RCTF · DIG+8 · AI-TPM
RCTF communication · DIG+8 · AI-TPM · Cornell CAIO · Sourced vs. Owned.
Bridge to a Brighter Tomorrow
AI Proficiency Accelerator · Flagship programme
Do not just learn AI. Learn to work differently with it.
In 1990, Excel felt like magic. Within a decade, everyone was expected to know it. AIPA prepares your workforce for the same shift — eight project-based sessions that make AI a natural part of how every professional works.
- Format
- 4 basic + 4 advancedOne session per week, workshop-style
- Method
- Project-based3-hour in-person session + 3-hour project task
- Audience
- All functions, all levelsFinance, HR, supply chain, sales, production and more
- Output
- AI-capable staffUp to 40% productivity gain
- Prerequisite
- NoneNo coding; a laptop for hands-on sessions
- Cost
- Low-cost toolsWidely available tools participants keep using
The difference
Project-based, not theory-based.
The goal is not a workshop attended. It is AI used in everyday work, by the people who do that work — without needing a technical team.
Participants are taught a concept clearly first. Then they take on a project based on their own work, complete it, get feedback and move on to the next session — practising AI augmentation on the work they already own.
Six promised outcomes
What changes for every participant.
- 01
Demystify AI
Cuts through the hype and the hesitation, so people judge AI for themselves and use it.
- 02
Augmentation, not training
The result is AI used in everyday work — not just a workshop attended.
- 03
Project-based learning
Each participant completes 10–16 project tasks applied to their own workplace.
- 04
Measurable productivity
~12% without tools, ~20% with tools, and up to 40% once they build their own AI workflows.
- 05
Self-learning
A way of learning that lets participants keep up to date on their own, without further support.
- 06
Automation on their own
Participants use AI the way they use Excel — on their own, without needing a technical team.
Leave able toBuild no-code appsAutomate workAnalyse dataDeploy agentsSelf-learn
Curriculum
Eight sessions. Two tracks.
Every session is workshop-style and project-based — participants apply each one to their own function between sessions.
Basic track · Sessions 01–04
Personal proficiency
Four mandatory sessions that make every participant personally proficient — communication, a second brain, their own no-code apps and safe data analysis. No coding or technical background required.
- Session 01Basic
AI Anthropology & Augmentation
Focus. AI evolution, philosophy and the DIG+8 framework in the enterprise.
- Session 02Basic
AI as Colleague: Communication & Second Brain
Focus. Structured prompting, chain of thought and RAG with NotebookLM.
- Session 03Basic
No-Code App Building & Work Automation
Focus. Problem definition, build, documentation, deployment — automate regular work.
- Session 04Advanced
Google AI Studio & Data Insight Workshop
Focus. Data prep and privacy, insight discovery, actionable reporting.
Advanced track · Sessions 05–08
The build track
Participants move from using AI to constructing it — agentic workflows, automation and persistent memory inside their own department.
- Session 05Semi-tech
Agentic AI, MCP & Context
Focus. Context engineering, MCP and connectors — building agentic systems.
- Session 06Semi-tech
Low-Code Automation
Focus. Workflow basics, agentic automation and workflow AI.
- Session 07Semi-tech
Building a Personal AI-OS
Focus. Combining everything into a personal AI operating system — workflows, skills, connectors and MCP working together.
- Session 08Advanced
Second Brain: Data Security & Connected Systems
Focus. Enterprise AI roadmap, data security and sanity, and building a second brain.
How a participant experiences it
Learn. Build. Prove it.
One session per week: about three hours in the room, then about three hours applying it to real work.
- 1
Learn the concept
The facilitator explains the capability and the reasoning behind it in a 3-hour, workshop-style session.
- 2
Apply it to real work
The participant takes on a 3-hour project task connected to their own role or workflow.
- 3
Submit and get feedback
The work is reviewed, so the participant learns from what they actually built.
- 4
Move forward
The next session builds on the capability already practised — from personal proficiency towards agents and automation.
Productivity we have measured
Measured on real tasks, not attendance.
Each level builds on the last — the more participants build for themselves, the larger the gain.
Frameworks & tools
Built on frameworks, run on tools you keep.
Low-cost, mostly public tools
Low-cost, widely available tools that participants keep using after the programme ends.
None
No coding or technical knowledge required; a laptop for hands-on sessions.
Designed for the workforce
Any function. Any level.
No technical background is required for the basic track. Each project is grounded in the participant’s own work.
FinanceAccountsProcurementHR & L&DSupply chainSales & marketingProductionQualityR&DIT & MISData & analyticsDigital transformationPMOLegal & complianceAudit & riskAdministrationStrategyLeadership
For senior managers who need to set direction for the whole organisation, AISA is the better fit.
Frequently asked
What people usually want to know.
How is AIPA structured?
Eight sessions, one per week: four basic sessions that make every participant confident with AI, then four advanced sessions in which they build AI agents, automations and their own personal AI setup.
How much time does it take each week?
A 3-hour, in-person workshop session plus a 3-hour project task applied to the participant’s own work.
Is AIPA just a course?
No. Sessions are workshop-style, but the programme is built around 10–16 project tasks that participants complete on their own work between sessions.
Do participants need a technical background?
No. The basic track needs no coding. Advanced sessions are marked semi-technical where they go further into agents, MCP and automation.
Can one department take AIPA?
Yes. AIPA can be shaped around one function or run across mixed teams.
What changes after AIPA?
People use AI in their everyday work, the way they use Excel, without needing a technical team. We have measured productivity gains of around 12% from using existing AI better, around 20% with tools, and up to 40% once participants build their own AI workflows. When a process needs a deeper, connected solution, AITA takes the next step.
Explore the Accelerator
See the other programmes.
Each programme answers a different AI question — and each one stands alone.