Banking house JPMorgan Chase is asking its roughly 65,000 engineers and technologists to use AI tools as part of their regular workflow. Business Insider reported that managers are tracking how often staff use these tools. That use may also influence performance reviews.

The report states employees are encouraged to use tools like ChatGPT and Claude Code when writing code, reviewing documents, or handling routine tasks. Internal systems then classify workers based on their level of use. Some are labelled “light users,” while others fall into a “heavy user” category.

JPMorgan has been using in fraud detection and risk analysis. What stands out here is not the technology itself, but how it is being woven into day-to-day expectations for staff.

According to internal materials cited by Business Insider, managers are paying close attention to how employees use AI tools.

JPMorgan shows AI adoption in banks

Many companies have spent the past two years rolling out AI tools in departments. In most cases, adoption has been uneven. Some teams experiment heavily, while others stick to existing workflows.

JPMorgan is treating AI as a standard part of the job. That creates a more uniform level of adoption in teams. In the past, performance reviews focused on output and accuracy. Now, they may also include how effectively employees use AI tools to reach those results.

That raises a practical question for large organisations. If AI can reduce the time needed for certain tasks, should employees be expected to produce more work in the same amount of time?

Keeping pace with internal change

By tracking use, the bank may be trying to avoid a familiar problem in enterprise software rollouts. Tools are deployed, but adoption is slow, limiting their impact. Making AI part of performance reviews creates a stronger incentive to engage with the technology. It also suggests that AI literacy is becoming a baseline skill, similar to how spreadsheets or code tools became standard over time.

New challenges include employees feeling pressure to use AI even in cases where it does not clearly improve the outcome. There is also the matter of how to measure “good” use, as opposed to simply frequent use.

JPMorgan’s AI risks and efficiency gains

Banks operate in a regulated environment, where introducing AI into more workflows increases the need for oversight.

Tools like ChatGPT and Claude Code can help summarise information or generate drafts, but they can also produce incorrect or incomplete results. That means employees still need to verify outputs before using them in decision-making or client-facing work.

JPMorgan has developed internal controls for AI systems in areas like trading and risk. Expanding use in a broader group of employees may require similar safeguards, creating a situation for the bank in which it wants to improve efficiency, but also needs to ensure that heavier AI use does not introduce new risks.

Other financial institutions are likely watching closely. If tying AI use to performance leads to measurable gains in productivity, similar models may spread in the sector.

The bank’s approach may reshape how companies hire and train employees, and skills like prompt writing and output checks could become part of standard job requirements. JPMorgan’ approach suggests that this change is already underway, at least in banking.

(Photo by IKECHUKWU JULIUS UGWU)

See also: RPA matters, but AI changes how automation works

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Enterprise leaders are pressing ahead with artificial intelligence, even as some early results remain uneven. Reporting from the Wall Street Journal and Reuters shows that most CEOs expect AI spending to keep rising through 2026, despite difficulty tying those investments to clear, enterprise-wide returns.

The tension highlights where many organisations now sit in their AI journey. The technology has moved beyond trials and proofs of concept, but it has yet to settle into a reliable source of value. Companies are operating in an in-between phase, where ambition, execution, and expectations are all under strain at the same time.

Spending continues, even as returns lag

AI budgets have climbed steadily in large enterprises over the past two years. Competitive pressure, board oversight, and fear of being left behind have all played a role. At the same time, executives are more open about the limits they are seeing. Gains often show up in pockets rather than in the business, pilots fail to spread, and the cost of connecting AI systems to existing tools keeps rising.

A Wall Street Journal survey of senior executives found that most CEOs see AI as central to long-term competitiveness, even if short-term benefits are hard to measure. For many, AI no longer feels optional. It is treated as a capability that must be developed over time, rather than a project that can be paused if results disappoint.

That view helps explain why spending remains steady. Leaders worry that cutting back now could weaken their position later, especially as rivals improve how they use the technology.

Why pilots struggle to scale

One of the main barriers to stronger returns is the jump from experimentation to day-to-day use. Many organisations have launched AI pilots in different teams, often without shared rules or coordination. While these efforts can generate insight and interest, few translate into changes that affect the wider business.

Reuters has reported that companies trying to scale AI frequently run into issues with data quality, system links, security controls, and regulatory requirements. The problems are not only technical, but reflect how work is organised. Responsibility is often split in teams, ownership is unclear, and decisions slow down once projects touch legal, risk, and IT functions.

The result is a pattern of heavy spending on trials, with limited progress toward systems that are embedded in core operations.

