Digital Innovation
Build practical skills in AI, automation, and solution design. Explore new tools and ways of working to apply immediately.
From AI practice to a tested solution.
Each course adds a capability you can use in the next.
- 01 · Professional AI Practice
AI Productivity for Project Professionals
Design repeatable AI-assisted tasks with clear inputs and quality checks.
- 02 · Workflow Design
Workflow Automation & No-Code Innovation
Connect triggers, decisions, handoffs, and exceptions into a usable workflow.
- 03 · AI Solution Architecture
Building AI-Enabled Solutions
Combine AI, data, and human review into a reusable solution.
- 04 · Applied Innovation
Digital Innovation Sprint
Bring the capabilities together: prototype, test, and improve a practical idea.
Same fundamentals. Different labs.
All four courses run in each cycle. Choose an offering that fits your year; return to a new lab to explore another application.
Build the foundations
Connect and test
Evaluate and refine
Read across a row to follow one course theme through the year. Select a lab to see its activities, tools, and takeaways.
Something you can use immediately.
- Apply a method
Start with a work problem and prepared materials.
- Build with tools
Create a focused prompt, workflow, or prototype.
- Test and improve
Use feedback and evidence to refine the result.
- Use immediately
Leave with your artifact and a clear next application.
Choose your way in
No-code
Visual tools and guided steps.
Low-code
Configuration, structured data, and connections.
Optional basic code
Inspect or adapt a short script.
The same learning objective and useful output, with different implementation routes where available.
Turn each course theme into something usable.
Choose a cycle, then expand a lab for activities, tools, and takeaways. Each live virtual lab is 90 minutes, 6:00–7:30 p.m. ET, $99.
January–April4 labs · Build the foundations
Professional AI Work Systems
Aligns with AI Productivity for Project Professionals
Turn a recurring task into a repeatable AI-assisted workflow. Create reusable instructions and quality checks, then test how they handle incomplete or conflicting information.
Tools & participation options: Featured: ChatGPT. Low-code: Langfuse prompt versioning. Optional code: Promptfoo testing.
View details: Professional AI Work Systems
You'll learn
Design a repeatable AI-assisted task and check the quality of its results.
You'll apply
Turn sample project notes into a draft update, test different inputs, and improve the instructions.
You'll build
A reusable prompt-and-workflow blueprint for one recurring professional task.
You'll leave with
Your prompt, input checklist, test results, and a review checklist you can reuse.
Named tools by route
Featured: ChatGPT. Low-code: Langfuse prompt versioning. Optional code: Promptfoo testing.
Ways to participate
No-code: adapt prompts and review results. Low-code: structure output fields. Basic-code: optionally add a simple validation check.
Automation Opportunity Map
Aligns with Workflow Automation & No-Code Innovation
Find the parts of a process worth automating. Map the work, identify handoffs and exceptions, and leave with a clear first automation opportunity.
Tools & participation options: No-code: Zapier, Make, Scribe, and bpmn.io process mapping. Low-code option: Activepieces.
View details: Automation Opportunity Map
You'll learn
Identify and prioritize automation opportunities based on value, effort, and risk.
You'll apply
Map a request process, examine where work stalls, and compare possible improvements.
You'll build
A current-state workflow map and prioritized automation opportunity brief.
You'll leave with
A justified first automation candidate and a sketch of how the improved process could work.
Named tools by route
No-code: Zapier, Make, Scribe, and bpmn.io process mapping. Low-code option: Activepieces.
Ways to participate
No-code: map and trace the process. Low-code: configure a rule or data mapping. Basic-code: optionally test the same rule in a short script.
AI Workflow and Agent Architecture
Aligns with Building AI-Enabled Solutions
Design how an AI-enabled workflow should operate before it takes action. Define its inputs, outputs, human review, and limits, then test what happens when information is missing.
