. The region includes highly digitised economies such as Singapore alongside markets where many smaller businesses are still building basic digital infrastructure. As a result, AI adoption is unlikely to follow a single regional trajectory.

According to the Asian Development Bank’s Asia Small and Medium-Sized Enterprise Monitor 2025, MSMEs account for an average 99.8 per cent of enterprises and 67.6 per cent of employment across its 26 developing member economies. For Southeast Asia, therefore, the economic significance of AI depends heavily on whether smaller businesses can convert increasingly accessible AI tools into productivity gains.

AI’s first advantage is operational, not transformational

For many SMEs, the immediate value of AI lies in improving existing processes rather than reinventing the business.

Customer service, finance and accounting, marketing, inventory management, demand forecasting and internal knowledge management are among the functions where AI can reduce repetitive work or accelerate decision-making. Singapore provides a useful illustration. Data from the country’s Infocomm Media Development Authority shows that SME AI adoption increased from 4.2 per cent in 2023 to 14.5 per cent in 2024. SMEs using AI-enabled solutions supported through the Productivity Solutions Grant reported average cost savings of 52 per cent in 2024.

Also Read: AI boom pushes chipmakers to expand across Asia Pacific, FedEx report finds

The significance of this example is not the specific percentage. It demonstrates that AI’s commercial case for smaller companies can begin with relatively narrow applications. A company does not necessarily need to build a proprietary AI system. It can use existing models to automate repetitive workflows, analyse information, assist employees or improve customer interactions.

This lowers the technological barrier, but it does not eliminate the organisational one.

The real constraint is digital readiness

AI systems depend on the quality of the processes and data around them. A company with fragmented spreadsheets, inconsistent customer records and largely manual workflows may find it difficult to obtain reliable results from increasingly sophisticated AI tools.

Recent research published by ADB on digital technology adoption among 681 entrepreneurs across Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam found that selected digital applications and business-model digitalisation were associated with greater experimentation and changes in business models.

This suggests an important sequencing issue. AI is often presented as the next step after digitalisation, but for many SMEs, basic digital infrastructure remains the prerequisite for meaningful AI adoption.

A 2026 analysis published through ADB’s Southeast Asia Development Solutions identifies substantial differences in AI readiness across the region, with infrastructure, skills and regulatory maturity influencing adoption. It also notes that Cambodian manufacturing SMEs, for example, continue to face gaps in basic digital systems.

Consequently, an SME that has not yet digitised its core operations may receive more value from improving its data and workflow infrastructure than immediately implementing advanced AI.

Southeast Asia will not have one AI adoption curve

The region’s diversity also means that national AI strategies and enterprise capabilities will shape adoption differently.

Singapore has developed comparatively mature AI infrastructure and policy support, while other Southeast Asian markets continue to address foundational connectivity, skills and digitalisation challenges. The result is likely to be a multi-speed AI economy, rather than a uniform regional transformation.

Also Read: AI chats are becoming the new starting point for online shopping

This matters for enterprise software providers and policymakers. A sophisticated AI platform designed for a highly digitised Singaporean SME may not address the constraints of a smaller business in Cambodia, Laos or parts of Indonesia. Solutions need to account for differences in connectivity, workforce capabilities, language, affordability and existing software systems.

The Asian Development Bank similarly argues that AI readiness, rather than adoption alone, will determine how effectively economies capture the technology’s benefits.

The SME advantage could be speed

There is, however, a structural reason to expect smaller businesses to remain important participants in AI adoption.

Large organisations often have more resources but also have complex legacy systems, multiple approval layers and established processes. SMEs have fewer resources, but they can sometimes introduce new tools across a smaller organisation with less organisational friction.

AI can therefore reduce some of the disadvantages traditionally associated with company size. A small retailer can use automated customer support. A manufacturer can apply predictive analytics to production data. A professional-services firm can use AI to organise documents and accelerate research. A small exporter can use AI-assisted translation and market research to communicate across markets.

