Agentic AI has disrupted everything across the enterprise in 2025, but AI-enabled engineering has been screaming from the rooftops. By next year, companies that use AI-enabled engineering to automate code, perform A/B testing, and write documentation will see their productivity double. While other companies are still stuck in pilot mode in 2026 – running clever demo products that will never hit their core stack – they’ll watch as AI-assisted engineering helps their competitors finish projects faster and cheaper, and with fewer bugs. It also prepares them for an AI-assisted future.
Companies that view AI-assisted software development as a natural part of software development are far more likely to embrace the technology. However, many companies are feeling unsure about their AI investments. According to MIT, 95% of corporate AI projects are failures. Software developer ServiceNow also reported that the average enterprise AI maturity score dropped nine points from 44 to 35 over the past 12 months. This suggests that enterprises are not keeping pace with the speed of innovation or using AI coding tools. They start pilots, but they never finish the job. It also demonstrates the behavior change required to adopt AI tools effectively within the enterprise.
For the past few years, businesses have heard many promises from a plethora of AI companies that have suddenly appeared to capture billions from their bank accounts. While these companies have a reasonable expectation of success, these same companies have not made it to the most important step of AI maturity: The production model. Adding AI assistance requires the technology to be in production.
How AI enables software engineering
There are three functional areas in which AI-enabled software engineering has the greatest effect. Those are:
- Engineering productivity: Using natural language prompts, generative and agentic coding tools provide context-aware help by writing boilerplate code, refactoring legacy modules, updating documentation, and creating test suites. Systems capable of code completion keep engineers in control to as reviewers, but they no longer start from a blank file. They also check code quality and keep work consistent with internal stylistic guidelines.
- System complexity: AI assistance handles integration across mobile, web, desktop, cloud, and enterprise applications. Models thrive in clean architecture with well thought-ought APIs, yet engineers still observe AI recommendations periodically to ensure they are performing as expected and they offer architectural guidance when patterns begin to drift.
- Product lifecycle: AI brings value to greenfield apps, but it also processes incremental changes as updates are available, performs other types of maintenance, and prepares digital products for their end of life. In greenfield scenarios, AI-assisted coding speeds up design and prototyping. In mature products, it clears backlogs and keeps codebases healthy and improves long-term maintainability. It also manages end-of-life tasks by helping products retire cleanly, such as scheduling automatic expiration and organizing the final updates needed to wind down the system.
As organizations increase their level of AI maturity, the benefits continue to grow. In the earliest stages of AI development, it is like a copilot for engineers. AI systems suggest snippets of code and help autocomplete tasks. At the next phase, AI becomes like a collaborator working under the supervision of a software engineer. This is part of the broader automation spectrum that includes proving help to fully autonomous agents. While AI systems will make intelligent suggestions and handle some tasks automatically, the engineer guides its output and still has the final say. Finally, companies at the highest level of the generative engineering maturity model, companies deploy well-orchestrated agents to run end-to-end deployments on their own. Yet even at this highest level, an engineer oversees the model and has the final say.
The benefits of AI-enabled engineering
Using generative AI as a coding copilot and documentation assistant represents a strategic shift in duties, where software engineers are adding prompt engineer to their skillsets. By embedding AI-enabled development in their digital contracts, companies ensure they enjoy these benefits:
1. A 100% gain in related productivity
AI-enabled engineering takes over a software engineer’s repetitive work at double the pace and puts it on auto-completion. Agents generate code and documentation in minutes. Even ide tools offer AI assistants that act like feature editors. AI coding assistants also can find orphaned functions, make pull requests from repositories with AI coding tools like GitHub copilot, and help to eliminate technical debt. They can spin up sandboxed environments and even move gated changes through CI/CD. Meanwhile, the development team stays focused on system design, validation, and the edge cases that need their skills and judgment.
2. 3x faster time-to-market
The software development lifecycle for AI-enabled software engineering is so short that products can be completed and moved to market sooner than previously possible.
3. 8x faster prototyping
With AI-assisted development, companies take their ideas from concept to prototype within hours rather than days. With proper guardrails, AI systems can produce a complete proof of concept in a single work session, making it easier to validate product ideas and reshape them as necessary.
4. 5x higher A/B testing bandwidth
AI-native software engineering also expands the capacity to experiment. For example, AI systems quickly produce variants of a program, allowing the engineers to test which works better for a task. AI systems also prepare the test conditions and supports rapid comparisons, giving product groups a cleaner analysis of what works without slowing down delivery.
Software development without AI-enablement
AI-enabled software development
Source: McKinsey & Co.: How an AI-enabled software product development life cycle will fuel innovation
A pragmatic approach to AI-enablement
Product engineering is one of many business functions that greatly benefits from AI-enablement. By early next year, for example, AI copilots will start handling the everyday tasks that place a burden on the legal and financial industries. They will update regulations, draft contracts, and run stress-tests on financial models. Bankers and lawyers will get information faster, but AI-enablement also reduces operating costs and gives them an edge when serving clients under tighter deadlines. These industries will also see even greater efficiency in billing and record-keeping work with the help of AI-enablement.
