Afleveringen
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In our latest episode, Lisa Weaver-Lambert dispels the belief that is incapable of delivering impact in her book "The AI Value Playbook." She also lays out principles for succeeding in your implementation of AI:
1. Your tech stack determines winners: Orgs that already were built to process and leverage data as part of core decision making are at a huge advantage. Especially those that are focused on leveraging insights to learn and iterate.2. Leadership and strategy matter: The vision, guiding principles, and culture matter. They will dictate the strategy or lack of a cohesive strategy.3. AI shouldn’t be added on top: AI should be viewed as the pathway ro removing layers, friction, and complexity.4. Getting from proof of concept to value is harder: AI reduces the barrier to creating proof of concepts while also layering in a lot more uncertainty about how to make it production-ready.5. Centralize AI strategy & decentralize implementation: Orgs should have a cohesive strategy owned by a centralized team. But the workflows and use cases defined by the teams that are seeking to gain specific value.
Listen on Spotify | Listen on Apple | Watch on Youtube
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New report showing use of Anthropic (Claude) doubled, while OpenAI lost 1/3
Menlo Ventures published their 2024 report: The State of Generative AI in the Enterprise. It shows the continued maturation of the AI market and clear use cases where the tech is being leveraged. Not surprising, task-level use cases that can be directly evaluated/audited are coming out on top.
Also, the layers of AI stack are becoming more distinct with some products starting to create their own moats. As we move into 2025 expect the Data layer to split as more orgs realize that they need a semantic layer to structure and make sense of first-party data.
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The LLM market share data makes OpenAI look like the big loser. But I suggest throwing out the 2022 and 2023 data since adoption was so low and leveraging the tech for experimentation rather than impact. 2024 is the year when AI became the workhorse for the first time powering countless products.
Nonetheless, it is compelling to see Anthropic and Claude shoot up. Their focus on UX seems to be paying dividends, that or OpenAI’s dilution of trust is.
Of no surprise, prompt engineering is falling off a cliff. It was a bandaid approach for a tech that had no standards yet. For reference a business that built their product through prompts often had to rebuild all those prompts whenever a model was updated.
Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.
AI use & impact assessment survey
Please share your experiences and point of view in our year-end AI research study.
Your lessons and opinions will shape a critically important assessment of how & if AI is positively impacting individuals and teams.
Less than 5-minutes of your time will help us a lot.
Perplexity is one-upping Google by introducing AI-powered shopping journeys
Perplexity, the upstart GenAI search form is firing shots at Google by taking a refreshing look at shopping. Rather than focusing on someone searching for a product (e.g. Patio furniture), they are taking a very human-centred approach by focusing on what a user is trying to accomplish (e.g. renovate my outdoor living space). The platform then provides ideas, support, and instructions. Plus, recommends products to buy.
While this is immensely helpful, it brings up the ever-present concern that AI will pick winners and losers for us. Where Google served up dozens or hundreds of results and encouraged us to make our own decisions, AI only shows a handful of options. This is the beginning of the platform as expert and it could change how we interact with the world in a huge way. It could lead to small merchants being shut out or even grow distrust of options that aren’t recommended by a platform.
Alarming data showing that achieving AGI could destroy market wages
Economics at the International Monetary Fund have modeled data that shows that if Sam Altman & crew succeed at bringing AGI to the world faster than expected, it could set into motion a total destruction of market wages (aka devalue everything).
Their model also showed that on the expected timeline of AGI, wages will continue to rise as humans continue to do the thinking for the machines.
Read the report
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The last two years have been extremely stressful for anyone working in tech. There’s been a consistent sense that we all need to do more with less. That our jobs are on the line. And now AI is being touted as the cheat code that will unlock productivity and profit gains.
In our latest podcast, Peter Merholz (add him on LinkedIn) doesn’t see AI helping much in the short-term because teams are too over-tasked to believe they have the time to try new models of working. He also believes that most organizations don’t have cultures and leadership that promote experimentation and reward learning.
Listen on Spotify | Listen on Apple | Watch on Youtube
What makes matters worse is that simply “using AI” won’t get you the results you need. Simply using ChatGPT or Claude will not give you and your business a significant boost because data is at the heart of AI. The more of your first-party data that you train models on and the more that you craft agents around specific workflows, the closer you’ll get to what AI acolytes are selling.
Accenture calls this AI maturity: Advancing from practice to performance. And this is where Peter Merholz believes that most orgs will be blocked. His experience working in mega-corps has found that most aren’t learning cultures. Introducing new tools, mental models, and ways of working aren’t well-received.
AI use & impact assessment survey
Please share your experiences and point of view in our year-end AI research study.
Your lessons and opinions will shape a critically important assessment of how & if AI is positively impacting individuals and teams.
Less than 5-minutes of your time will help us a lot.
Valuable lessons
💡 Nearly half of workers are uncomfortable admitting to their manager that they used AI for common workplace tasks
💡 Evaluations —or “Evals”— are the backbone for creating production-ready GenAI applications.
💡 Ten lessons that separate impactful training from mere AI showcases
💡 Even teams actively working with AI are wrestling with fundamental knowledge structuring challenges. The tools are advancing faster than our practices
Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.
