Stop Building the Wrong Things: How to Assess Product Viability for Internal Initiatives

Too many internal products move forward without clear evidence they should exist.

Teams respond to stakeholder requests, collect requirements, and start building - only to discover late that adoption is low, value is unclear, or the problem was never well understood.

This session focuses on how to assess product viability before and during delivery so effort is spent on the right things.

It outlines how product ownership creates clarity from competing inputs, how roles contribute to validating real needs, and how to move from ideas to evidence-based decisions.

Key areas covered include:

- Defining viability using adoption, process impact, and cost—not activity or output
- Structuring intake to handle multiple stakeholders without losing focus
- Validating assumptions early through prototypes, pilots, and technical exploration
- Establishing clear criteria to continue, pivot, or stop work
- Recognizing signals that an initiative is not viable and acting on them

The session is grounded in internal product scenarios, including examples where teams made the decision to stop work based on weak viability signals.

The focus is on practical guidance for making better decisions, reducing wasted effort, and ensuring internal products deliver real value.

Getting Started with AI Governance for Small and Medium Businesses (SMBs)

Generative AI is already embedded in everyday work across small and medium sized businesses. It shows up in emails, marketing content, customer communication, and internal decision-making. Most organizations did not formally introduce it. It appeared through tools already in use.

This creates a gap. AI is influencing outcomes, but governance, oversight, and clear expectations are often not in place. Responsibility has not changed, but the way work is produced and decisions are shaped has.

This session introduces a practical, lightweight approach to AI governance designed specifically for SMB environments. It reflects the reality that most organizations do not have dedicated governance teams, legal support, or technical specialists.

Participants will learn how to establish clear ownership, define simple guardrails, and apply governance directly within existing roles and workflows. The focus is not on heavy frameworks or compliance theater, but on making responsibility visible, setting boundaries that reduce risk, and enabling teams to use AI effectively.

The session walks through:

Why AI changes the risk profile of everyday work, especially in smaller organizations
The most common failure patterns, including unreviewed outputs, misuse of sensitive data, and over-reliance on “confident but wrong” results
What “minimum viable governance” looks like in practice, including simple policies, review expectations, and clear accountability
How to align AI use to real business value instead of ad hoc experimentation
A practical starting point using a 90-day, three-phase approach to introduce governance alongside real usage

This is not a theoretical or compliance-heavy discussion. It is a grounded, operational view of how SMBs can adopt AI safely while maintaining speed, trust, and accountability.

Intended for 45–60 minutes. No technical background required. Focused on practical application within existing roles rather than creating new governance structures. Suitable for organizations early in AI adoption or using AI informally without defined policies.

Target Audience:
Small and medium business owners and operators
Project managers, product managers, and delivery leads
Operations leaders and functional managers
Consultants and advisors supporting SMB clients
Teams using AI tools without formal governance or oversight

AI Governance for Agile / Lean Delivery Leaders

AI is already influencing day-to-day work across planning, delivery, documentation, analysis, communication, and decision support. For Scrum Masters, agile PMs, agile delivery leads, coaches, and similar leaders, the challenge is often less about the technology itself and more about how to help teams use it responsibly, consistently, and in ways that improve outcomes rather than create avoidable risk.

This session presents a practical, lightweight approach to AI governance for real delivery environments. It focuses on how leaders close to the work can help teams align AI use to meaningful business and delivery needs, apply appropriate data and tool discipline, and put workable guardrails in place around accountability, risk, and adoption.

Rather than treating governance as a heavy compliance exercise, this talk frames it as part of good delivery leadership. It is about creating clarity, improving judgment, and helping teams move forward thoughtfully as AI becomes part of everyday work.

Attendees will leave with a practical framework they can apply in project, agile, and cross-functional delivery settings without adding unnecessary bureaucracy.

Target audience: Scrum Masters, agile coaches, agile delivery leads, project managers, program managers, delivery managers, PMO practitioners, and related team and delivery leadership roles.

Suggested session length: 45-60 minutes, Q&A incorporated throughout the session.

Also suitable for audiences in project management, agile, product, software delivery, and digital transformation communities.

No technical setup required beyond standard presentation capability.

AI Governance for Project Managers: Managing Risk in an AI-Enabled World

Project managers have always been responsible for managing risk. That responsibility has not changed, but the nature of risk has.

AI is now embedded across how work gets done: in the tools PMs use, in how teams deliver, and increasingly in the products being built.

Each introduces new and often poorly understood risks that can impact quality, security, compliance, and outcomes.

This session provides a practical, PM-focused view of AI governance, not as a technical discipline, but as an extension of project risk management.