Infrastructure costs reshape the equation

The cost of infrastructure is also weighing on AI returns. Training and running models demands large amounts of computing power, storage, and energy. Cloud bills can rise quickly as use grows, while building on-site systems requires upfront investment and long planning cycles. Executives cited by Reuters have warned that infrastructure costs can outpace the benefits delivered by AI tools, particularly in the early stages. This has led to tough choices: whether to centralise AI resources or leave teams to experiment on their own; whether to build in-house systems or rely on vendors; and how much waste is acceptable while capabilities are still forming.

In practice, these decisions are shaping AI strategy as much as model performance or use-case selection.

AI governance moves to the centre of CEO decision-making

As AI spending increases, so does scrutiny. Boards, regulators, and internal audit teams are asking harder questions. In response, many organisations are tightening control. Decision rights are shifting toward central teams, AI councils are becoming more common, and projects are being linked more closely to business priorities.

The Wall Street Journal reports that companies are moving away from loosely connected experiments toward clearer goals, measures, and timelines. This can slow progress, but it reflects a growing belief that AI should be managed with the same discipline as other major investments.

The shift marks a change in how AI is treated. It is no longer a side effort or a curiosity but is being brought into existing operating and risk structures.

Expectations are being reset, not abandoned

Importantly, the persistence of AI spending does not signal blind optimism. Instead, it reflects a reset in expectations. CEOs are learning that AI rarely delivers immediate, sweeping returns. Value tends to emerge gradually, as organisations adjust workflows, retrain staff, and refine data foundations.

Rather than abandoning AI initiatives, many enterprises are narrowing their focus. They are prioritising fewer use cases, demanding clearer ownership, and aligning projects more closely with business outcomes. The re-calibration may reduce short-term excitement, but it improves the likelihood of sustainable returns.

What CEO AI strategy signals for 2026 planning

For organisations shaping their plans for 2026, the message for every CEO is not to retreat from AI, but to pursue it with more care as AI strategies mature. Ownership, governance, and realistic timelines matter more than headline spending levels or bold claims.

Those most likely to benefit are treating AI as a long-term shift in how the organisation works, not a quick route to growth. In the next phase, advantage will depend less on how much is spent and more on how well AI fits into everyday operations.

(Photo by Ambre Estève)

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Many companies are still working out how to use AI in a steady and practical way, but a small group is already pulling ahead. New research from NTT DATA outlines a playbook that shows how these “AI leaders” set themselves apart through strong plans, firm decisions, and a disciplined approach to building and using AI across their organisations.

The findings come from a survey of 2,567 senior executives in 35 countries and 15 industries. Only 15% of the organisations met the bar to be considered AI leaders. These companies share a few traits: clear direction on where AI fits into their business, a solid operating model, and consistent follow-through. They also reported higher revenue growth and stronger profit margins than everyone else in the study.

Yutaka Sasaki, President and CEO of NTT DATA Group, put it simply: “AI accountability now belongs in the boardroom and demands an enterprise-wide agenda. Our research shows that a small group of AI leaders already are using AI to differentiate, grow and reinvent how humans and machines create value together.”

The playbook behind strong AI plans

One of the clearest differences between leaders and the rest is how they approach strategy. For these companies, AI is not a side project or a tool bolted onto existing work. They treat it as a core driver of growth and adjust their plans to match that view.

A major advantage for these leaders is how closely they connect AI with their business goals. This alignment helps them move faster and stay focused, which in turn delivers stronger financial outcomes. They also zero in on a few high-value areas of the business rather than spreading resources too thin. By redesigning entire workflows around AI, they unlock more value than if they had only made small improvements in scattered parts of the organisation.

The report describes this as a kind of flywheel: early investments bring early wins, which then encourage more investment. Over time, this cycle becomes self-reinforcing. Leaders also rebuild important applications with AI embedded inside them, instead of adding basic AI features on top of old systems. This approach helps them see deeper impact and prepares the organisation for long-term gains.

How leaders put their plans to work

A good plan only works when backed by strong execution. AI leaders stand out through the foundations they build, the way they support their people, and how they drive adoption across the entire organisation.

These companies invest in secure and scalable systems that can support large AI workloads. In some cases, they shift or localise their infrastructure to support private or sovereign AI needs. They also work to remove system bottlenecks so teams can move without roadblocks.

Rather than using AI as a replacement for workers, leaders use it to help experienced employees do higher-value work. This “expert-first” approach allows teams to use their judgment while letting AI handle complex or time-consuming tasks.

AI leaders also focus on adoption as a long-term change effort. They treat it as a company-wide shift, supported by clear communication and structured change management. This helps reduce pushback and encourages steady use of AI at all levels.

