Why Invest in Product Training Now?

My Approach:

My Offering:

I facilitate training for organizations where I connect with the problem you are trying to solve and can deliver measurable impact. My approach is grounded in the sentiment that, in Product, those that do, should teach. I have spent my career building and implementing practices so that the organizations that I work for and with can achieve their goals, create value and grow. As I am always actively working on products and with teams, I stay current on what actually works versus what sounds good in theory.

Before any engagement, I invest time understanding your team's current capabilities, challenges, and strategic priorities. This allows me to tailor content to your organization's specific context, integrate your product examples, and focus on the AI capabilities most relevant to your roadmap.

My workshops are continuously updated to reflect the latest developments in product practice and AI tooling, ensuring your team learns frameworks that are battle-tested and immediately applicable

Examples Workshop

The four workshops below are examples of the types of programs that I have put together for past clients. I have developed a library of training content, and workshop structures, and can quickly and easily put together training programs that speak to your unique needs.

Data Collection, Research & AI Research Tools

Research ownership is shifting as AI tools democratize user research capabilities. Product teams can now conduct more research faster, but must understand when AI-powered tools enhance research quality versus when they introduce bias or miss critical nuance.

This program will cover:

  • Persona creation using AI segmentation and behavioral data analysis

  • Writing effective interview guides and using AI for question optimization

  • Conducting customer interviews with AI-powered transcription and tagging

  • Recording raw data with AI tools and maintaining research integrity

  • Synthesizing insights using AI pattern recognition while applying critical thinking

  • Field research techniques with real-time AI data capture

  • Creating impactful reports that combine AI-generated insights with strategic interpretation

  • Quantitative data collection tools, and how to build AI agents that can speed up quantitative insights

Outcomes:

  • PMs will leverage AI tools to identify relevant customer segments faster and more accurately

  • Product teams will understand how AI-enhanced personas drive more targeted research

  • PMs will confidently implement interviews using AI transcription while avoiding AI-introduced bias

  • Product teams will use AI for initial synthesis, then collaboratively apply judgment to distill strategic patterns

  • PMs will understand when field observation beats AI analysis for uncovering innovation opportunities

  • PMs will craft reports that demonstrate both AI-enabled speed and human strategic insight

Continuous Discovery: Shaping your Roadmap

In order for product teams to be able to build roadmaps that guide a product over the course of several quarters, they need to be constantly and consistently collecting data. But most teams are very start/stop when it comes to discovery practices. Luckily, Discovery has evolved beyond traditional research methods. AI tools can now accelerate synthesis, identify patterns across customer conversations, and easily allow teams to build and search data banks. But without strong foundational research and innovation skills, AI can’t help much.

This workshop is for teams who are looking to improve their customer experiences, find innovation opportunities, and develop a strong value proposition for their market.

This program will cover:

  • Foundation theory of design thinking and AI-augmented research

  • Scoping frameworks for Discovery

  • Building and validating personas using data powered segmentation analysis

  • Conducting customer interviews and using AI for qualitative synthesis

  • Running experiments with predictive analytics

  • Leveraging AI for collaborative synthesis and pattern recognition

  • How to build roadmaps that align to org goals

Outcomes:

  • Understanding research best practices and the ability to avoid actions that introduce bias, including bias introduced by AI.

  • Product teams can craft learning goals and deploy AI tools for faster insight generation

  • PMs can assess when AI synthesis adds value versus when manual analysis is superior

  • PMs can work with design and research to quickly generate prototypes, and then deploy experiments around them

  • PMs are capable of framing discovery sessions and leveraging AI for post-session analysis

  • Product teams can gather data, use AI for initial synthesis, and apply human judgment to distill strategic insights

The product landscape has fundamentally shifted. AI isn't just changing what products can do, it's transforming how teams discover, build, and scale them. Organizations that don't upskill their product teams risk falling behind competitors who are leveraging AI for faster discovery, better decision-making, and more sophisticated product capabilities.

Training delivers measurable ROI: teams with strong foundational skills and shared frameworks ship faster, make better prioritization decisions, and adapt more quickly to market changes. In the AI era, where product velocity and technical fluency are competitive advantages, investing in your team's capabilities isn't optional, it's strategic.

Building product teams that can scale in an AI-powered environment requires intentional capability development. The foundation of any high-performing team is a shared vocabulary and baseline understanding of how AI tools can multiply the value of strong, core capabilities.

My training programs combine proven product frameworks with practical AI integration strategies. I engage participants in active application with the projects that they are working on today. In each training, I explore how AI tools can accelerate discovery, enhance decision-making, and enable teams to ship more sophisticated products faster.

I take a critical thinking approach: product skills, AI tools, and processes are all "tools in a toolkit" that should be applied based on context, not doctrine. This creates empowered product thinkers who can evaluate when to deploy certain activities, and are constantly asking how they can improve their own processes, as well as their team processes.

The goal is simple: participants leave with the confidence to start applying new skills immediately, and the frameworks to evaluate which AI capabilities will actually drive impact for their specific product challenges.

Pulling together: Mapping Org Operations for Successful Product Roll-Outs

As evinced by Mind the Product, “Product Operations is set to be the backbone of product-led growth.” As product teams scale it is important to invest in consistency and transparency in how teams work. This workshop is for organizations that feel that their functions are never working together, where marketing doesn’t understand the value prop they need to convey and sale’s immediately says the new feature doesn’t do what it needs to do. Product practices can help pull organizations together, start the right conversations and development alignment as the product is being built, so you can launch strong and hit the market with all capabilities firing.

This program will cover:

  • Project kick-offs

  • Extracting data from the organisation

  • Service Blueprint and Customer Journey Mapping

  • Research Operations

  • Stakeholder management

  • Retrospectives

  • Reporting out

Outcomes:

  • Product teams will be able to ensure that all relevant functions are aligned to goals and outcomes

  • Product develops an in-depth understanding of dependencies and impact of product and experience changes

  • Product teams will feel empowered to take a continuous delivery mindset

  • Product Managers will have tools and frameworks to align with stakeholders in others functions, provide more impactful updates, and extract data from needed sources

Designing Delivery: Product-Engineering Collaboration in the AI Era

One of the most crucial hinges in any software eco-system is how product management collaborates with engineering. This workshop will cover how product managers can create the right conversations, establish trust and extract information out of their engineering teams. Engineering teams are increasingly working with AI/ML capabilities, LLMs, and complex data pipelines. Product managers must understand how to develop AI use cases, scope AI features, communicate technical constraints, and help organizations make smart trade-off decisions. In a world where development is moving faster than ever,

This program will cover:

  • Developing AI use cases in partnership with engineering

  • Project inceptions for AI feature development - aligning on goals, risks, data requirements

  • User story writing for AI/ML features with clear success criteria

  • User story mapping with technical dependency visualization for AI capabilities

  • Backlog management for iterative AI model development

  • Retrospectives that address AI-specific learnings (model performance, data quality, user acceptance)

Outcomes:

  • PMs will create efficient documentation that clearly scopes AI feature requirements and constraints

  • PMs will understand how to collaboratively plan AI feature implementation with data and ML considerations

  • PMs will effectively communicate AI capability trade-offs to stakeholders

  • Product Managers will collect feedback on AI feature performance and iterate based on user behavior data

  • PMs will build trust with engineering through clear communication of AI product requirements and realistic expectations