Exciting AI jobs
👉 USA | Anthropic | Strategic Product Management
👉 USA | World Economic Forum | Head of Data and AI Innovation
👉 USA | Google DeepMind | Group Product Manager, Generative AI Tools for Music Creators
👉 USA | Amazon Web Services | Generative AI Strategist, Generative AI Innovation Center
👉 Australia | Canva | Creative Technologist (Gen AI)
👉 Canada | Autodesk | AI Research 3D Dataset Creation & Annotation Manager
👉 Canada | Robinhood | Staff Product Designer, AI Investing
👉 Canada | McAfee | Sr Product Manager, GenAI
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Zijn er afleveringen die ontbreken?
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Speaking to Phillip Maggs on Design of AI had so many💡 moments:1. Want to use AI to get a career advantage? Consuming AI content isn't enough to get ahead, you need to experiment with the new material. Stretch what you believed was possible and you'll gain new capabilities.2. New careers and role are being defined right nowGenAI makes it possible for anyone to quickly learn about a topic or skill. You might think you're average but can quickly put together a unique skill profile that makes you a unicorn, especially if you're more committed to being curious about new technologies and how to leverage them.3. Much of design should be automatedWe forget that a lot of design tasks are literal assembly-line outputs: Banners, emails, ad variants. These rightfully should be automated because they exist in the world for such a short period. However, assets that represent your brand to millions or which will be in market for years must be hand-crafted.4. Design systems and brands are rulesThe more we codify what our products and brands should be, the more we unlock the augmenting powers of AI. Phillip imagines that a day will come when the LLMs about our brands will shine light on ideas we otherwise wouldn't have considered because of our own biases.5. A lot of AI design products are "party tricks"Sure a tool that can generate designs based on text prompts are cool but are they significantly saving time? Are they aware of what qualifies a good output for your brand? Do they understand how you communicate with customers? The outcome of these tools likely is not a significant ROI.Listen on Spotify | Listen on Apple
AI tool of the week: Cove.ai
Cove.ai is like Miro meets Claude. You can prompt and build assets, just like in Claude. But what makes this tool fascinating is that you can save our work to a visual board and invite others to collaborate with you.
The most surprising finding from using this platform is recognizing that in a typical project I’m outputting so many assets. The volume makes infinite scroll interfaces painful, and even makes Claude Project’s interface seem deficient. The visual board interface is much more functional since I can sort dozens of cards into a work surface that makes sense.
Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.
Our First 20 Episodes: 20 Lessons for How to Advance Your Career in the Era of AI
We’re being taught to fear AI and how it is expected to impact our jobs and workplaces. But our guests see distinct opportunities for us to embrace this time as an opportunity to advance our careers.
Lesson 1: Embrace AI as a tool to enhance creativity, not replace It
Maarten Walraven-Freeling, our guest on Episode 3, highlighted how AI tools like AIVA and Google Deep Mind's LIA can empower musicians to generate new music and expand their creative possibilities. Rather than fearing AI as a threat, musicians can leverage these advancements to enhance their craft and explore uncharted artistic territories.
Episode: The future of music in the era of generative AIListen on Spotify | Listen on Apple
Lesson 2: Understand the evolution of AI interfaces to design better products
In Episode 4, Emily Campbell traced the history of AI interfaces, from early chatbots to voice assistants and brain-computer interfaces. By understanding this evolution, product teams can better anticipate future trends and design AI products that are intuitive and user-friendly.
Episode: How AI is reshaping UX and the new role for designersListen on Spotify | Listen on Apple
Lesson 3: Address the copyright challenges posed by generative AI
Virginie Berger, in Episode 5, shed light on the ethical and legal implications of AI models trained on copyrighted data. Creatives, businesses, and policymakers must work together to establish fair compensation models and licensing frameworks to protect artists' rights in the age of generative AI.
Episode: GenAI's copyright problem: Training & derivative copiesListen on Spotify | Listen on Apple
Lesson 4: Prioritize problem-solving over technology when building AI startups
Ben Yoskovitz, our guest on Episode 6, emphasized the importance of focusing on real-world problems and customer needs rather than solely on AI technology. Startups that prioritize solving genuine challenges are more likely to achieve product-market fit and attract investment.
Episode: Venture building: Why AI products may fail Listen on Spotify | Listen on Apple
Lesson 5: Approach emerging technologies as an enabler of people, not magic
In Episode 7, Dr. Llewyn Paine cautioned against blindly embracing the hype surrounding emerging technologies like generative AI. To find the value of a technology we need to understand how people and teams work. The most valuable opportunities are buried in behaviors and assessing what they’re willing to adopt.
Episode: The secrets to researching potential emerging tech productsListen on Spotify | Listen on Apple
Lesson 6: Leverage AI to create personalized behavior change journeys
Dr. Amy Bucher, our guest on Episode 9, discussed how AI can revolutionize behavior change interventions by enabling true personalization. By tailoring communication and interventions to individual needs and contexts, AI can drive more effective outcomes in healthcare, education, and marketing.
Episode: AI can innovate behavior change strategies & transform personalization Listen on Spotify | Listen on Apple
Lesson 7: Focus on AI native workflows and integrations to stay ahead of the curve
In Episode 1, Peter Van Dijck explored the rapid growth of the generative AI ecosystem, with a surge in AI-powered consumer web products. To thrive in this dynamic landscape, developers should prioritize building AI-native workflows and seamlessly integrating multiple AI tools into their products.
