Why Choose AI Product Management: From Idea to Deployed AI Solution Training Course?
AI can create new products and improve existing services, but a promising idea is only the beginning. Product teams must identify a real user need, determine whether AI is suitable for the task, secure the right data and design an experience that people can understand and trust. They must also decide how success will be measured before committing to development.
Managing an AI product brings additional challenges throughout its lifecycle. Outputs may vary, performance can change as data and user behaviour change, and some decisions require human review. Product managers need to work closely with users, business sponsors, designers, data specialists, engineers, legal teams and operations to balance value, feasibility, cost and risk. A solution that performs well in a demonstration must still work within a reliable service once deployed.
This AI Product Management: From Idea to Deployed AI Solution training course follows the journey from an initial AI product idea to deployment and ongoing improvement. Participants learn how to discover opportunities, define product requirements, evaluate data and technical options, plan development and testing, and prepare for launch. Using a continuing case study, they develop an AI product brief, delivery roadmap and performance plan that can be adapted to their own organisation.
What are the Goals?
By the end of this course, participants will be able to:
- Identify user problems and business opportunities suitable for AI.
- Define a product vision, target users and measurable outcomes.
- Assess data readiness, technical feasibility, cost and risk.
- Translate user needs into AI product requirements and acceptance criteria.
- Design user experiences that account for uncertainty and human oversight.
- Plan prototypes, pilots and iterative product development.
- Evaluate AI performance alongside usability and business value.
- Coordinate deployment, adoption and operational ownership.
- Monitor product performance and prioritise improvements after launch.
Who is this Training Course for?
This training course is suitable to a wide range of professionals but will greatly benefit:
- Product managers and product owners
- Digital transformation and innovation professionals
- Business analysts and service designers
- AI programme and project managers
- Data and analytics leaders
- Technology managers working with AI delivery teams
- Business leaders sponsoring AI-enabled products
How will this Training Course be Presented?
The course combines instructor-led discussion, case studies and group exercises. Participants work through a continuing AI product scenario, from problem discovery and product definition to pilot evaluation and launch planning. They prepare product artefacts and present a deployment recommendation at the end of the course.
The Course Content
- Understanding the AI product lifecycle and the product manager’s role
- Identifying customer needs and operational problems
- Conducting user discovery and mapping current journeys
- Determining whether AI is appropriate for the proposed task
- Defining the target users, use cases and expected outcomes
- Reviewing alternative solutions and existing capabilities
- Assessing business value, feasibility and initial risks
- Writing an AI product opportunity statement
- Developing the product vision and value proposition
- Mapping user journeys and key interactions
- Defining functional and non-functional requirements
- Assessing data availability, quality and permissions
- Comparing build, buy and integration options
- Defining acceptable outputs, limitations and escalation paths
- Setting product success metrics and baseline measures
- Preparing a product brief and prioritised backlog
- Designing interactions that communicate AI capabilities and limitations
- Planning human review, feedback and correction mechanisms
- Building a prototype to test the core user experience
- Creating test cases for common, unusual and high-impact situations
- Evaluating output quality, reliability, speed and cost
- Conducting user testing and gathering structured feedback
- Identifying privacy, security and fairness concerns
- Refining requirements based on test evidence
- Coordinating product, engineering, data and business teams
- Planning development stages, dependencies and decision points
- Defining responsibilities for data, models, integrations and support
- Managing scope, trade-offs and changes during delivery
- Designing a pilot with clear success and exit criteria
- Measuring adoption, task performance and user outcomes
- Reviewing incidents, errors and unintended effects
- Deciding whether to stop, improve or proceed to deployment
- Preparing the product for operational deployment
- Planning user onboarding, training and communications
- Establishing support, monitoring and issue escalation
- Tracking quality, adoption, cost and business outcomes
- Managing updates as data, models and user needs change
- Prioritising enhancements using feedback and performance evidence
- Presenting a product launch and improvement roadmap
- Developing a 90-day action plan for an AI product initiative
Certificate
- AZTech Certificate of Completion for delegates who attend and complete the training course
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Register now or contact our team to discuss schedules, delivery formats, and customised options.