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1 You're not supposed to be here and other Dad wisdom 29:22
Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)
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161 - Designing and Selling Enterprise AI Products [Worth Paying For]
Manage episode 462222658 series 2938687
With GenAI and LLMs comes great potential to delight and damage customer relationships—both during the sale, and in the UI/UX. However, are B2B AI product teams actually producing real outcomes, on the business side and the UX side, such that customers find these products easy to buy, trustworthy and indispensable?
What is changing with customer problems as a result of LLM and GenAI technologies becoming more readily available to implement into B2B software? Anything?
Is your current product or feature development being driven by the fact you might be able to now solve it with AI? The “AI-first” team sounds like it’s cutting edge, but is that really determining what a customer will actually buy from you?
Today I want to talk to you about the interplay of GenAI, customer trust (both user and buyer trust), and the role of UX in products using probabilistic technology.
These thoughts are based on my own perceptions as a “user” of AI “solutions,” (quotes intentional!), conversations with prospects and clients at my company (Designing for Analytics), as well as the bright minds I mentor over at the MIT Sandbox innovation fund. I also wrote an article about this subject if you’d rather read an abridged version of my thoughts.
Highlights/ Skip to:
- AI and LLM-Powered Products Do Not Turn Customer Problems into “Now” and “Expensive” Problems (4:03)
- Trust and Transparency in the Sale and the Product UX: Handling LLM Hallucinations (Confabulations) and Designing for Model Interpretability (9:44)
- Selling AI Products to Customers Who Aren’t Users (13:28)
- How LLM Hallucinations and Model Interpretability Impact User Trust of Your Product (16:10)
- Probabilistic UIs and LLMs Don’t Negate the Need to Design for Outcomes (22:48)
- How AI Changes (or Doesn’t) Our Benchmark Use Cases and UX Outcomes (28:41)
- Closing Thoughts (32:36)
- “Putting AI or GenAI into a product does not change the urgency or the depth of a particular customer problem; it just changes the solution space. Technology shifts in the last ten years have enabled founders to come up with all sorts of novel ways to leverage traditional machine learning, symbolic AI, and LLMs to create new products and disrupt established products; however, it would be foolish to ignore these developments as a product leader. All this technology does is change the possible solutions you can create. It does not change your customer situation, problem, or pain, either in the depth, or severity, or frequency. In fact, it might actually cause some new problems. I feel like most teams spend a lot more time living in the solution space than they do in the problem space. Fall in love with the problem and love that problem regardless of how the solution space may continue to change.” (4:51)
- “Narrowly targeted, specialized AI products are going to beat solutions trying to solve problems for multiple buyers and customers. If you’re building a narrow, specific product for a narrow, specific audience, one of the things you have on your side is a solution focused on a specific domain used by people who have specific domain experience. You may not need a trillion-parameter LLM to provide significant value to your customer. AI products that have a more specific focus and address a very narrow ICP I believe are more likely to succeed than those trying to serve too many use cases—especially when GenAI is being leveraged to deliver the value. I think this can be true even for platform products as well. Narrowing the audience you want to serve also narrows the scope of the product, which in turn should increase the value that you bring to that audience—in part because you probably will have fewer trust, usability, and utility problems resulting from trying to leverage a model for a wide range of use cases.” (17:18)
- “Probabilistic UIs and LLMs are going to create big problems for product teams, particularly if they lack a set of guiding benchmark use cases. I talk a lot about benchmark use cases as a core design principle and data-rich enterprise products. Why? Because a lot of B2B and enterprise products fall into the game of ‘adding more stuff over time.’ ‘Add it so you can sell it.’ As products and software companies begin to mature, you start having product owners and PMs attached to specific technologies or parts of a product. Figuring out how to improve the customer’s experience over time against the most critical problems and needs they have is a harder game to play than simply adding more stuff— especially if you have no benchmark use cases to hold you accountable. It’s hard to make the product indispensable if it’s trying to do 100 things for 100 people.“ (22:48)
- “Product is a hard game, and design and UX is by far not the only aspect of product that we need to get right. A lot of designers don’t understand this, and they think if they just nail design and UX, then everything else solves itself. The reason the design and experience part is hard is that it’s tied to behavior change– especially if you are ‘disrupting’ an industry, incumbent tool, application, or product. You are in the behavior-change game, and it’s really hard to get it right. But when you get it right, it can be really amazing and transformative.” (28:01)
- “If your AI product is trying to do a wide variety of things for a wide variety of personas, it’s going to be harder to determine appropriate benchmarks and UX outcomes to measure and design against. Given LLM hallucinations, the increased problem of trust, model drift problems, etc., your AI product has to actually innovate in a way that is both meaningful and observable to the customer. It doesn’t matter what your AI is trying to “fix.” If they can’t see what the benefit is to them personally, it doesn’t really matter if technically you’ve done something in a new and novel way. They’re just not going to care because that question of what’s in it for me is always sitting behind, in their brain, whether it’s stated out loud or not.” (29:32)