Tools & participation options: Featured: Gemini. No-code: Tines workflow design. Low-code: Dify. Optional code: Google Agent Development Kit (ADK).
View details: AI Workflow and Agent Architecture
You'll learn
Design an AI workflow and decide when a fixed sequence or an AI agent fits the task.
You'll apply
Trace a sample policy-based drafting task through normal, missing-information, and out-of-scope situations, with sending simulated.
You'll build
An AI workflow architecture map with human review and escalation points.
You'll leave with
Clear input and output requirements, action limits, and examples of how the workflow handles problems.
Named tools by route
Featured: Gemini. No-code: Tines workflow design. Low-code: Dify. Optional code: Google Agent Development Kit (ADK).
Ways to participate
No-code: map and walk through the workflow. Low-code: configure a decision branch. Basic-code: optionally adapt a small routing script.
From Problem to Testable Prototype
Aligns with Digital Innovation Sprint
Turn a work problem into a prototype someone can try. Observe how a person uses it, improve one part, and leave with a focused plan for the next test.
Tools & participation options: Featured: Replit. No-code screen options: Google Stitch and Magic Patterns. Optional code: Streamlit (Python).
View details: From Problem to Testable Prototype
You'll learn
Frame a user problem and choose the smallest prototype that can test your idea.
You'll apply
Adapt a starter intake interaction using sample data, observe someone completing a task, and revise it using their feedback.
You'll build
A focused, testable screen flow or simple working prototype.
You'll leave with
Your prototype, test findings, and a focused experiment plan.
Named tools by route
Featured: Replit. No-code screen options: Google Stitch and Magic Patterns. Optional code: Streamlit (Python).
Ways to participate
No-code: build a guided screen or interaction. Low-code: configure fields and validation. Basic-code: optionally adapt a small interactive app.
May–August4 labs · Connect and test your ideas
AI Knowledge Architecture
Aligns with AI Productivity for Project Professionals
Make notes, sources, decisions, and actions easier to find and reuse. Organize knowledge around the work it supports and identify where AI can help.
Tools & participation options: No-code: Notion, Tana, Capacities, and the hosted Khoj app. Low-code: structured fields and retrieval rules in Notion or Tana.
View details: AI Knowledge Architecture
You'll learn
Structure professional knowledge so it supports useful tasks and decisions.
You'll apply
Organize sample meeting notes, connect decisions to their sources, and test whether someone can find the information they need.
You'll build
A redesigned knowledge workflow and a map of where AI assistance fits.
You'll leave with
A sample knowledge collection, a guide to its fields, and practical review rules.
Named tools by route
No-code: Notion, Tana, Capacities, and the hosted Khoj app. Low-code: structured fields and retrieval rules in Notion or Tana.
Ways to participate
No-code: organize and link information. Low-code: define fields and retrieval rules. Basic-code: optionally check missing information with a short script.
Event-Driven Automation
Aligns with Workflow Automation & No-Code Innovation
Design what should happen when one system sends an update to another. Test incomplete data, repeated messages, and failures so you can plan a reliable connection.
Tools & participation options: No-code: Make, with webhook.site for inspecting events. Low-code: n8n, with Hookdeck for webhook handling. Optional basic-code: Python.
View details: Event-Driven Automation
You'll learn
Connect systems through events and design useful responses when something goes wrong.
You'll apply
Use sample events in a sandbox workflow to map data, test incomplete and repeated messages, and trace a simulated failure.
You'll build
An integration blueprint with success, failure, and exception paths.
You'll leave with
A description of the data exchanged, case results, and recovery steps, identifying what you tested and what remains a design.
Named tools by route
No-code: Make, with webhook.site for inspecting events. Low-code: n8n, with Hookdeck for webhook handling. Optional basic-code: Python.
Ways to participate
No-code: adapt and test a visual workflow in Make. Low-code: configure data transformations and conditions in n8n. Basic-code: optionally edit a small event-processing script in Python.
