4 min read
Today, Kosli and Adaptavist announce a strategic partnership to help regulated enterprises automate governance for AI driven software delivery - making it automated, continuous, and evidence-driven rather than a manual checkpoint that sits apart from DevOps and CI/CD.
Adaptavist brings deep enterprise DevOps transformation expertise: assessment and strategy, DevSecOps integration, developer experience, and implementation across Atlassian, GitLab, and AWS. They work with some of the world’s largest organisations to align people, processes, and toolchains for faster, safer software delivery.
Kosli provides the Controls Engineering infrastructure that makes automated governance possible - capturing compliance evidence continuously across the delivery pipeline and enforcing policy at the point of action, not after the fact.
The partnership is timely. AI-driven software delivery is arriving fast, and for regulated enterprises, the governance gap it exposes is about to get a lot harder to ignore.
Governance Is the Last Manual Bottleneck in Regulated Software Delivery
A decade of DevOps investment has transformed how enterprises build and ship software. Pipelines are automated. Testing is continuous. Deployments happen dozens - sometimes hundreds - of times a day. The tools, the culture, and the practices have all moved forward.
But for regulated enterprises, one part of the delivery lifecycle has barely changed at all: governance.
Change Advisory Boards still convene to review tickets. Approval workflows still require human sign-off. Compliance evidence is still gathered manually, often weeks after the fact. And the final step before production - the last mile of delivery - still sits outside the DevOps loop, disconnected from everything that came before it.
That gap has always been costly. AI-driven software delivery is about to make it unsustainable.
When AI Writes the Code, Human Governance Can’t Keep Up
AI agents can now write, review, and prepare code for deployment at machine speed. The volume of change that regulated enterprises will need to govern is about to increase by an order of magnitude - and it will keep increasing.
Legacy governance processes were designed for a world where release cycles were measured in weeks or months. They were already under strain from modern DevOps practices. AI-driven development won’t just expose their limits. It will blow straight through them.
The collision is predictable: productivity lost to approval gates that can’t scale, compliance costs that escalate as manual processes handle ever-higher change volumes, and rising risk as the gap between what gets deployed and what gets documented grows wider.
Regulated enterprises need a fundamentally different approach to software delivery governance - one that is event-driven, evidence-based, and engineered to scale with AI-generated change volumes rather than against them.
The Last Mile Problem
For many enterprises, DevOps stops just short of production. Teams invest heavily in automation - CI/CD pipelines, automated testing, containerisation, developer portals - and then hand over to manual change boards and disconnected approval processes at the very end.
It’s the DevOps equivalent of building a six-lane motorway that ends in a roundabout.
The problem isn’t a lack of intent. Regulated enterprises operate under real compliance obligations. Change management processes exist because regulators require evidence of control over what gets deployed into production. The issue is that those processes were never designed to be part of the DevOps workflow - they were bolted on beside it.
The result is a bottleneck that undermines the speed, visibility, and auditability that DevOps promises everywhere else in the pipeline. And as AI-driven delivery accelerates change velocity, that bottleneck will only tighten.
What This Means in Practice
Regulated enterprises that work with Kosli and Adaptavist can expect:
Governance that moves at DevOps speed. Compliance evidence is captured automatically as code moves through the pipeline - no manual data gathering, no retrospective documentation, no approval delays for changes that are already well-evidenced.
A DevOps loop that extends to production. Change management becomes part of the workflow, not a handoff outside it. The last mile to production finally comes inside the loop.
An automated foundation for AI-driven delivery. As AI agents increase change velocity, governance scales with it - because it’s built on automation, not human bandwidth.
AI-driven software delivery isn’t a future concern for regulated enterprises. It’s arriving now. The organisations that will benefit most are those that address the governance gap before the volume of AI-generated change exposes it at scale.
