We’re all trying to figure out this constantly changing AI thing. How can we use it day to day? How do we use it to make our products better, more useful, more whatever your current KPI/OKR is.
So we were very excited to have Caitlin Blackwell from SEEK to share some of their experiences with incorporating AI into not only production applications but also a very well established product.
Photo by Patrick Li
Caitlin shared a couple of their experiments which have been running in production.
Experiment One:
Use generative AI via a chat to refine and improve job recommendations. They decided to use an LLM to retrieve a person’s preferences in order to refine the recommendations received.
What they learned: They had multiple objectives such as gathering information, adoption, satisfaction which made the measurement hard with no single primary objective. It was hard to measure the actual change on the recommendations relevance
Experiment Two:
Currently there’s a ‘You are a strong applicant’ badge which helps you understand your fit for the role based on your profile information and the job listing. This experiment was to make this feature more valuable and grow its usage. Using the sparkly ai icon, they rolled out an experiment which helped you understand the next steps and what skills you could grow.
Once again, the adoption was lower than expected – which left them wondering if the sparkly ai icon isn’t understood or do people just not want to use ai tools? They chose a model which was cheaper but they found it was blunt, not very conversational nor friendly. This led to spending more time tweaking the tone than had they gone with other models that were more expensive but nicer out of the box. They also fell into the trap of too many objectives again.The goal was to increase quality applications. This could be a great way to drive people to update their profile in order to have better matching but that was not the primary goal.
Photo by Jen Leibhart
Overall, Caitlin recommends:
Have a single objective for each experiment. Don’t try to do too much or get confused about your goals.
Don’t go too big. Focus on small well known problems that you know have desirability. Or experiment cheaply to determine the desirability eg fake door tests.
There is so much uncertain & unknowing right now, that you should be going fast with ideas.
Use your experiments to determine the costs overall (human & AI) and think about how much you want to be spending on the experiment. Make sure you have buy in from leadership.
Don’t get into analysis paralysis with the technology choices. Pick a model & go for it.
Realise you might not have the adoption you had hoped for. Think about how you will maximise the experiment if you don’t have high adoption.
Thank you!
Thank you Caitlin for sharing some of the challenges your teams have been working through.
AND thank you to EasyGo for hosting the evening! Thank you for your hospitality & letting the community eye up the race car! 😉
Both Seek & EasyGo are currently hiring so go check them out!
Last month we ran a workshop with a name we couldn’t quite put on the public event listing. “What the F**k is Impact” is a provocative title — but as presenter JJ Monester made clear from the opening minute, it’s an honest one. Because when you really start pulling at the thread of what impact means, how it’s formed, and how it’s measured, you quickly discover that almost no one has a satisfying answer. And that’s exactly why it’s worth a room full of product people spending an evening on it.
Setting the scene
JJ comes at this topic from an interesting vantage point. He works at The Conversation — a not-for-profit news media organisation where academics write evidence-based content, and editors translate it into something the rest of us can actually read. With over 220,000 articles published across 11 global editions, approaching 6 billion reads, and around 110,000 academics published, The Conversation is genuinely trying to democratise knowledge at scale.
But being a not-for-profit means operating with two goals instead of one. Most organisations can point to a North Star metric — user retention, revenue growth, conversion — and orient the whole team around it. Not-for-profits have to balance that commercial reality against a harder question: are we actually making a positive difference in the world?
JJ framed it this way: imagine riding a horse with impact on one rein and revenue on the other. Pull too hard in either direction and you crash. The skill — and the challenge — is the balance.
That balance became very concrete when JJ was tasked with improving member retention for The Conversation’s university partnerships. Universities from around the world had completely different structures, priorities, and ways of measuring the value of their research. In the UK, the Research Education Framework ties how universities report research impact directly to tens of billions of dollars in public funding. Impact, it turns out, isn’t just a feel-good concept for these institutions — it’s a bottom-line metric they have to prove to keep the lights on.
That discovery sent JJ deep down a rabbit hole. And rather than keep the rabbit hole to himself, he built a workshop around it.
The activity: mapping impact for a fake company
After a grounding video from the Impact Management Platform — a UN and OECD-backed initiative to create shared language around impact for sustainable development — groups were handed a fictional company and a big sheet of paper.
The task: map out inputs, outputs, business activities, and the value being created or eroded. Two questions anchored it: What’s the traditional North Star metric for this business? And: What could impact actually look like here?
