For CFOs, controllers, analysts and finance professionals who want to apply AI to a real everyday problem — from forecasting and reporting to automation and controls — with human review and an audit trail.
You leave with a real finance process, workflow or analysis — tested, documented and ready for a decision.
8 weeks · 24h live · 1 cumulative real problem · Python with no programming prerequisites
You can join alone and apply the project where you work, or bring the team to advance the finance function's problems.
You buy the seat and apply the project at the company where you work — the real finance problem becomes the core of your journey.
The organization brings the team and the finance function's problems and capabilities enter the journey, with common standards.
The program answers different problems depending on the role. Explore what each role learns and builds — the real problem remains the common core.
Decide where AI really creates value and govern the risk.
See problems, learning and deliverable
Problems
You learn
You build
Improve forecast, close and management narrative.
See problems, learning and deliverable
Problems
You learn
You build
Automate routines and raise the value of analytical work.
See problems, learning and deliverable
Problems
You learn
You build
Accelerate research, modeling and opportunity monitoring.
See problems, learning and deliverable
Problems
You learn
You build
Detect anomalies and structure controls with evidence.
See problems, learning and deliverable
Problems
You learn
You build
Structure faster, repeatable analytical deliverables.
See problems, learning and deliverable
Problems
You learn
You build
These are problem families, not limits. Bring your challenge and the journey is built around it.
Your problem is not on the list? Bring it anyway.
Teams already use AI on an ad-hoc basis. The challenge is to start from a real problem and advance it with method — controls, evidence and human review at every step.
Ad-hoc AI use that is not connected to a finance process that needs to improve.
Reports and analyses generated without audit trail, validation or quality criteria.
Without a baseline and before/after metrics, automation becomes hidden cost — not capability gain.
The idea starts from High Individual Contributor: professionals who use AI to expand their capacity to analyze, decide, build and deliver. At EBAC, we believe this only truly works with Human in Control: people keeping judgment, evidence and responsibility for what AI does.
More capacity to analyze, decide, build and deliver with AI.
People keep judgment, validation, evidence and responsibility.
Apply the method to a relevant problem, product, service or opportunity, with mentoring.
More capability with AI. People stay in control.
Every synchronous HIC program starts from a real problem. The domain changes; the method core does not: learn only what is needed to build, verify, and prepare application and transfer with human judgment.
Define the problem, people, baseline, constraints, expected value, and success criteria before choosing technology.
Technical and methodological depth follows the challenge and participant role — not a fixed tool checklist.
Turn learning into a solution, workflow, analysis, prototype, artifact, or decision that fits the context.
Test quality, evidence, risks, and limits while keeping review, accountability, and Human in Control.
Document, define ownership, and prepare next steps so the learning can be reused and improved beyond one person.
Implementation does not automatically mean production deployment: each context keeps its own security, approval, integration, and decision gates.
Structured method in 7 steps ensuring professional judgment, verifiable evidence, and governance.
Process candidate for automation
Current rules and controls
Where AI adds value
Sensitive data and secrets
Inputs, outputs, and quality criteria
Application with mandatory human verification
Before, after, and continuous impact metrics
8 weeks with practical deliverables applied to the vertical context.
Week 1
Frame the real finance problem, the baseline and success criteria before choosing any technology.
Topics
Lab
Brief of the finance problem from your context
Validation criteria
Week 2
Set up the Python environment and a reproducible analysis notebook, with no programming experience required.
Topics
Lab
Analysis notebook applied to the problem
Validation criteria
Week 3
Build a forecast with a baseline, error/backtesting and scenarios, with interpretation and human review.
Topics
Lab
Forecast applied to the finance problem
Validation criteria
Week 4
Generate reporting, research and documents with AI, anchored in sources and with human validation.
Topics
Lab
Applied reporting or research workflow
Validation criteria
Week 5
Automate finance routines with controls, human review and an audit trail.
Topics
Lab
Automation or agent pilot with controls
Validation criteria
Week 6
Consolidate learning by applying what each role needs to the real project, with peer review.
Topics
Lab
Role Lab oriented to the participant's role
Validation criteria
Week 7
Validate the solution focusing on quality, risks and controls before implementation.
Topics
Lab
Formal validation of the solution
Validation criteria
Week 8
Consolidate the solution, the business case and the implementation roadmap.
Topics
Lab
Final project consolidation
Validation criteria
Fill out the form below and our team will contact you.
Corporate cohorts
Talk to our team about in-company implementation and dedicated cohorts.
Still have questions? Talk to our team.
Chat on WhatsAppIn 8 weeks, turn it into a process, analysis or workflow with AI — tested, documented and ready for a decision.
