We are excited to announce the launch of Maximizing the Value of AI for Product Work, a new self-paced, on-demand course now available.
We created this course to bridge a critical disconnect in how AI costs are evaluated. Consider a quick calculation: producing a draft might appear to cost 4.3 cents on paper. However, when you factor in only the usable, high-quality outputs, the true cost rises to 11.4 cents—almost triple the original estimate. Most organization-level AI discussions overlook this discrepancy because standard metrics divide costs across every output generated rather than measuring only successful results.
Addressing this exact gap is the driving force behind our latest course.
What Maximizing the Value of AI for Product covers
The course provides a continuous framework to evaluate and demonstrate which AI initiatives generate genuine returns. For every AI capability across your organization, four fundamental questions guide the decision-making process: what assets exist, what permissions apply, whether the investment proves worthwhile, and how to direct its future path.
Throughout four practical modules, you apply these concepts to an organization—using either your own company or Meridian Freight, a mid-sized logistics firm featuring two core products, eight AI assets, and typical operational complexities. You construct a core output during the course: the AI Value Ledger. Each AI asset receives a dedicated entry evaluated through four structured sections, culminating in a fully documented record alongside an actionable executive summary tailored for decision-makers.
The course framework spans four key areas:
- Conducting an AI inventory — mapping the five essential dimensions of your environment: tooling and models, underlying data, infrastructure, internal expertise, and unaddressed governance gaps
- Activating your AI capabilities — establishing five permission models, defining your team's AI-Safe Zone, and determining whether to build, activate, or defer
- Aligning AI with targeted outcomes — measuring cost per successful output and establishing clear value verdicts
- Expanding returns on AI assets — deciding to scale, maintain, or decommission, categorizing reuse options across four models, and delivering actionable summaries for leadership
Who it's for.
Designed for product managers, designers, engineers, and product leaders who already know how to leverage AI, this course targets a different challenge: proving that your AI investments are truly paying off when leadership asks for hard evidence.
It also addresses two widespread organizational blind spots:
- Unused capabilities — paid-for features sitting idle and switched off.
- Unsanctioned tools — unapproved applications operating under the radar.
Both issues remain invisible until actively audited.
Who should skip this course?
- Organizations that have not yet adopted AI (as there are no assets to inventory).
- Individuals seeking basic prompt engineering or tool walkthroughs, as foundational AI fluency is assumed.
How it works.
Designed to be completely on-demand and self-paced, this 60-minute experience dedicates over half its duration to hands-on exercises. You will navigate an interactive landscape map, complete a permission stance card sort, adjust real-time costs against quality thresholds using an interactive slider, and evaluate a flawed verdict before its underlying logic is uncovered.
Within the first three minutes, you select your operational context. If you choose your current company, your AI Value Ledger populates with live data from your very first entry—generating tangible work product directly within the course. Alternatively, selecting Meridian Freight provides a pre-built environment loaded with thirty days of usage, financial, and quality metrics.
Once finished, the Ledger remains fully accessible to you. Far from a static download, it functions as an active tool engineered for immediate deployment across your organization's AI ecosystem.
Questions you might have.
Is this for someone in my role?
Absolutely—if you belong to a product team and face inquiries about the return on AI efforts. Built to be universally accessible without requiring specialized framework terminology, the course guides product leaders, engineers, designers, and managers through these same core questions.
Do I need access to real cost and usage data?
Not at all. Participants from active organizations evaluate their own systems using provided self-assessment benchmarks. Other participants utilize Meridian Freight, which supplies a full month of operational metrics spanning quality, usage, and cost. Developing the practice of asking these core questions matters far more than possessing flawless telemetry.
Sixty minutes — can that really change anything?
Course duration isn't what matters—it's what you build. By the end, you will walk away with a completed Ledger and rollup, along with a prompt to log a real entry for an active capability within 48 hours. Establishing that initial entry builds the habit; from there, it's just a matter of repetition.
Isn't this finance's job, or governance's?
This course delivers practical, asset-by-asset evaluations that seamlessly enrich your current portfolio reviews, viability tests, and investment choices—supplying the crucial perspective those frameworks often lack.
Won't this be stale in six months?
By focusing on core principles rather than specific tools or vendor interfaces, these lessons remain relevant through future tech updates. Regardless of upcoming tool changes, the fundamental evaluation remains centered on four key questions: inventory, permissions, cost per successful output, and whether to scale or retire.
Start today.
AI fluency is widespread today, but sound judgment remain rare. While metrics provide the evidence, rendering the final verdict is the real goal — and this course equips you to make that call.