Episode: Designing AI products: Building effective products with LLMsListen on Spotify | Listen on Apple
Lesson 8: View AI as a design material that enables intelligent & radically adaptive experiences
Josh Clark & Veronika Kindred, our guests on Episode 19, introduced the concept of “sentient design,” where AI becomes an integral material in shaping intelligent interfaces. To effectively design with AI, product teams must understand its capabilities, limitations, and potential impact on user experience. What were once static user experiences can be radically adaptive.
Episode: Authors of Sentient design: AI-powered self-aware experiencesListen on Spotify | Listen on Apple
Lesson 9: Rethink organizational structures and embrace AI to remain competitive
JP Holecka, in Episode 1, emphasized the need for advertising agencies to fundamentally adapt their operating models in response to AI's transformative potential. Traditional agencies must embrace change, form new business units, and develop AI-driven solutions to meet evolving client needs and remain competitive.
Episode: How AI is changing ad agencies & the creative processListen on Spotify | Listen on Apple
Lesson 10: Start using AI as a material to see what possible and what isn’t
In Episode 10, Alexandra Holness highlighted the importance of viewing AI as a new tool within the designer's toolkit. The sooner you begin integrating it, the sooner you’ll learn how easy/difficult it is to work within your particular situation. Avoid the search for perfect because you’re going to need to adapt your expectations to meet what the technology can actually deliver.
Episode: AI is disrupting the design & product delivery process [Lessons for startups, enterprise & UX]Listen on Spotify | Listen on Apple
Lesson 11: Recognize your role as an innovator: Are you a sea captain or a pirate?
Nick Sherrard, in Episode 11, discussed who he has seen driving innovation. There are archetypes. Firstly, the sea captain is the leader who has a destination in mind but not the expertise. Secondly, the pirates are misfits exploring new places and trying wild new techniques. Which are you? How can build the right team and allies to be able to align vision + expertise, passion + experimentation?
Episode: Innovation lessons for brands and product teams investing into AIListen on Spotify | Listen on Apple
Lesson 12: Codify your experience to scale your impact
Trisha Causley, in Episode 12, shared how AI can empower content designers by automating repetitive tasks and scaling their expertise. You add so much more value to your teams than you understand. Find ways to codify that knowledge into specific guidelines, key insights, and specifications. You then can unlock the real potential of LLMs.
Episode: Content design: How creatives are leveraging prompt engineering to innovate ecommerce platformsListen on Spotify | Listen on Apple
Lesson 13: Avoid innovation traps and learn what the technology is good and bad at
In Episode 13, Scott Jenson cautioned against blindly chasing hype cycles and urged product teams to prioritize customer needs and sustainable business models when implementing AI solutions. And more importantly, be the person on the team that knows what GenAI can and can’t do well so you avoid innovation traps.
Episode: Unlocking AI product success: Coaching teams to navigate uncertainty & design risksListen on Spotify | Listen on Apple
Lesson 14: Need to be specific about how to use AI
Jess Holbrook, our guest on Episode 14, stressed the importance of understanding what AI is. It might be easy to tell a team to go build with AI. But can we grasp its strengths, limitations, and ethical considerations? Do we have guidelines and principles that guide our ethos related to leveraging AI and products overall?
Episode: Researching & building responsible AI within tech’s biggest platforms Listen on Spotify | Listen on Apple
Lesson 15: Embrace radical transparency and challenge assumptions to deliver impactful AI solutions
In Episode 15, Arpy Dragffy, the show’s co-host, discussed the value of “radical transparency” in consulting and AI product development. By engaging in honest and sometimes uncomfortable conversations, teams can uncover hidden assumptions and ensure they are building products that genuinely meet customer needs. Quite often we’re using the wrong solution and tackling the wrong problem. AI can unlock new horizons for teams that think beyond the bounds of the obvious.
Episode: Futures design: Build AI products that customers want & find valuable use casesListen on Spotify | Listen on Apple
Lesson 16: Treat this as a time to experiment
Yasemin Cenberoglu, our guest on Episode 16, detailed her journey of being the first designer working on Microsoft’s Copilot in secret with OpenAI. It required blue-sky thinking and plenty of experiments to identify unexpected outcomes of this new probabilistic technology. You’ll discover the need to create guardrails and shift your thinking about how a product should be released.
Episode: Service design of AI: Designing the first Copilot w/ Microsoft & OpenAIListen on Spotify | Listen on Apple
Lesson 17: Go beyond the usual KPIs and find a way to measure time well spent
In Episode 18, Dr. Kristie J. Fisher emphasized the importance of finding the right KPIs and ways of evaluating whether the experience of using AI is time well spent. Users want product experiences that are enjoyable. Find ways to leverage AI to make user experiences more enjoyable and supportive based on the situational user needs.
Episode: Immersive GenAI experiences: Video games' KPIs & path to joy Listen on Spotify | Listen on Apple
Lesson 18: Use AI to build expertise in areas that complement you
Phillip Maggs, our guest on Episode 20, challenged the assumption that you need to be great to succeed. He sees technology mixed with curiosity as your path of unlocking new capabilities. Learn how to be average in many areas but connect those capabilities into something distinct and powerful. Engineers can now explore their impact on design. And creatives can better explain and demonstrate their ideas.
