科技棱镜2026：聚焦技术驱动的业务变革

Bringing tech-led businesschanges into focus Introduction3 Lens one:AI and software delivery Lens two:Preparing for agentic transformation 12 Lens three:In evolving interactions, AI reimagines the possibilities 19 Lens four:From data platforms to AI-ready data ecosystems 26 Lens five:Building your AI future on responsible foundations 33 Glossary40 Introduction Each year, Thoughtworks’ Looking Glass report looks beyond individualtechnologies to examine the forces reshaping how organizations build,run and evolve their technology estates. The aim is not to predict thefuture, but to help leaders make sense of the changes already underway— and to understand which of them will matter most in practice. This year’s trends reflect a moment of transition. Long-running shiftsaround platforms, data, security and experience design are convergingwith rapid advances in AI. The result is not a single disruptive technology,but a reconfiguration of how technology creates value across theenterprise. Systems are becoming more adaptive. Interactions moreintent-driven. Governance more embedded in day-to-day delivery. Across the Looking Glass, we explore what this means in concrete terms:how enterprises are rebuilding their core foundations, rewiring workflowsto support greater autonomy and reimagining the role technology playsin customer experience, decision-making and operations. AI featuresprominently, but always in context — as one of several forces acceleratingchange, rather than a solution in isolation. As ever, this report is grounded in what we see working with clientsaround the world. It is intended as a practical guide to navigating theyear ahead, with a focus on technologies that deliver real, durable value. Rachel Laycock Chief Technology Officer, Thoughtworks AI and software delivery Time to rebuild core systems Adoption of AI in software development continues to accelerate, but the real shift underwayis less about autonomy and more about addressing the long-standing structural challengesthat hold enterprises back. Rather than simply automating tasks, AI is beginning to be leveragedto rebuild the core of software delivery: modernizing legacy systems, improving architecturalintegrity, strengthening quality and stabilizing pipelines. AI-first software delivery (AIFSD) represents the end-to-end integration of generative and agenticsystems into the full lifecycle of developing software — requirements, design, development, testing,deployment and maintenance. As these capabilities mature, they don’t just speed up delivery; theyreinforce the foundations on which delivery depends. Systems will increasingly learn from product goals, user behavior, telemetry and operational signals,enabling continuous improvement. But these capabilities must sit within strong engineering oversightto avoid compounding technical debt, introducing vulnerabilities or creating brittle architectures.The opportunity is not fully autonomous development — it is AI-enabled core renewal. While generative AI tools can dramatically accelerate delivery and will change the way developers work,it’s important to maintain a balance. Regardless of how sophisticated they may become, AI systemsmust operate under rigorous engineering oversight. Without this, they risk introducing technical debt,security vulnerabilities or hallucinated requirements. Unchecked, AI-generated code may bypassproper architecture practices, or create subtle flaws that laterprove costly to fix.Industry analyseshave shown that generative AI can lead to maintainability concerns or vulnerabilities if governance,review and validation are not baked into the process. AIFSD has to be seen as a practice where human engineers and AI systems co-construct softwarein a complementary way, with the AI handling repetitive, scaffolding and optimization tasks; yetalways operating under human-in-the-loop stewardship to ensure accuracy, security and architecturalintegrity. Enterprises that strike the right balance will develop powerful adaptive capabilities thatfree them from tech debt and ready them to respond to change. AI and software delivery: Time to rebuild core systems How AI rebuilds the core Key trends Goal‑based development environments (GBDEs) Developers will verbally specify objectives like “build a scalable user onboarding flow,”while AI agents negotiate trade-offs, select libraries and assemble implementations.This shift collapses the distance between business intent and software creation. Continuous learning delivery systems Delivery pipelines evolve into neural delivery loops — closed systems where feedbackfrom users, telemetry and market signals immediately inform the next iteration or release. Neural software twins Digital twins are already being paired with generative AI in physical systems(seeMcKinsey, 2024). In software, the idea is nascent, but the analogy holds:maintain a living model of the running system (code + data + performance)that the AI can exp