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5 Key Learnings from How Top SaaS Companies are Actually Productizing AI with Paragon

SaaStr

The Three Types of Context Your AI Needs Your AI agent needs access to: Team documents & engineering specs (the “how it works” knowledge) Systems of record like CRM (the structured data) Unstructured info from Slack, Zoom, etc. the tribal knowledge) Without all three, you’re flying blind.

AI 232
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Going Multi-Product in the Age of AI with Webflow, Rubrik, Zoom, and ProductBoard

SaaStr

People are not going to want to take recommendations around security and what’s happening within their data infrastructure from an AI agent unless they’re confident that what’s being told to them is going to help make their environment better and not worse. The biggest challenge in all of that has been around quality.

AI 258
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10 Key Lessons from Calendly’s CPO and Head of UX on Building AI that Actually Works

SaaStr

His background combines deep product strategy with practical AI implementation experience. 4 Unexpected Learnings: Parallel AI experiences often fail : Calendly’s experiment with a conversational scheduling chatbot couldn’t retain users because the original experience was more efficient.

AI 192
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UX Analytics: It’s Not Just About Data Collection and Methods

User Pilot

Without effective UX analytics that goes beyond collecting data, you’re losing valuable customers. Unfortunately, the research backs this up, with a staggering 90% of users reporting that they stopped using an app due to poor performance. Basically, anything that ruins the user experience.

Data 105
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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.

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Where AI Really Matters in Vertical SaaS With CEOs of Owner, Alloy Automation, and DoNotPay

SaaStr

Rather than committing to single solutions, successful companies are: Testing multiple AI models continuously Using self-served platforms for rapid experimentation Empowering engineers to make model-selection decisions Avoiding long-term vendor lock-in Data Management Data quality and integration have emerged as critical factors in successful AI implementation. (..)

AI 212
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How to Use Generative AI and LLMs to Improve Search

TechEmpower SaaS

For instance, if a user searches for "low carb options," even though "low carb" might not be a category saved in the database, the LLM can still match and suggest "keto" products, understanding the user's intent. This advanced approach greatly enhances the user experience, making product discovery more intuitive.

AI Search 519
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The Definitive Guide to Dashboard Design

Dashboard design can mean the difference between users excitedly embracing your product or ignoring it altogether. Great dashboards lead to richer user experiences and significant return on investment (ROI), while poorly designed dashboards distract users, suppress adoption, and can even tarnish your project or brand.

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Business Intelligence 101: How To Make The Best Solution Decision For Your Organization

Speaker: Evelyn Chou

We’ll explore essential criteria like scalability, integration ease, and customization tools that can help your business thrive in an increasingly data-driven world. You’ll discover how successful companies align BI capabilities with their growth strategies and learn what to look for when it comes to user adoption and implementation.

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Personalize Your User Experience Using Behavior Change Science

Speaker: Amy Bucher, Ph.D., Vice President of Behavior Change Design, Mad*Pow

Why motivation is the key to user empowerment. How to determine the data needed to effectively personalize a product. Best practices for personalization that fit timeline and budget while empowering users. In this webinar, you will learn: The basics of motivation and the types of motivation that last.

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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Understanding these trends is not only essential to staying ahead of the curve, but critical for those striving to remain competitive and innovative in an increasingly data-driven world.

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The Product Corner: Maximizing Impact, Reducing Hours, and Accelerating Roadmaps with Data

Speaker: Edie Kirkman - VP, Digital at Focus Brands

To overcome this challenge, it is crucial to build core product and technology competencies that provide actionable insights through qualitative and quantitative data analysis. By leveraging data-driven insights, companies can accelerate time-to-market, enhance product quality, and align offerings with customer needs.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.

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What is Contextual Analytics? The Next Evolution of Embedded Analytics

Download this whitepaper to learn what contextual analytics is, how BI platforms like Yellowfin revolutionize the way users discover insights from their data with native contextual analytics, and how it adds value to your software solution by elevating the user experience.