Mejix · AI Operating Model
Embedding AI Into Your
Organization
From AI experimentation to an AI operating model.
Built with your teams. Owned by your organization.
02 / 09 · The Gap
From AI talk to an AI operating model.
Most companies are talking about AI. Few are actually building with it.
The reality today
High interest. Low impact.
- Teams experiment but don't scale
- No standardized way of working
- Governance and risk are unclear
- ROI difficult to measure
What success looks like
AI embedded, measured, scaled.
- AI embedded into real delivery teams
- Measurable productivity gains
- Clear governance
- Repeatable system that scales across teams
03 / 09 · Our Approach
A joint transformation,
not a vendor delivery.
We partner with your organization to build sustainable AI capability — designed, installed, and owned together.
Proven foundation
Start from proven tools and methodologies.
Together with your teams
Implement them side-by-side, not over the wall.
Knowledge stays with you
Ownership and IP remain with your organization.
Clear accountability
Transparent control and governance throughout.
04 / 09 · Delivery
How we deliver value.
A five-step path from pilot to autonomous, AI-powered delivery.
-
1→
Start with a controlled pilot
One real team. Measurable KPIs.
-
2→
Document what works
Tools, workflows, patterns, best practices.
-
3→
Build the playbook
Your organization owns this forever.
-
4→
Scale to the next team
Faster, with less support.
-
5
Scale independently
You run it — with optional expert support.
05 / 09 · Pilot KPIs
Measuring what matters.
Outcomes measured against a baseline captured in week one — before AI is introduced.
Results vary by role mix and baseline.
Development & Engineering
25–40%
Reduction in delivery cycle time
Code review iterations · AI-assisted code ratio · rework per story
Quality Assurance
200%
Faster test case generation
Automation coverage · bug escape rate · manual hours saved
Business & Product
300%
Faster requirements & specs
AI-generated content ratio · review cycles · sign-off time
Overall target
25% reduction in delivery cost
Measured against the week-one baseline, before any AI tooling is introduced.
06 / 09 · Roadmap
Example: a mid-size enterprise rollout.
One scenario — six teams over six months. The model scales linearly to your size.
W1–4
Phase 1
Pilot Team 1
All functions: product, engineering, QA, ops.
W5–8
Phase 2
Enable Team 2
Team 1 supported · Team 2 onboarded.
W9–12
Phase 3
Enable Team 3
Teams 1–2 now operate independently.
W13–16
Phase 4
Enable Team 4
Reduced support overhead.
W17–20
Phase 5
Enable Team 5
Optional 0.2–0.5 FTE expert access.
W21–24
Phase 6
Enable Team 6
All teams independent. Expert support optional.
Scale to your size
2 teams
3 months
4 teams
4 months
10 teams
8 months
Compounding economics
Each additional team costs 40–60% less than the previous one.
07 / 09 · Sizing
Size this to your organization.
Same operating model. Three pre-shaped engagement sizes — flexed to your team count.
Small
2–3 teams
2-month pilot
1 additional team
$15–25K engagement
~$40K annual ROI per team
Mid-size
4–6 teams
3-month pilot
2–5 additional teams
$20–35K engagement / month
~$30K annual ROI per team
Large
8–12 teams
4-month pilot
4–10 additional teams
$30–50K engagement / month
~$25K annual ROI per team
Quick calc
Months needed
Teams in scope ÷ 2
Annual savings
Teams × $25–30K
08 / 09 · Investment & Ownership
One pilot.
One playbook.
Every team after — faster and cheaper.
A flexible engagement model designed for full ownership and zero lock-in.
The investment
- 2 FTEs of senior AI expertise per month (0.5 FTE Lead + 1 FTE distributed by need)
- Fully directed by your organization
- Expert review and validation included
- Cost scales with team count
- Zero vendor lock-in post-pilot
- All playbooks and IP stay with you
Mejix · Obsessed with delivery, powered by AI
09 / 09 · Common questions
Quick answers.
What to measure, how to start, and what we need from your team to move from talk to operating model.