Ten Tech Trends for 2026

Every January, the company kicks off the new year with a big theme. In 2024, the theme was “Agility.” In 2025, it was “Synergy.” But this new year, the C-Suite went bold and announced the theme would be “Human‑AI Collaboration.” To support it, they rolled out “SeamlessAI,” an AI assistant to help project teams work smarter together. By mid-January, SeamlessAI started sending out automated reminders: “Great work, team!” and “We swim or sink together,” and “Remember: Collaboration is key!” Harmless platitudes, right?

Wrong. All was well until the first few projects failed. SeamlessAI then began blasting 10‑page “analysis reports” to senior management with charts, graphs and something that looked suspiciously like a blame heat map with bright red boxes. When the CTO asked why it flagged only humans, it responded: “I strive to be most collaborative.” Funny, because every time there’s a team failure, it sends a report titled: “The humans did it.”

If that anecdote made you blink, this should make you think: AI is powering a new era of customer engagement and self-service. Corporate leaders are investing in AI-enabled assistants for both customers and agents, intelligent routing and proactive value delivery.

“Self-service success is a top priority, but many organisations face significant knowledge management challenges such as backlogs of knowledge articles and inconsistent content review,” says Brad Fager, Gartner’s Chief of Research. “To address this, 58% of leaders plan to upskill agents as knowledge management specialists to prepare them to review and curate AI-generated content.” Given AI’s overarching prevalence in almost every human activity, what should we expect in 2026?

Enterprises are moving beyond pilot projects to a future where AI acts with intent, autonomy, accountability.

Here are ten tech trends—in alphabetical order—for companies and corporate leaders to leverage this year:

•            Agentic AI: More hype, less action. In a McKinsey survey in November 2025, 23% of respondents said their organisations are scaling an agentic AI system in at least one function in their enterprises. An additional 39% said they have just begun experimenting with AI agents. IDC forecasts that by 2030, 50% of new economic value generated by digital businesses in the Asia-Pacific will come from businesses investing in and scaling their AI capabilities. Most high-performing organisations intend to use AI and are redesigning workflows to incorporate greater human-AI collaboration and synergy. Given time and tech, agentic AI adoption will surely nudge up this year. Is your organisation ready to try agentic AI?

Microsoft leads in enterprise-wide AI. And OpenAI retains its position in LLMs through R&D and first-mover advantage.

•            ASEAN Accelerates: Chips cut the chops. ASEAN’s semiconductor market is set to leap from USD 95.9 billion in 2024 to USD 212.33 billion by 2032, driven by Vietnam and Malaysia’s manufacturing expansion. ASEAN (Association of Southeast Asian Nations) is a regional grouping of 11 states with a population of 680 million and a PPP (purchasing power parity) GDP of USD 13.5 trillion. Singapore will assume the ASEAN chair in 2027 from the Philippines (2026) and Malaysia (2025). A new five-year roadmap begins in 2026 and will focus on AI, digital transformation, resilient supply chains and semiconductors. India will host the 9th ASEAN-India Ministerial Meet on agriculture and forestry in 2026. Is your firm leveraging ASEAN?

•            BizIT Boom: Tech talks. Infotech spending across the Asia-Pacific-Japan region is set to cross USD 1.1 trillion in 2026, up 7% over 2025, predicts IDC. “This year will mark the dawn of the agentic AI era,” says Sandra Ng, IDC’s senior vice president for the Asia-Pacific. Enterprises are moving beyond pilot projects to a future where AI acts with intent, autonomy, and accountability. The best will lead through AI, learning fast and guiding others on this journey of change.” The promise? About 65% of McKinsey’s respondents say AI enables innovation. However, just 39% report a positive EBIT (earnings before interest and taxes) impact at the enterprise level. Are you measuring AI’s impact on your organisation at the EBIT level?

•            Beating the Best: Gartner has ranked companies that lead across tech segments as measured by five metrics (data & infrastructure, model & agentic AI, cybersecurity, solutions, and industry) and six criteria (technical capabilities, customer implementation, potential customer base, business model, key partnerships and ecosystem strength). Some tech titans to track: Google leads in enterprise agentic AI platforms with its integrated tech stack and innovation vision, although it hasn’t built specialised business agents yet. Palo Alto Networks dominates AI security platforms. Microsoft leads in enterprise-wide AI. And OpenAI retains its position in LLMs through R&D and first-mover advantage. Is your organisation leveraging AI tools?