This does not make AI a substitute for scale. Rather, it can lower the cost of performing certain activities that previously required specialised staff or external providers.

Also Read: AI labs are chasing a slice of the corporate pie in Southeast Asia

The productivity question is more complicated

There is a tendency to equate AI adoption with productivity automatically. The evidence suggests a more conditional relationship.

AI can reduce the time required for particular tasks, but the resulting productivity gain depends on what employees do with the time released. Poorly implemented automation can simply move work elsewhere, introduce errors or create additional verification requirements.

This is particularly important for SMEs, where a single employee may perform several functions. Automating one task does not necessarily produce a proportional reduction in costs if the employee remains essential for related activities.

ADB’s broader research on digital transformation highlights both the productivity opportunities and the risks, including digital divides, labour-market disruption, cybersecurity, data privacy and governance challenges.

The economic impact of AI should therefore be measured through business outcomes such as processing time, error rates, customer response times, revenue per employee and operating costs, rather than simply the number of AI tools deployed.

Governance will become an SME issue

As AI moves into finance, recruitment, customer service and other decision-making processes, governance becomes increasingly relevant to smaller companies.

ASEAN’s Guide on AI Governance and Ethics provides a regional framework covering issues including transparency, fairness, security, accountability and human oversight. ASEAN subsequently expanded the guidance to address generative AI specifically.

For SMEs, governance does not necessarily require large compliance departments. It can begin with practical questions: What data is being provided to an AI system? Who reviews its outputs? Which decisions can AI make independently? What happens when the system produces an incorrect or biased result?

These questions become more important as AI moves from assisting employees to executing parts of business workflows.

From experimentation to infrastructure

The next phase of AI adoption among Southeast Asian SMEs will be less about experimenting with individual tools and more about integrating AI into core business operations.

The real value will come from connecting AI with customer data, finance, inventory and internal workflows. As adoption grows, success will depend not simply on access to AI, but on digital readiness, skills, governance and practical use cases.

The challenge is no longer accessing AI. It is turning AI into measurable, repeatable productivity.

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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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Malaysia has put a number on its startup ambitions: MYR450 million (US$110.4 million). That is how much the National Fund of Funds Jelawang Capital and Dana Perintis, the startup vehicle of the Retirement Fund (KWAP), will invest in developing startups, Prime Minister Anwar Ibrahim said in his Budget 2027 speech to parliament on Friday.

“Startups need support at every stage, from ideas to seed capital and scaling up,” said Anwar, who is also Finance Minister.

Also Read: Malaysia’s digital economy’s second wave looks nothing like the first

It is a welcome commitment. It is also, by some distance, not the biggest cheque in the speech. Read the budget end to end and a clear pecking order emerges: mid-tier companies and semiconductors first, research and AI skills next, and early-stage startups a little further down the list.

Follow the money

Anwar said MYR2.1 billion (about US$515 million) from the NIMP fund (linked to the New Industrial Master Plan 2030), KWAP’s Dana Pemacu and Khazanah’s MTC Fund will go towards strengthening mid-tier companies in electrical and electronics (E&E), digital technology, aerospace and medical devices. These firms, he said, “must rapidly become self-reliant to lead in high-value industries.”

Set side by side, the startup allocation is roughly a fifth of the mid-tier pot. Add the MYR1.2 billion (about US$294 million) Khazanah and KWAP will put into local semiconductor companies, and the MYR6 billion (about US$1.47 billion) earmarked for research, development and commercialisation across ministries, and the startup line starts to look like a footnote.

That is not necessarily a mistake. Mid-tier firms hire at scale, export and anchor supply chains, and Malaysia’s growth story rests on climbing value chains rather than minting unicorns. But for founders, the size of the fund matters less than who manages it and how quickly the money actually moves.

KWAP’s presence on both sides of the ledger is worth watching. The pension fund spent part of this year containing the damage from the eFishery fraud shock. Retirement money in venture capital invites political scrutiny, and whether that makes Dana Perintis a more disciplined investor or simply a slower one remains to be seen.

