Yes, but only if the system around it changes. AI won’t fix broken delivery, but will expose it.
Companies should rethink how software gets built and what foundation they lay in the bases. At Coherent Solutions, that rethink is already underway.
AI-native engineering as an operating model
AI can generate in hours what once took days. But then human engineers would spend days validating, testing, and deploying the output. This is a “productivity paradox” that shows up unless following a human-in-the-loop approach. To avoid that paradox and start seeing real digital value, companies need mature CI/CD, strong QA automation, and site reliability discipline.
AI changes roles
AI changes the role of the project manager and the function of the PMO. If you don’t redefine those roles, friction builds up, and you end up moving faster at one step and slower at the next. At Coherent Solutions, this shift reflects years of building structured engineering practices across enterprise clients. And AI made the framework indispensable.
Measurement before momentum
Many companies seek to improve productivity with AI, but few can prove their baseline, including lead time, defect rate, quality under pressure, deployment frequency, and delivery rhythm. Without these metrics, AI will just move effort from writing to reviewing.
The path to AI-native is three-stage
Max Belov in his article explains a pragmatic model, shaped by our own evolution from a traditional SDLC to what we call the Coherent Continuous Delivery Loop:
- Tool adoption
- Workflow automation
- Feedback loop
What’s in it for enterprises
For enterprise leaders, that implies the demand to adapt workflows as deliberately as they adopt tools.
For Coherent Solutions, this reflects a broader shift from service execution to strategic partnership. Helping clients build AI-ready operating models is now as critical as implementing the technology itself.
Read the full story at Forbes Technology Council.
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