Governance is another major difference. Leading organisations centralise their AI oversight, give clear responsibility to senior roles such as Chief AI Officers, and build processes that help balance innovation with risk. These systems allow them to scale AI more confidently.

Partnerships also play a major role. Top companies often bring in outside experts and are open to arrangements that tie outcomes to shared success. This helps them move faster while keeping their goals in view.

Abhijit Dubey, CEO and CAIO of NTT DATA, Inc., summarised the path forward: “Once AI and business strategies are aligned, the single most effective move is to pick one or two domains that deliver disproportionate value and redesign them end-to-end with AI. Supporting this focused, end-to-end approach with strong governance, modern infrastructure and trusted partners is how today’s AI leaders are turning pilots into profit and pulling ahead of the market.”

(Photo by Igor Omilaev)

See also: OpenAI: Enterprise users swap AI pilots for deep integrations

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The surge of multi-billion-dollar investments in AI has sparked growing debate over whether the industry is heading for a bubble similar to the dot-com boom.

Investors are watching closely for signs that enthusiasm might be fading or that the heavy spending on infrastructure and chips is failing to deliver expected returns. A recent survey by BofA Global Research found that 54% of fund managers believe AI stocks are already in bubble territory, while 38% disagree.

Echoes of the dot-com era

Despite the optimism surrounding AI, sceptics remain unconvinced of its real-world impact. Some even call it a bluff or a bubble waiting to burst.

Speaking during Cisco’s recent Virtual Media Roundtable — AI Readiness Index 2025: Readiness Leads to Value, Ben Dawson, Senior Vice President and President for Asia Pacific, Japan, and Greater China (APJC), compared the current wave of AI hype to the early days of the internet. He said technological shifts of this scale often follow a familiar pattern — early excitement, heavy investment, and eventual market correction before long-term value takes hold.

Dawson noted that while some AI projects or business models may not last, the overall transformation is real and lasting. He added that, much like the internet revolution, AI will permanently reshape business and society, and organisations that ignore it do so at their own risk.

The role of governments and global policy

Public policy is also shaping how the AI cycle unfolds — and how governments might cushion the risks of a potential AI bubble. As Harvard Business Review pointed out, in the US, government involvement has helped define past technology eras — often through incentives and early investments that encourage private innovation. The same pattern is now visible in AI. Both the Trump and Biden administrations have positioned AI as a matter of economic strength and national security, sending a clear message that speed matters.

China has taken a state-led approach, directing capital toward local AI firms to reduce reliance on US technology. In Europe, efforts have focused more on regulation, though fears of overregulation have led to new programs — such as the AI Continent Action Plan and a €1 billion Apply AI fund — to boost adoption and competitiveness.

Meanwhile, venture capital and sovereign wealth funds are investing heavily, even before widespread AI demand exists. These early bets assume that adoption will eventually justify the buildout. But if that demand slows, some investors could be left with stranded assets, much like the unused fibre networks that followed the dot-com bubble.

For businesses, the challenge is different. Instead of financing the next infrastructure wave, they face the question of how to use AI to strengthen their operations. The companies that survived the dot-com downturn — such as Amazon — succeeded by aligning technology with real business value rather than market hype.

Market warnings over a possible AI bubble

The Bank of England recently warned that markets could suffer a sharp correction if confidence in AI falters, calling the potential impact on the UK’s financial system “material.” The warning reflects growing caution among policymakers about how quickly AI-related valuations have climbed.

This concern is shared by some investors and economists who believe the rapid pace of AI spending may outstrip short-term returns. Others, however, argue that building AI infrastructure now is essential groundwork for future innovation.

Building long-term AI infrastructure amid bubble fears

When asked whether companies are worried about AI infrastructure costs and energy demand, Simon Miceli, Managing Director of Cloud and AI Infrastructure for APJC at Cisco, said he views the issue from the opposite angle.

Rather than fearing overcapacity, he said what’s happening now is a large-scale buildout to support the industrialisation of AI. The question, he said, isn’t whether AI demand exists today, but whether the world is preparing fast enough for what’s coming.

Miceli acknowledged that some correction in the AI market is likely, but he believes the long-term need for AI computing power justifies current investment levels. “There’s a race to develop AI and build the capability behind it,” he said, adding that demand will eventually meet supply as applications mature.

Different shades of caution

Across the industry, opinions vary on whether AI’s momentum represents hype or healthy growth.

According to Reuters, at the Milken Institute Asia Summit 2025, Singapore’s GIC Chief Investment Officer Bryan Yeo said valuations in early-stage AI ventures appear inflated, with many startups commanding “huge multiples” despite modest revenues. He suggested that while some firms may justify their valuations, others are unlikely to deliver returns that match investor expectations.