Nudging private capital off the sidelines

The more interesting moves may be the ones designed to pull private money in. Anwar announced MYR270 million (about US$66 million) to support SMEs, mid-tier companies and social enterprises raising through equity crowdfunding (ECF) and peer-to-peer (P2P) lending platforms.

Also Read: AI demand lifts Malaysia’s chip sector, but not every player wins

Income tax exemptions for individual investors on ECF platforms, and for angel investors backing early-stage tech startups, have been extended to December 31, 2030. That four-year runway matters more than it sounds. Angels plan around portfolios and holding periods, not single budget cycles, and a sunset clause that keeps moving is a poor basis for a long-term bet.

Cradle Fund, meanwhile, gets MYR41 million (about US$10 million) to implement the Malaysia Startup Ecosystem Roadmap and to hand out grants for prototype development and commercialisation. For a roadmap meant to reshape the national ecosystem, it is a slim line item. Its impact will depend on how tightly Cradle targets the grants.

Semiconductors, again

If there is a single obsession running through the speech, it is chips. MTDC’s SemiconStart programme will provide follow-on equity for startups in IC design, advanced packaging and semiconductor materials. Khazanah is partnering with the Selangor state government on a fund to develop the state’s semiconductor industry. Khazanah and InvestPenang will also set up a MYR100 million (about US$24.5 million) Strategic Investment Fund for early-stage semiconductor and advanced manufacturing firms.

The follow-on detail is the smart part. In deeptech, first cheques are rarely the problem. Startups stall at the second and third rounds, through long and capital-hungry design cycles. Firms such as GreatAsic, which is trying to pivot Malaysia from chip assembly to indigenous design, are exactly the kind that need patient capital. The risk is the old one: the sector has already faced questions over governance and credibility, and more state money will not settle them on its own.

AI for everyone, on someone else’s model

On AI, the budget leans heavily on access. The free AI subscription initiative will be expanded by another 100,000 subscriptions and extended to new applications such as ChatGPT. Rakyat Digital, meanwhile, offers free digital courses through a partnership with Coursera.

There is an irony here. Paying for subscriptions to an American chatbot sits awkwardly beside Putrajaya’s sovereign AI rhetoric. Local AI startups may wonder why public money is building habits on a foreign platform rather than theirs.

Also Read: Why Malaysia’s AI Nation 2030 plan matters for B2B startups

AI Malaysia Bhd will receive almost MYR15 million (about US$3.7 million) to reinforce a safe and ethical AI ecosystem, towards a target of 200,000 AI-skilled workers. MDEC’s MYR30 million (about US$7.4 million) fund will help 4,000 MSMEs adopt AI and automate their operations, and will also train and certify 5,000 AI professionals. Do the maths and that is at most MYR7,500 (about US$1,840) per business, before the training component takes its share. Given how wide Malaysia’s AI skills gap already is, that is a starter kit rather than a transformation.

Platforms, gig workers and quantum threats

The government and Grab will jointly fund a MYR160 million (about US$39 million) package from 2027 to boost net earnings and improve the welfare of e-hailing and p-hailing (parcel delivery) workers. It is a pragmatic arrangement, though one in which the regulator and the dominant platform now share the bill for gig worker protection. Riders will judge it by what lands in their pockets, not by the press release.

On e-commerce, the government is examining legislative and enforcement measures to shield local MSMEs from unfair competition from foreign platforms and to ensure product safety. An E-commerce Bill will be tabled in the next parliamentary sitting. It continues a broader pattern in which Malaysia regulates while Singapore funds.

For growth-stage companies eyeing an exit, the Securities Commission Malaysia and Hong Kong’s securities regulator have formalised cooperation to help Malaysian companies list in both markets, with matching grants to cover listing costs. Forest City’s Single Family Office scheme offers zero tax on qualifying income, and a Multi-Family Office model will let licensed fund managers serve several families.

Green tech tax incentives have been enhanced and extended to December 31, 2030. They include an Investment Tax Allowance of up to 100 per cent for green and sustainable technology projects, EV charging stations, and green assets bought for own use.

