Spec-Driven Development vs. Traditional Engineering: Where Delivery Teams Actually Stand Today

AI is changing how fast teams can write code. What it has not changed, at least not yet, is how clearly teams define what they are building in the first place.

That gap is becoming the defining challenge of AI-native engineering. Code can now be generated in minutes, but if the requirements feeding that process are vague or open to interpretation, AI does not resolve the ambiguity. It reproduces the ambiguity faster, and at a greater scale.

This is the shift behind the growing interest in spec-driven development: defining what you’re building precisely enough that AI accelerates the work instead of amplifying its flaws. To understand where the industry actually stands — and what it takes to make the move — we sat down with Phong Bui, Senior Vice President of Technology at KMS Technology.

Key Takeaways

• Spec-driven development gives AI clear, structured specifications, reducing ambiguity, rework, and delivery risk.
• Traditional engineering remains valuable, but teams must make requirements more structured and AI-ready to fully benefit from AI-native software development.
• The shift to spec-driven engineering is an operating model transformation requiring better specifications, stronger BA and QA capabilities, and AI-native workflows.

From Loose Requirements to Executable Specifications

The way teams capture requirements is changing. For years, epics, user stories, and acceptance criteria were the standard unit of work. They are still useful, but in an AI-native model they often leave too much room for interpretation.

There is a shift away from loosely written requirements toward more structured, testable, and executable specifications. In an AI-native engineering model, requirements need to be clearer, more complete, and easier for both humans and AI agents to understand. That is why spec-driven development is gaining attention: it helps teams move from describing intent to defining expected system behavior in a more precise way.

The reason this matters now comes down to input quality. AI can only accelerate delivery when the input quality is strong enough. Faster code generation has limited value if the specification driving it is unclear.

Why Traditional Delivery Is Hard to Leave Behind

Spec-driven engineering is gaining ground, but traditional requirements-based delivery is still the norm for most teams, and for good reasons.

Traditional delivery remains common because it is familiar. Most teams already know how to work with epics, user stories, acceptance criteria, and sprint-based planning. It still makes sense when the product is evolving quickly, the domain is not fully understood, or the client needs flexibility more than strict upfront structure.

The limitation is that this model depends heavily on human interpretation. Different people can read the same requirement differently, which creates rework, missed edge cases, and gaps between business expectations, implementation, and testing.

The answer is not to abandon what works. Traditional delivery is not disappearing, but teams will need to make requirements more structured and AI-ready over time.

One Platform, Two Models: Meeting Teams Where They Are

Most organizations are not choosing between traditional and spec-driven delivery in a single step. They are somewhere in between, which is why VELOX, KMS’s AI-native engineering platform, is designed to support both.

For teams still working in a traditional model, VELOX plugs into the inputs they already have: epics, user stories, documents, tickets, code repositories, and test cases. It helps analyze context, generate or refine requirements, support development activities, and connect requirements with testing.

For teams moving toward spec-driven engineering, VELOX supports a more structured flow — grounding requirements in real system context, helping turn intent into testable specifications, and linking those specifications directly to validation so gaps surface before they reach production. In other words, KMS is not only advocating for spec-driven delivery; it is building the capabilities to help teams adopt it in practice.

What this reveals about the market is telling. Most clients are not fully spec-driven yet. They want the benefits of AI-native engineering, but they also need a practical path from their current process. That is why KMS believes the right approach is not to force one model, but to support both and help teams mature step by step.

The New Skill Set: BAs and Testers as System Behavior Designers

Spec-driven engineering does not just change tooling. It changes roles, especially for business analysts and testers.

The biggest shift is that BAs and testers need to think more like system behavior designers. For BAs, the work moves beyond writing user stories and acceptance criteria toward defining business rules, edge cases, constraints, workflows, and data expectations in a way that both engineering teams and AI tools can understand.

For testers, the role moves earlier in the process. Instead of only validating after implementation, testers can help shape the specification by identifying ambiguity, missing scenarios, and testability gaps upfront.

This raises the bar on capability. Stronger analytical thinking, deeper domain understanding, structured writing, and the ability to work effectively with AI tools all become core skills. These will define modern delivery teams.

Where Adoption Really Stands

For all the interest in spec-driven engineering, adoption is still early. Many teams are interested, especially because of AI, but only a smaller group is truly ready to operate this way at scale.

The barrier, importantly, is not the technology. It is the operating model and skill set. Teams need better requirement discipline, clearer ownership between business and engineering, stronger BA and QA capabilities, and a delivery process built to support structured specifications. Without that foundation, the warning is simple: AI may make teams faster, but not necessarily better.

Success Story: How an InsurTech Leader Achieved 75% Faster Releases with AI-Native QA

Client: A global InsurTech leader providing digital solutions for the life insurance and financial services industry sought to modernize its quality engineering practice to support faster, more reliable software delivery.

Challenge: Manual testing, fragmented automation, and growing release complexity slowed delivery, increased maintenance effort, and limited the organization’s ability to scale quality alongside development.

Solution: KMS implemented an AI-native, automation-first quality engineering model that modernized testing, strengthened validation, and integrated quality throughout the software delivery lifecycle.

Outcome: The transformation enabled 75% faster software releases, improving release velocity while maintaining quality and confidence at enterprise scale.

→ Read the full case study

The Bottom Line

The move from traditional delivery to spec-driven engineering is less a tooling decision than an operating-model decision. AI raises the ceiling on speed, but only a strong specification, clear ownership, and capable teams turn that speed into reliable outcomes.

That is why KMS is investing on both sides of the equation: the VELOX platform and the delivery capability around it. The goal is to help clients adopt AI-native engineering in a practical way, starting from where they are today, then gradually moving toward a more structured, specification-driven model. 

VELOX helps teams start from their existing delivery artifacts and move at a practical pace, whether that means creating traditional epics and stories, structured specifications, or a hybrid of both. This gives teams a realistic path from today’s delivery model toward a more traceable, specification-driven engineering workflow.

For teams weighing the shift, the takeaway is encouraging. You do not have to be fully spec-driven tomorrow. You need a clear path, the right skills, and a platform that meets you where you are.

Ready to turn requirements into clearer specifications, stronger validation, and faster AI-native delivery? Connect with KMS Technology to explore how VELOX can support your team’s next step.