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This post outlines how product managers can effectively use AI by evaluating whether problems are deterministic or probabilistic. It provides a diagnostic tool for selecting the appropriate AI approach and a strategic framework for implementing AI solutions that align with business objectives.
This comprehensive exploration delves into the essence of delegation, outlining practical strategies and considerations tailored to various roles within the organization, including product managers, regulatory & compliance personnel, and executives.
An overview of UAT, common pitfalls, a checklist for setting up a good UAT, and some ideas for communication plans, troubleshooting guides and script libraries that might be helpful for preparing for your next UAT.
The inherent complexity, unpredictability, and ethical considerations surrounding AI demand a tailored approach to setting and achieving objectives. Read on for OKR setting.
A recent evening at the SF Jazz center in San Francisco transported me into the captivating world of musical mastery.
In the dynamic world of business, individuals often aspire to climb the leadership ladder, transitioning from managers to leaders and eventually to executives.
Wonder how to unlock your potential in the AI revolution? I share some considerations and questions to ask when evaluating where to spend your hard earned professional development dollars.
AI's current hype mirrors the early mobile era, blending optimism with caution. Let's discuss the hype and how to evaluate the AI use case.
Introducing an SLDC approach, that not only embraces AI's capabilities but also addresses the complexities of modern software development, including risk management, data models, and validation processes.
Exploration of a product positioning frame from Obviously Awesome and expansion into a sales tool.
Explore some of the traditional prioritization methods (Kano, RICE, MoSCoW) used in product management and discuss how to modify them for use in regulated industries.
A mindmap for AI validation and actionable steps Product Managers can take when incorporating AI/ML into digital health, biopharma, or medical device products in order to speed the product to market.
The allure of adding "just one more thing" is strong. A cool new configuration option, a fancy widget, a feature so niche it practically has its own zip code. That's when the most important "no" you might ever say is the one you whisper to yourself.
Assessing an organization's security posture concerning AI (Artificial Intelligence) is crucial as AI technologies become increasingly integrated into various business processes.
Here's a non-technical dive into the challenges of securing AI systems and algorithms, designed for executives that want a little more than the bare minimum, but don’t want a degree in cybersecurity. What risks are these systems susceptible to? How can we mitigate these risks? Let's explore.
Lessons Learned from MDR & the Rights-focused product lifecycle. This piece will discuss parallels with the EU MDR transition & lessons learned and how manufacturers can integrate ethical evaluations into their product development lifecycle.
As we step into 2024, medical device manufacturers are poised for a year of regulatory evolution, guided by the FDA's upcoming guidance documents.
This is the first post in a series on using AI/ML in drug development, considerations for benefit-risk analysis, and potential mitigations to support responsible AI.