On cybersecurity, Malaysia says it is now recognised at Tier 1 for readiness to transition to post-quantum cryptography. The National Cyber Security Agency (NACSA) and the Malaysian Communications and Multimedia Commission (MCMC) will develop home-grown data protection technologies built to withstand attacks from quantum computers, while CSCDC Bhd’s capabilities are strengthened. “Home-grown” is the operative word. The country’s cyber startups have been battling a funding drought, and they will want to know whether that work flows to them or stays inside government agencies.

The real test is deployment

Anwar projected growth of 4.2 to 5.2 per cent in 2027, a touch softer than the 4.8 to 5.3 per cent expected this year. In a slower year, capital that sits in committees is capital that does not exist.

Also Read: Mid-year 2026 look at Malaysia’s digital and technology regulatory updates

Budgets are statements of intent. This one is clear about its priorities: build the middle, double down on chips, and give everyone a chatbot. Startups got a seat at the table. Whether they get fed depends on how fast Jelawang, KWAP and Cradle turn announcements into term sheets.

The post Malaysia’s Budget 2027 hands startups US$110.4M but the bigger cheques go elsewhere appeared first on e27.

Ask a Singapore small business owner what AI has done for them, and the answer is time. Ask whether their systems are ready to let AI run more of the business, and the answer gets a lot less certain.

Also Read: Beyond automation: How AI is reshaping the SME landscape in Southeast Asia

That tension runs through “The State of AI in Small Business 2026 – Singapore”, a new report from small business accounting platform Xero. Research firm Pureprofile conducted the survey between July 29 and August 5. It polled 1,018 business owners and senior decision-makers at Singapore companies with fewer than 200 employees and annual revenue of up to SGD100 million (about US$77 million).

The starting point is familiar to anyone who has run a small company. Some 76 per cent of respondents said their leadership and finance teams lose anywhere from three to more than 10 hours a week to manual administrative work. AI, the report argues, is buying that time back.

Where the reclaimed hours go is the more flattering part of the story. Nearly half (46 per cent) of SMEs said they channel them into growth strategies and market expansion, 38 per cent into product and service innovation, and 33 per cent into upskilling existing staff for higher-value tasks.

“The real dividend of AI isn’t simply doing the same work faster. It’s the capacity it gives back, and business owners are clear they’d reinvest that time in growth, innovation and their people,” said Koren Wines, Managing Director of Xero APAC.

It is a tidy narrative, and a convenient one for a company that sells AI-embedded accounting software. It is also worth noting what the survey measures: where SMEs say the hours go, not whether those hours turned into new customers or revenue.

The 82 per cent problem

On sentiment, Singapore’s SMEs are bullish. Some 82 per cent said AI returns have met or exceeded expectations, and 69 per cent plan to increase total AI spending over the next 12 months.

Dig into the operational numbers, though, and the confidence thins out. Only 18 per cent feel fully confident that their current software has enough context to support automated decision-making, such as data history, clean ledgers and connected systems. Nearly a third (31 per cent) named poor data quality as the main reason AI pilots never made it into regular production.

Also Read: Why Singapore SMEs should treat cybersecurity as a sales issue

Cost and talent remain sticking points too. Integration costs (38 per cent) and upskilling staff (36 per cent) are the top hurdles to expanding adoption.

Read together, the figures suggest SMEs are pleased with what AI does today, which is largely task-level admin relief. They are far less sure it can be trusted with anything more consequential, and high satisfaction may simply reflect modest expectations.

The harder question is what happens after the pilot phase, when AI is expected to make decisions rather than draft emails. As one e27 analysis put it, the region’s small businesses arguably have a record problem before they have an AI problem.

Guardrails, at least on paper

Xero frames Singapore’s SMEs as leaders in responsible AI use. Some 67 per cent have established formal internal usage frameworks, and 90 per cent say vendor data privacy is a priority.

