Looking Glass 2026
Adoption of AI in software development continues to accelerate, but the real shift underway is less about autonomy and more about addressing the long-standing structural challenges that hold enterprises back. Rather than simply automating tasks, AI is beginning to be leveraged to rebuild the core of software delivery: modernizing legacy systems, improving architectural integrity, strengthening quality and stabilizing pipelines.
AI-first software delivery (AIFSD) represents the end-to-end integration of generative and agentic systems 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; they reinforce 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 oversight to 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 systems must operate under rigorous engineering oversight. Without this, they risk introducing technical debt, security vulnerabilities or hallucinated requirements. Unchecked, AI-generated code may bypass proper architecture practices, or create subtle flaws that later prove costly to fix. Industry analyses have 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 software in a complementary way, with the AI handling repetitive, scaffolding and optimization tasks; yet always operating under human-in-the-loop stewardship to ensure accuracy, security and architectural integrity. Enterprises that strike the right balance will develop powerful adaptive capabilities that free them from tech debt and ready them to respond to change.
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 feedback from 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 (see McKinsey, 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 experiment upon or predict regressions and propose changes before applying them in production.
- Synthetic engineers. In research settings, early prototypes of synthetic engineers — composite AI entities composed of multiple specialized models — are beginning to manage entire development streams. These entities collaborate, debate and self-correct to deliver complex systems with minimal human intervention. Though experimental, they hint at the emergence of collective AI design teams.
- Multimodal collaboration and real-time translation. As translation, voice synthesis and multimodal reasoning mature, AI plays the role of universal collaborator, instantly bridging gaps between design, engineering, QA and product in different languages and modalities. This ensures global collaboration at human speed.
Signals
AI is already reshaping foundational software engineering practices:
AI-augmented development environments can help streamline testing, debugging and code quality, easing technical debt pressures.
Predictive quality engineering. AI systems are now capable of predicting test coverage gaps, security vulnerabilities and performance issues before code is deployed. This is enabling more proactive approaches to software quality assurance and vulnerability remediation that is providing critical leverage in industries like e-commerce and financial services.
AIOps-enhanced pipelines. The incorporation of AI into DevOps pipelines, or AIOps, is automating incident detection, root-cause analysis and self-healing actions, helping optimize resources and making DevOps teams more productive.
Intent-based, goal-oriented coding. Frameworks are appearing that enable developers to put autonomous coding assistants to work simply by expressing what they want to build, rather than how to build it. It is the AI system that decides the best means of achieving the stated objective. A similar trend is cognitive debugging, where experimental tools use large context windows and neural-symbolic reasoning to detect and explain bugs at the intent level, rather than just sharing their syntax.
AI-empowered maintenance and continuous architecture improvement. In examining existing code bases, predictive agents are able to identify decaying dependencies, outdated frameworks or performance regressions humans may miss. AI-driven refactoring tools can also suggest optimizations to enterprise architecture based on performance and scalability data in real time. The net result is a more efficient tech estate where less time is spent ‘keeping the lights on.’
The rise of ‘adaptive governance’. AI systems are being leveraged to track compliance, license usage and policy enforcement dynamically, improving the oversight of third-party service usage and ensuring software evolves in a responsible way.
Each of these signals contributes to a stronger, more maintainable core.
The opportunities:
By getting ahead of the curve on this lens, organizations can:
Break through modernization barriers
The difficulty of untangling legacy systems is one of the biggest barriers enterprises and development teams face when building for the future. AI-enabled tools like CodeConcise are proving a massive accelerant to this process, slashing the time and effort needed to reverse engineer code bases and complete the shift to modernized architectures that allow teams to focus on creating value.
Elevate the way teams build
Claims about the ability of AI to speed up the software delivery process are often wildly exaggerated, but we’ve observed a typical uplift of up to 15%. More significantly, by acting as an engine of customer insights, AI is fueling faster, more intelligent product prototyping and design, and helping teams deliver deeply personalized customer experiences.
Lower operational risks and enhanced reliability
Leveraging AIOps for capabilities like code quality checks and real-time alerting increases the likelihood of catching and addressing software problems and vulnerabilities before they become crippling. Studies have found integrating AIOps into incident management typically improves mean time to detection by over 70%, and reduces mean time to resolution by more than 60%.