Links
105 פרקים
Manage episode 462222658 series 2938687
With GenAI and LLMs comes great potential to delight and damage customer relationships—both during the sale, and in the UI/UX. However, are B2B AI product teams actually producing real outcomes, on the business side and the UX side, such that customers find these products easy to buy, trustworthy and indispensable?
What is changing with customer problems as a result of LLM and GenAI technologies becoming more readily available to implement into B2B software? Anything?
Is your current product or feature development being driven by the fact you might be able to now solve it with AI? The “AI-first” team sounds like it’s cutting edge, but is that really determining what a customer will actually buy from you?
Today I want to talk to you about the interplay of GenAI, customer trust (both user and buyer trust), and the role of UX in products using probabilistic technology.
These thoughts are based on my own perceptions as a “user” of AI “solutions,” (quotes intentional!), conversations with prospects and clients at my company (Designing for Analytics), as well as the bright minds I mentor over at the MIT Sandbox innovation fund. I also wrote an article about this subject if you’d rather read an abridged version of my thoughts.
Highlights/ Skip to:
- AI and LLM-Powered Products Do Not Turn Customer Problems into “Now” and “Expensive” Problems (4:03)
- Trust and Transparency in the Sale and the Product UX: Handling LLM Hallucinations (Confabulations) and Designing for Model Interpretability (9:44)
- Selling AI Products to Customers Who Aren’t Users (13:28)
- How LLM Hallucinations and Model Interpretability Impact User Trust of Your Product (16:10)
- Probabilistic UIs and LLMs Don’t Negate the Need to Design for Outcomes (22:48)
- How AI Changes (or Doesn’t) Our Benchmark Use Cases and UX Outcomes (28:41)
- Closing Thoughts (32:36)
- “Putting AI or GenAI into a product does not change the urgency or the depth of a particular customer problem; it just changes the solution space. Technology shifts in the last ten years have enabled founders to come up with all sorts of novel ways to leverage traditional machine learning, symbolic AI, and LLMs to create new products and disrupt established products; however, it would be foolish to ignore these developments as a product leader. All this technology does is change the possible solutions you can create. It does not change your customer situation, problem, or pain, either in the depth, or severity, or frequency. In fact, it might actually cause some new problems. I feel like most teams spend a lot more time living in the solution space than they do in the problem space. Fall in love with the problem and love that problem regardless of how the solution space may continue to change.” (4:51)
- “Narrowly targeted, specialized AI products are going to beat solutions trying to solve problems for multiple buyers and customers. If you’re building a narrow, specific product for a narrow, specific audience, one of the things you have on your side is a solution focused on a specific domain used by people who have specific domain experience. You may not need a trillion-parameter LLM to provide significant value to your customer. AI products that have a more specific focus and address a very narrow ICP I believe are more likely to succeed than those trying to serve too many use cases—especially when GenAI is being leveraged to deliver the value. I think this can be true even for platform products as well. Narrowing the audience you want to serve also narrows the scope of the product, which in turn should increase the value that you bring to that audience—in part because you probably will have fewer trust, usability, and utility problems resulting from trying to leverage a model for a wide range of use cases.” (17:18)
- “Probabilistic UIs and LLMs are going to create big problems for product teams, particularly if they lack a set of guiding benchmark use cases. I talk a lot about benchmark use cases as a core design principle and data-rich enterprise products. Why? Because a lot of B2B and enterprise products fall into the game of ‘adding more stuff over time.’ ‘Add it so you can sell it.’ As products and software companies begin to mature, you start having product owners and PMs attached to specific technologies or parts of a product. Figuring out how to improve the customer’s experience over time against the most critical problems and needs they have is a harder game to play than simply adding more stuff— especially if you have no benchmark use cases to hold you accountable. It’s hard to make the product indispensable if it’s trying to do 100 things for 100 people.“ (22:48)
- “Product is a hard game, and design and UX is by far not the only aspect of product that we need to get right. A lot of designers don’t understand this, and they think if they just nail design and UX, then everything else solves itself. The reason the design and experience part is hard is that it’s tied to behavior change– especially if you are ‘disrupting’ an industry, incumbent tool, application, or product. You are in the behavior-change game, and it’s really hard to get it right. But when you get it right, it can be really amazing and transformative.” (28:01)
- “If your AI product is trying to do a wide variety of things for a wide variety of personas, it’s going to be harder to determine appropriate benchmarks and UX outcomes to measure and design against. Given LLM hallucinations, the increased problem of trust, model drift problems, etc., your AI product has to actually innovate in a way that is both meaningful and observable to the customer. It doesn’t matter what your AI is trying to “fix.” If they can’t see what the benefit is to them personally, it doesn’t really matter if technically you’ve done something in a new and novel way. They’re just not going to care because that question of what’s in it for me is always sitting behind, in their brain, whether it’s stated out loud or not.” (29:32)
Links
105 פרקים
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1 168 - 10 Challenges Internal Data Teams May Face Building Their First Revenue-Generating Data Product 38:24

