The gap between speed and value
AI has unquestionably made individual developers faster. Code generation, documentation, and unit testing now happen at a pace that would have seemed unrealistic just a few years ago. Organizations that invested heavily in coding assistants expected this speed to ripple through their entire delivery chain. Instead, many are watching a frustrating reality unfold: more output, but not meaningfully improved outcomes.
Coherent Solutions, a global digital product engineering and technology consulting company, calls this the “Digital Value Gap,” which is the growing distance between what AI tools produce for individuals and what the organization actually ships.
According to Vittesh Sahni, Senior Director of AI Engineering at the company, the root cause isn’t the tools themselves. It’s the operating model that they’re being used in. Traditional software delivery systems were designed around human-paced work. There are slow handoffs, sequential review cycles, and governance at each stage. When AI accelerates the front end of that process, the downstream stages feel the impact. Adding another AI model doesn’t fix that, either. It often makes the problem worse.
Why governance is now a strategic asset
Gartner has identified an AI-assisted productivity ceiling that many organizations are already hitting. Research from BCG suggests that most of the real value from AI initiatives comes not from the tools themselves, but from the people, processes, and systems around them. Buying AI is the easy part. Redesigning how work flows through an organization takes careful observation and attention.
This is where Coherent Solutions’ Continuous Delivery Loop (CDL) comes into play. Developed and stress-tested inside the company’s own delivery operations, the CDL is a tool-agnostic framework that organizes AI-powered workflows into a coordinated, continuously improving system. The four activity centers include Problem Identification, Validation & Exploration, Design & Engineering, and Observation & Scale. They’re connected feedback loops, designed so that insight from production informs the next cycle of development from the very start.
The operating system, not the tool
Some enterprises today sit in what the company describes as the “adoption” stage of AI maturity. AI is being used daily, but teams remain siloed, inconsistent, and the lessons learned by one team rarely reach another. This can come with challenges because it feels like progress when they’re really leaving a lot of value on the table. Coherent Solutions reports an average delivery-performance improvement of approximately 30% across CDL-activated teams.
Shawn Torkelson, the Chief Marketing & Strategy Officer at the company, explains that the organizations that will lead in software delivery are not necessarily those with the most sophisticated AI tools, but those that have redesigned how work flows around AI. For enterprise leaders watching their AI investment produce code without proportional returns, CDL offers a practical framework for closing that gap.
The information provided in this article is for general informational and educational purposes only. It is not intended as legal, financial, medical, or professional advice. Readers should not rely solely on the content of this article and are encouraged to seek professional advice tailored to their specific circumstances. We disclaim any liability for any loss or damage arising directly or indirectly from the use of, or reliance on, the information presented.