Enhance developer satisfaction
AIFSD will redefine what it means to ‘ship software.’ With AI taking over more mundane or repetitive aspects of the development process, teams will move from builders to curators of intelligence, designing the objectives and constraints within which AI systems will operate. This will free up teams to devote more time to higher-value work that supports product innovation and engagement with the business.
What we’ve done
One of our clients, a leading automotive manufacturer, had developed a complex system over three decades based on a tech stack for which engineering talent was proving increasingly difficult to find. To update this system and ensure it could continue to deliver value, the company was faced with the task of rewriting the entire code base, which ran to millions of lines. Reverse engineering 10,000 lines of code alone was taking an average time of six weeks. Drawing on the CodeConcise tool, we were able to reduce this cycle by two-thirds — potentially saving the company up to 60,000 person-days, and helping teams approach modernization with renewed confidence.
Trends to watch
Adopt
Analyze
-
AI assistance across the entire software development lifecycle: requirements, design, coding, testing and deployment.
-
Development teams systematically using AI tools throughout the engineering workflow.
-
Integrated security platform protecting cloud-native applications across development and runtime environments.
-
Treating datasets as products with defined SLAs, documentation and consumer focus.
-
AI models fine-tuned for specific industries or functions, delivering higher accuracy than general-purpose models.
-
Programmable Linux kernel instrumentation for deep observability without code changes.
-
Interfaces controlled through hand gestures and body movements without physical contact.
-
Platforms analyzing development data to measure productivity, identify bottlenecks and optimize processes.
-
Cloud practices minimizing environmental impact through efficient resource use, renewable energy and carbon tracking.
Anticipate
-
User interfaces automatically generated and adapted by AI based on context and user needs.
-
Technology embedded in environments that responds contextually without explicit interaction.
-
Cryptographic algorithms resistant to quantum computer attacks; NIST finalized standards in August 2024.
Adopt
Analyze
Anticipate
-
AI systems matching human-level intelligence across all cognitive tasks, capable of reasoning, learning and adapting without task-specific programming.
-
AI systems capable of independently conducting scientific research, generating hypotheses, designing experiments and making Nobel-worthy discoveries.
Actionable advice
Things to do (Adopt)
- Grasp the modernization opportunity. The potential of GenAI to decipher archaic coding languages and address documentation gaps means the time, complexity and cost burdens that have prevented the organization from tackling modernization may no longer apply, easing the path to more resilient, productive systems.
- Encourage AI adoption, and ensure development teams are ‘agent‑ready.’ While AI is already a daily reality for the majority of developers, research points to a rise in cynicism about the technology, rooted in concerns about its impact on jobs and issues with accuracy. It’s important that enterprises are transparent about their vision for AI; how and where they expect to integrate it into the development lifecycle; and how the use of agents may alter the engineer’s role.
- Institute clear guardrails, checks and balances. With surveys indicating AI-generated code can introduce serious vulnerabilities, enterprises will need to retool and elevate software quality control and risk management. AI evals are emerging as an essential element of the development process. Teams will also have to view the tools they use, and their outputs, with an ethical lens. Frameworks like the Responsible Tech Playbook present a range of methods and benchmarks designed to ensure development remains principled. A balance has to be struck between instituting clear standards and guidelines, while providing teams room to adopt the technology in the ways that are most relevant for them.
Things to consider (Analyze)
- Expanding AI’s role in the development process. As systems grow more sophisticated there is clear potential to push use cases well beyond coding into more creative and strategic aspects of development, from evaluating new features to modeling the most likely user response.
- Rethinking measures of engineering performance. With human engineers and AI agents increasingly working in tandem, the standard DORA metrics may become less relevant; the speed at which AI can create code vastly exceeds typical deployment benchmarks for example. KPIs will need to be redefined both to account for developers focusing on higher-value tasks, and for systems that will depend on these metrics to make and execute decisions.
Things to watch for (Anticipate)
- The emergence of neural software twins. The digital twin techniques often adopted in manufacturing are increasingly pushing into the software domain, allowing for the modeling of entire codebases and system behavior to simulate future states. Such simulations could reduce complexity and introduce more clarity into the development lifecycle, informing future strategies and priorities.
- The rise of the synthetic engineer. First came synthetic data; now prototypes of synthetic engineers — composite AI entities composed of multiple specialized models — are beginning to manage entire development streams. These entities are capable of collaborating, debating and self-correcting to deliver complex systems with minimal human intervention. Though in the early stages, they could have major long-term implications for the engineering practice, and hint at the emergence of phenomena like collective AI design teams.