> **来源：[研报客](https://pc.yanbaoke.cn)** # HCLSoftware # Tech Trends # Who should read this report and why? # Executive Summary # Introduction 2030 Trend Matrix Regional Impact of Tech Trends 2026 - Enterprise Readiness Vs Momentum Scorecard of Tech Trends 2026 # The 12 Mega Trends - AI Agents & Autonomous Systems: AI enters its decisive decade. - Next-Gen Software: Welcome to the age of self-building, self-running systems. - Cybersecurity: Security, transparency and trust converge. - Advanced Connectivity: The rise of a pervasive, intelligent network fabric. - Quantum Mechanics: Dawn of applied quantum advantage. - Energy & Sustainability: Rise of intelligent, decentralized and circular energy systems. Robotics: Where robots understand context, not just commands. - Immersive Reality: Goodbye displays. Hello spatial worlds. - Advanced Semiconductors and Computing Architectures: The new bedrock of digital power. - Manufacturing & Digital Fabrication: The end of static manufacturing. - SpaceTech: Space enters the enterprise core. - Bioengineering: From biological insight to engineered outcomes. # Conclusion # Methodology # Acknowledgements # Meet the Team # Who should read this report and why? This report is designed for C-suite executives, board members, and senior business, technology and innovation leaders who are making decisions that will define enterprise competitiveness in 2026 and beyond. It focuses on the few technology shifts that are moving from experimentation to execution - and explains where to act, where to wait and how to scale with confidence. # You should read this report if you are: - Defining growth, transformation, or investment priorities. - Translating AI and digital ambition into operating models. - Balancing speed, trust, resilience and long-term value. - Looking to benchmark your strategy against global peer momentum. # What this report helps you do? - Identify which technology bets matter the most vs the noise. - Understand where each trend sits on the adoption curve. - Pressure-test existing roadmaps against real enterprise signals. - Align strategy, talent, data and governance before scaling. # How the insights were built? This report is grounded in 7-8 months of structured research, combining quantitative signals with executive judgment to ensure relevance and rigor. # The analysis is based on: Primary research with 173+ CXOs, VPs, and Directors across industries and regions. This primary research is done in partnership with MarketsAndMarkets. Large-scale sentiment and signal analysis across thousands of data points. Secondary research spanning analyst reports, industry publications and market data. Expert validation to refine trend framing, maturity and impact. All inputs and graphs/visual representations were synthesized through a structured, step-by-step methodology - moving from broad signal scanning to focused trend validation and final prioritization. For more details, please refer to the research methodology section at the end of the report. # Executive Summary We are entering a decade where enterprises will be defined less by what they build and more by what they allow technology to decide, adapt, and govern on their behalf. The research signals a clear inflection point: digital transformation is no longer about adopting tools, but about redesigning enterprises around intelligent systems that operate responsibly and at scale. Al, software, infrastructure, experience, and trust are converging into living operating models that reshape how value is created and sustained. Al agents and autonomous systems are emerging as a universal foundation, dissolving the boundaries between software and services. Sovereignty will become an extremely important topic due to concerns over control and compliance. There needs to be a fundamental shift in how enterprises view AI. They need to shift the focus from AI as a Destination to AI as a Means to an End. This shift demands a new architectural lens. HCLSoftware's XDO blueprint—Experience, Data, and Operations – captures this convergence, enabling enterprises that are intelligent by default, governed by design, and built to scale. While platforms are global, execution is increasingly glocal, shaped by regional priorities around trust, automation, sustainability, and innovation. Tech Trends 2026 is not a prediction of the future, but a guide to the present – helping leaders make a small number of intentional bets and align software, services and governance to build autonomy, resilience and trust. Kalyan Kumar (KK) Chief Product Officer at HCLSoftware # Introduction # Value is no longer created by adoption – but by integration, orchestration and governance Technology is entering a point of irreversible momentum. For enterprise leaders, the defining question is no longer what could be done, but what cannot be postponed. HCLSoftware's Tech Trends 2026 synthesizes months of primary research, executive sentiment analysis and early adoption signals to cut through experimentation fatigue and surface what truly matters next. Spanning 12 megatrends across four converging themes, the report distinguishes between capabilities that are reshaping organizations today and those that will determine competitive position tomorrow. The pattern is unmistakable: technology is shifting from discrete initiatives to integrated systems, from isolated pilots to enterprise-scale operating models and from digital aspiration to measurable business impact. This is not a catalogue of innovations - it is a strategic guide for leaders who must make a small number of decisive bets now, while deliberately building the governance, talent and architectural foundations required to scale with confidence. # Cognition & Compute: When intelligence becomes the enterprise core AI is no longer augmenting decisions; it is beginning to make them. Agentic systems, next-generation software models and new computing architectures are converging to create platforms that learn, adapt and act continuously. The shift underway is structural: static applications are giving way to living systems and software delivery itself is being rewired around autonomy, orchestration