The split within that 90 per cent is telling. Only 36 per cent will work exclusively with vendors that explicitly guarantee customer data will not be used to train external models. The remaining 54 per cent prefer vendors that offer a clear opt-out. That quietly shifts the burden onto the business owner to find, and remember to flip, the right setting.

A framework on file is also not the same as one that is enforced. Larger Singapore firms adopting agentic AI have been found to keep thin audit trails, and many fear data sovereignty failures while remaining underprepared for them. There is little reason to assume a 30-person trading company is more rigorous.

The grant question

For Xero at least, the report’s most commercially useful finding is that 52 per cent of SMEs now use AI built directly into core software they already rely on. That includes cloud accounting, inventory tracking and customer management tools. It is precisely the pitch embedded-software vendors make, so the figure is best read through that lens.

Also Read: The Philippines uses AI as a burnout shield; Singapore uses it for deep work

The other number that deserves attention is 63 per cent: the share of SMEs using government digital grants to fund technology deployment and training. Singapore has made AI adoption a national priority, most visibly in its AI-first Budget 2026, and the grants clearly lower the barrier to entry.

They also raise a fair question. How much of that planned increase in AI spending, reported by 69 per cent of SMEs, is driven by their own conviction, and how much by the availability of subsidies? The answer will matter if grant priorities shift.

Ahead of the region, still short of the finish line

By regional standards, Singapore’s small businesses are in a strong position. A similar Xero study found Malaysian SMEs grappling with a growing confidence gap in AI adoption. Globally, SMEs have been adopting AI four times slower than large enterprises.

But being ahead of the pack is not the same as being ready. For founders building software for Southeast Asia’s SMEs, the report contains a clear signal: the bottleneck is plumbing, not models. Clean books, connected systems and trustworthy data handling are what stand between an SME saving a few hours a week and an SME letting AI make real decisions. It is also, not coincidentally, exactly where Xero wants to sell.

“What’s encouraging is that AI is reshaping roles rather than replacing them,” Wines said. “For Singapore’s SMEs, it’s no longer about adoption. It’s about what happens next.”

Also Read: Why Singapore, Indonesia, and Vietnam are losing the AI race they think they are winning

She is right about that. The first phase of SME AI was measured in hours saved. The next will be measured in revenue earned, and on that score, Singapore’s small businesses have yet to show their workings.

The post Singapore SMEs buy back time with AI, but messy data keeps pilots stuck appeared first on e27.

Before a Vietnamese shopper adds anything to a cart these days, there is a good chance a chatbot has been consulted. As many as 62 per cent of Vietnamese buyers use AI, including generative AI, to research purchases before they buy, according to a new survey by Ho Chi Minh City-based market research firm Q&Me.

Only 3 per cent of respondents said they “never” use AI for pre-purchase research. Frequent use peaks among people in their 30s (68 per cent), while those in their 40s are more likely to say they use it “sometimes” (44 per cent).

Also Read: The invisible shopper rewriting Asia’s e-commerce playbook

On the surface, it is another data point for the thesis that AI chats are becoming the new starting point for online shopping. Read the rest of the survey, though, and a more nuanced picture emerges: AI has joined the shopping journey, but it has not taken it over.

The old guard still runs the funnel

When respondents were asked which research methods they actually use, generative AI came in fifth, cited by 58 per cent. Search engines such as Google led with 86 per cent, followed by e-commerce platforms such as Shopee and Lazada (77 per cent), Facebook (59 per cent) and TikTok (57 per cent).

In other words, the chatbot has become one more tab among many rather than a replacement for them. A typical Vietnamese buyer is likely to ask ChatGPT what to look for, check Google for reviews, compare prices on Shopee, and watch a TikTok creator unbox the product before paying. That fragmentation is a headache for brands, which now have to show up credibly in all five places.

It also matters for the platforms. TikTok, which is investing US$980 million in a logistics project in Ho Chi Minh City, is betting that discovery and checkout happen in the same app. A shopper, who does the thinking inside a chatbot and only arrives at the marketplace to pay, weakens that bet, and hands the most valuable part of the funnel — the moment of consideration — to someone else.