Two groups shared back at the end of the session.
BulkBreed Energy — a fictional smart energy and green optimisation platform with around $380M in revenue and engineering teams across Berlin and Austin — started where most companies do: revenue. But as the group worked through the mapping exercise, a richer picture emerged. Reduced waste. Increased renewable energy adoption. Lower electricity bills for consumers. Reduced pollution. But also: potential unemployment in mining towns as traditional energy sources wind down. Their overall impact statement landed somewhere honest and ambitious — better for people, better for customers, better for the world — while acknowledging that “better” isn’t always straightforward.
Atlas Freight Systems — a fictional SaaS platform for global logistics optimisation — found that complexity quickly multiplied. Their North Star was a universal freight platform that delivers everything on time and on budget. Clean enough. But once they started mapping, the picture got messy in interesting ways. Inputs covered workforce on both the tech and freight sides, physical materials, and time. Activities included navigating relationships with governing bodies across multiple jurisdictions. Outputs were primarily their SaaS platform — with secondary outputs including freight movement itself, and all the waste that comes with it.
The impact layer was where things got genuinely uncomfortable in the best way. Atlas could create positive environmental outcomes. Or significant negative ones. Spoiled freight, missed shipments, delayed supply chains — each one a potential value-eroding event. The group landed on customer satisfaction and environmental value creation as key measures, while honestly acknowledging that almost every impact dimension could tip either way depending on execution.
The question to take home
JJ closed with a provocation worth sitting with.
In 1970, Bhutan decided not to measure national progress by GDP alone. They created Gross National Happiness — nine categories, full measurement infrastructure, national surveys — and have used it as a legitimate alternative metric ever since.
If an entire country can build a framework for something as complex and contested as happiness, what’s stopping your organisation from getting more intentional about impact?
The ask wasn’t grand. JJ put it simply: if you leave this session and ask yourself just one question you weren’t asking before — What is my product doing in the wider world? Who does it influence? What value is it creating? — then the night was a success.
Thanks to JJ for bringing the rabbit hole to us. Resources from the session can be found in the reading.
Thank you to our sponsors!!
Thank you Revity & Amplitude for teaming up to support this community!
Amplitude: We help companies build better products.
We help companies unlock the power of their products.
Revity helps startups, scaleups and established organisations deliver product-led quality software outcomes.
We partner with businesses to build high-performing teams, uplift engineering capability, and deliver product outcomes that last beyond the project. From applied AI and mobile apps to global expansion and customer lifecycle platforms, we focus on practical, scalable solutions that drive business value. At Revity, we don’t just deliver projects, we empower teams to learn by doing, so organisations grow stronger with every engagement
Product Managers often say the C level just doesn’t understand – so what does it look like when you reach that level and the tables are turned? What does the C level want to see from product? Ana moved from Product to COO and gave us this low down.
While PMs often feel misunderstood and a disagreement on a roadmap can sometimes feel like a personal attack, Ana says we need to remember an executive is constantly trying to balance short term survival with long term growth. Do we grow? Where do we invest? Where do we cut? Do we do this thing now? – are some of the questions going through their minds.
How you can help is to have a product strategy which has the that balance & shows you understand both short and long term goals. And of course your outcomes need to be aligned to the business goals. Those short term wins are what fund the long term strategy.
You need to have both clarity & confidence when it comes to research – both in understanding why it’s needed and what the results show.. Don’t just provide a report or data. Make sure you’re clear why are you dong the research, what opportunities you are unlocking, what are the risks are you’re trying to in/validate. Frame your research as minimising risk and driving growth.
People at the exec level are thinking at a different levels than product – they need to be thinking about budgets, talking to investors & analysts. They want to make sure the vision is being executed – but they do not need to be in the weeds for this. As the PM, you’ve been hired to be in the weeds. You have the detail but need to translate it to ‘why now’ instead of your usual ‘how’ thinking. As a PM, make sure you transfer the product strategy into business impact. Communicate why from the lens of customers, the market, sales, ROI, this the most important thing we should be doing. Knowing the entire business is helpful with this (be interested in financials, talk to other departments).
Product managers often joke that execs come up with pet projects and “dumb ideas”. You know the conversation – when someone has read a blog post or heard about something another company is doing or just had a shower idea that you think is ‘dumb’.