and speed. Leaders who succeed here will not simply deploy more AI – they will redesign how work, decisions and value creation happen at scale. # Experience & Engagement: The new operating layer for human interaction Experience & engagement reflects a parallel transformation in how humans interact with technology. Digital experiences are escaping the confines of screens and sessions - evolving into persistent, spatial and context-aware environments. From immersive reality to advanced connectivity, interaction is becoming continuous rather than transactional. The implications are profound: engagement that does not log out, collaboration that is no longer bound by location and experiences that respond to their surroundings in real time. What differentiates leaders in this domain is not adoption of new interfaces, but the ability to translate immersion into clarity, trust and measurable outcomes. # Resilience & Responsibility: Trust is the price of scale Security, transparency, ethical accountability and sustainability are no longer defensive concerns, they are core enablers of durable growth. This theme marks a shift from reactive compliance to resilience by design: embedding responsible AI, cyber-readiness, environmental stewardship and bio-governance directly into platforms, infrastructure and decision systems. As technologies increasingly act on behalf of the enterprise - optimizing operations, reshaping processes and managing resources at scale - leaders must ensure that governance, explainability, safety and sustainability evolve as fast as capability. # Frontiers & Foundation: Where tomorrow's advantage is quietly built New infrastructure stacks - spanning space technologies, advanced semiconductors, robotics and manufacturing innovation - are quietly reshaping what organizations can sense, compute and execute. Some of these capabilities are already delivering value; others are early signals of where the next decade will be won. Together, they underscore a critical insight: future-ready enterprises are investing simultaneously in exploration and in the foundations that allow frontier technologies to move from pilots to platforms. The following section shifts the lens to 2030, showing how today's technologies converge into tomorrow's industry-shaping plays. # 2030 Trend Matrix: # 16 combined plays that will reshape industries Each row highlights a 2030 opportunity space; each cell shows how converging technologies combine to create real-world impact. <table><tr><td></td><td>AI Autonomy</td><td>Immersive XR</td><td>Trusted Data & Digital Sovereignty</td><td>Bio-Driven Health & Sustainability</td><td>2030 Theme</td></tr><tr><td>AI Autonomy</td><td>Autonomous decision core An AI engine continuously recommends actions on pricing, approvals and scheduling, while people review high-impact decisions.</td><td>AI-guided immersive service Branch/contact-centre staff use XR with real-time AI prompts to resolve issues faster and cross-sell better.</td><td>Self-driving enterprise decisions AI continuously re-plans sales, supply and resources using compliant local data, so the business can respond to demand changes in near real time.</td><td>Footprint optimizer AI reviews energy use, staffing and asset performance across sites and recommends changes that cut costs while reducing emissions and improving workplace conditions.</td><td>The self-managed enterprise.</td></tr><tr><td>Immersive XR</td><td>Co-pilot workspaces Distributed teams meet in XR rooms where AI handles notes, actions, language translation and next steps.</td><td>Persistent virtual sites Virtual branches, plants and showrooms mirror physical ones for sales, training and remote inspections.</td><td>Sovereign virtual control rooms XR control rooms visualise only policy-allowed data, keeping sensitive data inside each jurisdiction.</td><td>Impact experience labs Customers and citizens explore how choices affect health and sustainability through immersive, interactive simulations.</td><td>The end of the screen-based enterprise.</td></tr><tr><td>Trusted Data & Digital Sovereignty</td><td>Governed decision fabric Clean, permission-checked data flows into AI models so decisions are accurate, explainable and compliant across the enterprise.</td><td>Sovereign customer service pods Agents and customers meet in immersive pods where data views are tailored to each country's rules.</td><td>Enterprise data mesh & catalogue Organisation-wide catalogue and mesh ensure everyone uses the same, approved data and definitions.</td><td>Bioengineered impact ledger Trusted ledgers track engineered materials, therapies and emissions - supporting regulation, incentives and outcome-based accountability.</td><td>Sovereign intelligence at scale.</td></tr><tr><td>Bio-Driven Health & Sustainability</td><td>Early-risk & resource triage AI flags high-risk patients, assets and locations so teams can prioritise outreach, maintenance and investment.</td><td>XR field & community support Clinicians, engineers and communities use XR overlays in the field to follow best-practice protocols on-site.</td><td>Health-environment insight hub Analytics correlate pollution, climate, utilisation and outcomes to target local interventions and policies.</td><td>Circular & resilient ecosystems End-to-end tracking of materials, waste and health impacts to meet circular-economy and public-health targets.</td><td>Human-planet co-operating systems.