Episode: Future of Design: Leveraging Design Systems & Brand to Automate WorkflowsListen on Spotify | Listen on Apple
Lesson 19: Leverage AI analyze data in bulk
Weidan Li, our guest on Episode 8, explained how GenAI is imperfect at analyzing data but that as the models get better it can greatly expand how much and how deeply we can analyze data sets. This opens opportunities to unlock insights that may have otherwise not been considered because of the effort required. Remember, it shouldn’t replace human insight generation.
Episode: Case studies: Leveraging AI to build conversational bots & analyze conversationsListen on Spotify | Listen on Apple
Lesson 20: Automation is coming and we need to prepare for it
A common theme from many guests has been that automation will happen. Many tasks, specifically low-level ones, will be taken over by AI. We need to treat this time as an opportunity to redefine the type of work and level of impact we want to have. We can either be operators of the AI automation platforms or we can envision new ways of using technology that will 10x our impact and 100x the possibilities for our teams. As Phillip Maggs said, have a bias for building with AI, not just consuming AI content.
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GenAI’s promise is that digital experiences will become more intelligent. Big Medium Founder Josh Clark and his daughter, Veronika Kindred, are the authors of the upcoming book “Sentient Design” and the latest guests on the podcast. They see products that are radically adaptive to our situational needs and collaborate with users in ways that seemed insane a few years ago.
Listen on Spotify | Listen on Apple Podcasts
But what struck me the most were three things:
* Veronika, a GenZer who figuratively grew up inside of tech because of her father’s work, sees the role of AI much differently than what us older folk would expect. There’s an awkward comfort with the centralization of power within these systems and the expectation that we, the users, will decide whether it is used for good or bad.
* Not building towards personalization. Josh knows that it requires far too much data for a system to understand us and what we truly need. So they’re better suited to inferring where we are in our journey, making assumptions about what might have changed about us, and adapting to meet us where we are.
* Josh is a champion for embracing the weirdness of AI. Rather than be intimidated and worried about hallucinations, use the not-so-perfect technology in ways that provide unexpected results.
The counter-point to intelligent products continues to be how much intelligence a user wants and how much personal information they are willing to give up for it. There’s nothing more uncomfortable than a salesperson who doesn’t get your signals.
Adobe’s Project Concept is the start of something huge
Embracing the weirdness is exactly what Adobe’s new product, Project Concept does. Better you watch the video than me try and explain. It will be interesting to see how agencies respond to the further commoditization of their expertise.
Always remember, GenAI is great at the boring stuff
Amazon, in its quest for greater efficiency, has developed new systems to shave seconds off each package delivery and to help customers make faster buying choices, even for new product types that they may know little about. The company announced Wednesday it has created spotlights within its trucks to guide delivery people to packages for each stop along a route."When we speed up deliveries, customers shop more," said Doug Herrington, CEO of Amazon worldwide stores in remarks at the event. "Once a customer experiences fast delivery, they will come back sooner and shop more."
Interestingly, this also highlights the tech’s ability to imagine solutions to problems that humans may not be able to see otherwise. You could call that embracing the weirdness again.
We’ll go into this conversation in detail when we interview Lisa Weaver-Lambert, the author of The AI Value Playbook. In the book she interviewed business leaders to document exactly where and how AI has been delivering value.
Multi-modal AI: 8 ways computer vision will change our lives
While GenAI has been monopolizing the headlines, Apple, Meta, and Snap continue to invest in augmented reality headsets. Apple's Vision Pro landed with a thud —largely due to the price and home-bound use cases— but the others stirred buzz because they focused on lightweight and fashionable eyewear (courtesy of their partnership with Ray-Ban).
We've been here before though. Google Glass famously failed. And no one remembers Snap's previous eyewear.
But now is different.
AI researchers have made huge advancements related to computer vision. If AI enables computers to think, computer vision enables them to see, observe and understand.
Continue reading the article on LinkedIn…
Want to join as a contributor?
Contact us [email protected] to help us collect the best resources about how AI is shaping the world around us.
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In this newsletter:
* Podcast episode with Kristie J. Fisher, PhD, the Sr. Director of Global User Research, PlayStation Studios.
* Guide to designing a GenAI product: From vision to content strategy
* Poll for the AI community
The biggest challenge facing AI products isn’t whether they would use your product, it’s whether you’re delivering reasons to convince them to switch from their existing solution.
This is extra difficult when leveraging an emerging technology, like GenAI, because of key factors:
* GenAI tools ask users to give up control and have faith that the system knows what’s right—the exact opposite of what we’ve been training users to expect from productivity tools
* GenAI is still nascent and doesn’t always get it right, meaning that in some situations it will deliver an inferior output (and need to be re-prompted)
* Users quickly run out of ideas about what to prompt because they don’t know what the tech is capable of
So as much as product teams can focus on the incremental delivery of value to users, those efforts are likely to fail because we’re asking users to take a leap of faith. Something that users, especially B2B and enterprise, don’t want to do.