If this happens, ask yourself – if my strategy is strong why are they coming up with something new? Use those ‘dumb idea’ conversations as a SIGNAL. Consider this as a symptom of a shift, or something going wrong – especially when YOU think there’s already a good roadmap
Is there a low confidence in the current strategy, or your roadmap simply didn’t hit the mark? Or maybe they have some information you haven’t received yet, like a market shift for example. It’s your chance to shine. Use the exchange to understand where they are coming from and lean into your problem discovery skills. See if your current strategy already addresses it, and if you can accommodate testing and validating the idea. It’s a great opportunity to strengthen the relationship with your exec.
PMs have to keep learning and sometimes you need to re-learn.
Roana reflected on the ‘invisible work’ which defines a veteran PM’s career. Success in our field is often silent because if you are doing well at your job, everything looks effortless, chaos is kept at bay and all goes well. For example, who really sees the effort that goes into having that roadmap? Who sees all the conversations and market research? The internal conversations & alignment? The stressing over how to put together a good workshop?
So then, how do you measure your success and growth?
Consider:
Ditch the vanity metrics. Just like we do with products, don’t measure your value with vanity metrics like how many slack emojis your post got. Measure it by whether the team is moving faster and if customers are ‘less unhappy’
Lean into your spectrum. Product management is not just one role but a whole spectrum of activities such as strategy, technical depth or SME knowledge.
Let go. You can’t and should not control everything. You need to let the team make a mistake and recover from it. (queue the song from Frozen)
When you have that imposter syndrome (which we all do…) because someone else has better tech understanding or someone makes amazing decks or someone else has tons of domain knowledge when you’re new – lean into your super powers. Learn what your strengths. Ro talked about realising she enjoys and is good at strategy and having clarity when under pressure. Leaning into those skills made her job more enjoyable.
As a product person, you have to lean into leadership no matter what and one of her re-leaned lessons was stepping back and letting the team make a mistake (and recover from it). Now the team has learnt that & as the product manager she’s seen the team grow.
Ro closed by saying that product management is like parenting. You are the one holding the tension between ‘now’ and the ‘later’. You are the one between chaos and clarity. While it can be a thankless job, being the person who understands the full picture is your ultimate superpower.
Like so many of us, Jithesh is learning and reflecting on using ai, including how it impacts society. Especially when we’re in this early stage of something, questions are important. In product, we try to stay in the question space and not jump to solutions right away so this should be a comfortable space for us. It’s in the questions that Jithesh focused this talk.
This talk was inspired by building ai features into products & his thoughts of their impact on society with the purpose of us asking questions of ourselves and teams. While Ai can synthesis research and help to speed up thinking, it cannot replicate the human ‘lived experience’ that informs tasteful design.
Jithesh started by asking us to quickly ask our favourte LLM to create a presentation on the same topic so we could later compare & contrast what it got right – and what it was missing.
Questions to ponder:
What is the gap between intention and reality?
What is the simplest creation process?
If creation is abundant, why should we create?
Whom are we creating for?
What drives us to consume?
What informs our taste & judgement?
So how was Jithesh’s talk different to what ChatGPT gave me? Well it didn’t ask provoking questions – it told me things like ai is creating tools which make it easy to create and ‘execution is abundant‘ but that means judgment by humans is more important (which is shown in taste, trust & craft). Ai can generate many options but ‘taste’ which can recognise quality and what should not be built is important. It even pointed out that ‘in an ai world, trust becomes even more fragile‘ and users wonder if something is accurate or if it should be trusted with their data.
And it says with ai, all 3 of these things are more important since the average quality will fall as the cost of building is lower so a tasteful, well crafted, trustworthy product will be a competitive advantage. 🙂
Zain has been experimenting with various ai tools to help simplify his work & shared how he’s using several small Ai assistants.
Using Relevance Ai, Zain created 4 agents – a market researcher, persona strategist, prototype designer and product marketer. His experiment showed they were great for:
Generating 1st drafts of time consuming items – like user documentation or user stories
Uncovering blind spots – one agent suggested a value prop that Zain hadn’t considered
The big question – will the agents replace Product Managers? Zain doesn’t think so as there is a long way to go for the agents to be good at stakeholder management or negotiation or even understanding the significance of the tasks.
If you are going to investigate using agents, keep in mind
Review everything because you still own the quality
Use ai to shift time by off-loading things like formatting or writing so you can spend time on strategy and thinking and alignment.
Start experimenting now. The tools are mature enough and the learning curve is worth it.