1 167 - AI Product Management and Design: How Natalia Andreyeva and Team at Infor Nexus Create B2B Data Products that Customers Value 37:34

1 166 - Can UX Quality Metrics Increase Your Data Product's Business Value and Adoption? 26:12

1 165 - How to Accommodate Multiple User Types and Needs in B2B Analytics and AI Products When You Lack UX Resources 49:04

1 164 - The Hidden UX Taxes that AI and LLM Features Impose on B2B Customers Without Your Knowledge 45:25

1 163 - It’s Not a Math Problem: How to Quantify the Value of Your Enterprise Data Products or Your Data Product Management Function 41:41

1 162 - Beyond UI: Designing User Experiences for LLM and GenAI-Based Products 42:07

1 161 - Designing and Selling Enterprise AI Products [Worth Paying For] 34:00

1 160 - Leading Product Through a Merger/Acquisition: Lessons from The Predictive Index’s CPO Adam Berke 42:10

1 159 - Uncorking Customer Insights: How Data Products Revealed Hidden Gems in Liquor & Hospitality Retail 40:47

1 158 - From Resistance to Reliance: Designing Data Products for Non-Believers with Anna Jacobson of Operator Collective 43:41

1 157 - How this materials science SAAS company brings PM+UX+data science together to help materials scientists accelerate R&D 34:58

1 156-The Challenges of Bringing UX Design and Data Science Together to Make Successful Pharma Data Products with Jeremy Forman 41:37

1 155 - Understanding Human Engagement Risk When Designing AI and GenAI User Experiences 55:33

1 154 - 10 Things Founders of B2B SAAS Analytics and AI Startups Get Wrong About DIY Product and UI/UX Design 44:47

1 153 - What Impressed Me About How John Felushko Does Product and UX at the Analytics SAAS Company, LabStats 57:31

1 152 - 10 Reasons Not to Get Professional UX Design Help for Your Enterprise AI or SAAS Analytics Product 53:00

1 151 - Monetizing SAAS Analytics and The Challenges of Designing a Successful Embedded BI Product (Promoted Episode) 49:57

1 150 - How Specialized LLMs Can Help Enterprises Deliver Better GenAI User Experiences with Mark Ramsey 52:22

1 149 - What the Data Says About Why So Many Data Science and AI Initiatives Are Still Failing to Produce Value with Evan Shellshear 50:18

1 148 - UI/UX Design Considerations for LLMs in Enterprise Applications (Part 2) 26:36

1 147 - UI/UX Design Considerations for LLMs in Enterprise Applications (Part 1) 25:46

1 146 - (Rebroadcast) Beyond Data Science - Why Human-Centered AI Needs Design with Ben Shneiderman 42:07

1 145 - Data Product Success: Adopting a Customer-Centric Approach With Malcolm Hawker, Head of Data Management at Profisee 53:09

1 144 - The Data Product Debate: Essential Tech or Excessive Effort? with Shashank Garg, CEO of Infocepts (Promoted Episode) 52:38

1 143 - The (5) Top Reasons AI/ML and Analytics SAAS Product Leaders Come to Me For UI/UX Design Help 50:01

1 142 - Live Webinar Recording: My UI/UX Design Audit of a New Podcast Analytics Service w/ Chris Hill (CEO, Humblepod) 50:56

1 141 - How They’re Adopting a Producty Approach to Data Products at RBC with Duncan Milne 43:49

1 140 - Why Data Visualization Alone Doesn’t Fix UI/UX Design Problems in Analytical Data Products with T from Data Rocks NZ 42:44

1 139 - Monetizing SAAS Analytics and The Challenges of Designing a Successful Embedded BI Product (Promoted Episode) 51:02

1 138 - VC Spotlight: The Impact of AI on SAAS and Data/Developer Products in 2024 w/ Ellen Chisa of BoldStart Ventures 33:05

1 137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen 44:50

1 136 - Navigating the Politics of UX Research and Data Product Design with Caroline Zimmerman 44:16

1 135 - “No Time for That:” Enabling Effective Data Product UX Research in Product-Immature Organizations 52:47

1 134 - What Sanjeev Mohan Learned Co-Authoring “Data Products for Dummies” 46:52


1 132 - Leveraging Behavioral Science to Increase Data Product Adoption with Klara Lindner 42:56

1 131 - 15 Ways to Increase User Adoption of Data Products (Without Handcuffs, Threats and Mandates) with Brian T. O’Neill 36:57

1 130 - Nick Zervoudis on Data Product Management, UX Design Training and Overcoming Imposter Syndrome 48:56

1 129 - Why We Stopped, Deleted 18 Months of ML Work, and Shifted to a Data Product Mindset at Coolblue 35:21

1 128 - Data Products for Dummies and The Importance of Data Product Management with Vishal Singh of Starburst 53:01

1 127 - On the Road to Adopting a “Producty” Approach to Data Products at the UK’s Care Quality Commission with Jonathan Cairns-Terry 36:55

1 126 - Designing a Product for Making Better Data Products with Anthony Deighton 47:38

1 125 - Human-Centered XAI: Moving from Algorithms to Explainable ML UX with Microsoft Researcher Vera Liao 44:42

1 124 - The PiCAA Framework: My Method to Generate ML/AI Use Cases from a UX Perspective 21:51
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