Build AI product and delivery skills
Learn to choose useful workflows, define acceptance, evaluate outputs, and support adoption.
Start here if: Product managers, analysts, operations leads, and implementation professionals working with engineering teams.
Try the relevant skills checkUseful AI products need people who understand users, shape scope, and make quality observable. You can develop these skills without claiming to be the engineer who builds every component. Technical fluency helps you make better decisions with that engineer.
Begin with a workflow you understand. Define the problem before selecting a model, write acceptance conditions before reviewing a demo, and include the people who will operate the result.
What to study, in order.
- 01
Understand the work and its baseline
- Study
- Start with AI Fluency or AI Foundations, then study inputs, decisions, handoffs, and exceptions in a real process.
- Practice
- Map a fictional service workflow and identify its slowest or most error-prone step.
- Keep as evidence
- Create a problem brief with the user, outcome, current baseline, and unresolved assumptions.
- 02
Define useful behavior
- Study
- Learn to specify observable results, source requirements, human review, and actions the system may take.
- Practice
- Write examples of a good answer, an incomplete answer, and a case requiring escalation. Review them with someone playing the domain expert.
- Keep as evidence
- Keep acceptance conditions tied to examples and a record of disagreements resolved.
- 03
Evaluate the prototype with engineering
- Study
- Read evaluation guidance and learn why representative cases and error categories matter.
- Practice
- Compare a manual baseline with an AI-assisted process. Inspect accuracy, review effort, and failure handling; avoid relying only on average time saved.
- Keep as evidence
- Produce a result table, failure notes, and a justified decision to revise, narrow, or continue the pilot.
- 04
Prepare adoption and ownership
- Study
- Learn user onboarding, feedback collection, operating responsibility, and change control.
- Practice
- Have a volunteer follow the new workflow, then simulate an unavailable system and an ambiguous case.
- Keep as evidence
- Deliver user instructions, escalation contacts by role, fallback steps, and a follow-up measurement plan.
Learn enough technology to ask precise questions
Understand the difference between prompting a model, retrieving source material, and calling a tool that changes another system. These choices affect what can go wrong and who needs to approve it. Ask engineers to show a request moving across the actual boundaries.
A useful product conversation asks where data comes from, how access is checked, what happens when a source is stale, and which failures need a person. You do not need to pretend to own implementation details to insist on observable behavior.
Measure the workflow people experience
An answer that arrives quickly but takes longer to correct may not improve the job. Consider time to an accepted result, rework, unanswered cases, and the operator’s ability to find supporting evidence. Select measures suited to the workflow rather than copying a universal scorecard.
Use interviews and observed use alongside numerical results. State the sample, environment, and limitations. A classroom exercise is useful practice; an operational claim needs evidence from an appropriately authorized setting.
A reviewable AI pilot proposal
Design a fictional internal knowledge assistant for a small service team. Use public or invented documents. Work through the operating decision even if your prototype is a manual simulation.
What to produce
- Workflow map and user problem statement.
- Example inputs, expected behavior, and escalation cases.
- Baseline comparison and a pilot decision with reasons.
- Adoption guide, feedback method, and ownership map.
Ask a reviewer
- Which errors matter most to the user?
- Who can approve the proposed action?
- What evidence would cause you to stop or narrow the pilot?
Courses and resources for this path.
Choose resources for the gap you are working on. Course access and access to paid products or cloud services are separate.
AI Fluency: Framework & Foundations
How to choose suitable tasks, communicate intent, inspect AI outputs, and take responsibility for AI-assisted work.
- For
- People beginning to use AI, and experienced users improving their judgment.
- Before you start
- Designed for learners across experience levels; programming is not required for this learning path.
- Access
- Free course content. The provider lists a completion badge; that is separate from FDE Benchmark practice or employer assessment.
AI Foundations
AI and language-model basics, useful instructions and context, output review, and responsible everyday use. The academy also offers Applied AI Foundations and Agents and Workflows for later practice.
- For
- People new to AI and ChatGPT.
- Before you start
- The academy identifies this course as a starting point for people new to AI.
- Access
- Follow the provider link for current enrollment and access details. Product plans and course participation are separate; no certification claim is made here.
Evaluation best practices
Set evaluation objectives, choose representative cases, compare implementations, and calibrate automated grading with human review.
- For
- Developers and delivery teams assessing an AI feature.
- Before you start
- A defined task and examples of the outputs or behavior you want to assess.
- Access
- Public documentation, not a course or credential. Running model-based evaluations can incur API costs.
Common questions.
Can a product manager or analyst become an FDE?
Your discovery and stakeholder skills are relevant, but engineering-heavy FDE roles require software ability. You can deepen product and delivery work now, or add programming and application projects if hands-on engineering is your goal.
Do I need an AI certification to lead a pilot?
A course can improve your understanding. Leading a pilot also requires an agreed mandate, suitable collaborators, and evidence that the workflow is useful and operable. A course badge does not grant authority to process data or release software.
How do I show ability without customer access?
Use a clearly labeled simulation, public sources, and volunteer feedback. Make assumptions visible and preserve the artifacts. Explain what would still need to be tested with actual users and approved operational data.
Sources and editorial basis.
Original guidance and practice projects by LockedIn Labs. Provider references describe their own offerings; inclusion is not an endorsement of this site.
Put the next step into practice.
Try the role-specific scenarios, keep the feedback, and bring a work sample into guided training.