Could AI be your next Product team member?
Jon Block, former Chief Product Officer of VIOOH, shares his vision of a near future where humans and AI work closely together. The agenda includes concrete feedback, new organizational models, and practical advice to start this transformation with agility.
Jon Block
Product Advisor & Consultant chez syllepsis.io
Jan 28, 2025
To kick off 2025, we’re excited to Open! the floor to a special guest. This edition features Jon Block, a seasoned Product Leader and Advisor with whom we had the pleasure of collaborating during his time at VIOOH.
Recently, Jon has shared inspiring insights on the transformative impact of AI on product development and team organisation. We invited him to continue the conversation and explore these ideas further with us.
Recent Experiments
You created an app using AI tools and documented the process in your first article. Can you tell us more?
Jon: Absolutely! Over the holidays, I dove into Bolt.new, a new AI-powered no-code tool, spurred on by a very well-timed Source design trends newsletter. I was so impressed by how effortlessly an ideas-person like myself could build an app that I decided to create my own.
I created RateMyApp.ai, a platform for creators to showcase prototypes, get feedback and discover innovative ideas - a bit like ProductHunt or DevHunt, but focused on early-stage no-code AI prototypes.
Glad that our newsletter helped! Can we talk about your process?
The experience was eye-opening and incredible. I fell into a pattern of using ChatGPT as my advisor and Bolt.new as my developer, quickly producing a working prototype.
The core functionality came together in just a couple of hours, but the refinement - polishing, testing, tweaking - took far longer. Partly because these tools let me obsess over the details without the usual constraints of team-driven development.
Did you face any challenges? That sounds almost too easy.
The limitations are surprisingly minor when you consider how early we are in this journey. Most of the challenges stem from the context window - how much information can be sent to and retained by the AI. This means having to re-explain what needs to be done, which can be frustrating at times.
Additionally, the AI excels at small, gradual changes, but struggles with sweeping transformations.
That said, these issues are temporary. As AIs become more powerful and their context windows expand, these challenges will diminish rapidly.
Implications for product teams
In your follow-up article, you outlined a future model for product teams. Could you explain how it works in practice?
Jon: I proposed the Augmented Quad Model - a blueprint for AI-augmented teams that balances efficiency with creativity. In practice, it comprises four key roles: two hybrid human team members and two specialised AIs.
The Human Roles:
A Product-focused role, driving vision, user needs and prioritisation.
An Engineer/QA hybrid, responsible for code quality, automated testing and troubleshooting.
The AI Roles:
The Developer AI, focusing on generating and refactoring code, with its “context” centred on the codebase.
The Orchestrator AI, managing broader product and project responsibilities (its “context” is higher-level), including writing requirements, managing workflows and documenting progress.
This model empowers small, lean teams to deliver with incredible speed and precision while preserving human creativity and strategic oversight.
How would these Quads interact with the rest of the organisation?
Crucially, these Quads are supported by two centralised functions:
UX Research/Product Design: Ensuring user-centric design across multiple Quads.
Technical/Architectural Oversight: Maintaining scalability and alignment with organisational objectives.
These central functions operate as shared resources, typically supporting two or three Quads, ensuring expertise is distributed while keeping teams agile and focused.
The result? A collaborative structure where AI drives efficiency and humans contribute innovation - creating a seamless partnership between technology and talent.
What new skills will be crucial for teams to thrive in this AI-augmented environment?
Beyond adaptability to this rapidly evolving landscape, I see three critical skill areas for product and design teams:
Context Engineering: Going beyond basic “prompt engineering” to structure and guide AI tools effectively.
Strategic Thinking: Focusing on the “why” behind a product to ensure AI efforts align with user needs and business goals.
Iterative Design: Embracing rapid prototyping and learning through experimentation with AI.
These skills will empower teams to leverage AI for execution while focusing human effort on creativity, empathy and strategy.
We believe Product Designers will need to be familiar with no-code tools to deliver prompted prototypes. Do you see this becoming a standard?
Yes, but I see this evolution as an opportunity to elevate the role of Product Design rather than diminish it. AI tools are becoming invaluable partners, but Designers will remain central to shaping how products are envisioned, built and experienced.
Designers who can quickly translate ideas into “prompted prototypes” will bridge the gap between ideation and execution, enabling faster iteration cycles and clearer communication with stakeholders.
Equally, Designers who embrace the opportunity to train and collaborate with AI team members will find themselves shaping the future of Design in new and meaningful ways
About our past collaboration
Looking back at our collaboration at VIOOH, how would you describe the process we had?
Jon: Our process at VIOOH was highly collaborative and focused on tight integration between Product and Design. What worked particularly well was our commitment to user-centricity (in a very complex area) and our ability to iterate quickly based on feedback. However, the tools we used at the time required a lot of manual effort to prototype, build and refine features.
If we were to start the same project today, do you think the Augmented Quad Model could be applied effectively?
Yes. The Augmented Quad Model is designed for lean, high-output teams and I believe it would have been a natural fit for the project. With AI team members - one focused on development and another on project orchestration - we could have accelerated delivery while maintaining creative and strategic oversight. The dynamics would likely shift toward rapid experimentation, enabling faster validation, smoother iteration and more efficient results.
