ProductCon London 2025 

Roger on 20/02/2025

A woman in a red suit stands on a stage with a big screen behind her and a large audience in front of her.

Takeaways

Product management is becoming more responsible for business success and revenue. 

The Product School CEO shared The Value Stick and talked about how product management is involved in protecting margin by increasing willingness-to-pay and reducing cost. Vinay Ramani, CPO at Tide, shared his playbook for market-first innovation and how they expanded into new markets by relying on local knowledge and separating from their established approaches before bring successful things into their core business later. 

AI in product management, products and organisations 

AI was definitely a theme, as you might expect. Lots of organisations are trying to figure out how they should use it. The trend seems to be in using AI behind-the-scenes to make organisations more efficient than creating new opportunities or value by building it into products. The Financial Times considered whether AI would change their core mission or support elements of their existing business and landed on the latter, with journalists using AI to make their research more efficient. 

Everyone is modernising, migrating or replatforming 

Ok, maybe not everyone but organisations you might expect to have a modern tech stack still struggle with how the tech keeps pace with the rate of change in the business. It was interesting to hear how TIER Dott, a company that rents electric bikes and scooters, managed bringing together two platforms and apps into one following a merger, and how Mastercard introducing SAFe to help them manage legacy tech platforms failed. 

Talks

Videos of the talks.

  • Augmenting Your Product’s Value Proposition with AI by Debbie McMahon, CPO at Financial Times 
  • The Future of Product in 2025 by Carlos González De Villaumbrosia, Founder & CEO at Product School 
  • Product Localisation Playbooks for International Expansion by Vinay Ramani, CPO at Tide, Ex-Meta, Google, Uber 
  • Product & Culture Integration After M&A by Pénélope Carlier, VP of Product at TIER Dott 
  • Killing SAFe, Safely: Breaking Bureaucracy to Unlock True Agility by Simone Paul Tamussin, CPO at Mastercard Gateway 
  • Partnering with AI Agents to build Stronger Teams and Smarter Products by Christine Itwaru, VP of Product at Beamer Userflow; Sally Fuller, Head of Mobility Products at BT; Susanne Jones, Senior Partner & Customer Transformation Leader at IBM and Sang Ha Park, AI Agent Workforce Leader at Sendbird 
  • Scaling & Monetising Marketplaces by Carlos González De Villaumbrosia, Founder & CEO at Product School and Tanya Cordrey, CPO at Motorway. 
  • Practical AI Use Cases for Product Leaders to 10x Impact Today by Dave Killeen, VP of Product at Pendo 
  • Don’t Leave Money on the Table: Optimising Payments to Reduce Churn by Chetan Pandya, SVP of Product at DAZN 
  • Streamlining Product Operations for Category Expansion by Sam Hancock, VP of Product at Deliveroo 

Three ways for product managers to think about AI

Roger on 16/02/2025

As a product manager, I think about AI in three ways: AI in the market, AI as a tool, AI in products.

AI in the market

Understanding how AI is affecting the market you operate in is essential. It’s a must. As with an emerging tech, product managers need to be watching how the tech is developing, what use cases come out and how organisations use it to meet those needs.

The introduction of AI is mostly ‘technology push’ which means it’s a solution in search of a problem, but also means that it’s widely applicable to lots of problems. If we believe even a small part of the hype, we can expect AI to change every industry and so affect every person’s life in some way. It’s more of a question of when and by how much, rather than if. So as a product manager, even if you don’t work in ecommerce or healthcare or fintech, your users are being affected by AI used in those sectors, and it will change their behaviour and expectations (just like the internet and mobile devices did) for your sector and product. Understanding and getting ahead of the trends is part of the job.

AI as a tool

Using AI as a tool means using it to be more efficient in what product managers already do. I’ve heard it said that “AI won’t replace you, but someone using AI will”. Again, when, not if. Can you imagine working as a product manager and not using that new-fangled email for communicating? It won’t be long until we think of AI in the same way.

Product managers can (and increasingly, should) use AI to write user stories or create prototypes or summarise workshop notes. Current thinking seems to be that AI isn’t reliable enough to be used without a human-in-the-loop, so as that human, products managers have to exercise the kind of critical thinking they would is other areas of their work. Its AI as a tool, not AI as an avatar/assistant (yet, but it’s coming), and a bad worker blames their tools.

AI in products

Using AI in products relies on product managers asking, “does it solve a worthwhile problem?” In the rush to find the problems technology push allows, adding AI into a product without that problem being worth solving could be wasteful but there are advantages to exploring ambiguous spaces and building an organisation’s capability for the future.

Product managers could be discovering worthwhile problems in two complimentary ways at the same time.