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That’s why this week’s episode with Kristie J. Fisher, PhD was so fascinating. Having worked on launching new products and features at XBox, Google, and Playstation, she has learned how to dive deeper into the psyche of users and gamers. In there is the secret to making a product enjoyable: defining metrics to ensure a user’s time is well spent.
When building and researching we must be committed not only to delivering value, but ensuring that the experience is enjoyable and worth changing your workflows for.
So when building your GenAI product, always create evaluative metrics for the level of impact. The higher you score, the more likely a switch. It also offers and opportunity to qualitatively investigate where and how the impact is happening so you mine valuable product ideas.
💡 Have questions about your GenAI project, post them on the Design of AI LinkedIn page.
💡 Or contact me via email to privately discuss your project
Kristie J. Fisher, PhD, has spent the last 15 years conducting user experience research and building and leading research teams across a variety of product domains, primarily in gaming. She currently leads the global PlayStation Studios User Research team. The mission of her team is to empower PlayStation's Studios to get to great faster by being vision-led and data informed. At Google she worked on Stadia, Gmail, and Ads and was a co-author of Google's People + AI Research Guidebook. Prior to Google she was at Xbox Research, collaborating with game producers and development teams to improve player experience on Xbox, Xbox Kinect, and Windows.
Guide to designing a GenAI product: From vision to content strategy
Working with GenAI requires designers to shift their mental models from deterministic to probabilistic output. Not only are you working with a new material, the technology is so new so there aren't any best practices (yet).
This guide is an overview of the technology and lessons I've learned in my own AI consulting projects working at PH1 Research and from the amazing experts we've had as guests on the Design of AI podcast (Spotify - Apple).
🎯 Continue reading the guide
Sections in this guide
* Background & reality-check
* Rationale for AI
* AI product vision
* AI product strategy
* AI product principles
* Design's role in crafting GenAI products
* Content strategy
Poll: We want to help our community better so we can deliver better resources.
We started Design of AI to help teams quickly learn how to best leverage hashtag#GenAI. In the coming months, we're launching some initiatives to improve knowledge sharing to address concerns we've heard:- Lack of archive of products/tools/features others have built- Lack of best practices- Lack of visibility on why initiatives have failed- Lack of mentorship & sense of doing it all aloneIf you have any questions or want to help with building out resources for some of these, contact us [email protected]
Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
Episode 17. Our guest is Glenn MacDonald who was Spotify’s Data Alchemist, building it into an algorithmic powerhouse.
We’re critically evaluating algorithms' effectiveness and why GenAI probably isn’t the best technology for many problems.
Some key insights:
#1. As Spotify's former data alchemist, I expected huge advocacy for hashtag#ML & hashtag#AI as a predictive technology. Instead, we must not play god with algos. They should be assistive tool to get people to where they're headed. Prediction leads to errors.#2. You must be able to evaluate algorithms. Too often we're deploying fancy tech with no way to know it is performing better than an alternative. hashtag#GenAI has a huge risk of this because the assumption is that it solved everything. But the cost of deploying it is also very high."I think the main thing I've learned Is actually not to think about it as prediction, I think the thing that happens to you when you start thinking about things as prediction, and I think this applies to thinking about LLM, LLM outputs as predicting text. It also applies to A& R and music as like predicting hit artists. The moment you start thinking about it as prediction, you've sort of internalized sort of ugly idea that the future is kind of determined and you're just attempting to guess what it's going to be and thus profit by anticipation. And I think it's a lot more productive to not think about the future as something you're predicting, but it's something you're making. ""I think a lot of the time we evaluate new tech against really Poor baselines, like against randomness or against the most popular things, or like you said, against just like our intuitive guesses. And in those contexts, sometimes the fancy tools seem like, Oh, they're clearly better. But then when you compare them against, Oh, what if we just did some math and you realize. Oh, the math's even better. It's a lot simpler. "
The episode is hosted by:
Arpy Dragffy Guerrero (Founder & Head of product strategy, PH1 Research) https://www.linkedin.com/in/adragffy/
Brittany Hobbs (VP Insights, Huge) https://www.linkedin.com/in/brittanyhobbs/
Glenn McDonald is a music evangelist, algorithm designer, software engineer and technology strategist. He created the music-exploration website Every Noise at Once, and for 12 years was the Data Alchemist at the Echo Nest and Spotify. He has written about music online since before "blog" was a word, and his first offline book, You Have Not Yet Heard Your Favourite Song: How Streaming Changes Music, is available now from Canbury Press.
00:24 Meet Glenn MacDonald: Spotify's Data Alchemist
01:50 The Evolution of Music Discovery
08:39 The Role of AI in Music and Beyond
13:29 Challenges and Future of AI in Music
29:14 Navigating AI in the Workplace
31:25 Designing User-Friendly Algorithms
34:59 Challenges with Algorithmic Recommendations
39:42 Evaluating AI and User Testing
47:41 The Future of Music and AI
Thank you for listening to the Design of AI podcast. We interview leaders and practitioners at the forefront of AI. If you like this episode please remember to leave a rating and to follow us on your favorite podcast app.
Take part in the conversations about AI https://www.linkedin.com/company/designofai/
And subscribe to our newsletter for additional resources https://designofai.substack.com/
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Our guest is Yasemin Cenberoglu, who was the first designer to work on Microsoft’s Copilot, all in secret, before the world was exposed to ChatGPT for the first time.