That said, adopting this model isn’t just about tools - it’s about mindset and change management. Integrating AI team members fundamentally alters how teams operate and making this shift positively and collaboratively is key.
Practical takeaways
For companies hesitant about adopting these tools or models, what’s a good first step?
Jon: Start small. Introducing Orchestrator AIs like ChatGPT into workflows - drafting requirements, managing tasks or creating documentation - is a low-risk way to explore AI’s potential without disrupting existing processes.
For a deeper dive, I’m developing workshops to help teams create and train their own Orchestrator AIs - no technical expertise required.
And for companies that are more mature or ambitious with AI, what would you recommend?
Another effective step is using no-code AI tools for prototyping. Dedicate a small team or even just one day a week to build a feature prototype. This exercise can reveal efficiencies and demonstrate the transformative impact of AI-driven development.
Any final advice for organisations starting their AI journey?
Crucially, companies need to treat AIs as part of the team, not just as tools for point solutions. Like any new hire, AIs need onboarding, training and context to perform at their best. Encouraging this collaborative mindset will be key to unlocking AI’s potential.
Every organisation’s journey will be unique, but the principles are universal: start small, communicate clearly and embrace the opportunities.
And remember, as the singularity edges closer (my money’s now on 2028), treating AI as a valued team member might not just improve your workflows - it might even help ensure you’re on its good side when that time comes. Better safe than sorry, right?
About the Author
Jon Block is a product and tech expert with over 25 years of experience. Recently, as Chief Product Officer at VIOOH, he led the creation of a real-time advertising platform that generated over 100 million pounds in programmatic revenue in just three years, while launching an innovation lab dedicated to rapid prototyping.
Today, Jon advises organizations on their innovation and growth challenges, focusing on product strategy, team structuring, and leveraging cutting-edge AI to accelerate prototyping and transform business models.
In his free time, he is a father of three, a long-distance cyclist, a board game enthusiast… and a jazz standards performer on the ukulele… but rarely all at once!
To kick off 2025, we’re excited to Open! the floor to a special guest. This edition features Jon Block, a seasoned Product Leader and Advisor with whom we had the pleasure of collaborating during his time at VIOOH.
Recently, Jon has shared inspiring insights on the transformative impact of AI on product development and team organisation. We invited him to continue the conversation and explore these ideas further with us.
Recent Experiments
You created an app using AI tools and documented the process in your first article. Can you tell us more?
Jon: Absolutely! Over the holidays, I dove into Bolt.new, a new AI-powered no-code tool, spurred on by a very well-timed Source design trends newsletter. I was so impressed by how effortlessly an ideas-person like myself could build an app that I decided to create my own.
I created RateMyApp.ai, a platform for creators to showcase prototypes, get feedback and discover innovative ideas - a bit like ProductHunt or DevHunt, but focused on early-stage no-code AI prototypes.
Glad that our newsletter helped! Can we talk about your process?
The experience was eye-opening and incredible. I fell into a pattern of using ChatGPT as my advisor and Bolt.new as my developer, quickly producing a working prototype.
The core functionality came together in just a couple of hours, but the refinement - polishing, testing, tweaking - took far longer. Partly because these tools let me obsess over the details without the usual constraints of team-driven development.
Did you face any challenges? That sounds almost too easy.
The limitations are surprisingly minor when you consider how early we are in this journey. Most of the challenges stem from the context window - how much information can be sent to and retained by the AI. This means having to re-explain what needs to be done, which can be frustrating at times.
Additionally, the AI excels at small, gradual changes, but struggles with sweeping transformations.
That said, these issues are temporary. As AIs become more powerful and their context windows expand, these challenges will diminish rapidly.
Implications for product teams
In your follow-up article, you outlined a future model for product teams. Could you explain how it works in practice?
Jon: I proposed the Augmented Quad Model - a blueprint for AI-augmented teams that balances efficiency with creativity. In practice, it comprises four key roles: two hybrid human team members and two specialised AIs.
The Human Roles:
A Product-focused role, driving vision, user needs and prioritisation.
An Engineer/QA hybrid, responsible for code quality, automated testing and troubleshooting.
The AI Roles:
The Developer AI, focusing on generating and refactoring code, with its “context” centred on the codebase.
The Orchestrator AI, managing broader product and project responsibilities (its “context” is higher-level), including writing requirements, managing workflows and documenting progress.
This model empowers small, lean teams to deliver with incredible speed and precision while preserving human creativity and strategic oversight.
How would these Quads interact with the rest of the organisation?
Crucially, these Quads are supported by two centralised functions:
UX Research/Product Design: Ensuring user-centric design across multiple Quads.
Technical/Architectural Oversight: Maintaining scalability and alignment with organisational objectives.
These central functions operate as shared resources, typically supporting two or three Quads, ensuring expertise is distributed while keeping teams agile and focused.
The result? A collaborative structure where AI drives efficiency and humans contribute innovation - creating a seamless partnership between technology and talent.
