Presentation transcript from Rollie Stephens, Co-Founder of QAT Global, delivered at the Nebraska IT Symposium 2026.
Hello and welcome.
Before we dive into the slides, I want to start somewhere a little different.
I want to ask you a few questions and I want you to answer them honestly in your own head as we go.
Question one.
Most of you are probably already using AI in some forms such as copilots, chat assistants, or code suggestions.
But ask yourself, is AI actually changing how your team delivers software, or is it just helping individual developers type a little faster?
Question two.
Think about the last feature your team shipped.
How many tools did that requirement pass through from the original idea all the way to production, and how much context was lost along the way?
If you’ve ever seen a developer build something that didn’t quite match what the business intended, then you already know the answer.
Question three.
Think about how much of your engineering capacity goes into rework.
This includes rejected pull requests, misunderstood requirements, and bugs caught too late.
Has AI tooling actually reduced that, or does it just feel like the same problems are happening faster?
Question four.
When a test fails in your pipeline, how long does it take to figure out whether the problem is in the test itself, the service, or the original requirements?
For most teams, the honest answer is too long.
Question five.
And this one is for the leaders in the room.
If someone asked you right now how much your team spent on large language model calls last Sprint, broken down per feature, could you answer that?
For most organizations, AI spend is a black box.
And one last question, would you let an AI agent release code to production on its own with no human review and no final sign off?
Most people instinctively say no.
That instinct matters.
We’ll come back to why a little later.
Keep those questions in the back of your mind because everything I’m about to show you was designed to answer them.
Let’s start with the distinction that changed how we think about AI and software delivery.
AI Assisted vs. AI Accelerated
AI assisted development is not the same thing as AI accelerated.
Today, I believe most teams are at the AI assisted stage, and there’s nothing wrong with that.
It’s a reasonable place to be.
But it’s important to recognize that AI assisted does not equal AI accelerated.
Traditional vs. Diamond AI Accelerated Framework
The Diamond AI accelerated framework is integrated and fast.
The key insight is this: it’s not about individual tooling, it’s about team wide collaboration.
When the entire team operates within the same workflow, you get real results.
Maturity Levels
Level 0 is traditional methodology.
Level 1 is where most teams are right now with copilot assisted autocomplete.
Level 2 is where Diamond AI operates with shared context and zero manual handoffs.
The Diamond AI Methodology
The five phases start with business requirements, project planning, development, quality assurance, and deploy.
Every stage has at least one AI agent assigned to it.
Every stage has a gate between it and the next one.
And the entire flow sits on top of a human in the loop foundation.
Phase 1: Business Requirements
This is where we turn business intent into clear executable requirements.
The analyst agent helps capture and refine requirements from interviews and unstructured data.
By the end of phase one, you have a buildable specification.
Phase 2: Project Planning
We define the project charter and create the product backlog.
We design architecture aligned to business objectives.
We embed governance, security, and compliance into the plan itself.
Phase 3: Development
This is accelerated development with AI.
The developer agent generates the code, outputs are validated and code is reviewed.
Quality gates are applied and the work is packaged and handed off to QA.
Phase 4: Quality Assurance
This ensures every release is production ready.
We run automated smoke regression, cross service journey and performance tests.
The output is a clear release recommendation — go, conditional go, or no go.
Phase 5: Deploy
Software is delivered with consistency, quality and traceability.
We deploy through governed CI/CD pipelines.
Humans approve, review, and decide at every gate.
The Diamond AI Reference Architecture
There are three engineered pillars feeding one source of truth.
Requirements.
Agentic workflow development.
Quality assurance.
Everything works as one integrated system, not three disconnected tools.
Why Diamond AI is Different
Most AI development tools today are code copilots.
They help individual developers write code faster, but they don’t change how teams work together.
Diamond AI is built for spec driven development.
The entire team operates within a single unified workflow with shared context.
Built-In Cost Intelligence
Diamond AI tracks all LLM costs natively per work item per phase.
The dashboard shows total spend right alongside delivery statistics.
Leadership gets a transparent view showing exactly how AI is being used, what it’s delivering, and what it costs per work item.
The Diamond AI Control Panel
This is a real dashboard running in production today.
Project managers, developers, and leadership all get visibility into what’s happening across the delivery pipeline.
There is one source of truth for delivery status, agent activity, and spend.
Closing Thoughts
Diamond AI was built to answer the questions organizations are asking about AI delivery, governance, visibility, and speed.
This isn’t AI assisted — this is AI accelerated.
At QAT Global, your success is our mission.