What people actually ask the bots

Shopping is not even the main reason Vietnamese users turn to generative AI. According to Q&Me, 78 per cent use it for information search, 62 per cent for personal advice, 58 per cent for product research and 57 per cent for work tasks.

The information-search figure is the one to watch. Chatbots are increasingly doing the job search engines used to do, a shift regulators elsewhere are starting to notice; Europe’s decision to classify ChatGPT as a search engine is one sign of where that leads. For shoppers, the risk is that a confident-sounding answer replaces a comparison. Product data feeding AI systems is often incomplete or simply wrong, and a chatbot rarely tells a buyer when it is guessing.

Also Read: How Vietnam is emerging as a leading AI builder ecosystem in Southeast Asia

The 62 per cent seeking personal advice deserves its own caution. Advice on what to buy shades quickly into advice on whether to buy, and few of these tools disclose how products end up in their recommendations.

ChatGPT leads, Google closes in

Among generative AI services, ChatGPT is the clear leader, used by 80 per cent of respondents, followed by Gemini (68 per cent) and Meta AI (25 per cent). Claude, Copilot, DeepSeek, Grok and Perplexity trailed behind.

Gemini’s strong second place is no accident. Google has been pushing hard on distribution and local-language capability across Southeast Asia, and it owns both the Android phones most Vietnamese carry and the search engine 86 per cent still rely on.

Meta AI’s 25 per cent, by contrast, looks modest given how deeply Facebook is woven into Vietnamese commerce, which suggests users have not yet learned to treat the assistant inside their social feed as a shopping adviser.

Notably absent from the list of popular services is any homegrown Vietnamese assistant, despite the country’s ambitions to become a regional AI builder and a growing body of work on models trained for Vietnamese, Thai and Indonesian. Vietnamese consumers, for now, are doing their AI-assisted shopping on American platforms.

Read the fine print

The survey’s headline numbers should be handled with care. Q&Me polled just 200 men and women aged 20 to 49 in Ho Chi Minh City and Hanoi. A sample that size carries a margin of error of roughly seven percentage points either way, and the age-group splits are drawn from smaller pools still, so the gap between people in their 30s and 40s may be narrower or wider than it looks.

The sample is also urban, working-age and, almost by definition, online, which is exactly the slice of Vietnam most likely to have embraced AI. It says little about older shoppers or buyers outside the two biggest cities, where much of the next wave of e-commerce growth is expected to come from.

There is a definitional wrinkle too. The survey says 62 per cent use AI for pre-purchase research, yet only 58 per cent name generative AI as one of their research methods, and the same 58 per cent use it for product research. The difference may come down to how respondents interpreted “AI”: some may be counting the AI features already built into search results and shopping apps rather than a standalone chatbot.

What it means for brands and founders

None of this undercuts the direction of travel. Even with a small sample, near-universal exposure among urban buyers is a strong signal, and the 30-something cohort, the one with the most purchasing power, is the heaviest user.

For brands selling into Vietnam, the practical lesson is that being findable now means being legible to machines as well as to people: accurate product data, consistent reviews and clear specifications that a chatbot can parse and repeat. For startups, the gap is in the middle of the funnel, building tools that help merchants show up correctly inside AI answers, and that help shoppers verify what those answers tell them.

Also Read: Why Singapore, Indonesia, and Vietnam are losing the AI race they think they are winning

For now, Vietnamese shoppers are using AI the way they use a well-read friend: worth asking, rarely the final word. Google, Shopee and TikTok still close the sale. The question for the next survey is how long that lasts.

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Generative AI has made video creation remarkably fast. A scene that once required a camera crew, actors, locations and hours of post-production can now begin with a prompt and an idea.

But after working with AI-generated cinematic videos, I have learned that generating a video quickly and producing a video that is actually usable are two very different things.

An AI model can create an impressive scene on the first attempt. At first glance, everything may look great: cinematic lighting, realistic environments, convincing characters and smooth camera movement.