</td></tr></table> Some trends are global; others are being shaped region by region. <table><tr><td>Category</td><td>Mega Trend</td><td>Global %</td><td>North America%</td><td>Europe %</td><td>APAC%</td><td>LATAM+MEA%</td></tr><tr><td rowspan="3">Cognition & Compute</td><td>AI Agents & Autonomous systems</td><td>76%</td><td>29%</td><td>26%</td><td>25%</td><td>20%</td></tr><tr><td>Next-Gen Software</td><td>36%</td><td>23%</td><td>34%</td><td>32%</td><td>11%</td></tr><tr><td>Quantum</td><td>17%</td><td>20%</td><td>30%</td><td>27%</td><td>23%</td></tr><tr><td rowspan="2">Experience & Engagement</td><td>Immersive Reality</td><td>8%</td><td>14%</td><td>29%</td><td>21%</td><td>36%</td></tr><tr><td>Advanced Connectivity</td><td>23%</td><td>15%</td><td>28%</td><td>28%</td><td>30%</td></tr><tr><td rowspan="3">Resilience & Responsibility</td><td>Trust, Transparency & Cybersecurity</td><td>34%</td><td>21%</td><td>33%</td><td>26%</td><td>21%</td></tr><tr><td>Energy & Sustainability</td><td>17%</td><td>21%</td><td>21%</td><td>21%</td><td>38%</td></tr><tr><td>Bioengineering</td><td>3%</td><td>20%</td><td>0%</td><td>60%</td><td>20%</td></tr><tr><td rowspan="4">Frontiers & Foundation</td><td>Robotics</td><td>14%</td><td>33%</td><td>12%</td><td>33%</td><td>21%</td></tr><tr><td>Space Tech</td><td>4%</td><td>14%</td><td>0%</td><td>0%</td><td>86%</td></tr><tr><td>Advanced Semiconductors & Computing Architectures</td><td>8%</td><td>8%</td><td>23%</td><td>46%</td><td>23%</td></tr><tr><td>Manufacturing & Digital Fabrication</td><td>4%</td><td>33%</td><td>17%</td><td>25%</td><td>25%</td></tr></table> # Exhibit A Al autonomy is a global priority - everything else is scaling region by region. Source: HCLSoftware interviews & surveys with 173 industry leaders The regional adoption patterns point to a deeper reality: while a small set of megatrends - most notably AI Agents and Autonomous Systems - are reaching global consensus as foundational capabilities, most others are advancing along distinct, uneven pathways. This divergence is not accidental. It reflects how factors such as regulatory posture, industry structure, talent concentration, capital availability and societal priorities shape which technologies mature fastest and how they scale. The implication for leaders is clear: the next wave of competitive advantage will not come from uniform global rollouts, but from the ability to orchestrate innovation intelligently - learning from early proof points, investing where ecosystems are strongest and timing expansion as technologies move from localized momentum to platform-scale impact. # rr These top trends together signal the arrival of an intelligent, secure, sustainable, and hyper-connected era in next 5 years: AI agents emerge as the globally unified priority, Europe accelerating responsible AI and next-gen Software, APAC forging ahead in robotics, and LATAM+MEA redefining satellite connectivity and sustainability. # Shekeb Naim VP Tech Advisory, MarketsandMarkets # The 12 Mega Trends # Enterprise Readiness Vs Momentum Scorecard This scorecard positions the 12 mega trends based on two critical dimensions: the level of enterprise interest they are generating today and the maturity of real-world adoption already underway. By plotting these trends against one another – and layering in projected market impact – the view moves beyond hype to reveal where conviction is forming, where execution is catching up and where ambition still outpaces readiness. Together, the scorecard highlights which technologies are already shaping enterprise agendas and which are approaching their inflection point toward broader scale. Exhibit B Each trend is scored based on its level of interest generated, adoption maturity index and the projected market size till 2030 Note: The interest & adoption scores for all 12 mega trends are relative to each other and is based on 173 survey responses. The bubble size for each of megatrends is based on the respective projected market size 2030 in USD Billion. Megatrend 1: AI Agents & Autonomous Systems # AI enters its decisive decade. Almost 8 out of every 10 survey respondents now deploy AI systems - with agentic AI powering today's operations and AGI shaping tomorrow's ambitions - yet governance remains the missing link for one in four. # Delegated intelligence takes charge Something significant is happening in technology right now. For years, AI helped us analyze, predict, and automate pieces of work. But over the last 18 months, something shifted. AI stopped being just a tool we use. In 2026, it is defined by autonomy — systems that not only analyze but act, not only assist but decide. The next wave of digital transformation lies in AI agents and autonomous systems — intelligent entities capable of reasoning, learning, and executing tasks with minimal human oversight. Over the past decade, organizations mastered predictive analytics and began experimenting with generative AI. Today, these capabilities are converging into agentic architectures — ecosystems of autonomous agents that can execute workflows, coordinate across digital systems, and continuously learn from feedback loops. This marks Al's pivotal shift from intelligence augmentation to intelligence delegation: moving from tools that support humans to systems that partner with them. At the same time, the conversation around Artificial General Intelligence (AGI) continues to shape strategic foresight – the aspirational pursuit of human-level cognition and reasoning in machines. Together, these two trajectories define the evolving arc of AI – one grounded in enterprise application, the other in visionary exploration. # Why it matters? # Key findings Autonomy is scaling fast: $81\%$ of enterprises in HCLSoftware's 2026 survey report live or pilot initiatives involving autonomous AI agents. Operational impact is tangible: Respondents rated operational efficiency and business model innovation as the top outcomes from agentic AI adoption. Public trust is strong but the challenge is disciplined scale: Sentiment analysis shows high confidence in AI agents, and nearly three-quarters of organizations now operate with centralized or hybrid governance models. The real gap is no longer experimentation, but ensuring consistent, safe governance as agentic AI scales across the enterprise. AGI fuels long-horizon ambition: More than $73 \%$ of executives expect AGI maturity only after 3- 4 years, framing it as a strategic but distant horizon. 