When AI Doesn't Fulfill the Productivity Promise

Lessons from Enterprise Usecases

Large gains in productivity has been elusive

Your feeds must be filled with Vibe coding example videos with fully functioning, amazingly beautiful, websites or apps built using Vibe coding within minutes. You must be thinking - wow software engineering is dead! Why do you need large teams when AI can just do that? The promise? A 3-10x productivity boost, instant feature delivery, and significant reductions in engineering spent. You are also hearing about a general slowdown in hiring of software developers and even layoffs in large tech giants where they cite AI based improvements as the reason for staff reductions. I heard from several sources that many companies are demanding 40-45% reductions in annual costs from software outsourcing vendors as time comes for contract renewals, and many are obliging since they are seeing their competitors bidding at these lower levels.

But as the dust settles, and as you examine the data, the real improvements that you see, even in tech giants who have been at the forefront of this AI revolution, is around 10% improvement in productivity. So where is the disconnect?

While AI coding tools can deliver impressive productivity gains in narrow contexts, the reality is that large-scale enterprise development is a different beast. In this article, we'll explore why the much-touted 30-50% productivity boost is often elusive, and how the complexity of large codebases, legacy systems, and human processes can dampen the initial promise of AI. Finally, we'll consider how platforms like Kavia can help bridge these gaps and offer a more balanced approach to AI-assisted development.

"The most important metric, and we carefully measure it, is how much has our engineering velocity increased as a company due to AI?" he said. The company estimates that it's so far seen a 10% boost.

— Sunder Pichai, Google CEO

The Early Promise: Bold Claims from Tech Giants

Many AI coding tools entered the market with bold claims. Vendors and tech giants often tout productivity uplifts of 20--50%, suggesting that developers can code faster and deliver features at unprecedented speed. However, more cautious internal reports and studies paint a different picture. While there are certainly gains, they tend to be more modest in real-world, large-scale projects.

For instance, studies have shown that while AI tools can indeed reduce time spent on routine tasks---like writing boilerplate code or simple unit tests---they struggle in more complex environments. In one survey, a notable percentage of employees actually reported that AI tools made them less productive due to the overhead of integrating and validating AI-generated code. Another study found that initial productivity boosts were often concentrated in simpler, smaller-scale tasks, and that when teams moved to larger, interconnected systems, the gains diminished significantly. In other words, the real-world uplift is often less dramatic than the marketing headlines suggest, especially in large enterprise environments.

Where the Gains Truly Matter --- and Where They Don't

In scenarios where you're building small utilities, prototypes, or standalone apps, AI tools can indeed deliver impressive productivity improvements. They excel at generating boilerplate code, writing documentation, and handling routine refactoring. For example, a startup building a simple proof-of-concept app might see development times cut by 80-90%. Many of our smaller customers and startups have reported that they are able to get a working prototype up and running in just a few days.

Diminishing Returns in Large Codebases:

However, once you move into large, complex enterprise systems, those gains start to diminish. The context and interdependencies in a mature codebase mean that AI-generated code often requires significant human oversight. When you take a small component in a large codebase with even less than a thousand lines of code, you would see that the code handles not just the primary functionality, but also several tweaks or adjustments to handle very specific real-world use-cases or changes to integrate with intricacies of the larger project codebase. AI often misses these and produces vanilla code that handles the primary functionality based on its own training and understanding.

Debugging AI-generated code becomes more challenging, as developers need to spend additional time understanding not only what the AI produced, but also how it fits into the architectural constraints and existing business logic.

Over time, the net productivity gains are eroded by the overhead of integration, testing, and maintenance. In fact, what starts as a 50+% boost in a small project might dwindle to a much smaller net improvement once you factor in the long-term costs of dealing with technical debt and architectural consistency.

The "Invisible Work" That AI Doesn't Help Much With

Beyond just writing code, a large portion of software development involves defining features, aligning them with business goals, navigating approval processes, and ensuring architectural consistency. These human-centric tasks haven't seen the same level of acceleration from AI tools. As a result, even if coding speed improves, the overall time-to-market may not improve proportionally---especially as complexity increases.

Furthermore, AI-generated code often needs more rigorous review and validation. Developers must spend additional time debugging, refactoring, and ensuring that the AI-produced code meets quality and security standards. Over time, this additional overhead can offset the initial coding speed gains, resulting in a more modest net improvement. In fact, the "invisible work" of aligning features, navigating human-centric processes, and maintaining architectural coherence is where AI tools offer limited help. This can reduce the overall impact on time-to-market, especially as the complexity of the existing product increases.

Why Many Enterprises Aren't Seeing more than 5-10% Net Gains

When we put it all together, it becomes clear why many enterprises won't see the sweeping productivity gains that were initially promised. The combination of high context complexity, legacy code, invisible human work, and the overhead of validation and integration all contribute to more modest net gains. While AI tools are incredibly valuable, they aren't a silver bullet.

Next phase of Vibe Coding

This is where a platform like Kavia comes into play.

Kavia creates and actively maintains an Enterprise Knowledge Graph that processes existing code and makes sure that all nuances are captured in this KG. Keeping a KG based on code is not easy since code is always changing. Every repository may contain hundreds of branches and keeping KGs for 100s of repositories across 100s of branches required some out of the box thinking.

Kavia's micro agents are then able to effectively make use of knowledge tools to retrieve the right information for each step to ensure that AI-generated code aligns with existing patterns and architectural concepts. This reduces the burden on developers and helps maintain architectural coherence, even in large codebases.

Kavia's Full Lifecycle approach means the system tracks not just code, but also requirements, architecture, detailed design, test cases, etc. Information is not lost as the project progresses across various phases of SDLC.

Kavia has designed an orchestration system that can be fine-tuned based on the workflows which are unique to an enterprise. Kavia is built with the expectation that people are still the driving force behind all innovations. Organizations will have several teams working on different parts of the system and the need to collaborate within teams and across departments does not go away. Each session within Kavia is tracked and can be examined, modified or extended by others in the same team or other approvers - similar to how Git manages Pull Requests. If the sessions are for code generation, you could track all changes within those sessions and decide when to merge.

Real-World Impact

Recently one of our customers had specified some new requirements (in PDF with flow architectural diagrams and flow diagrams which illustrate the changes needed) on a large codebase. It would have taken weeks for experts with experience in that codebase to even fully understand the requirements, and would have taken several more weeks to come up with detailed designs and test plans. The customer had expected the project to last for around 4-6 months and be done by 3 engineers. With Kavia, the team was able to complete the project in roughly 2 week's time and another two weeks for the final approval for all the deliverables.

Conclusion

In conclusion, while AI coding tools hold great promise, it's important to approach their adoption with a nuanced understanding of their limitations. The bold claims of a 50+% productivity boost often don't fully materialize in large, complex systems. However, by using platforms like Kavia that thoughtfully integrate AI with human expertise, enterprises can capture sustainable improvements and truly reimagine the way they build software.

Author Bio

Labeeb Ismail is Founder and Chief Executive Officer of Kavia.ai, an enterprise platform redefining how software is built through generative AI, knowledge graphs, and micro-agent automation - transforming how enterprise software is developed through end-to-end automation, intelligent tooling, and scalable DevOps infrastructure. He has a track record of leading large-scale innovation in enterprise software and automation. Former SVP at Comcast, where he built and led a global team of 2,000+ engineers managing 100M+ devices and delivering 7,000+ software releases annually.

A core architect of RDK's global success and an early adopter of generative AI in product development, he is passionate about helping organizations accelerate innovation by eliminating manual bottlenecks and rethinking the software delivery lifecycle.

When AI Doesn't Fulfill the Productivity Promise