Firstly, testing hypotheses about user adoption, even if that is done quite bluntly by plugging an LLM into the product to see if anyone uses it is a completely viable approach given the state of the market mentioned above. There are other ways to understand user adoption, of course, but it’s important to understand that what doesn’t work today might work next year because of how user expectations will be quickly changed by other organisations using AI.

At the same, story-telling about the possibilities, building the case for investment, and creating longer-term technical capability for using AI as part of the product tech stack are all good things for productbmanagers to be doing. As well as, or instead of, surfacing AI to the user as part of the interface, it can include using AI to make predictions, analyse images, automate proceses with uncertain outputs, etc., etc.

It is also for product managers to balance the opportunity with the risks.

Product managers could be doing supplier due diligence, data protection and information rights assessment, figuring out an IP protection stance, analysing the total cost to run, assessing the risks, understanding ethical concerns about privacy and bias, and environmental impact of AI’s use of energy and water, and all the other things that come with introducing an emerging technology to an organisation, their product and their users.

That’s my current thinking on how product managers can think about AI. It’ll probably change, but for now I’d say product managers must be understanding AI in the market, should be using AI as a tool, and could be introducing AI to their products.

Weeknotes 446

Roger on 14/02/2025

I did:

  • Wrote up last week’s workshop with the Delivery Manager’s community which focused on reporting and conflict resolution.
  • Talked about vibe, and using it for workshops as a way of setting shared expectations. My go-to vibe is ‘rocket dog’, a happy jack russell riding a rocket. To me, it means we’re going to do this at pace, jump around a bit, and stay positive.
  • Started planning a workshop to design team responsibilities, mandate level, interactions, etc.
  • Met my new line manager. Was good to talk through the product vision and possible routes for getting there over the next few years.
  • Did some more market analysis and theory of change work on a new opportunity, and lined up getting buy-in from senior stakeholders.
  • Talked about how Profession and Community complement each other, with Profession providing the top-down ‘what’ and the Community creating the bottom-up ‘how’.
  • Got my ticket for ProductCon next week.

The numbers

Tasks completed: 37

Minutes in meetings: 825

I read/listened:

The Impact of AI on Product Management: A Systematic Review and Future Trends

The integration of AI has greatly increased its functionality in product management across innovation and market development from the initial concept to the actual market share. AI tools have helped product managers to improve traditional processes as it is an advanced tool which can analyze a large dataset, identify the patterns and facilitates to generate efficient strategies. AI product managers are essential in driving the identification of business problems best solved with AI, defining the overall strategic plan, and guaranteeing that AI is implemented ethically, safely, and with transparency and reliability. The paper provides an idea of what is an AI product manager, how does AI influences more traditional marketing models (B2B and B2C). These advancements accentuate how AI holds promise for constant enhancement and sustaining competitiveness in a rapidly changing market environment.

The challenges of studying in the ‘platformised’ university

University life is now increasingly mediated by digital platforms. Joe Noteboom’s research looks at the everyday realities of studying through platforms, and how students’ dependence on these technologies can lead to a number of problems.

Remote-first team interactions

The tragedy of the anticommons

The ‘tragedy of the anticommons‘ occurs when a resource has many owners, all of whom have the ability to exclude others from using it, leading to the under-utilization of that resource. I wonder if/how this happens in organisations?

I thought about:

Looking back

Thought about how hard it is to see progress in the moment and how it only makes sense when we look back. Maybe this is why product histories are so important for helping teams see how far they’ve come.

AI for product managers

Few things going around this week about AI and product so I thought I’d try to get some of my thoughts down.

I see three interconnected layers of AI.

Starting at the bottom is what’s going on with AI even if you do nothing. This is about having a way of dealing with any emerging tech. It includes horizon-scanning, market analysis, trends, behaviours, etc. It also includes how AI effects you and your users, because it will.

The middle layer is using AI in how we do our jobs, which for now probably just means using Gen AI and Machine Learning, but which will probably include Agent AI in the near future. Understanding how to use AI is like understanding how to use a mobile phone or email. It’s another tool we’ll use to do our work regardless of whether we use it well (I’m looking at you, email). So, we might as well apply some of that critical thinking and figure out how to use it well (when is it worth it for the environmental impact, for example).

The top, and most interesting layer, is how we might use AI in the products we build. I’d suggest we won’t do this very well if we aren’t doing the other two layers because we won’t have wide and deep contextual knowledge about how people are using AI more generally. This layer of AI also includes machine learning, image recognition, data analysis and decision-making, and all the other kinds of AI that is used in products without the user necessarily knowing. But they are still part of the product so product managers need to understand how they fit.