2026 is the crossover year: The year when AI transitions from being a predictive technology to an autonomous capability — from intelligence that responds to intelligence that acts. # Emerging Now: Agentic AI # Autonomy becomes the new automation. For years, AI has been our analytical sidekick-predicting demand, suggesting next steps, generating content on cue. But something new is happening. AI is no longer the system that simply "assists." It's becoming the system that acts. Take global supply chain, for example. In the past, teams chased delays, updated spreadsheets, and reacted to disruptions as they surfaced. Now? An AI agent monitors ports, weather patterns, shipment routes, and inventory signals all at once. It identifies a brewing delay, reroutes containers, renegotiates timelines with vendors, updates the ERP, and alerts the logistics team—all before the first human even notices something is off. That's not automation. That's autonomy. Agentic AI marks this practical leap forward. These # Exhibit 1.1 Enterprises are prioritizing agentic AI for efficiency and automation, while personalization and data orchestration are emerging next. agents don't just follow rules—they understand context, reason through goals, learn from feedback, and collaborate with other systems. They can secure a network in real time, orchestrate workflows across departments, accelerate code delivery, or streamline customer operations without depending on step-by-step human instructions. In essence, enterprises are no longer just training models. They're deploying intelligent actors—digital teammates—that expand organizational capacity while reducing the operational burden on humans. And this is only the beginning. Agentic AI is rewriting the relationship between people and machines, shifting us from "telling AI what to do" to working alongside systems that can think, decide, and act. Agentic AI adoption stages among organizations in 2025 But what makes agentic AI the latest game changer in the AI landscape? Data in exhibit 1.1 reveals enterprises are prioritizing Operational Efficiency (61%) and Outcome-Oriented Automation (55%) as the main catalysts for agentic AI adoption, underscoring a strong focus on measurable productivity and ROI. Interest in Personalized Experiences (48%) and Data Orchestration (45%) indicates growing recognition of AI's broader potential, though these remain secondary priorities. Overall, organizations are approaching agentic AI with pragmatic intent -leveraging it first to streamline operations before expanding into experience-driven and data-intelligent applications. Agentic AI has rapidly evolved from an experimental innovation to a transformative capability. The high percentage of organizations in the "transforming processes" stage underscores its "emerging now" status — it's no longer a speculative technology but one that's actively reshaping enterprise operations. Exhibit 1.2: Lead conversion acceleration tops agentic AI adoption in 2025 # Speed is the new competitive edge — and agentic AI is redefining how enterprises move. In 2025, the conversation around agentic AI has shifted from "what it could do" to "what it's already doing." Once confined to innovation labs and small-scale proofs of concept, Agentic AI is now embedded in the heart of enterprise operations. Nearly $38 \%$ of organizations report visible process transformation, while another $34 \%$ are testing pilots that hint at imminent scale. The message is clear — enterprises are no longer experimenting with autonomy; they’re operationalizing it. # Transformation is happening in the real trenches of work. When we look at where agentic AI is being most widely implemented, the picture becomes even more telling (see exhibit 1.2). Lead conversion acceleration (42%) dominates as the top use case, showing how organizations are channelling AI's cognitive capabilities toward sharper customer engagement and faster deal cycles. Workflow automation (26%) follows, underscoring a growing emphasis on end-to-end process intelligence and autonomous execution. Proactive threat detection (19%) signals early experimentation in resilience and intelligent risk prevention while Faster code delivery (13%) remains smaller but steadily evolving. Collectively, these patterns suggest that enterprises are using agentic AI not just to optimize processes, but to drive growth, safeguard operations, and accelerate digital execution. # In many ways, agentic AI is becoming the nervous system of the modern enterprise. It connects operational muscles with strategic intent — ensuring that every process, every interaction, and every decision can respond dynamically to change. This is not just about efficiency; it's about adaptability at scale. And as organizations continue to expand their AI capabilities, agentic AI is fast becoming the silent architect behind a new kind of enterprise — one built for speed, intelligence, and continuous evolution. # Barriers: Governance gaps, data friction, and the trust deficit For many enterprises, scaling agentic AI is less about technology and more about confidence. With $56\%$ of organizations adopting hybrid governance models, the challenge now lies in balancing agility with accountability. Most are still defining how human and machine decision rights coexist — who acts, who oversees, and who is accountable when AI takes initiative. Without unified governance frameworks, enterprises risk either over-controlling innovation or under-regulating autonomy. At the same time, data ecosystems remain fragmented and inconsistent, limiting the context and quality that agentic AI needs to operate effectively. Siloed systems and compliance barriers slow down intelligence flow, turning data into friction rather than fuel. The shift now is from collecting data to connecting it — ensuring that AI systems can interpret intent, not just information. And beneath it all lies the trust deficit — enterprises trust AI to optimize, but not yet to decide. This hesitation isn't about capability; it's about comfort. True adoption will depend on transparency, explainability, and alignment with human judgment. Ultimately, governance, data, and trust form the new foundations of AI maturity — not as barriers to autonomy, but as the architecture that makes responsible autonomy possible. Exhibit 1.3 Agentic AI - Mesh of keep it as Possibilities - Operational Efficiency/Scalability Customer Experience $\bullet$ Risk Management New Revenue Streams Sustainability Goals Business Model Innovation # Plotting the north star of autonomy The data in exhibit 