Digital Innovation
Building AI-Enabled Solutions
Move from a one-off AI task to a reusable solution. Connect the user problem to inputs, instructions, knowledge, tools, outputs, and handoffs. Design human review and operating boundaries, then prototype and test an AI workflow or assistant.
Build practical capability
Design AI around a real need, not the novelty.
This course guides participants from problem framing through solution design, testing, governance, and adoption planning.
Move beyond individual AI tools and learn how to shape useful, responsible AI-enabled solutions. Participants explore user needs, workflow design, data considerations, testing, governance, and adoption.
What you will learn
Course learning objectives
By the end of this course, you will be able to:
Define a useful solution
Identify the user, problem, intended outcome, constraints, and success criteria.
Structure the solution components
Connect inputs, instructions, knowledge, tools, outputs, and handoffs in a practical architecture.
Design accountable human review
Set approval points, escalation routes, sensitive-decision limits, and action boundaries.
Prototype a reusable workflow
Build a focused AI workflow or assistant for a professional use case.
Test reliability
Use normal and edge cases to evaluate unsupported claims, consistency, and failure behavior.
Plan ongoing operation
Document ownership, monitoring, feedback, and a next iteration before wider use.
What you will do
Apply the methods to practical work
Define a professional use case, map its solution components, and decide when people must review or intervene. Prototype a reusable workflow or assistant, test normal and difficult inputs, record failures, and prepare an ownership and monitoring checklist.
Techniques you will practice
Problem and success framing: Make the intended outcome and constraints clear before adding AI.
Component mapping: Describe how inputs, instructions, knowledge, tools, and outputs work together.
Human-review and escalation design: Define which decisions AI can support and which actions require a person.
Test-case and failure logging: Record expected behavior, observed results, and the conditions that caused a failure.
Operating-boundary checklists: Specify ownership, monitoring, feedback, and limits for a reusable solution.
How the course is structured
Demonstration, guided practice, testing, and improvement
The course moves through the stages below. Instructor demonstrations and guided activities lead into participant application, critique, and a practical next-use plan.
- Frame the use case
Define the user, problem, intended outcome, and constraints.
- Map the architecture
Connect the solution components and identify handoffs.
- Design review and limits
Set approval, escalation, accountability, and sensitive-decision boundaries.
- Demonstrate reliability checks
Inspect edge cases, unsupported claims, inconsistent results, and failure records.
- Prototype and test
Create a bounded reusable workflow or assistant and challenge it with test inputs.
- Prepare the next iteration
Document ownership, monitoring, feedback, and improvements needed before wider use.
Choose an approach that fits your experience
Start with visual solution mapping and guided prototype work. Participants with configuration experience can examine structured inputs, outputs, and decision rules more deeply. Optional scripting may support a suitable implementation, but becoming a developer is not the course goal.
Use a sample or anonymized work situation. Keep confidential information out of shared tools, verify AI-generated claims, and retain human judgment over consequential decisions.
2027 schedule
Plan the experience into your learning year.
Choose a scheduled offering below. Course registration opens December 10, 2026. You can reserve your interest now and request an opening reminder; no payment is collected by the reservation form. A completed paid registration is required to secure a seat.
Live virtual · 4-hour course
9:30 AM–1:30 PM ET
Eastern Time (America/New_York)
Course investment: $449
Design a reusable AI workflow, map its components and human review, prototype it, and test reliability.
You will leave with: A solution map, bounded prototype, test cases, and an operating checklist.
Reserve this offeringLive virtual · 4-hour course
9:30 AM–1:30 PM ET
Eastern Time (America/New_York)
Course investment: $449
Design a reusable AI workflow, map its components and human review, prototype it, and test reliability.
You will leave with: A solution map, bounded prototype, test cases, and an operating checklist.
Reserve this offeringLive virtual · 4-hour course
9:30 AM–1:30 PM ET
Eastern Time (America/New_York)
Course investment: $449
Design a reusable AI workflow, map its components and human review, prototype it, and test reliability.
You will leave with: A solution map, bounded prototype, test cases, and an operating checklist.
Reserve this offeringThe listed dates, times, and prices match the governed 2027 calendar. This reservation expresses interest; it is not a paid enrollment or a guarantee that an offering will run.
Find your pathway
Choose the next step that fits your goals.
Explore the full 2027 calendar and see how Building AI-Enabled Solutions connects to your Digital Innovation pathway.
Explore the 2027 calendar