Quick bits

🧩 Strategy: What to do when your Stakeholders come to you with solutions and not problems

📊 Trends: Product Management Is Dead, So What Are We Doing Instead?

🛠️ Training & Tools: From Good to Great: Master Prompting Essentials in 5 Minutes

Welcome AI Quick Bytese

🧩 Strategy

This article captures a strategically critical approach for today’s AI-driven landscape, especially given the rush by leadership teams to launch AI solutions before identifying real problems. In my experience, one of the most common pitfalls is underestimating what it truly takes to make AI impactful—beyond just the tech itself, it requires time, cultural shifts, and substantial resources. But the deeper issue I’ve seen is the lack of a clear problem-solution alignment. Without a true understanding of what customer pain points AI can address, even the most sophisticated initiatives risk falling flat on ROI.

This is why a forward-thinking approach like the one in this article is so powerful. Rather than dissecting the problem retrospectively, it guides leaders to think through the expected impact of an AI solution: what it will tangibly achieve for customers and for the organization. This mindset can transform stakeholder alignment, ensuring AI projects aren’t just “shiny new solutions” but genuine value-drivers for both the business and the customer.

Dealing with Stakeholders coming with Solutions, not Problems

Executive Summary:

Dealing with stakeholders who come with solutions instead of problems can be challenging for product teams. Rather than working backwards to discover the problem, a forward-focused approach can lead to better alignment and uncover the real issue without resistance. By exploring the anticipated outcomes of a suggested solution, product managers can guide stakeholders to identify underlying needs organically. This technique transforms potentially confrontational conversations into collaborative discussions, paving the way for effective product discovery.

Key Points:

1. Move Forward, Not Backward, with Solutions

Instead of directly asking stakeholders, “What problem does this solve?”, try casting forward with their idea. This technique helps stakeholders envision the impact of their solution, making it easier for them to articulate the real outcomes they’re aiming for and creating a shared understanding without defensiveness.

2. Reverse Engineer from Outcomes to Problem Spaces

Using the forward-focused approach, stakeholders identify desired outcomes, such as increased speed or reduced costs. With these benefits as a foundation, you can then reverse-engineer back to the actual problem areas, making it clear why the solution is relevant. This reframing can turn ambiguous solution requests into actionable problem spaces.

3. Create a Natural Path to Product Discovery

This method often brings up underlying assumptions that need validation. By surfacing these assumptions early, teams are better equipped to move into product discovery, helping avoid rushed implementations and ensuring that solutions align with true organizational needs.

Author: Ant Murphy

📊 Trends

Product Management Is Dead, So What Are We Doing Instead?

This talk is one of the most grounded, insightful reflections on AI’s impact within corporate enterprise that I’ve encountered. Claire cuts through the hype to show how profoundly—and how quickly—the knowledge work landscape is evolving, using clear, relatable examples that every product leader will recognize. The most striking insight? The shift from traditional team structures to empowered "teams of one" who can design, productize, and engineer their own solutions. This vision of agile, AI-augmented talent is both exhilarating and challenging: it opens up unprecedented opportunities for those ready to adapt, while putting pressure on everyone else to catch up. The talk is a powerful reminder that the future belongs to those who embrace AI’s potential to reimagine and reshape every corner of enterprise work.

Executive Summary:

In a bold keynote, Claire (CPO) argues that AI is radically transforming product management, making traditional roles and tasks obsolete. By automating repetitive work, augmenting skills with AI, and breaking down rigid roles, teams can increase speed and creativity. The future, she argues, belongs to versatile "AI-powered triple threats" who can manage product, design, and engineering all at once, using AI as an integral part of the team. Her insights offer a roadmap for product leaders to embrace AI, empowering their teams to become more agile, impactful, and adaptable.

Key Points:

1. Automate and Accelerate Product Management Tasks with AI 

AI tools can now handle a wide range of product management tasks—document drafting, prioritizing features, even generating slides. Claire suggests creating an "anti-to-do list" of tasks that should always be automated, freeing up PMs to focus on high-impact strategy rather than manual busywork.

2. Embrace Versatile, AI-Powered Talent for a New Product Team Model 

Claire predicts that the classic product, design, and engineering "triad" is being replaced by flexible AI-powered professionals who span multiple disciplines. These "AI-powered triple threats" bring adaptability, cutting across traditional roles to accelerate product development and reduce team friction.

3. Prepare for a Future of Customizable, Skill-Driven Teams 

Claire advises leaders to rethink how teams are built, focusing on diverse skillsets that align with immediate project needs. She emphasizes the importance of adapting team structures to fast-evolving AI capabilities, allowing for greater agility and customized solutions in product strategy and execution.

🛠️ Training & Tools 

Five proven prompt engineering techniques (and a few more-advanced tactics)

When I first launched AI Quick Bytes, I dedicated a section to prompting, but we covered the essentials fast. Personally, I've never struggled with getting well-written responses from AI—my prompt research and practice seem to have tuned my prompt-writing instincts naturally. However, Mike Taylor’s article provides a fantastic refresher on the nuances of prompt engineering, breaking down techniques that make it easy to see why some prompts yield better results. With his clear examples and expert tips, this piece is a great guide to crafting prompts that bring out the best in AI. Enjoy!

Executive Summary:

In today’s AI landscape, achieving exceptional results with language models like ChatGPT hinges on mastering prompt engineering. Professional prompt engineer Mike Taylor reveals that, much like giving clear instructions to a colleague, well-crafted prompts enable AI to deliver highly relevant and sophisticated outputs. From adopting personas to crafting step-by-step directives, Taylor outlines five foundational prompt techniques and three advanced methods to optimize AI responses effectively. His insights empower users to create tailored, impactful AI interactions, significantly enhancing productivity and precision.

Key Points:

1. Prompting Techniques for Superior AI Performance 

Taylor shares five tried-and-tested prompting tactics to get better, more customized responses from AI. From using "role-playing" to make AI emulate an expert to "few-shot learning" for guiding responses through examples, these techniques allow users to tailor AI outputs to specific needs or audiences, no matter the model.

2. Style and Emotion-Based Prompts for Authenticity 

Techniques like "style unbundling" and "emotion prompting" allow users to instruct AI to incorporate elements of a specific communication style or add emotional context to tasks. By clearly defining the desired tone and stakes, users can enhance the depth and empathy in AI’s responses, making them feel more genuine and aligned with the task’s goals.

3. Advanced Tactics for Complex Tasks 

For more sophisticated prompting, Taylor introduces methods such as “synthetic bootstrap,” where AI generates diverse example responses for further refinement, and “task splitting” for breaking down complex tasks. These methods support better AI understanding, making them invaluable for nuanced or layered tasks requiring extra precision.

Author: Mike Taylor