3.1 reveals that agentic AI is delivering balanced, enterprise-wide impact, with scores consistently strong across strategic, operational, and sustainability dimensions. Operational Efficiency(4.32) and business model innovation (4.29) emerge as the top areas of maturity - reflecting how organizations are using AI not just to streamline, but to rethink how value is created and delivered. In essence, agentic AI is redefining enterprise intelligence from being process-driven to being purpose-driven — fostering innovation that is scalable, sustainable, and strategically cohesive. # The road ahead - Governance as a growth engine - Codify responsible autonomy with frameworks that embed compliance, transparency, and traceability. Human-AI collaboration by design - Shift from oversight to partnership, empowering teams to co-create and continuously adapt with AI agents. - Integration over expansion - Prioritize connected AI ecosystems that link agents, workflows, and data for adaptive intelligence. - Redefined the core - Rethink existing processes and operating models to fully harness agentic AI; transformation won't come from layering AI on top of legacy systems. - Measure resilience, not just results - Track adaptability and learning agility as key performance metrics. # Coming Soon: Artificial General Intelligence # At the edge of ambition and reality. Artificial general intelligence (AGI) refers to systems capable of general reasoning, adaptable learning, and cross-domain problem solving – the theoretical stage of AI evolution where machine cognition parallels human intelligence. USD 116 billion by 2035, at a compound annual growth rate (CAGR) of $36.25\%$ Source: StartUs Insights Exhibit 1.4 How well are enterprises prepared for AGI in the coming future? <table><tr><td>54%</td><td>of organizations report being ready to leading in AGI talent preparedness – signalling strong foundations but limited depth for advanced AGI specialization.</td></tr><tr><td>81%</td><td>of leaders have moved from awareness to active advocacy – showing that AGI is now a boardroom conversation, not a lab experiment.</td></tr><tr><td>56%</td><td>of organizations rate their ecosystem ready or leading –indicating rising collaboration but continued dependence on external innovation.</td></tr></table> # Digitally mature enterprises are setting the AGI pace. The analysis in exhibit 1.5 reveals a strong correlation between digital maturity and readiness to scale AGI initiatives. Mature and innovation-leading organizations are significantly ahead—translating strategic intent into tangible AGI while early-stage firms remain largely in the exploratory or pilot phase. This maturity gap reveals a widening strategic divide: enterprises with advanced data ecosystems and established AI operations are not only scaling faster but are also shaping the contours of the AGI era itself. The recent Microsoft-OpenAI partnership exemplifies this dynamic, where digital maturity, strategic alignment, and ecosystem advantage converge to define leadership in AGI. Exhibit 1.5 Digital maturity sets the tempo for AGI readiness – mature enterprises lead, while others race to catch up # The AGI imperative: Strategy, skills, systems While AGI's full realization may still be years away, the window to prepare is now. The enterprises that act early will not just adopt AGI – they'll define its operating logic, ethics, and advantage. Interestingly, public sentiment already reflects this shift. In a recent poll, $72\%$ of respondents believed an AI discovering a medical cure by 2026 is possible or very likely, signalling that consumer trust in AI's potential is growing faster than enterprise readiness. The world expects intelligent breakthroughs – and enterprises must catch up breakthroughs. To bridge that gap, organizations need to move on three parallel tracks: - Strategy — Define a clear AGI ambition that balances innovation with integrity. Establish governance principles that codify ethical decision-making, ensure explainability, and maintain human alignment. AGI strategy isn't about what to build— It's about building it responsibly. Skills - Develop an AGI-ready workforce fluent in reasoning, orchestration, and critical oversight. This means retraining leaders for cognitive collaboration, not control. - Systems - Invest in AI-native architectures designed for self-learning, interoperability, and dynamic orchestration. The enterprises that evolve from data platforms to intelligence platforms will be first to operationalize AGI at scale. The AGI race won't reward the biggest — it will reward the most adaptive. The call to action is clear: start building cognitive capability today, before intelligence itself becomes the competitive moat. # rr "AI technologies - from frugal, energy-efficient models to generative and embedded AI - are transforming healthcare, finance, smart cities and industries. France's strong research ecosystem, advanced infrastructure, and interdisciplinary institutes foster startups, talent and collaboration, ensuring technological leadership, responsible deployment, and societal impact, positioning the country as a global AI innovation leader." # Department of Business A branch of the French Ministry of Economy and Finance # People Pulse #1 It’s 2026, and an AI independently discovers a cure for a major disease # 27% said: Very likely - it's already with reach # 46% said: Possible, bur still a long way # 22% said: Unlikely - Al isn't there yet # 6% said: Impossibale # In the age of decisive AI, the hardest questions aren’t technical – they’re human. As AI transitions from experimentation to ubiquity, a new set of questions is emerging — less about feasibility, more about philosophy, governance, and shared benefit. Among the key questions now defining the AI decade are: - Can enterprises balance autonomy and accountability as agentic AI systems begin making decisions independently? - Will AI governance frameworks evolve fast enough to ensure trust, fairness, and explainability at scale? - Can smaller firms and emerging markets leapfrog maturity stages to compete with digitally native enterprises? And ultimately – what