Yasemin is a Principal Design Manager at Microsoft, leading the Copilot product for Teams Meetings, Calling, and Devices. She’s the first designer to shape what Copilot is today. Previously, she served as the Director of Design at Digitalist. Yasemin is an advisory board member at IDEA School of Design at Capilano University. She studied in Germany and then at Cal State, in the Bay area.
00:49 Yasmin's Background and Role 02:09 Design Differences: Europe vs North America 03:44 Service Design Methodologies 03:58 Co-Creating with OpenAI 04:38 Blueprints and Customer Journeys 05:27 Rapid Prototyping and Testing 06:20 Reconnecting with Yasmin 07:06 The Excitement of Innovation 10:04 Defining Value Drivers 11:50 Building High-Level Scenarios 12:49 Managing Feasibility and Vision 15:53 Lessons Learned from GenAI 21:05 Testing and User Feedback 22:51 Iterative Design and AI 31:52 Building Trust in AI 34:12 Service Design in AI 39:11 Deciding Between Co-Pilot, Agent, or Chatbot 43:41 Future of Assistive Software 47:27 Advice for Aspiring AI Designers
Episode is hosted by:
Arpy Dragffy Guerrero (Founder & Head of product strategy, PH1 Research) https://www.linkedin.com/in/adragffy/
Brittany Hobbs (VP Insights, Huge) https://www.linkedin.com/in/brittanyhobbs/
Thank you for listening to the Design of AI podcast. We interview leaders and practitioners at the forefront of AI. If you like this episode please remember to leave a rating and to follow us on your favorite podcast app.
Take part in the conversations about AI https://www.linkedin.com/company/designofai/
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
How should product teams be leveraging GenAI? Product teams are struggling to find the use cases which deliver the most value to customers and where the technology can be effective. And teams that have built AI products are finding that there’s often a mismatch between what customers find valuable and what the technology can do.
Our guest is Arpy Dragffy Guerrero, the founder of PH1 Research where he has consulted Spotify, Microsoft, Mozilla, National Football League, to research and strategize how to best leverage emerging technologies. He’s worked on products across AI, personalization, Web3, location-sensing, and more. His focus is creating product & testing strategies to quickly pinpoint where the best opportunities are for new products.
Follow him on social:
https://www.linkedin.com/in/adragffy/
https://twitter.com/arpyd
Arpy maps out Futures Design: How to build AI products that customers want. We discuss strategies for product teams:
‣ Learning from failure & the struggles of early AI
‣ The challenge of identifying the impactful use cases of AI
‣ The importance of value drivers (& why they aren’t JTBD)
‣ Applying systems thinking to AI products & strategies
‣ People hate chatbots —agents will open new possibilities
‣ Examples of how agents could transform use cases and roles
Please subscribe to: Design of AI: The podcast for product teams, on Spotify, Apple podcasts, Youtube, substack. We interview leaders and practitioners at the forefront of AI to help product teams navigate where and how to leverage AI.
Substack newsletter https://designofai.substack.com/ Join the conversation on LinkedIn https://www.linkedin.com/company/103164463/
This Design of AI episode is brought to you by PH1: A research & strategy consultancy that helps clients build AI products that customers want https://ph1.ca
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
What is the path to building responsible AI products? We have a special guest: Jess Holbrook, the Head of UX Research for Microsoft AI.
We discuss:
‣ Responsible AI: What it is and how orgs need a clear vision for it
‣ Data transparency: Ensuring you are communicating appropriately
‣ Becoming one of Google’s first user researchers working on machine learning
‣ Philosophical differences to user research at Google, Meta, and Amazon
‣ Bridging academic research and the practical development of AI products
‣ The paradigm shift that big tech is expecting AI to deliver
‣ Why the last thing you should want is a user over-trusting your product
As one of the first user researchers working on AI products, Jess offers a deep and informed perspective on the challenges and opportunities of working with this new technology. He challenges organizations to build values into their products, unwaveringly and without vagueness.
Jess Holbrook is the Head of UX Research for Microsoft AI. Prior to that he was Director of UX Research for Generative AI and Responsible AI at Meta. He got his start in human-AI research about 10 years ago at Google where he was a founder and lead of Google’s People + AI Research group (PAIR). Prior to joining Google, he was a UX Researcher at Amazon and Microsoft. He received his Ph.D in Psychology from the University of Oregon and a B.S. in Psychology from the University of Washington
Follow Jess: https://linkedin.com/in/jessholbrook/ https://x.com/jessscon
Resources mentioned by Jess:
https://pair.withgoogle.com/
https://research.google/teams/responsible-ai/
https://runwayml.com/
Please subscribe to: Design of AI: The podcast for product teams, on Spotify, Apple podcasts, Youtube, substack. We interview leaders and practitioners at the forefront of AI to help product teams navigate where and how to leverage AI.
Have questions? Join the conversation in our LinkedIn community: https://www.linkedin.com/company/designofai/
Hosted by:
Brittany Hobbs https://www.linkedin.com/in/brittanyhobbs/
Arpy Dragffy Guerrero https://www.linkedin.com/in/adragffy/
This Design of AI episode is brought to you by PH1: A research & strategy consultancy that helps clients build AI products that customers want https://ph1.ca
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
AI is changing the role of the designer and shifting how product teams succeed. We have a special guest: Scott Jenson, formerly from Apple, Google, and Frog Design.