Then you look closer. A car is moving in the wrong direction. A character suddenly looks different. An important object disappears. The environment does not accurately represent the intended location. A person appears twice. The camera moves differently from what the story requires.

The video may look impressive, but it is not necessarily usable.

Good-looking is not the same as correct

One of the most interesting things about AI video is how easily visual quality can hide mistakes. A generated scene can look highly realistic while containing small problems that make the entire shot unusable.

This becomes particularly important when creating content based on real locations, products or brands. If a scene is supposed to represent Singapore, for example, recognisable landmarks, roads, architecture and the overall environment need to make sense. A beautiful futuristic city is not enough if it no longer feels like Singapore.

The same applies to branded content. A logo that changes shape, text that becomes unreadable or a product that changes appearance can quickly turn a visually impressive generation into something that cannot be published.

This means creators need to evaluate AI-generated footage beyond visual quality. Accuracy matters too.

Also Read: AI boom pushes chipmakers to expand across Asia Pacific, FedEx report finds

Consistency is one of the biggest challenges

Creating one impressive AI-generated shot is becoming increasingly easy. Creating 10 shots that feel like they belong to the same film is much harder.

A character may look correct in one scene and slightly different in the next. Clothing can change. Buildings can move. Objects can disappear. Lighting can suddenly shift.

Individually, each generation might look good. Together, however, they may not feel like one continuous story.

This has changed the way I approach AI video creation. Instead of thinking only about individual prompts, I increasingly think about continuity: What must remain the same? What can change? Where should the character be? What direction should objects move? What should the camera be doing? What details from the previous scene need to remain visible?

Consistency becomes part of the creative process.

Prompting is becoming more like directing

Early AI prompts could be relatively simple. “Create a futuristic city.” Today, producing more controlled cinematic content often requires much more thought.

A creator may need to define the subject, action, environment, camera movement, framing, lighting, visual style, timing and restrictions. Instead of simply asking AI to create something beautiful, you begin directing what should happen. The difference is important.

A prompt might specify that the camera slowly pushes forward while the main character remains visible, vehicles continue moving naturally in the correct direction, the environment stays consistent and the shot ends at a particular visual moment. At that point, prompting starts to feel less like giving a computer a sentence and more like communicating instructions to a virtual production team.

The ability to describe an idea clearly becomes increasingly valuable.

Also Read: AI chats are becoming the new starting point for online shopping

Human judgement still decides what is usable

AI can generate alternatives very quickly, but it does not always understand why one result works better than another. A human still needs to notice when something feels wrong.

Is the movement natural? Does the location look accurate? Is the character consistent? Does the camera movement support the story? Is the branding correct? Does the scene communicate the intended message?

These decisions require judgement rather than generation. This is why I do not see AI video simply as a replacement for traditional creative work. It changes where some of the work happens.

Instead of manually producing every visual element, creators increasingly spend time defining ideas, setting constraints, reviewing generations, correcting problems and deciding which results deserve to become part of the final production.

Businesses should look beyond generation speed

For businesses considering AI-generated video, speed is understandably attractive. But “How quickly can AI generate a video?” may not be the most useful question. A better set of questions might be:

• How many generations are required before the result is usable?
• Can characters, products and brand elements remain consistent?
• How much human review is required?
• Can the technology accurately communicate the intended message?
• Does the final content actually feel appropriate for the brand?

AI can dramatically reduce some production barriers, particularly for ideas that would otherwise require expensive locations, visual effects or complex production. But businesses should still treat AI-generated content as creative production rather than a one-click solution.

The role of the creator is changing

AI video technology will continue to improve. Many problems that are difficult today will probably become easier.

Characters will become more consistent. Physical movement will improve. Models will understand environments better. Creators will gain more precise control.

But better technology does not eliminate the need for creative decisions. Someone still needs to decide what the audience should see, how the story should unfold, what feels authentic and whether the final result communicates the right idea.

The tools are changing rapidly, but storytelling, direction and judgement still matter.

AI can generate the footage.

Someone still has to direct it.

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

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