AI operating model — from ideas to validated impact.
We help companies turn scattered AI ideas, pilots, and tools into a managed flow of business value: with clear roles, priorities, gates, risks, and validated impact.
The first step is a diagnostic of your current AI adoption process.
1–2 weeks · 3–5 interviews · blocker map and 3–6 month plan
3 initiatives at risk
New7
In review11
Completed12
Economic impact
RUB mln
Top initiatives
impact
AI resume scoringRUB 24 mln
Support RAGRUB 18 mln
CodeAgent reviewRUB 12 mln
Problem
AI exists.
Results do not.
The company already invests in AI, but the management loop does not keep up with pilots, tools, and business demand.
Solution
You need an AI operating model.
AIOM defines rules, roles, processes, and gates for the full initiative path: from idea to adoption and validated impact.
Result
AI becomes a managed flow of value.
Leadership sees the portfolio, risks are managed early, and initiatives are evaluated by impact, feasibility, and adoption readiness.
What this looks like inside the company
There are already enough ideas, tools, and budgets. But without shared strategy, coordination, and a clear link to business impact, AI initiatives drift into separate loops.
Different teams and vendors launch pilots, buy tools, and build similar solutions in parallel. Some AI tools move into shadow IT — outside architecture, security, and access control.
Infrastructure budgets grow, but leadership does not see one picture: where the money is, where the risks are, what is moving, and which initiatives actually create impact.
Business
AI office
Security
AI initiatives
Architecture
Data warehouse
IT
Finance
Scattered AI pilots
Pilots start without shared strategy, prioritization, or a clear route from idea to adoption.
Similar solutions are built in parallel
Teams and vendors create overlapping functionality without coordination or reuse.
AI moves into shadow IT
Solutions appear outside architecture, security, access control, and the shared technology loop.
Business impact is not proven
Impact is not measured or remains disputed, while infrastructure spend grows without a transparent link to results.
The problem is not AI.
The problem is the missing operating model that turns AI initiatives into repeatable results.
AIOM — AI operating model
AIOM helps a company build a managed loop for creating AI value: from idea intake to validated impact in the business process.
New
Assessment
Delivery
Awaiting impact
On support
AIOM makes AI adoption more than a one-off set of initiatives.
It is a repeatable management process where ideas move to value by clear rules.
Why we understand this problem
The methodology comes from practice, not AI slideware.
It is based on hands-on experience building an AI function, managing an AI initiative portfolio, and aligning decisions with business, security, architecture, data, IT, and finance.
Experience from a large company with high regulatory, technological, and organizational complexity.
First step
AI adoption diagnostic
The diagnostic helps you understand where the company is now, where value is lost, and which operating model elements should be implemented first.
For companies that already have AI pilots, but do not yet have a managed adoption and impact validation process.
How the diagnostic works
- 1–2 weeks
- 3–5 interviews
- Analysis of current initiatives and artifacts
- Final workshop
A practical next-step map
- Map of AI initiatives, ideas, pilots, and products
- Blockers across security, architecture, data, IT, finance, and ownership
- Prioritization by value, feasibility, and risk
- Leadership summary: portfolio, blockers, priorities, and next steps
- Draft target operating model
- 3–6 month plan
- Recommendations: continue, stop, keep manual, or move into the system
Result: a decision-ready package — map, priorities, and plan.
The methodology is documented openly
AIOM is not a slide deck. The methodology describes principles, roles, processes, gates, artifacts, and playbooks for building managed AI adoption.
Principles
How to separate value from hype and lead initiatives to validated impact.
Roles
How responsibility is distributed across business, AI office, security, architecture, data, and IT.
Processes
How an initiative moves from intake and assessment to delivery, adoption, and impact.
Artifacts
Which documents and templates prevent decisions from getting stuck between functions.
AIOM can be run manually. But it works better in a system built around AIOM.
The methodology can start in a corporate wiki, spreadsheets, task trackers, whiteboards, or other tools. But once initiatives grow, a single working loop becomes necessary.
After diagnostic / when scaling
A platform that turns AIOM into daily operational work
AI Conveyor is the operating layer on top of AIOM: one loop where initiatives, owners, gates, tasks, and impact live in the workflow instead of scattered files.
- single flow of AI initiatives;
- initiative portfolio, statuses, and owners;
- gates, risks, and approvals;
- delivery tracks and tasks;
- expected and validated impact;
- reporting for AI function and leadership.
3 initiatives at risk
New 7
In assessment 11
Completed 12
Economic impact
RUB mln
Main initiatives
impact
AI resume scoringRUB 24 mln
Support knowledge baseRUB 18 mln
Code review agentRUB 12 mln
AI inside the platform
AI Agent Ensemble
Hover over an agent to see its role in the process.
Analyst
Builds the business case and shapes requirements.
Analyst
We need a business case for ML scoring of loan applications
📊
Business case is ready download .pdf
Questions for business clarification:
1. Current approval rate for applications?
2. Expected monthly application volume?
3. Historical data for 2+ years?
Questions for business clarification:
1. Current approval rate for applications?
2. Expected monthly application volume?
3. Historical data for 2+ years?
Finance
Builds the financial model and estimates economic impact.
Finance
Estimate the economic impact of ML scoring
💰
Financial model is ready download .xlsx
Key metrics:
NPV: +RUB 47 mln over 3 years
Payback: 14 months
Return on investment: 3.2×
Key metrics:
NPV: +RUB 47 mln over 3 years
Payback: 14 months
Return on investment: 3.2×
Security
Gets security approvals moving without delays.
Security
What is needed for security approval?
🔐
Requirements checked. Needed:
• Data classification: personal data to ISPDn
• Security committee: ~2 weeks
Request template is ready fill in
• Data classification: personal data to ISPDn
• Security committee: ~2 weeks
Request template is ready fill in
Architect
Prepares technical and architecture documentation.
Architect
Prepare an architecture description for the solution
🏗️
Architecture decision
Components:
feature data mart
model registry
Inference API: p99 < 200ms
Components:
feature data mart
model registry
Inference API: p99 < 200ms
Data
Finds the right data marts and shapes data requirements.
Data
Where is the data for scoring?
D
Found the right data marts:
• scoring_features
Owner: data team
Coverage: 98%, updated daily
open in catalog
• scoring_features
Owner: data team
Coverage: 98%, updated daily
open in catalog
Infrastructure
Prepares environments, deployment, and monitoring for the AI product.
Infrastructure
What is needed to move the model into production?
IN
Checklist is ready:
• production environment
• 4 CPU / 8 GB memory
Deployment: process template
Monitoring connected
• production environment
• 4 CPU / 8 GB memory
Deployment: process template
Monitoring connected
Developer
Builds a prototype and shows the idea in the interface.
Developer
Build an MVP to demonstrate scoring
💻
Prototype is ready in 2 hours open demo
interface + model + data mart
12 features from the mart
Accuracy: AUC 0.81
interface + model + data mart
12 features from the mart
Accuracy: AUC 0.81
AI assistant
Orchestrator
Analyst
Finance
Security
Architect
Data
Infrastructure
Developer
Want to turn AI adoption into a managed flow of value?
Start with a diagnostic. We will review your current situation, define priority scenarios, risks, roles, and the next step toward an AI operating model.
A typical diagnostic takes 1–2 weeks and ends with a blocker map, priorities, and a 3–6 month plan.