We all know there’s a lot of hype around Ai and one of the more recent developments has been the ‘Ai PM’. What is an ‘ai PM’ and how can you become one – or are you already one?
Li Xia, is a product person who’s been building Sondar.ai, his own startup (with ai), and shared a bit of his journey plus broke out 3 different ways to view the ‘ai pm’.
Thank you Li for this fantastic talk including great practical examples of what you’ve been learning and how you’ve been using it within your product!
Session Wrap Up:
Adapting a framework from Amar Khan, Li sees the Ai Product Manager as actually 3 different roles.
The Prompt Master
This type of Ai PM use Ai tools to make their existing job easier, faster, more efficient. This could be helping with document creation, prototyping, synthesis, to help brainstorm and more. Based on the show of hands in the room, we’re almost all ai PMs using this lens.
The Foundational Model PM
These are the PMs that work for organisations like OpenAi, Google, Anthropic, etc and build the core tech that others build on. When we’re using the LLMs and APIs like ChatGPT or Claude, we’re building on the work that these product people have made possible.
The Builder
The Builder Ai PM is responsible for creating ai features or products using those off the shelf foundational models to solve our customer problems.
How to move from Prompt Master to Builder
This is where a skill set change is needed and Li stepped us through a playbook of what he’s learned from while working in his startup – sort of like creating your own Iron Man suit.
Setup your Ai Lab
A conversational ai tool like ChatGPT is like driving a car with adaptive cruise control where the computer makes most of the decisions. To truly build, you must use developer tools which let you change the settings and all the knobs that are in the car to adapt them to what you need.
To to so, you need to understand the limitations of the tech, the strengths & weaknesses of the LLM model, tweak the settings, know the cost and drive the output (ie JSON objects).
Li suggests getting hands on with these tools is needed so you can communicate better with your tech team and have a better understanding of constraints.
Build your Ai Brain
The base ai model is a machine without personality or direction. It’s your job to give it a brain, a voice & a personality.
You can do this via system prompts and guardrails which is achieved via system prompts. Li showed us some of the prompts he’s using for functionality within his startup, Sondar.ai. The part of Sondar Li shared wants the system to behave as if it’s a senior UX researcher coach. The prompt defines that persona, ensures it has key context questions and uses specific methodologies .
Because building ai experiences are non-deterministic, it may answer in different ways depending on the input which requires rapid iteration.
Instead of PRDs or similar, Li has been writing a ‘prompt spec’ which he gives to the engineering team. This spec details system prompts, parameters and desired outputs. Sample input and those expected outputs are key.
Give Ai its Senses
Dynamic data is the ‘secret sauce’ – prompting is only half of the story.
While everyone has access to the same foundational models, it’s the unique experience you provide in the product that makes it valuable. In order to do that, enriching the ai with your product’s valuable data is how you differentiate your product.
In Sondar, there’s a ‘Ask Ai’ feature which lets you important & transcribes customer interviews so you can talk to your data to get answers & quotes.
Don’t forget about personalising by using dynamic data valriables like names or other context.
Li found learning simple SQL queries to retrieve data and understand popular data formats like JSON & Markdown enabled him to move faster by getting the data he needed to progress without needing to wait on developers to help.
Takeaways
There’s different ways to define an ‘ai pm’
You don’t need a PhD to work in this space – focus on building practical skills aka your Iron Man suit
Having some technical skills will benefit and help you build trust with your tech team. Being able to query data & understanding API calls are at the top of that list.
OUR SPONSOR
Our wonderful sponsors for the evening – Revity!!!
Photo by Ellie Care
Revity helps startups, scaleups and established organisations deliver product-led quality software outcomes. We partner with businesses to build high-performing teams, uplift engineering capability, and deliver product outcomes that last beyond the project. From applied AI and mobile apps to global expansion and customer lifecycle platforms, we focus on practical, scalable solutions that drive business value. At Revity, we don’t just deliver projects, we empower teams to learn by doing, so organisations grow stronger with every engagement. Follow on Linkedin
What a February 2025 talk on digital sustainability revealed about the hidden environmental cost of the tools we build and use every day.
When we talk about sustainability in tech circles, the conversation usually drifts toward electric vehicles, renewable energy and corporate net-zero pledges. Rarely do we turn the lens on ourselves – on the Jira boards we manage, the SaaS platforms we ship and the AI queries we fire off a dozen times a day.
