Slow Down to Speed Up: Jake McKee’s Guide to AIX (A Chat with Cross-Functional Experts)

Dianna Deeney interviews Jake McKee to explore AI Experience Design (AIX), the practice of designing relationships between humans and intelligent AI systems, and how it’s reshaping product development in today’s rapidly advancing technological landscape.

In this eye-opening conversation, Jake draws a powerful parallel between today’s AI transformation and the digital transformation of the early 2000s. The key difference? Scale and speed. While the early web had natural boundaries, AI presents an almost limitless frontier advancing at breathtaking pace. This creates unique challenges for product teams caught between executive demands for AI innovation and the practical realities of implementation. Jake explains how this pressure often leads to a predictable cycle of over-reliance followed by algorithm aversion before teams eventually find balance.

Rather than viewing AI as a replacement for human capabilities, Jake advocates for seeing it as a “creative and critical partner” that enhances our thinking and processes. He shares practical examples of how product teams can thoughtfully integrate AI – from using it to test early concepts against customer data to employing it as a collaborative ideation tool. Throughout our discussion, Jake emphasizes that successful AI integration depends on maintaining human relationships at the center of product development, not pushing customers further away behind technological barriers.

This interview is part of our series, “A Chat with Cross Functional Experts”. Our focus is speaking with people that are typically part of a cross-functional team within engineering projects. We discuss their viewpoints and perspectives regarding new products, the values they bring to new product development, and how they’re involved and work with product design engineering teammates. [Read more…]

Uncovering Customer Desires: Understanding Benefits in Concept Development

In product development, we often get caught up in the technical specifications and features we’re creating, forgetting the fundamental reason we’re building products in the first place: to provide benefits that improve users’ lives. This foundational concept of benefits versus features deserves revisiting regularly, especially when we’re deep in the weeds of development work.

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Blank Flipcharts Don’t Make Magic, But Templates Do

In product development, the “fuzzy front end” of concept development often represents both tremendous opportunity and significant challenge. During this critical phase, teams are tasked with defining problems, understanding customers, and generating solutions—all before any engineering begins.

How we navigate this phase dramatically impacts bottom-line results, market share, customer satisfaction, and whether projects even launch at all.

What makes concept development particularly difficult is that teams typically lack something concrete to discuss. Without prototypes or developed products, conversations can become abstract and unfocused. This challenge is compounded when cross-functional team members approach problems from vastly different perspectives, sometimes unknowingly working to solve entirely different issues altogether.

The traditional approach of gathering everyone in a room for open brainstorming sessions has proven remarkably ineffective. Research confirms what many of us have experienced: teams engaged in unstructured brainstorming typically generate fewer ideas, and those ideas are often of lower quality compared to more structured approaches.

The solution lies in using visual models and templates—structured frameworks that guide the creative process and facilitate meaningful team collaboration.

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Why Your Cross-Functional Team Isn’t Communicating Effectively (And How to Fix It)

Have you ever watched a promising product idea slowly die in the fuzzy space between “great concept” and “actual development”? You’re not alone.

The journey from product idea to market-ready solution contains a critical yet often overlooked phase: concept development. This is where cross-functional teams must align their diverse perspectives to create a solid foundation for design. But as many product developers discover, this is precisely where communication frequently breaks down.

In this episode, we dive deep into why cross-functional teams struggle to communicate effectively during early concept development and how to fix it.

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The Hidden Costs of Poor Concept Development in Product Design

The hidden costs of poor product development can devastate your project timeline, budget, and ultimate market success. Drawing from Dr. Robert Cooper’s research, this episode reveals how skipping proper concept development—the critical “fuzzy front end” of product design—leads many teams into a costly “ready-fire-aim” approach.

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Brighten Your Creative Spark

Ever hit that wall where your creative tank feels bone dry? That moment when you’ve been grinding away at your projects, head down for so long that when someone asks for innovation, you come up empty? You’re not alone.

Creative slumps happen when we get too immersed in our specialized domains. As engineers and designers, we develop expertise through consistent application of familiar tools and techniques. But that same specialization creates mental echo chambers where we recycle the same ideas and follow habitual thought patterns. The result? When innovation is needed most, we feel frustratingly blocked.

The solution lies in cross-pollination – deliberately exposing ourselves to diverse inputs that spark unexpected connections.

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Choosing a Confidence Level for Test using FMEA

We’re developing requirements for our product, including setting reliability requirements. Or we’re setting acceptance criteria for our test plans.

What confidence levels do we choose? We don’t have to blindly set them – we can base it off the risks of failure, using our FMEA (failure mode effects analysis).

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AI in Design: Coming Full Circle

As a Generation X engineer, I’ve witnessed remarkable shifts in how we approach design engineering.

Recently, I saw an article suggesting Gen X is frustrated because the skills we learned early in our careers no longer apply in today’s technological landscape. This characterization made me pause and reflect. While our tools have certainly evolved dramatically, I believe we’re experiencing something more nuanced than obsolescence.

With AI in design, we’re coming full circle, with artificial intelligence and machine learning enhancing rather than replacing the fundamental skills we developed.

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5 Aspects of Good Reliability Goals and Requirements

Good reliability requirements are going to drive our design decisions relating to the concept, the components, the materials, and other stuff. So, the moment to start defining reliability requirements is early in the design process. But, what makes a well-defined reliability requirement? There are five aspects it should cover: do you know what they are? 

We’ll describe what makes a good reliability requirement and examples of common (but not good) requirements.

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Supplier Agreements: The Good, The Bad, and The Quality

In today’s fast-paced manufacturing landscape, the link between product quality and supplier agreements is more critical than ever.

Custom product development can be complex, and regarding suppliers, we can uncover hidden challenges. These come up when suppliers are not aligned with the quality standards necessary for our products to be successful.

Join us as we discuss various types of supplier agreements commonly encountered in new product launches. They are important, but do they have what you need to ensure quality?

Tune in to learn how to transform your supplier partnerships into an invaluable asset in your quest for quality excellence.

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Practice Makes Improvement in Subjective Probability Estimations

Does the phrase “Subjective Probability Estimation” make you feel uncomfortable? If you’re a data-driven professional, you’re likely wary of each of those terms on their own, let alone combining them into one thing.

But we sometimes need to do it. And we can practice to get better at it.

In this episode, we emphasize the importance of subjective probability estimations in decision-making, especially in situations where concrete data may be unavailable or impractical.

We talk about:

• Exploring the discomfort of subjective probability estimations
• Utilizing Monte Carlo simulations for complex systems analysis
• Addressing bias and improving estimation accuracy

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