0 -> 35
Dedicated CoE members
Anonymized engagement brief
EnterpriseAI Capability Building
AI Center of Excellence from Scratch
From no in-house AI capability to a fully operating CoE.
Engagement answer
NeoBram designed and stood up an enterprise AI Center of Excellence end to end - structure, hiring, governance and delivery model, plus hands-on developer training. The client went from zero in-house AI capability to 35 dedicated CoE members in 6 months, with developers trained to build with AI and use AI in their daily work.
Reported outcomes
What changed in the stated scope.
6 months
Time to stand up
Anonymized engagement brief
Daily use
Developer AI enablement
Anonymized engagement brief
Self-sufficient
Delivery capability
Anonymized engagement brief
These figures are not a guarantee or a benchmark for another organization. A new project requires its own baseline, scope, measurement method and acceptance test.
Client context
Who the brief describes
A large enterprise that wanted AI capability in-house but had no team, no operating model and no AI best practices in place.
Business problem
What needed to change
The organization wanted AI capability in-house but had no team, no operating model and no AI best practices. Typical industry practice: hire slowly, learn by trial and error, stay dependent on vendors.
Baseline
Where the work started
- No dedicated AI team or CoE
- No defined AI operating model or governance
- No in-house AI delivery playbooks or best practices
- Heavy dependency on external vendors for any AI initiative
Data
Evidence used by the system
- Current org structure, roles and skills inventory
- Existing technology stack and data platforms
- Active AI use cases and pilots across business units
- Internal compliance, security and procurement standards
Solution
What NeoBram built
NeoBram designed and stood up the AI CoE end to end: structure, hiring, governance and delivery model, plus hands-on developer training in AI best practices and using AI to multiply their own productivity.
Integration
Systems and interfaces
- CoE operating model aligned to existing enterprise governance
- Hiring profiles and onboarding plan for AI and ML roles
- Delivery playbooks for discovery, build, MLOps and run
- Developer enablement program embedded into daily workflows
- Executive scorecards to track CoE maturity and outcomes
Timeline
Reported delivery sequence
Operating model and first hires in 8 weeks. 35 dedicated CoE members onboarded and productive within 6 months.
Governance
Controls described in the brief
- CoE structure aligned with enterprise risk and compliance
- Clear RACI between CoE, business units and central IT
- Hands-on developer training in responsible AI use
- Knowledge and IP retained in-house, not with vendors
- Independent client ownership of the operating model and playbooks
Measurement method
How to read the outcome
- The page preserves the engagement's reported baseline, implementation scope and outcome labels.
- The customer dataset, calculation workbook and acceptance records are not publicly available for independent review.
- Any percentage, time or accuracy value applies only to the stated scope and should not be treated as a forecast for another site.
Limitations
What this brief does not prove
- Customer identity is withheld, so public reference checking is not possible.
- Performance depends on the original data, process, hardware, users, thresholds and review workflow.
- The brief does not establish causality beyond the engagement's reported comparison.
- Future buyers should define their own baseline, held-out test and acceptance criteria.
Build your own evidence record
Define the baseline and acceptance test before the pilot.
NeoBram will help turn one operating problem into a scoped, reviewable AI project and hand over the production capability.