Human-in-the-Loop Agent Controls
Aligns with Building AI-Enabled Solutions
Keep people in control when AI is part of a process. Design approval, rejection, and escalation steps, then test what happens when a decision is delayed or a draft changes.
Tools & participation options: Featured: Copilot Studio. No-code: Tines. Low-code: gotoHuman review steps. Optional code: LangGraph.
View details: Human-in-the-Loop Agent Controls
You'll learn
Design human review that lets an authorized person pause, reject, or approve a specific draft before the next step.
You'll apply
Test approval, rejection, timeout, and changed-draft situations, including when a new approval is needed. Keep the final action simulated.
You'll build
A human-review, approval, and escalation control map.
You'll leave with
Review requirements, rules for who can approve which draft, and a record of the situations you tested.
Named tools by route
Featured: Copilot Studio. No-code: Tines. Low-code: gotoHuman review steps. Optional code: LangGraph.
Ways to participate
No-code: map and test review steps. Low-code: configure when work can resume. Basic-code: optionally test decision states in a short script.
Hypothesis-Driven AI Experiment
Aligns with Digital Innovation Sprint
Test whether an AI idea improves a task. Compare a small working prototype with the current approach and use the results to decide what to improve or test next.
Tools & participation options: No-code: Gumloop. Low-code: Langflow and PostHog experiment measurement. Optional code: Python components in Langflow.
View details: Hypothesis-Driven AI Experiment
You'll learn
Turn an AI idea into a testable hypothesis with clear measures of success.
You'll apply
Adapt a small working AI request-routing starter and compare it with a manual approach using the same sample cases.
You'll build
A working AI prototype for one bounded task and an assumption-and-test card.
You'll leave with
Scored sample results, observations about failures, and a decision about your next experiment.
Named tools by route
No-code: Gumloop. Low-code: Langflow and PostHog experiment measurement. Optional code: Python components in Langflow.
Ways to participate
No-code: adapt a visual prototype and score results. Low-code: configure categories and rules. Basic-code: optionally adapt prototype checks or scoring.
September–December4 labs · Evaluate and refine
Evidence-Grounded AI Analysis
Aligns with AI Productivity for Project Professionals
Use AI to support a decision while keeping the evidence visible. Verify claims, compare sources, and make uncertainty clear in a recommendation someone else can check.
Tools & participation options: No-code: NotebookLM, Gemini, ChatGPT, Elicit, and Zotero. Optional structured-data activities use the same evidence set.
View details: Evidence-Grounded AI Analysis
You'll learn
Evaluate sources, challenge assumptions, and connect conclusions to supporting evidence.
You'll apply
Compare options, inspect original source passages, and revise claims that the evidence does not support.
You'll build
A source-backed decision brief and an evidence-and-assumptions matrix.
You'll leave with
A recommendation with traceable sources, stated limitations, and clear reasoning.
Named tools by route
No-code: NotebookLM, Gemini, ChatGPT, Elicit, and Zotero. Optional structured-data activities use the same evidence set.
Ways to participate
No-code: verify sources and write the brief. Optional low-code or basic-code: structure evidence records or check for missing source information.
From Automation to Operational System
Aligns with Workflow Automation & No-Code Innovation
Connect the pieces that make a process usable: data, interfaces, automation, and people. Design a request workflow with visible status, clear ownership, and a way to recover from failures.
Tools & participation options: No-code: Airtable, Make, and Baserow. Low-code: Retool and Appsmith. Optional code: API and validation rules.
View details: From Automation to Operational System
You'll learn
Design an operational system that people can use, monitor, and maintain.
You'll apply
Adapt one sandbox intake-to-review workflow, trace its records and status, and test a simulated failed handoff or duplicate request.
You'll build
A blueprint connecting data, interfaces, automations, and system connections.
You'll leave with
A data model, workflow stages, ownership and access rules, and recovery notes.