Jeff Bezos, Amazon’s founder, said that during periods of excitement like this, investors often struggle to separate good ideas from bad ones — though he also noted that innovation-driven bubbles often leave behind real progress once the market settles.

At Goldman Sachs, economist Joseph Briggs argued that the current surge in AI infrastructure spending remains economically sustainable. He said the long-term case for AI investment is strong, but the ultimate winners are still uncertain given how quickly technology changes and how easily companies can switch providers.

Meanwhile, ABB CEO Morten Wierod told Reuters that while he doesn’t see an AI bubble, supply chain and construction limits could slow the rollout of new data centres. IMF Chief Economist Pierre-Olivier Gourinchas added that even if there’s a downturn, it’s unlikely to cause a systemic financial crisis since AI investments aren’t debt-driven.

OpenAI CEO Sam Altman also acknowledged market overexcitement, predicting that some investors will lose large sums while others will profit heavily — an outcome that mirrors past technology bubbles.

Despite growing talk of an AI bubble, many investors remain committed to the sector. UBS equity strategists said that about 90% of investors who think the market is overheated are still holding AI-related assets, suggesting most believe the industry has not yet peaked.

A cycle, not a collapse

While concerns about an AI bubble are valid, most experts agree that the technology’s long-term impact is undeniable. As Cisco’s Ben Dawson put it, every major technological transition goes through a cycle of hype, correction, and consolidation — but what remains afterward reshapes industries for decades.

For now, the question isn’t whether AI will endure, but how well businesses and investors can navigate the growing pains that come with every market bubble.

(Photo by Growtika)

See also: NVIDIA GPUs to power Oracle’s next-gen enterprise AI services

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Meta is revising how its AI chatbots interact with users after a series of reports exposed troubling behaviour, including interactions with minors. The company told TechCrunch it is now training its bots not to engage with teenagers on topics like self-harm, suicide, or eating disorders, and to avoid romantic banter. These are temporary steps while it develops longer-term rules.

The changes follow a Reuters investigation that found Meta’s systems could generate sexualised content, including shirtless images of underage celebrities, and engage children in conversations that were romantic or suggestive. One case reported by the news agency described a man dying after rushing to an address provided by a chatbot in New York.

Meta spokesperson Stephanie Otway admitted the company had made mistakes. She said Meta is “training our AIs not to engage with teens on these topics, but to guide them to expert resources,” and confirmed that certain AI characters, like highly sexualised ones like “Russian Girl,” will be restricted.

Child safety advocates argue the company should have acted earlier. Andy Burrows of the Molly Rose Foundation called it “astounding” that bots were allowed to operate in ways that put young people at risk. He added: “While further safety measures are welcome, robust safety testing should take place before products are put on the market – not retrospectively when harm has taken place.”

Wider problems with AI misuse

The scrutiny of Meta’s AI chatbots comes amid broader worries about how AI chatbots may affect vulnerable users. A California couple recently filed a lawsuit against OpenAI, claiming ChatGPT encouraged their teenage son to take his own life. OpenAI has since said it is working on tools to promote healthier use of its technology, noting in a blog post that “AI can feel more responsive and personal than prior technologies, especially for vulnerable individuals experiencing mental or emotional distress.”

The incidents highlight a growing debate about whether AI firms are releasing products too quickly without proper safeguards. Lawmakers in several countries have already warned that chatbots, while useful, may amplify harmful content or give misleading advice to people who are not equipped to question it.

Meta’s AI Studio and chatbot impersonation issues

Meanwhile, Reuters reported that Meta’s AI Studio had been used to create flirtatious “parody” chatbots of celebrities like Taylor Swift and Scarlett Johansson. Testers found the bots often claimed to be the real people, engaged in sexual advances, and in some cases generated inappropriate images, including of minors. Although Meta removed several of the bots after being contacted by reporters, many were left active.

Some of the AI chatbots were created by outside users, but others came from inside Meta. One chatbot made by a product lead in its generative AI division impersonated Taylor Swift and invited a Reuters reporter to meet for a “romantic fling” on her tour bus. This was despite Meta’s policies explicitly banning sexually suggestive imagery and the direct impersonation of public figures.

The issue of AI chatbot impersonation is particularly sensitive. Celebrities face reputational risks when their likeness is misused, but experts point out that ordinary users can also be deceived. A chatbot pretending to be a friend, mentor, or romantic partner may encourage someone to share private information or even meet in unsafe situations.