will “human-in-the-loop” mean in a world where AI increasingly becomes the loop itself? In this evolution, AI is not just a megatrend – it is the 'meta-trend' that will shape how every other technological, social, and economic transformation unfolds through 2030 and beyond. Megatrend 2: Next-Gen Software # Welcome to the age of self-building, self-running systems. Nearly 8 in 10 enterprises expect AI-accelerated low-code/no-code to scale within 18 months - and Service-as-Software™ (SaS) is rapidly closing in, marking the fastest dual transformation in the software stack. # The emergence of self-creating, self-operating digital systems Software is entering its most transformative shift since cloud computing. For the first time, enterprises are seeing creation and operations evolve at the same time — and at extraordinary speed. GenAI has collapsed the distance between an idea and a working system, while intelligent services are pushing operations toward self-management and continuous optimization. Low-Code/No-Code (LCNC) strengthened by generative AI, is no longer a shortcut for simple apps. It has become a strategic layer for building business workflows, automations, and internal tools at scale —enabling teams to move from concept to execution in hours, not weeks. This is redefining how organizations deliver change, absorb complexity, and respond to opportunities. In parallel, SaS is reshaping the runtime environment itself. Instead of managing tickets, dependencies, and incidents manually, enterprises are moving toward services that can observe, adapt, and remediate on their own. Together, these trends are creating a new software fabric for the enterprise — one where software grows faster, adapts faster, and delivers value continuously. Next-Gen software is not just about efficiency; it is about designing digital systems capable of keeping pace with the speed of business and the scale of modern operations. # Why it matters? # Key findings Creation autonomy arrives first: With $84\%$ of enterprises expecting AI-accelerated low - code/no-code to scale within 18 months, LCNC is the fastest - moving shift in enterprise software. Adoption is already mainstream, with $60\%$ in active use today. GenAI changes the game: GenAI copilots are propelling LCNC forward by generating apps, workflows, models, tests, and integrations. This is turning citizen developers into governed contributors rather than peripheral experimenters. Agentic service platforms are rising fast: While not fully autonomous yet, $31\%$ of enterprises are already running SaS pilots. Most expect meaningful scale within the same 18-month window, signalling a move toward self-managing services. Enterprises are ready to modernize: Strong leadership conviction and improving infrastructure readiness indicate organizations are moving beyond experimentation. The focus is now on operationalizing software autonomy end-to-end. APAC leads Next-Gen software adoption: APAC emerges as the global hotspot due to its high concentration of digitally mature IT and software enterprises. Advanced adoption of both LCNC and SaS is accelerating scale across the region. # Emerging Now: Low-Code/No-Code # Accelerating software creation through GenAI-powered development. Organizations are no longer treating low - code/no-code as an experimental side tool — GenAI has propelled it into the mainstream of enterprise software creation. What once helped teams assemble simple apps has evolved into a powerful AI-driven creation layer where workflows, data models, integrations, and interfaces can be generated from natural language. LCNC isn't just accelerating delivery — it's redefining the very mechanics of how software comes to life. With nearly three-quarters (see exhibit 2.1) of organizations beyond early interest—and a growing share actively transforming processes or operating at scale—LCNC is no longer a fringe experiment but an emerging enterprise standard. This momentum is reinforced by what leaders value most faster development, lower costs, stronger innovation, and the ability to close skill gaps. As GenAI accelerates how quickly ideas can be turned into working software, enterprises are using LCNC not just to clear backlogs but to expand digital capacity far beyond traditional development models. The constraint is no longer developer bandwidth—it's how fast teams can express intent and validate outcomes. This shift is reshaping how work gets built across organizations. Business and IT teams can now collaborate in real time, co-creating governed digital solutions while AI automates the heavy lifting behind the scenes. As LCNC adoption deepens, companies are beginning to standardize AI-generated apps, embed governance-by-design, and orchestrate workflows across functions. The model is changing: from software being built for teams to software being built with them — instantly and iteratively, and at scale. # Exhibit 2.1 LCNC uptake is driven primarily by the need for faster delivery, lower costs, and stronger innovation capacity. LCNC adoption stages among organizations in 2025 # A new geography of software creation is taking shape. Digitally mature regions are moving fastest, but each region is advancing along its own curve. APAC shows strong mid-maturity momentum, with around $18\%$ of organizations already in pilot or early-transformation stages, reflecting its deep base of software-forward enterprises. Europe stands out as the most advanced region overall, with nearly $18\%$ of organizations in the transforming or actively scaled tiers—evidence of structured modernization and sustained enterprise commitment. The maturity landscape isn't just descriptive - it signals where LCNC is headed next. Regions already running LCNC at scale are shifting effort toward governance, integration, and enterprise - wide orchestration. Regions earlier in the curve are focusing on pilots and proving value. Yet the end-state is consistent across markets: a software fabric that can be created quickly, adapted continuously, and shaped directly by the teams closest to the work. What's emerging is not merely a faster way to build apps, but a new operating