AI spend needs accountability
Most teams buy tools before defining operating metrics. This track starts with ROI baselines and adoption targets.
BUNKROS AI Training
Build AI workflows that ship measurable business results.
From strategy to execution: align teams, automate repetitive work, and keep governance tight while scaling AI across operations.
Why This Matters
Strategic relevance before tactical execution.
Execution beats experimentation
You will move from one-off prompts to repeatable workflows with owners, SLAs, and escalation paths.
Governance is non-negotiable
Privacy, access controls, and model risk review are integrated into delivery from day one.
What You Will Learn
Practical capabilities you can apply immediately.
- Map high-impact processes for AI augmentation and automation.
- Design prompt and review workflows that reduce operational errors.
- Build KPI dashboards for throughput, quality, and cost-per-task.
- Set governance policies for model use, approvals, and auditability.
- Define rollout plans for executives, managers, and frontline teams.
- Create an AI operating cadence with weekly optimization rituals.
Curriculum Modules
A structured path from foundations to implementation.
Module 1: AI Opportunity Mapping
Prioritize workflows using volume, complexity, and financial impact.
Module 2: Workflow Design and Controls
Translate tasks into repeatable AI-assisted SOPs with review checkpoints.
Module 3: Team Adoption Systems
Set role-based playbooks and change-management loops that stick.
Module 4: Metrics and Business Cases
Track value with hard numbers, from cycle time reduction to margin lift.
Module 5: Risk, Compliance, and Security
Implement policy, access, and logging standards without slowing delivery.
Module 6: Scale Playbook
Expand from pilot to organization-wide deployment with governance intact.
30-Minute Training
One focused sprint to move from theory to repeatable execution.
00:00 - 05:00
Introduction
Define the problem this track solves, pick one real workflow, and set a measurable target for the session.
05:00 - 11:00
Theory Block 1
Map the core principles so your decisions are based on system behavior, not trial-and-error prompting.
11:00 - 17:00
Exercise Block 1
Run a controlled build task with explicit constraints, then measure output quality against your rubric.
17:00 - 23:00
Theory Block 2
Add governance, validation, and failure modes so the workflow remains usable in production.
23:00 - 30:00
Exercise Block 2 + Check
Refine your first build, run a quick knowledge check, and prepare your next learning sprint.
Theory Blocks
Foundations that keep your outputs reliable.
AI spend needs accountability
Most teams buy tools before defining operating metrics. This track starts with ROI baselines and adoption targets.
Execution beats experimentation
You will move from one-off prompts to repeatable workflows with owners, SLAs, and escalation paths.
Governance is non-negotiable
Privacy, access controls, and model risk review are integrated into delivery from day one.
Hands-On Exercises
Short builds designed for immediate skill transfer.
Exercise 1: Module 1: AI Opportunity Mapping
Prioritize workflows using volume, complexity, and financial impact.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 2: Module 2: Workflow Design and Controls
Translate tasks into repeatable AI-assisted SOPs with review checkpoints.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 3: Module 3: Team Adoption Systems
Set role-based playbooks and change-management loops that stick.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Knowledge Check
Validate comprehension before scaling the workflow.
Open Resources
Continue learning with high-quality public material.
Glossary
Key terms you should be fluent in for this track.
SLA
Service-level target for turnaround, quality, and reliability of an AI-supported workflow.
Adoption Loop
Weekly cycle of usage review, friction removal, and process refinement.
Tools Covered
Tooling choices tied to workflow outcomes.
ChatGPT Enterprise
Claude
Gemini
Notion AI
Zapier
Make
Power BI
Miro
Confluence
Who This Is For
Built for operators, builders, and strategic teams.
- Operations managers and transformation leads.
- Founders and executive teams implementing AI at scale.
- Consultants building AI-enabled delivery models.
- Team leads responsible for process efficiency and quality.
Outcomes and Career Impact
Execution outcomes with direct professional value.
Outcome
Launch a production-ready AI workflow in under 45 days.
Outcome
Reduce repetitive workload by 20 to 40 percent in target processes.
Outcome
Establish governance artifacts suitable for internal audit review.
Outcome
Present an AI expansion roadmap backed by measurable outcomes.
Signals from Practice
Operator-level feedback and implementation sentiment.
"Finally a business AI training that starts with operations reality, not hype."
"We moved from experiments to a board-ready AI operating model."
Access Models
Free, cohort, and enterprise pathways.
Starter
EUR 0
Orientation workshop, sample templates, and readiness checklist.
Pro Cohort
EUR 599
6-week course, live reviews, and implementation support.
Enterprise
Custom
Department rollout, governance setup, and leadership coaching.
Ready to Start
Turn AI strategy into operating reality.
Book a readiness call and align your first implementation sprint with clear KPIs.
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4-week prompt lab
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