That’s exactly what our digital sustainability advocate KB (Katherine) Buzza set out to change at the Product Anonymous Melbourne meetup, armed with a copy of Tom Greenwood’s Sustainable Web Design and a lot of inconvenient data.
The message? Sustainability isn’t just an environmental issue. It’s a product problem – and product people are uniquely positioned to fix it.
The Sector We Don’t Talk About
Most of us are vaguely aware that data centers use energy. Few of us know the scale.
Research from Ericsson and university partners found that the information and communication technology (ICT) sector consumed around 4% of global electricity in 2020, accounting for 1.4% of global greenhouse gas emissions. That figure is projected to climb to 15% of global electricity by 2030 – driven by AI, surging data center demand and the sheer proliferation of devices.
A recent study from the French research organisation GreenIT.fr breaks down where that footprint actually lives:
60% comes from end-user devices – manufacturing, purchasing and running our phones, computers and (perhaps surprisingly) televisions
20% from networks
20% from data centers
The manufacturing and embodied carbon of our hardware is the single biggest lever. Not the cloud. Not the server. The device sitting on your desk.
This reframes the conversation entirely. The greenest phone isn’t the one with the best energy-efficiency rating – it’s the one you didn’t buy.
“The Cloud Is Material and Computation Is Metabolic”
Cloud anthropologist Stephen Gonzalez put it plainly: the language around cloud services has obscured the physical reality of information storage, creating a fantasy of infinite, weightless abundance.
There are no fluffy cumulus clouds holding your data. There are warehouses full of humming servers, cooled by enormous quantities of water, powered by electricity that may or may not come from renewable sources – often located thousands of kilometres from the users they serve.
Every query, every file upload, every forgotten duplicate in your S3 bucket has a material cost.
The AI Question Nobody Wants to Answer
Generative AI has supercharged this problem – and the companies building it aren’t being transparent about it.
None of the major generative AI providers have published clear environmental commitments. In their absence, a Washington Post study offered a useful proxy: generating a 100-word response via ChatGPT consumes approximately 500ml of water and the equivalent energy of leaving an LED light on for an hour.
That’s per query.
For teams that have integrated AI into their daily workflows – code review, documentation, customer support, sprint planning – the cumulative impact is worth thinking about seriously.
Big Tech’s Report Card
The talk took a candid look at four of the most common tech suppliers in the product world:
Microsoft Azure makes bold commitments – carbon negative by 2030, removing all historical emissions by 2050. But a former senior sustainability lead recently left the company citing a fundamental contradiction: Microsoft’s custom cloud work for fossil fuel companies is actively enabling greater extraction, potentially exceeding any carbon savings the company achieves elsewhere. Opening three new data centers a week compounds the challenge.
OpenAI has made no meaningful environmental commitments. Full stop.
AWS takes a more measured approach – fewer sweeping claims, but genuinely useful resources for builders who want to make greener architectural choices. Worth exploring if you’re making infrastructure decisions.
Atlassian earns the gold star. Their sustainability strategy document (cheekily titled Don’t F** the Planet*) is transparent, detailed and backed by action – including paying employees to use green energy at home and building Atlassian Central in Sydney, set to be one of the tallest timber-framed buildings in the world.
The Green Software Foundation’s Six Principles
For teams ready to embed sustainability into their practice, the Green Software Foundation offers a practical framework:
Carbon efficiency – emit the least greenhouse gases possible for any given task
Energy efficiency – use the least energy to perform that task
Carbon awareness – schedule compute-intensive work when the grid is running on cleaner energy
Hardware efficiency – minimise embodied carbon by extending device lifespans and avoiding unnecessary hardware
Measurement – you can’t improve what you can’t measure
Climate commitments – understand the actual mechanism behind any carbon reduction claim, not just the marketing
These aren’t abstract ideals. They’re engineering and product decisions that most teams already make – just without sustainability as a criterion.
What Product Teams Can Do Right Now
Here’s where this becomes an action list, not just a lecture.
Hardware and E-waste
Extend device refresh cycles. Fight the instinct to push features that demand newer hardware.
Repair over replace – a new battery costs less than a new laptop, in every sense.
Dispose of E-waste responsibly. In Victoria, putting E-waste in landfill is illegal. Are your organisation’s policies keeping up?
Measure how much E-waste your organisation generates annually.
Data storage
Encourage a “digital spring clean” – delete what’s not needed, manage duplicates and archive rather than actively store stale data.
Audit dead customer accounts. They’re a security liability and an unnecessary load on your infrastructure.
