This Week’s Updates

Enabling the Team

Redesigning Workflows For AI by Jakob Nielsen
AI creates more value when teams redesign the whole workflow instead of just speeding up one task at a time. Removing handoffs, automating the glue work, and shifting people toward higher-judgment exceptions can unlock bigger gains than isolated productivity wins.

Mockups Were Never The Hard Part by Luke Wroblewski
AI makes it easier for anyone on a team to generate mockups, but the real design challenge is still creating coherent products with clear intent, shared mental models, and strong cross-functional alignment.

Product Direction

My Proven Success-Metric Framework by Büşra Coşkuner
Success metrics get clearer when teams start with the user journey, identify the turning-point steps that matter most, and then define signals that show whether those steps are working. The GSM framework helps turn vague goals into measurable progress, making it easier to choose better metrics even in B2B or low-data environments.

Competitive Differentiation Is Poison by Ravi Mehta
Product strategy gets stronger when teams stop asking what makes them different and start asking what makes them better for customers. Real advantage comes from improving the things people value most and building strengths that compound over time, not from inventing differences that sound distinctive but do not improve the product.

Continuous Research

Your Best Work Won’t Speak For Itself: Tried-And-Tested Sales Tactics For Researchops Professionals by Glenn Familton
ResearchOps work gets more traction when teams explain it in terms that leaders care about, not just in terms of activity or effort. Framing the need, the solution, and the business benefit makes it easier to win buy-in, build champions, and get important infrastructure work understood before its impact is fully visible.

User Personas Of Consequence by Chris R Becker
Personas create more value when teams treat them as a rigorous representation of real people, not a fast template to fill in. Slowing down, adding more inclusive context, and resisting AI-generated shortcuts can help teams build personas that support better design decisions instead of generic user fiction.

Continuous Design

Designing Trust For Agentic AI In Enterprise Software by Brooke Bell, Shrut Kirti Saksena
Agentic AI works better when teams match the interface and level of autonomy to the risk and complexity of the task. Clear previews, visible reasoning, checkpoints, and reversible actions help users trust AI enough to use it without losing control when the stakes are higher.

The Last Interface by Joshua Leigh
As agents take on more of the navigation and execution, design work shifts from arranging screens to defining the rules, guardrails, and defaults that shape what users get. Teams will need to design simpler core interfaces, support outcome-first workflows, and make sure dynamically generated experiences still stay usable, accessible, and coherent.

Continuous Development

Harness Engineering For Coding Agent Users by Birgitta Böckeler
Coding agents become more useful when teams surround them with clear guidance, fast feedback, and checks they can use to self-correct before humans step in. Strong tests, linters, architecture rules, and review loops help reduce repeated mistakes, improve code quality, and make higher-autonomy AI delivery safer to trust.

From T-shape To Comb-shape: How Developer Knowledge Needs To Evolve In The AI Era by Luca Mezzalira
As AI takes on more of the routine implementation work, developers create more value when they can judge trade-offs across architecture, domains, and system constraints rather than relying on narrow technical depth alone. Building knowledge across several important areas makes it easier to direct agents well, catch bad assumptions earlier, and focus human effort on the parts of software that matter most.

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Video Of The Week

AI in Learning & UX Design: Augmenting, Not Replacing

AI is already changing how teams work, but the biggest gains are not always coming from full automation. Often, the real value is in smaller process improvements that save time, improve quality and still keep humans firmly in the loop.

In this talk, Mike Brown, Head of Design at Barclays, shares how his team tested AI across internal learning workflows, what produced measurable gains, and where the tools fell short. A practical watch for anyone trying to adopt AI inside complex or regulated organisations, where speed matters, but accuracy, oversight and risk management matter even more:

Want to go into how careers and leadership are shifting as AI compresses the ladder? My ebook Managing Your Career In The Age Of AI explores how to build judgment, relationships, and influence in a world that keeps trying to automate the surface of the work.