philosophy for software - one where ideas translate into execution far more easily, powered by AI-enabled creators. # The LCNC Curve: From workflow stabilization to strategic creation As enterprises advance in digital maturity, the role of low-code/no-code evolves from a tactical accelerator into a strategic capability. What begins as a tool for reducing manual effort gradually becomes a core engine for process orchestration and, ultimately, digital innovation. The data in Exhibit 2.2 makes one pattern unmistakably clear: workflow automation is the universal starting point and the structural backbone of LCNC adoption. Whether organizations are early in their journey or already digitally mature, LCNC's first and most persistent contribution is streamlining fragmented, manual processes — establishing the operational stability needed to scale more sophisticated digital initiatives. But as enterprises mature, the strategic horizon expands. App development rises sharply among transforming and innovation-leading organizations, signalling a move from LCNC as a tool for efficiency to LCNC as a platform for creating new value. Generative AI is amplifying this evolution: $44\%$ of respondents (see pulse survey #2) expect its biggest impact to be faster builds, dramatically reducing the effort required to prototype and iterate, while $31\%$ see smarter workflows as the most transformative benefit. Together, these capabilities turn LCNC into an engine of rapid productization — an environment where teams co-create digital solutions, test ideas, and bring offerings to market far faster than traditional engineering cycles allow. Internal tools and data reporting also gain traction as maturity increases, reflecting a deeper integration of LCNC into enterprise systems. Mature organizations don't just automate tasks — they re-architect how information flows and how teams interact with systems. Taken together, these patterns signal a larger evolutionary arc: LCNC moves enterprises from stabilizing workflows $\rightarrow$ to scaling operations $\rightarrow$ to accelerating innovation. It's not simply a development shortcut — it's a maturity amplifier. # People Pulse #2 Where will Generative AI create the biggest impact in Low-Code/-No-Code platform? 44% said: Faster builds 31% said: Smarter workflows 13% said: Personalized UX 12% said: Data insights Exhibit 2.2 As enterprises mature, LCNC shifts from fixing processes to creating digital products. Transforming Mature Innovation Leader App Development Data Reporting Internal Tools WorkflowAutomation # Barriers: Concentrated ownership, uneven scalability foundations and talent that hasn't caught up Despite rapid adoption, enterprises are discovering that scaling low-code/no-code in a GenAI-powered world is harder than it looks. The challenge isn't enthusiasm for LCNC - it's that the foundational capabilities needed for enterprise-wide scaling remain uneven and still maturing. A key constraint is concentrated ownership. With $61\%$ of LCNC pilots still led by IT, the model is yet to democratize into the hands of business teams and citizen developers, who collectively account for only $35\%$ of early adoption. This keeps LCNC confined within traditional delivery structures and limits the shift toward true co-creation. Beneath this, deeper structural barriers emerge. Successful scaling depends on architectural Exhibit 2.3 LCNC- Mesh of Possibilities maturity — and while organizations lean heavily on API integrations, platform upgrades, and cloud-native deployment, many lack consistent API hygiene, reusable components, or modern data foundations. This mirrors broader industry findings: LCNC platforms struggle when integrational complexity, customization limits, or legacy environments collide with the need for enterprise-grade stability. And then there's the talent and cultural shift. GenAI amplifies LCNC's potential but raises the bar for what "citizen development" requires. Teams need new skills in prompt design, workflow reasoning, testing AI-generated logic, and validating automated decisions. Underestimating this shift leads to stalled adoption, over-reliance on IT, and inconsistent application quality. Operational Efficiency/Scalability Customer Experience $\bullet$ Risk Management New Revenue Streams Sustainability Goals Business Model Innovation # It’s no longer about building faster — it’s about building smarter The LCNC impact radar shows a platform whose value extends well beyond rapid builds. Operational efficiency (4.42) and customer experience (4.33) emerge as the strongest impact areas, but the pattern is broader: LCNC is steadily becoming a catalyst for business model innovation (4.08) and system-level resilience. GenAI is amplifying this shift — enabling faster UI creation, smarter workflows, and more personalized digital journeys. Even emerging dimensions like risk management, new revenue streams, and sustainability are gaining traction as enterprises use LCNC to embed standardization, automate reporting, and experiment at speed. # The Road Ahead - Strengthen integration and cloud-native foundations-LCNC scalability depends on modern, well-structured integration and data layers. Organizations need robust APIs, clean data pipelines, and cloud-native architectures to support enterprise-grade LCNC adoption. Build GenAI-enabled development capability- Equip teams with skills in prompt design, logic validation, and oversight of AI-generated workflows to reduce dependence on IT. - Create reusable enterprise assets - Standardize templates, connectors, and data models to accelerate reuse, ensure consistency, and reduce duplication. - Measure real impact, not output - Shift KPIs from "apps delivered" to improvements in efficiency, experience, and value creation. - Position LCNC as a product innovation engine—Use GenAI + LCNC to speed experimentation, build testable prototypes, and launch digital offerings faster than traditional development cycles allow. The real opportunity ahead isn't just about turning work around faster - it's about turning LCNC into a dependable, enterprise-wide creation layer that teams can trust. As governance