Agentic AI has disrupted everything across the enterprise in 2025, but AI-enabled engineering has been screaming from the rooftops. By 2026, companies that use AI-enabled engineering to automate coding, perform A/B testing, and write documentation will see their productivity double. Other companies that are still stuck in pilot mode — running clever demo products that will never hit their core stack — will watch as AI-assisted engineering helps competitors finish projects faster, cheaper, and with fewer bugs. It will also prepare those competitors for an AI-assisted future.
Companies that view AI-assisted software development as a natural evolution of their development workflow are far more likely to embrace AI technology across the organization. However, many companies are feeling unsure about their AI investments. According to MIT, 95% of corporate AI projects fail. Software developer ServiceNow also reports that the average enterprise AI maturity score dropped nine points, from 44 to 35, during 2025. 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 and are eager to capture billions in revenue across all sectors of the economy. While these AI companies have a reasonable expectation of success, they have not made it to the most important step of AI maturity: the production model. And significantly, for businesses to add AI assistance, AI must be in production.
How AI enables software engineering
There are three functional areas in which AI-enabled software engineering has the greatest effect:
Engineering productivity: Using natural language prompts, engineers can interact with generative and agentic coding tools that 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 as reviewers, but engineers 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 architectures with well-thought-out APIs, yet engineers still observe AI recommendations periodically to ensure that models are performing as expected and 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, keeps codebases healthy, and improves long-term maintainability. It also manages end-of-life tasks to help products
retire cleanly, such as by 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, AI is a copilot for engineers. AI systems suggest snippets of code and help autocomplete tasks. At the next stage, AI becomes a collaborator working under the supervision of a software engineer. This is part of the broader automation spectrum that includes fully autonomous agents. While AI systems will make intelligent suggestions and handle some tasks automatically, the engineer still guides their output and has the final say. Finally, 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 final say.
Benefits of AI-enabled engineering
Using generative AI as a coding copilot and documentation assistant represents a strategic shift in duties, allowing software engineers to add prompt engineer to their skillsets. By embedding AI-enabled development in their digital contracts, companies can enjoy these benefits:
1. 100% gain in related productivity
AI-enabled engineering can double the pace of a software engineer’s repetitive work and put 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 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 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 can quickly produce variants of a program, allowing engineers to run A/B tests to determine which works better for a task. AI systems also prepare test conditions and support rapid comparisons, giving product groups a cleaner analysis of what works without slowing down delivery.
The current software development lifecycle
AI-native software product development lifecycle
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 stands to benefit greatly from AI enablement. By early 2026, 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 use AI copilots to get information faster, but AI enablement will also reduce operating costs and give them an edge when serving clients under tight deadlines. These industries will also see even greater efficiency in billing and record-keeping with the help of AI.
The benefits of AI 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. Many enterprises still treat AI as a one-size-fits-all monolith. In reality, 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 — existing documentation, code repositories, logs, and so on — must be error-free. Therefore, companies must focus on preparing data and acquiring access to 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.
- 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. Just as ISO 27001 guides IT security, ISO/IEC 42001 helps companies develop ways to prevent AI bias while governing AI privacy and security.
- The NIST AI Risk Management Framework along with the NIST GenAI evaluation program give organizations a way to identify risks, measure them, and explain how a model performs under different conditions.
- The EU Artificial Intelligence Act goes further by setting obligations for anyone developing or deploying AI in higher-risk settings, including rules for data pipelines, model training, and testing.
To remain in compliance, companies should include audit trails and logs for AI-enabled software development processes. These compliance checks are like an immune system against poor-quality or biased output because they detect when an AI model begins to drift. Data from audit trails and logs help explain a model’s decisions to ensure they adhere to local and global rules.
Prioritize security and privacy
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 by KPMG, 67% of business leaders 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 in 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 helps clients achieve a solid return on their investments.
Caregiver app
A digital healthcare services provider that develops 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, and cost-effective alternative. They also needed it to be completed quickly to avoid renewal costs.
Intellias set up AI pods that included a cross-section of specialists to help guide the use of AI. These pods 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 for compliance with the Health Insurance Portability and Accountability Act (HIPAA) in the US.
This AI-enabled software development process 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 compared to previous versions, 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 used AI agents to quickly develop the prototype. The agents generated much of the UI. 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 workflow 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 AI engineering handling the everyday tasks, product development becomes leaner and smarter, better positioning companies to outshine the competition.
Ready to see how AI-enabled engineering can supercharge product development? Let Intellias show you how AI is changing the velocity of software engineering.