We discuss:
* Why designers feel like their entire job will go away
* What advice he offers to the teams and individuals he coaches
* How AI is over--hyped and where it will have impact
* Lessons from working at the forefront of mobile technology
* Why Google, Apple, Meta, Microsoft are all racing to get there first
* Recommendations to build successful products today
This conversation is more of a coaching session for the designers, researchers, and product teams trying to navigate this time of great change.We try and cut through the hype to distill out key lessons that will help you all in your careers.
Scott Jenson has worked in user interface design and strategic planning for over 30 years. The first member of the System Software Human Interface group at Apple in the late 80s, working on System 7, the Apple Human Interface guidelines and the Newton digital assistant. After Apple, was a freelance design consultant, doing work for Netscape, Mayo Clinic, American Express, and several web startups. Then director of product design for Symbian, then managed Mobile UI design at Google for 6 years. Left to become creative director at frog design for 2 years but returned to Google to explore advanced UX concepts for IoT and Android at Google. 35+ patents. https://www.linkedin.com/in/scottjenson/
Please subscribe to: Design of AI: The podcast for product teams, on Spotify, Apple podcasts, Youtube, substack. We interview leaders and practitioners at the forefront of AI to help product teams navigate where and how to leverage AI.
Have questions? Join the conversation in our LinkedIn community: https://www.linkedin.com/company/designofai/
Hosted by:
Brittany Hobbs https://www.linkedin.com/in/brittanyhobbs/
Arpy Dragffy Guerrero https://www.linkedin.com/in/adragffy/
This Design of AI episode is brought to you by PH1: A research & strategy consultancy that helps clients build AI products that customers want
https://ph1.ca
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
This conversation is a deep case study into what the capabilities of the technology are today and how product teams must leverage both creative experts and these emerging technologies, side-by-side. Our guest is Trisha Causley from Shopify.
Topics we discuss:
▪ Why Trisha went from an AI skeptic to a champion
▪ What types of creative tasks GenAI is best at
▪ Tactical lessons for leveraging GenAI across product experiences
▪ Why prompt engineering must become part of your toolkit
▪ Shopify’s plan to leverage GenAI to scale & personalize brand-building
▪ Why GenAI enhances the role of creatives by expanding what you do
Trisha Causley is a Senior Staff Content Designer at Shopify in Toronto, Canada, where she works on AI-powered product features. She previously worked with IBM and on the Watson team. https://www.linkedin.com/in/tcausley/
The Design of AI podcast is available on Spotify, Apple Podcast, and Youtube.
Have questions? Join the conversation in our LinkedIn community: https://www.linkedin.com/company/designofai/
Subscribe to the Design of AI podcast for more in-depth resources for product teams.
Hosted by:
Brittany Hobbs https://www.linkedin.com/in/brittanyhobbs/
Arpy Dragffy Guerrero https://www.linkedin.com/in/adragffy/
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
Why are brands investing into AI? How can they succeed? What can we learn from how experts in the field of innovation lead transformation projects? Where will AI actually deliver impact in the near term? Joining us is Nick Sherrard, who is involved in these conversations across Fortune 500, government, and startups.
He is a co-founder of Label Sessions, the global innovation expert network, and Label Ventures, the venture studio. He is also a board member at Substrakt, the digital agency, and Collective art gallery in Edinburgh. Nick is often said to be the only person to have run an innovation lab inside a bank, a government department, a big 4 consultancy and a circus. His approach to making change happen in organisations fuses his more classic brand and product development background, with the devising mindset of arts producer. Nick advises boards and entrepreneurs globally.
In this episode we cover:
* Top-down and bottom-up approaches to leading AI projects
* History of art and innovation is the history of rejection
* Leaders of AI projects often don’t anticipate what’s needed
* The problem with design thinking when building AI products
* How the creative & consulting worlds are enhanced by AI
* Use cases where AI will have impact
Also find us Apple Podcast & Spotify
Have questions? Join the conversation with other product leaders on LinkedIn https://www.linkedin.com/company/designofai/
Subscribe to the Design of AI podcast for more in-depth resources for product teams.
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
Building products with GenAI brings powerful new capabilities but also a whole new set of uncertainties. Teams can't rely on best practices because the technology is changing so quickly and users are cautiously adopting change. Designing and shipping products can no longer be thought about as a linear process.
Alexandra Holness, Senior Lead Product Designer at Klaviyo, joins to share lessons, cautions, and a path forward to help product teams build AI products that customers want. She sees that successful product teams will depend on designer, data scientists, engineers working more closely than ever because it is very hard to predict how customers will use models until you've shipped them.
Topics discussed:
* How she created her role leading AI design
* Assumptions the team had about how to leverage AI
* What works and doesn’t from a design perspective
* AI models being so nascent that its hard to design a UX
* Designers-data-engineers working together in new ways
* Building AI products is very different than traditional
* Building effective AI products requires culture change
* Why you need to test out potential futures
Have questions? Join the conversation https://www.linkedin.com/company/designofai/
Subscribe to the Design of AI podcast for more in-depth resources for product teams.