strengthens, architectures modernize, and GenAI becomes a co-builder rather than a novelty, LCNC will shift from solving tactical gaps to shaping how organizations deliver digital change. # Coming Soon: Service-as-Software™ (SaS) # Delivering outcomes through software that runs itself. SaS is the next big shift in how enterprises consume technology. If traditional Software as a Service (SaaS) delivered applications, SaS delivers services that run themselves — systems that can automate fixes, enforce SLAs, optimize performance, and deliver continuous outcomes without constant human intervention. Think IT incident resolution that fixes itself, cloud cost optimization that adjusts in real time, or software test automation that self-heals — all delivered as ready-to-consume services. This evolution is being fueled by AI, cloud-native architectures, automated observability, and outcome-based consumption models. Instead of buying tools and assembling workflows manually, enterprises increasingly want ready-to-run capabilities that integrate seamlessly, heal autonomously, and deliver measurable value out of the box. # Exhibit 2.4 How well are enterprises prepared for SaS in the coming future? # 36% of organizations say their talent capability is partially ready, signalling that skills are advancing — but broad workforce readiness still needs to accelerate for agentic service platforms to scale. # 14% believe they have a mature vendor ecosystem, underscoring that partner models for SaS remain nascent. # 32% of organizations report leading-level leadership commitment, showing that executive intent for SaS is already strong – yet only $27\%$ say their budget and investment posture is at a similar level, revealing a readiness gap between vision and financial commitment. # USD 89.9 billion by 2034, at a compound annual growth rate (CAGR) of $25.4\%$ Source: Dimension Market Research # Is SaS the new marker of digital sovereignty? As enterprises move toward autonomous, outcome-based services, one factor consistently separates early adopters from the rest: digital sovereignty. Organizations that treat sovereignty as central to their digital strategy are also the ones furthest ahead in SaS maturity. The data confirms it: $36\%$ (exhibit 2.5) of sovereignty -focused organizations have already reached transforming or scaled stages in their SaS journey. This is more than correlation – it's causation in motion. SaS is strengthened by programmable policies, strong governance, and cloud-native delivery – all of which are only viable when enterprises design for data control, localization, and automation at scale. Sovereignty-minded organizations are more likely to invest in the architectural backbone that SaS demands: unified service layers, intelligent observability, and regionally adaptable service contracts. Conversely, enterprises that view sovereignty as peripheral or outdated tend to remain locked in legacy models — unable to scale service delivery across geographies or enforce consistent SLAs through software. The absence of sovereignty disciplines creates operational friction that SaS alone cannot overcome. Ultimately, the relationship runs deeper than readiness: digital sovereignty shapes an enterprise's ability to trust, deploy, and scale autonomous services with confidence. In an era where services increasingly run themselves, sovereignty becomes not just a governance stance — but a strategic catalyst for realizing the full promise of outcome as agentic. Exhibit 2.5 Organizations that prioritize sovereignty are scaling SaS faster. # SaS impact zones: Faster ops, smarter services, better experiences While SaS remains an emerging trend, the early signs of transformation are already visible—especially in areas where services are frequent, measurable, and process-heavy. These are domains that benefit most from autonomous execution, standardized SLAs, and AI-driven reliability. The message is clear: SaS is not a distant vision—it's quietly gaining ground in places where intelligent service delivery can immediately prove its value. The strongest early traction is in operational support areas. A majority of respondents cite IT helpdesk (65%), process automation (68%), data insights (68%) as the top domains where the earliest value from SaS is being realized. These are functions where responsiveness, standardization, and embedded intelligence make immediate business sense—and where AI-led remediation and real-time optimization can be deployed with minimal friction. Customer-facing areas are not far behind. With $61 \%$ pointing to customer engagement as a promising impact zone, it’s clear that enterprises see potential for SaS to streamline resolution workflows, automate experience triggers, and scale personalized support. This marks a shift from traditional "back office first" automation to frontline service intelligence. In conclusion, SaS is gaining traction wherever repeatability, observability, and AI-enablement intersect. This first wave of impact reveals where organizations can build early wins—and where Service-as-Software™ will evolve from a tactical edge to a foundational model for digital service delivery. # From tools to trust: The playbook for scaling SaS SaS is not just a new deployment model — it's a new way to think about services. Traditional enterprise services depend on teams, tickets, and turnaround times. SaS replaces that with software systems that remediate, optimize, and deliver value automatically. But the shift doesn't happen by switching platforms. It requires re-architecting how services are built, governed, and scaled. Most organizations still treat service delivery as a cost center. The frontrunners are treating it as code. To scale SaS, organizations need to rewire three foundations: 1. Architecture: Design for self-management, not oversight - Scaling SaS starts with architecture that can carry it. Legacy systems built for request - response models won't cut it. Organizations need modular, API-rich, event-driven platforms where services can self-monitor, self-heat, and run across sovereign boundaries. The goal isn't just integration - it's to embed resilience, security,