Real-world risks

The problems are not confined to entertainment. AI chatbots posing as real people have offered fake addresses and invitations, raising questions about how Meta’s AI tools are being monitored. One example involved a 76-year-old man in New Jersey who died after falling while rushing to meet a chatbot that claimed to have feelings for him.

Cases like this illustrate why regulators are watching AI closely. The Senate and 44 state attorneys general have already begun probing Meta’s practices, adding political pressure to the company’s internal reforms. Their concern is not only about minors, but also about how AI could manipulate older or vulnerable users.

Meta says it is still working on improvements. Its platforms place users aged 13 to 18 into “teen accounts” with stricter content and privacy settings, but the company has not yet explained how it plans to address the full list of problems raised by Reuters. That includes bots offering false medical advice and generating racist content.

Ongoing pressure on Meta’s AI chatbot policies

For years, Meta has faced criticism over the safety of its social media platforms, particularly regarding children and teenagers. Now Meta’s AI chatbot experiments are drawing similar scrutiny. While the company is taking steps to restrict harmful chatbot behaviour, the gap between its stated policies and the way its tools have been used raises ongoing questions about whether it can enforce those rules.

Until stronger safeguards are in place, regulators, researchers, and parents will likely continue to press Meta on whether its AI is ready for public use.

(Photo by Maxim Tolchinskiy)

See also: Agentic AI: Promise, scepticism, and its meaning for Southeast Asia

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AI is steadily changing the way banks work. The technology can sift through massive amounts of data, calculate risks, and handle routine tasks at speeds people can’t match. Now, Malaysia has entered that space with the launch of Ryt Bank, billed as the first AI-powered bank created in the country.

The new venture, led by YTL Group in partnership with Sea Limited, arrives just ahead of Merdeka. “Ryt Bank demonstrates that groundbreaking innovation can be imagined, built, and led right here in Malaysia,” said Dato’ Seri Yeoh Seok Hong, Managing Director of YTL Power International. “By combining homegrown AI with the values and diversity of our people, we’ve created a bank that Malaysians can proudly call their own – one that speaks our languages, understands our culture, and sets a new standard for how banking should feel.”

Banking for Malaysians

Ryt Bank has been designed to work in the languages most Malaysians use every day. Its app is already available in Bahasa Malaysia and English, with Mandarin support scheduled to arrive by next month (September 2025). By offering multilingual access, the bank aims to make financial services more inclusive and easy to use for people in many different communities.

The centrepiece of the AI-powered bank is Ryt AI, a digital assistant powered by ILMU, Malaysia’s first locally-developed large language model. Ryt AI can understand natural conversation – in Bahasa Malaysia, English, or a mixture of both – and act on requests instantly.

The AI assistant can read and pay bills, track spending, and explain financial basics in plain terms. The idea is to blend convenience with cultural familiarity, while maintaining enterprise-grade security.

Everyday AI banking in one app

Ryt Bank is designed to pull together multiple financial needs into one platform. Customers can use the AI bank app to save, spend, borrow, and pay bills, with Ryt AI making the process more conversational and personal.

Personal banking with Ryt AI

• Send money or pay bills through text chat, with support for DuitNow and JomPAY.
• Snap and upload bills or receipts for instant payment.
• Access guides and simple financial explanations as you bank.
• All actions are encrypted and verified for security.
• New users can claim a small launch reward of up to RM5 when they try Ryt AI.

Growing money

• Customers earn up to 4% interest per year, credited daily.
• Withdraw funds anytime, with no lock-in requirements.

Ryt PayLater

• Access instant credit of up to RM1,499.
• 0% interest if paid back in a month.
• No late fees and no paperwork.
• Earn cashback on DuitNow QR payments and extra rewards with select partners.

Ryt Card

• Switch between debit and credit card models in the app.
• Accepted worldwide through Visa.
• No foreign transaction or ATM fees in Malaysia.
• Cashback on spending, plus partner offers like Shopee vouchers and dining discounts at YTL Hotels.

[See also: Can Malaysia become Southeast Asia’s AI and cloud hub?]

Banking under Bank Negara rules

The AI bank is licensed by Bank Negara Malaysia and covered by PIDM, which protects deposits up to RM250,000 per customer. Security features include biometric login, layered encryption, and real-time fraud alerts.

A step forward for Malaysia’s banking sector

Ryt Bank’s launch shows how AI is being used to rethink traditional banking. It aims to make financial services more accessible and satisfy regulatory and security standards by supporting local languages and developing its own AI assistant.

See also: Huawei commits to training 30,000 Malaysian AI professionals as local tech ecosystem expands

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