The session focuses on three critical areas every project manager must understand:

- AI used by the PM: where productivity tools can introduce accuracy, confidentiality, and decision risks

- AI used by the team: where AI-assisted delivery (e.g., development, automation) introduces quality, security, and IP risks

- AI embedded in the product: where traditional approaches to testing, validation, and accountability no longer apply

Participants will leave with a clear mental model for identifying where AI is influencing their projects, what risks it introduces, and how to manage those risks within existing project governance practices.

Target audience: Project managers, program managers, PMO leaders, and delivery leaders working in environments where AI is emerging or already in use

Session format: Interactive lecture with facilitated discussion

Preferred duration: 60 minutes (including Q&A)

Level: Intermediate (no technical AI background required)

Technical requirements: Standard presentation setup (screen/projector)

Agile, Product, and Software Craft in AI-Enabled Delivery

Session Title
Agile, Product, and Software Craft in AI-Enabled Delivery

Description
Agile teams are being asked to improve speed, quality, and collaboration while adapting to new ways of working shaped by AI-assisted development. At the same time, many teams still deal with a familiar problem: planning, technical context, and execution often drift apart.

This session explores how agile leadership, product thinking, and software craft can work together more effectively in that environment. The focus is on stronger feedback loops across roles, clearer work definition, better collaboration between those guiding the work and those building it, and practical ways to support continuous improvement without losing delivery discipline.

Through the combined perspectives of an agile coach and a software engineer, this talk examines how teams can connect intent to implementation more clearly, use planning artifacts such as acceptance criteria in more actionable ways, and improve the quality of delivery through better shared context. The session also connects these ideas to kaizen and continuous improvement, with an emphasis on practical application in modern software teams.

Attendees will leave with a clearer view of how to strengthen collaboration across agile, product, and engineering roles in AI-enabled delivery environments, and how better feedback loops can support both team effectiveness and higher-quality outcomes.

Target audience: agile coaches, scrum masters, product managers, product owners, engineering managers, software engineers, delivery leads, and cross-functional technology leaders.

Preferred session duration: 60 minutes.

Format: joint presentation from an agile coach and a software engineer.

Focus: practical, cross-functional session for teams working at the intersection of delivery leadership, product thinking, and software development.

Level: intermediate. Accessible to mixed audiences across agile, product, and engineering.

Minimum Viable Change: Scaling Small Wins into Organizational Momentum

Most large-scale change efforts fail not because of poor intent, but because they are too big, too disruptive, and too disconnected from how work actually happens. This session introduces Minimum Viable Change as a practical alternative, focusing on small, deliberate interventions that create immediate signal, reduce risk, and compound over time.

Rather than relying on top-down transformation, the approach centers on identifying the smallest meaningful change, validating it quickly, and expanding it organically across connected teams such as product, engineering, and shared services. The result is progress that is visible, adaptable, and sustainable.

This session focuses on how to:

Define and apply Minimum Viable Change in real delivery environments
Design small interventions that produce fast, actionable feedback
Make work visible to reinforce effective behaviors and surface learning
Expand proven practices across adjacent teams without heavy coordination overhead
Avoid over-scaling and preserve momentum as adoption grows

Grounded in modern, AI-enabled and hybrid delivery contexts, this session emphasizes practical execution over theory, enabling teams to build lasting change through consistent, incremental progress.

Target audience: Delivery leaders, product managers, engineering managers, program managers, and transformation practitioners

Session format: Practical talk with real-world examples

Preferred duration: 45 to 60 minutes, interactive Q&A throughout
Technical requirements: HDMI and standard projector/large monitor, no special setup required for in-person events otherwise.

Doing Good, Doing Better, Doing Well: A Practical Approach to Modern Consulting

This 60-minute session introduces a pragmatic, experience-based approach to consulting that prioritizes real outcomes over activity, capability over dependency, and partnership over performative expertise. Drawing from Caitlin and John's book, Values-Based Consulting - A Field Guide to Doing Good, Doing Better and Doing well, the session challenges traditional consulting models and presents principles and practices that lead to measurable impact.

The focus is not on frameworks or theory for their own sake, but on what works in real environments where context matters, resistance is real, and results are expected. Attendees will walk through how to shift from deliverables to outcomes, how to build trust and credibility early, and how to ensure that clients are stronger after the engagement than before.

Format: 60-minute webinar (45 minutes content, 15 minutes Q&A)
Delivery: Virtual (Zoom, Teams, or similar platform)
Audience: Consultants, consulting leaders, transformation leads, and client-side sponsors of consulting engagements
Level: Intermediate. Assumes basic familiarity with consulting or advisory work