Named tools by route
No-code: Airtable, Make, and Baserow. Low-code: Retool and Appsmith. Optional code: API and validation rules.
Ways to participate
No-code: adapt a form and operator view. Low-code: configure a query or connection. Basic-code: optionally add a record-validation or workflow rule.
RAG Architecture and AI Evaluation
Aligns with Building AI-Enabled Solutions
Design and test AI answers that use your source documents. Explore retrieval-augmented generation, or RAG, and evaluate what happens when evidence is missing, outdated, or misleading.
Tools & participation options: No-code: AnythingLLM. Low-code: n8n with a retrieval component such as Qdrant, Weaviate, or Supabase. Optional code: Chroma and Ragas.
View details: RAG Architecture and AI Evaluation
You'll learn
Evaluate how a document-based AI system retrieves evidence and uses it to answer questions.
You'll apply
Test a prepared document-based assistant, compare retrieved passages with its answers, and distinguish missing evidence from unsupported answers.
You'll build
A RAG solution design, evaluation set, and failure checklist.
You'll leave with
Test questions, expected evidence, observed results, and a documented improvement.
Named tools by route
No-code: AnythingLLM. Low-code: n8n with a retrieval component such as Qdrant, Weaviate, or Supabase. Optional code: Chroma and Ragas.
Ways to participate
No-code: test answers and inspect sources. Low-code: adjust retrieval settings. Basic-code: optionally adapt how documents are divided, retrieved, or tested.
Open-Stack Solution Architecture
Aligns with Digital Innovation Sprint
Choose and defend the tools behind a practical solution. Demonstrate how the pieces work together, explain their tradeoffs, and leave with a record of your architecture decisions.
Tools & participation options: No-code architecture: IcePanel. Optional code: Log4brains, Ollama, and Open WebUI. Choose commercial or open-source components to fit your solution.
View details: Open-Stack Solution Architecture
You'll learn
Evaluate tool choices against the problem, cost, reliability, portability, and maintenance needs.
You'll apply
Use a starter solution or prior lab artifact to compare options for one component, demonstrate a working path and a limitation, and improve your design after feedback.
You'll build
A bounded solution demonstration and an architecture decision record.
You'll leave with
A component map, the reasons behind your choices, and a next-step validation plan.
Named tools by route
No-code architecture: IcePanel. Optional code: Log4brains, Ollama, and Open WebUI. Choose commercial or open-source components to fit your solution.
Ways to participate
No-code: demonstrate a visual solution and explain the choices. Low-code: adapt a component or connection. Basic-code: optionally modify a small adapter or validation rule.
Participant questions
Do I need to write code?
No-code participation is part of the lab design. Low-code and basic-code activities offer additional ways to explore the same problem. Writing code is optional.
What if I already have technical experience?
Use the configuration or scripting activities to examine data, decision rules, integrations, and testing in more depth. You will still work toward the same practical outcome.
What will I take away?
Each lab identifies a specific output, such as a reusable prompt, process map, prototype, test set, or decision record, along with a way to apply it afterward.
How do tools fit into the learning?
Tools make the method practical. You will examine why an approach fits a problem, test its limitations, and make choices you can use beyond that particular product.
Do I need every tool listed?
Tool options illustrate different ways to approach the same problem. You use the tools for your chosen participation route.
Do the lab platforms and tools have free tiers?
Many do. ChatGPT and Make, for example, offer free plans. Other tools may offer a free community edition or a time-limited trial. Features and usage limits vary by provider. The lab details identify the tools and participation routes so you can choose an option that fits your access.
Are paid tool subscriptions required to participate?
No paid subscriptions are required for standard lab participation. Use the free-tier participation route or provided examples when a paid feature is demonstrated. You may need a free account for hands-on activities. Optional API usage, advanced features, or hosting outside class can incur charges; these extensions are optional.
Choose your next course and Lab.
See all 2027 dates and build a progression that matches the work you want to redesign.