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
Dr. Amy Bucher literally wrote the book on behavior change.She joined the podcast to discuss how hashtag#GenAI can transform what tech is possible of achieving on a human outcome level:- How AI can open entire new possibilities for behavioural change and lead to monumental outcomes- Opportunities and risks of leveraging AI personalization- Reinforcement learning and what it is- Objective-driven AI and how we should start focusing more on outcomes - Why wearables may open new possibilities- Considerations around proprietary vs. commercially-available AI- And - Why having a AI scientist will be critical for any team and that it may not be as hard to hire for as you think
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
How can AI make our workflows and products more effective? It’s a question every product team is asking itself as they decide to invest into developing or licensing products. Let’s learn from two building and leveraging AI today.
Two case study presenters from the upcoming Rosenfeld Design with AI Conference (June 4 & 5) will be with us to detail out how they leveraged GenAI. Savannah Carlin, Staff Product Designer at Marqeta, will detail how to design conversational interactions with AI. Weidan Li, the Design Research Lead at SEEK.com, will outline AI’s performance in analyzing qualitative data.
Design of AI, the podcast for product teams Hosted by Brittany Hobbs & Arpy Dragffy Guerrero. Find us on LinkedIn https://www.linkedin.com/company/designofai/
Subscribe on Spotify, Apple, YouTube for weekly interviews with leaders at the forefront of AI.
And join our substack newsletter to get resources, insights, and strategies for product teams
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
Building products using emerging technologies is more difficult. As we’re seeing with building AI products today, teams are often chasing which use case and customer profiles to focus on. It’s harder because the new technologies make us obsess over what’s possible rather than what people actually need. Dr. Llewyn Paine joins us to share lessons and strategies from her advising teams working on spatial computing, virtual reality, and robotics. Her expertise is helping teams make better product decisions through research. We’ll discuss how to identify your best potential customers and design higher-value products and services they’ll love to use.
She is an innovation strategy consultant with nearly two decades of experience in emerging technologies, including mixed reality and AI at Microsoft, and experimental media for Disney. She has helped emerging technology teams launch flagship products and secure investments of over $300M. designofai.substack.com to get additional resources.
Apple:
Spotify:
She’s speaking at the Designing with AI conference on June 4-5 where she’ll be diving into her most recent work: Protecting biometric data of research participants by leveraging AI
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
There are so many new GenAI products coming to market that it is hard to believe even a fraction of them will become sustainable businesses. Ben Yoskovitz, Founding Partner of Highline Beta and author of Lean Analytics, joins us to discuss how many of these startups will fail to find a product-market fit. By rushing to get to market they’re likely skipping key steps that would typically improve their likelihood of success. We discuss the process his venture studio uses and where he sees opportunities for AI products to deliver more value to consumers.
Ben’s newsletter: https://www.focusedchaos.co/
Design of AI, the podcast for product teamsHosted by Brittany Hobbs & Arpy Dragffy
Subscribe to the podcast
https://www.youtube.com/@DesignofAI
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
AI has the potential to be a transformational technology. But how is it trained and how can you track authenticity? Virginie Berger, Chief Business Development and Rights Officer at Matchtune, joins us to discuss the developments with copyright issues related to creative fields in hopes of shedding light on what this means for other industries. A particular issue is what happens to business models when you can get replicas elsewhere and have no clarity on how they were derived?
We explore how product teams can and should adapt. Important is protecting the rights of your users and leveraging LLMs that are ethically processing the data that you input into them.
Episode of hosted by Brittany Hobbs & Arpy Dragffy Guerrero.
Please subscribe to the Design of AI, the podcast for product teams who want to leverage AI to transform their industries.
Visit https://designof.ai to get AI news & tools that matter to product teams.
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
Emily Campbell joins us to discuss the future of UX. Her Shape of AI newsletter and community have become the go-to resource for AI product design patterns. She sees AI products getting to market with far less involvement from design than they should have. Design will undoubtedly experience shocks —with roles changing, and anti-patterns emerging— but also entirely new opportunities for design to shape adaptive experiences that offer users new capabilities to personally interact with products. We discuss what comes next after prompt-based, text interfaces.
Episode of hosted by Brittany Hobbs & Arpy Dragffy Guerrero. Please subscribe to the Design of AI podcast. We speak to leaders at the forefront of AI to learn how great AI products are designed and how they’re transforming industries
To contact us visit our website designof.ai
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com -
Maarten Walraven-Freeling, co-editor of MUSIC x and the co-CEO of Symphony Media joins the Design of AI podcast to discuss how AI will impact the music industry. We look at how digital streaming platforms and algorithmic discovery have already led to monumental changes to the business and what to expect now that generative AI tools, like Suno, are making music creation easier and more accessible. It is clear that music is one of the first and most important battleground where we see the potential of AI as a creative tool but also where concerns are growing about GenAI platforms being trained on content without the permission of copyright holders.
The show is hosted by Brittany Hobbs & Arpy Dragffy Guerrero
Subscribe on Spotify, Youtube, or Apple to get our latest episodes
We speak to leaders at the forefront of AI to learn how great AI products are designed and how they’re transforming industries
To contact us visit our website designof.ai
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com - Laat meer zien