Renewable energy
Ask whether your infrastructure runs on renewable energy. If you have a choice of cloud region, it’s worth factoring in.
Low-carbon design and development
Reuse and repurpose content and code rather than generating from scratch.
Choose efficient file formats.
Consider the environmental footprint of your coding language choices – there’s a real hierarchy and it matters at scale.
Ask where your data center is relative to your users. Proximity reduces latency and transmission energy.
Supplier interrogation
Before renewing contracts with major cloud or SaaS providers, ask about their environmental strategy. Request transparency on water and energy consumption. The question alone shifts incentives.
The Business Case Is Already There
This isn’t just good ethics – it’s good engineering.
Sustainable software tends to be efficient software: leaner, faster, cheaper to run and more resilient. The principles that reduce carbon footprint also reduce infrastructure costs, tighten security posture and improve development velocity.
Sustainability, framed correctly, is a performance enhancer.
Product managers and developers sit at the intersection of every decision that determines a product’s environmental impact – from the infrastructure it runs on, to the features that drive device obsolescence, to the AI tools baked into the workflow. That’s not a burden. That’s leverage.
The question isn’t whether the tech sector will need to confront its environmental footprint. It will. The question is whether product teams will lead that conversation – or be dragged into it.
KB (Katherine) Buzza’s career begun in marketing before embarking on a journey to discover how business can drive positive environmental and social change. Having worked across sectors, she has maintained a passion for sharing knowledge and climate positive solutions.
As a product manager for carbon account software company Climate Zero, she wants to keep expanding the conversation about sustainability in tech beyond data centres and decisions outside of our control.
Our Wonderful Host:
Chargefox is part of the AMS Group. Every day thousands of drivers charge their vehicle on the Chargefox network – the largest and fastest growing EV charging network in Australia. We’re owned and operated by the NRMA, RACV, RACQ, RAA, RAC and RACT. The same companies supporting drivers for over 100 years https://www.chargefox.com/
Our March meetup topic was ideation & collaboration – but the real focus was having small groups of attendees get hands on experience by using a specific method – Crazy 8s!
It was a super rainy night in Melbourne! Thank you everyone for attending – including a few folks who were absolutely drenched when they showed up!
The Talk
When we’re looking to innovate or problem solve, it’s easy to get stuck into our existing scenarios. How can we break out of this? How can we, and how can we help our teams, think in new ways?
One exercise that can be used to quickly come up with new ideas is Crazy 8s.
Crazy Eights is a brainstorming technique designed to rapidly generate a wide array of ideas within a constrained timeframe.
Lucy explained it’s a mix of convergent & divergent thinking plus has an element of prioritisation that helps you narrow scope. It’s a very time efficient method and that timeboxing helps you to not overthink ideas and focuses us to think outside the box.
Since Crazy 8s challenges individuals to sketch eight distinct ideas in eight minutes. This fast-paced exercise promotes quick thinking and minimises the tendency to dismiss unconventional ideas, fostering a creative and uninhibited environment. It’s especially useful for teams aiming to push the boundaries of conventional thinking and explore a broad spectrum of possibilities.
When to Use Crazy Eights
Lucy likes to use Crazy 8s when
We know what we are doing but wondering how do we design the right thing in the right way
When you have a lot of subject matter experts / stakeholders and want to include them
When you are creatively blocked or not sure how to solve the problem
Benefits of Crazy Eights
Implementing Crazy Eights in brainstorming sessions offers several advantages:
Encourages Creativity: The rapid pace and emphasis on quantity help bypass mental blocks, allowing creative ideas to emerge.
Inclusive Participation: By providing a structured yet open framework, all team members can contribute, ensuring a diverse range of perspectives. The individual brainstorming assists with the ‘loudest voice in the room’ problem.
Efficient Ideation: The time-boxed nature ensures that sessions are productive and focused, yielding a substantial number of ideas in a short period.
How to Conduct a Crazy Eights Session
Divide into small groups of 3-4
State your challenge: Make sure everyone knows what the problem or challenge you’re working on
Prepare the Template: Surprise! There is no fancy ‘template’. Just take some paper and fold in so you have 8 boxes!
Start the Timer: Allocate 1 minute for participants to sketch their idea. Do this 8 times so everyone has 8 sketches, ensuring a brisk and focused session. Even though people are divided up in groups, this is an individual task. AND sketching is the idea! Not words!