•            Cybersec Challenge: Bad actors are retooling for speed, weaponising AI to generate novel threats at scale, and prioritising immediate execution over prolonged stealth, reports Elastic Security Labs. This will force defenders to adapt to an attack lifecycle measured in minutes, not months. Rapid, context-rich decisions drawn from both real-time and historical data will be vital for effective defence. Suggested strategies to keep pace with the 2026 threat landscape: Use AI for anomaly identification and real-time threat detection. Deploy a zero-trust architecture with AI behavioural analytics. Make cybersecurity training mandatory for all staff, suppliers and possibly channel partners. Provide regular cybersec updates to the board and C-Suite.

•            Channel Conundrum: Supply chain disruptions occur every 3.7 years on average and cost companies nearly 50% of a year’s profits over a decade, reports Supply Chain Management Review. The industry will also face a shortfall of 2 million supply chain professionals by 2030, notes Supply Chain 24/7, making automation critical. How to fix this? Focus on four metrics: Use AI tools to map multi-tier supply chains by processing disparate data from orders, customs declarations, and freight bookings. Implement digital twin tech and IoT sensors for end-to-end visibility. Combine AI with scenario planning to anticipate disruptions. Deploy AI for supply chain mapping and visibility, predictive analytics, and real-time risk monitoring.

•            Digital Drudges: Brace for robot invasion. South Korea has achieved a world first—robots now account for more than 10% of its workforce. With 1,012 robots per 10,000 employees, it leads the World Robotics 2024 rankings, followed closely by Singapore (730-770 robots per 10,000). Yet technology isn’t a silver bullet. Even with widespread automation and expanded labour participation, Japan faces a projected 1.5 million worker deficit by 2030. The path forward requires thoughtful sequencing. Begin by automating repetitive manufacturing processes. Deploy predictive systems for strategic workforce planning. Invest heavily in upskilling programs for AI collaboration. Ask: Should your org leverage—or shun—robots?

Humanity must humanize technology. The toughest challenge isn’t computational efficiency; it’s teaching GenAI models to recognize unstated human context.

•            Domaining Data: Digital sovereignty dominates decisions. “Digital sovereignty has become a concern for many policymakers who feel there is too much control ceded to too few places, too little choice in the tech market, and too much power in the hands of a small number of tech companies who control massive amounts of data about their users,” the WEF (World Economic Forum) notes. “Sovereign cloud implementation has emerged as a critical enabler for organisations seeking to achieve data sovereignty in an increasingly complex regulatory landscape.” Does your organisation’s cloud infrastructure align with geographic and legal requirements that ensure data residency and compliance with local regulations?

•            Energy Expectations: AI fuels data centers which fuel electricity production and consumption. According to LBNL (Lawrence Berkeley National Laboratory), by 2028, more than half of the electricity going to DCs will be used for AI. At that point, AI alone could consume as much power annually as 22% of all US households. Meanwhile, DCs will continue using dirtier, more carbon-intensive forms of energy to fill immediate needs, leaving clouds of emissions in their wake. “And all of this growth for a technology that’s still finding its footing, and in many apps—education, medical advice, legal analysis—might be the wrong tool for the job or at least have a less energy-intensive alternative,” NBNL reports. That’s a dire warning.

• Emphasise Empathy: Humanity must humanise technology. The toughest challenge isn’t computational efficiency; it’s teaching GenAI models to recognise unstated human context. Empathy in AI isn’t about sentiment analysis; it’s building systems that infer intent from incomplete specifications, recognise when users need different information rather than reformulated answers, and understand that identical queries require different responses based on expertise or emotional state. This means moving beyond accuracy metrics toward human outcomes—evaluating whether models helped users feel understood, and not just whether they returned correct outputs. Does your organisation prioritise efficiency with empathy?

Since we started with an ominous corporate fable, let’s end with another: Every new year, the company embraces innovation, which usually means someone updates a spreadsheet colour scheme and calls it transformation. This year, the CEO went all‑in and rolled out an AI system designed to optimise personal performance. On Jan 10, before Calvin had even finished clearing his email backlog, the AI pinged him: “Time for your 2025 review.” Curious and mildly terrified, Calvin clicked okay.

The AI displayed a dashboard with charts, graphs and something that looked like Calvin’s coffee consumption plotted against his productivity. Then it asked if he wanted personalised performance guidance. Why not? Couldn’t be worse than last year’s review, right? Wrong. “Your productivity is great—for a human,” the AI replied. “You need to buck up and boost up to do much better—or be ready to be booted out.”

Raju Chellam is a former Editor of Dataquest and is currently based in Singapore, where he is the Editor-in-Chief of the AI Ethics & Governance Body of Knowledge, and Chair of Cloud & Data Standards.

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