What new skills will be crucial for teams to thrive in this AI-augmented environment?
Beyond adaptability to this rapidly evolving landscape, I see three critical skill areas for product and design teams:
Context Engineering: Going beyond basic “prompt engineering” to structure and guide AI tools effectively.
Strategic Thinking: Focusing on the “why” behind a product to ensure AI efforts align with user needs and business goals.
Iterative Design: Embracing rapid prototyping and learning through experimentation with AI.
These skills will empower teams to leverage AI for execution while focusing human effort on creativity, empathy and strategy.
We believe Product Designers will need to be familiar with no-code tools to deliver prompted prototypes. Do you see this becoming a standard?
Yes, but I see this evolution as an opportunity to elevate the role of Product Design rather than diminish it. AI tools are becoming invaluable partners, but Designers will remain central to shaping how products are envisioned, built and experienced.
Designers who can quickly translate ideas into “prompted prototypes” will bridge the gap between ideation and execution, enabling faster iteration cycles and clearer communication with stakeholders.
Equally, Designers who embrace the opportunity to train and collaborate with AI team members will find themselves shaping the future of Design in new and meaningful ways
About our past collaboration
Looking back at our collaboration at VIOOH, how would you describe the process we had?
Jon: Our process at VIOOH was highly collaborative and focused on tight integration between Product and Design. What worked particularly well was our commitment to user-centricity (in a very complex area) and our ability to iterate quickly based on feedback. However, the tools we used at the time required a lot of manual effort to prototype, build and refine features.
If we were to start the same project today, do you think the Augmented Quad Model could be applied effectively?
Yes. The Augmented Quad Model is designed for lean, high-output teams and I believe it would have been a natural fit for the project. With AI team members - one focused on development and another on project orchestration - we could have accelerated delivery while maintaining creative and strategic oversight. The dynamics would likely shift toward rapid experimentation, enabling faster validation, smoother iteration and more efficient results.
That said, adopting this model isn’t just about tools - it’s about mindset and change management. Integrating AI team members fundamentally alters how teams operate and making this shift positively and collaboratively is key.
Practical takeaways
For companies hesitant about adopting these tools or models, what’s a good first step?
Jon: Start small. Introducing Orchestrator AIs like ChatGPT into workflows - drafting requirements, managing tasks or creating documentation - is a low-risk way to explore AI’s potential without disrupting existing processes.
For a deeper dive, I’m developing workshops to help teams create and train their own Orchestrator AIs - no technical expertise required.
And for companies that are more mature or ambitious with AI, what would you recommend?
Another effective step is using no-code AI tools for prototyping. Dedicate a small team or even just one day a week to build a feature prototype. This exercise can reveal efficiencies and demonstrate the transformative impact of AI-driven development.
Any final advice for organisations starting their AI journey?
Crucially, companies need to treat AIs as part of the team, not just as tools for point solutions. Like any new hire, AIs need onboarding, training and context to perform at their best. Encouraging this collaborative mindset will be key to unlocking AI’s potential.
Every organisation’s journey will be unique, but the principles are universal: start small, communicate clearly and embrace the opportunities.
And remember, as the singularity edges closer (my money’s now on 2028), treating AI as a valued team member might not just improve your workflows - it might even help ensure you’re on its good side when that time comes. Better safe than sorry, right?
About the Author
Jon Block is a product and tech expert with over 25 years of experience. Recently, as Chief Product Officer at VIOOH, he led the creation of a real-time advertising platform that generated over 100 million pounds in programmatic revenue in just three years, while launching an innovation lab dedicated to rapid prototyping.
Today, Jon advises organizations on their innovation and growth challenges, focusing on product strategy, team structuring, and leveraging cutting-edge AI to accelerate prototyping and transform business models.
In his free time, he is a father of three, a long-distance cyclist, a board game enthusiast… and a jazz standards performer on the ukulele… but rarely all at once!
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Since 2014, we’ve been turning complex challenges into clear and desirable user experiences.
From product strategy to full-scale rollout, our team brings structure, speed and sharp execution — with no compromises.
Enjoyed this article? You’ll love Open!
Join our newsletter to get the very best of our content every month — insights, client stories and design experiments, straight to your inbox.
Work with Source.paris
Since 2014, we’ve been turning complex challenges into clear and desirable user experiences.
From product strategy to full-scale rollout, our team brings structure, speed and sharp execution — with no compromises.
Enjoyed this article? You’ll love Open!
Join our newsletter to get the very best of our content every month — insights, client stories and design experiments, straight to your inbox.
Work with Source.paris
Since 2014, we’ve been turning complex challenges into clear and desirable user experiences.
From product strategy to full-scale rollout, our team brings structure, speed and sharp execution — with no compromises.
Enjoyed this article? You’ll love Open!
Join our newsletter to get the very best of our content every month — insights, client stories and design experiments, straight to your inbox.
Work with Source.paris
Since 2014, we’ve been turning complex challenges into clear and desirable user experiences.
From product strategy to full-scale rollout, our team brings structure, speed and sharp execution — with no compromises.
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