Have each group share & discuss their sketches: Each person in the group explains their 8 sketches
Each group should vote on the group’s ideas. What 1 idea would you like to move forward with?
Time Management: Assign a timekeeper to provide regular updates, helping participants allocate their time effectively across all eight sketches.
Iterate as Needed: Repeat the process to delve deeper into promising ideas or explore new directions.You can take the top 3 and continue to build on them. You can get all the groups to vote. Keep collaborating & iterating.
But wait! Before you start…
Steve and Lucy added a new twist to Crazy 8s as a warm up – first we needed to get out all the BAD ideas for the topics. Since this was a warm up – we did 4 minutes with a bad idea per minute. We needed to exorcise all those bad ideas!
You put down all the crazy stuff in there (we had lots of groups talk about burning things… hilariously). Interestingly, it’s good to put down ideas that have already been done – because that is a bad idea to pursue. People in each group shared their bad ideas with each other.This was good practice for the real session.
With a brand new A4 page (folded thrice) – we had 8 squares. Personally, I found the 6th box the hardest to fill – but that’s where real growth comes. And that includes the art of possible.
The best part is the voting mechanism – because that depends on what each group selects – which changes the outcomes as well. Another important aspect to remember when using this technique!
Lucy Serret is a passionate problem solver and inclusive design practitioner with 6 years of experience in agencies and startups. She specialises in end-to-end solutions, including research, design strategy, and accessible product delivery. Her commitment to accessibility, research, and design drives her to ask the big questions and challenge assumptions through creative problem solving
Steve Bauer is the Chief Product Officer at 1Breadcrumb, master of festivities at Product Camp Melbourne and owns many articles of clothing emblazoned with flamingos.
Our wonderful hosts and sponsor:
Zendesk started the customer experience revolution in 2007 by enabling any business around the world to take their customer service online. Today, Zendesk is the champion of great service everywhere for everyone, and powers billions of conversations, connecting more than 100,000 brands with hundreds of millions of customers over telephony, chat, email, messaging, social channels, communities, review sites and help centers. Zendesk products are built with love to be loved. The company was conceived in Copenhagen, Denmark, built and grown in California, now expanded all over the world.
Our first speaker from within our community for the May event is Felicity Bodgeron discussing her playbook on “How to become a Killer Product Manager in 3 Easy Steps.” This talk is for product managers who want to level up to become killer pms, creating a personal professional playbook will enable you to transform your team of mercenaries into missionaries while also fending off the monster chewing on your leg, unlike your run of the mill corporate values that only have the half-life of a power point presentation.
Our second speaker from within our community for the May event is Marc Vandamme – Creating alignment with your teams and leaders. Marc is Senior Product Manager @Performio. Loves climbing and is looking for indoor & outdoor lead climbing buddies!
Our third speaker from within our community for the May event is Nick Kardamitsis, discussing how to go “Beyond the numbers: Harnessing data for smarter product decisions.” Nickolas is an outcomes-focused, customer-centric problem solver, passionate about hitting organisational goals. With over 10 years in digital product experience, Nickolas navigates the end-to-end product lifecycle and leads both technical and non-technical teams, effortlessly switching between strategy and execution. He loves everything about product—especially the words, “Yes, but…”
One of ProdAnon’s goals is to help our community members learn and grow – and what better way to achieve that than give our members the chance to do short talks to share what they learned and get some public speaking experience. Several people who have spoken at ProdAnon have gone on to present at larger conferences which is awesome!
For our May session, we did a call out for folks interested in doing 15 min talks. And we had 4 wonderful speakers share their knowledge:
If you’re interested in doing a talk, reach out to Jen & Liz(slack, email, at an event). Typically, ProdAnon sessions are workshops, panels or individual speakers plus we sometimes do the short talk sessions.
Thank you for hosting Culture Amp! Culture Amp is the world’s leading employee experience platform, revolutionising how 25 million employees across more than 6,500 companies create a better world of work. Culture Amp empowers companies of all sizes and industries to transform employee engagement, drive performance management, and develop high-performing teams. Powered by people science and the most comprehensive employee dataset in the world, the most innovative companies including Canva, On, Asana, Dolby, McDonalds and Nasdaq depend on Culture Amp every day. Culture Amp is backed by leading capital venture funds and has offices in the US, UK, Germany and Australia. Culture Amp has been recognised as one of the world’s top private cloud companies by Forbes and most innovative companies by Fast Company.