The benefits will continue to grow as more industries find use cases that help them offload redundant and repetitive tasks. For example, in the legal industry, AI-enablement will help lawyers focus on advisory work, negotiations, and client relationships. However, many enterprises still treat AI as a one-size-fits-all monolith. Instead, AI shows up in five distinct lanes that require different operating models:
- Individual productivity applications
- Enterprise functions (i.e. Finance, HR, Sales)
- IT operations
- Engineering productivity
- Innovation
Several principles define a pragmatic approach to AI-enabled engineering.
Treat data as the fuel, not the exhaust
AI models need high-quality training data to develop programs and write documentation like software engineers. This data – like existing documentation, code repositories, or logs – must be error-free. Therefore, companies must focus on preparation efforts and data access for the data used to fine-tune the model during the pilot phase.
Build-in governance from the start
Regulations, standards, and frameworks for AI are still relatively new. They include:
- ISO/IEC 42001 is the first international standard for an Artificial Intelligence Management System (AIMS). It provides a framework for organizations to responsibly manage AI risks and opportunities by ensuring AI systems are ethical while fostering innovation. Like the way ISO 27001 guides IT security, the standard helps companies develop ways to prevent AI bias while also governing its privacy and security.
- The NIST AI Risk Management Framework along with its GenAI evaluation program, gives organizations a way to identify risks, measure them, and explain how a model performs under different conditions. The EU AI Act goes further by setting obligations for anyone developing or deploying AI in higher-risk settings. For engineering groups working with AI-enabled tools, these expectations shape everything from how models are selected to how testing pipelines and audit trails are built.
- The EU Artificial Intelligence Act goes further by setting obligations for anyone developing or deploying AI in higher-risk settings. For engineering groups working with AI systems, these expectations include rules for data pipeline, model training, and testing.
To remain in compliance, companies should include audit trails and logs for AI-enabled software development processes. These checks act like an immune system that detects when an AI model begins to drift. These details will help explain the model’s decisions to ensure they are adhering to local and global rules.
Security and privacy are default behavior
AI-enabled product engineering can introduce new security and privacy risks, which means they must be part of the entire AI lifecycle from the start. According to a survey in KPMG, 67% of business leaders said they plan to invest in cybersecurity and data protections for their AI models. That means 23% are not making security and privacy a top priority.
AI-enabled product engineering with Intellias
Intellias has been an innovator of software development for more than 20 years. We use AI to help our clients more rapidly bring their projects to fruition. Two recent projects show how AI-powered engineering with Intellias helped clients achieve a solid return on their investments.
Caregiver app
A digital healthcare services provider that developed prescription digital therapeutics needed to rebuild an application used by caregivers. The legacy app relied on an aging technology stack and carried recurring license fees. The company wanted a modern, compliant, cost-effective alternative. They also needed it to be completed quickly to avoid renewal costs.
Intellias set up an AI pod model. AI pods include a cross-section of specialists to help guide the use of AI. They reverse-engineered and rebuilt the application on a modern platform in the cloud. AI agents generated code and wrote documentation for the product. They also assisted with security checks and vulnerability scanning, which was essential compliance with the Health Insurance Portability and Accountability Act (HIPPA) in the U.S.
Using AI-enabled software development was highly successful. The client cut their investment by more than half compared with rebuilding the application using traditional software development practices. Time to market improved and defects at user acceptance testing dropped by roughly 40%. Furthermore, the company avoided more than $700,000 in license renewal costs by replacing the legacy system in time.
Intelligent traffic
A global company that offers real-time maps and navigation services wanted to showcase an intelligent traffic solution at a major industry conference. A demo-quality prototype had to be built in a matter of days even as the product needs continued to change.
As their development partner, Intellias engineers used AI agents to quickly develop the prototype. The agents generated much of the UI and made in based on open-source map data. We delivered a working prototype in just one day, then refined the product over the following weeks until it was a fully integrated solution. AI code generation dropped development time from several months to about six weeks, while the cost was about 70% lower than it would have been using traditional software development methods.
The final product was demonstrated at the conference. The AI-assisted development also set a standard for future product prototypes.
Moving forward with AI maturity
As the New Year approaches with fresh expectations, companies that have reached AI maturity and are using AI-enabled product engineering will lead the rest through 2026. With the confidence of AI-engineering handing the everyday tasks, product development becomes leaner, smarter, and better positioned to outshine the competition in 2026.
Ready to see how AI-enabled engineering can supercharge product development? Let Intellias show you how AI is changing the velocity of software engineering.
