AI in 60 Seconds 🚀 - Dec 4, 2024
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Your bi-weekly dose of AI insights!Dec 4, 2024 Building AI That People Use: How entrepreneurs win over tech giants in the first leg of the AI Enterprise adoption.Two years into the AI revolution, a pattern emerges: while specialized AI tools thrive, the AI chat-based features added to the leading productivity and CRM solutions struggle to gain adoption. Our latest insights, now powered by over one billion data points through our alliance with Surveil.co, reveal why—and it’s not about the technology. Of enterprises that started Generative AI trials, less than 10% considered them successful, and most face change management challenges. 🎧 Dive deeper into this week’s insights in our “AI in 60 Seconds—The 10-Min Podcast” version. Available on Apple Podcast, Spotify, Amazon Music, or your favorite app. 📊 The Integration ImpactIf we put the superusers aside, most workers have difficulty adapting their workflows to benefit from AI added as chat interfaces to productivity tools. In contrast, those using native AI tools thrive.
⚡ The Native AI AdvantageOur research matches industry findings: The learning curve is longer than expected, particularly as the expectation is there's no learning curve. We're not talking about learning new software or a UI; we're talking about a new way of working. Marketing messaging from large software vendors often contributes to this confusion and misleading expectations, while small AI providers succeed through ease of use and delivering on the fundamental promise of automation. High Alpha’s research (Nov 2024) confirms our findings: AI-native companies are growing at twice the rate of horizontal SaaS companies, primarily due to better user experience and faster time to value. 🎯 The Expectation GapThe disparity in early market traction becomes even more evident when we examine the expectations gap in the market. People want to use it—they're excited about it—they're just not quite sure how to use it, hurting Microsoft, OpenAI, Google, and others from a deployment perspective. The disconnect is striking:
The Marketing Misstep - Inflated ExpectationsWhile specialized AI tools succeed with focused promises—”write better emails,” “personalize donor outreach,” and “optimize customer service”—large vendors’ marketing creates an expectations crisis. Their ambitious vision of AI transforming every aspect of work is inspiring but collides with today’s reality. It’s worth noting that super users achieve excellent results with ChatGPT, Claude, and Copilot, but they represent less than 10% of users. The majority of employees lack the prompt engineering expertise these tools currently require. Marketing messages and executive speeches seem to be directed at superusers rather than the general population.
This gap between sweeping promises and ground-level realities isn’t just about marketing—it reflects a more profound misunderstanding. After 50 years of menus and search boxes, we’re asking users to adopt an entirely new interaction model while simultaneously promising them it requires no adaptation. The result? Frustrated users and stalled deployments. Meanwhile, focused AI tools thrive by setting realistic expectations and delivering tangible, immediate value. Their success comes not from grand visions but from pragmatic solutions to specific problems. 💡 The Hidden Cost of “Just Add AI” Without Reimagining the Experience
🚨 Most Enterprise AI Deployments Miss the Mark
Success patterns from AI4SP research:
🔮 One More Thing…For 50 years, we've designed software around menus and search boxes. Adding a chat interface to this paradigm is like putting a steering wheel on a horse—it fundamentally misunderstands the old and new ways of working. The next wave of software doesn’t just add AI—it is reimagined from the ground up with AI at its core. Stanford University’s Symbolic Systems program points the way forward, combining computer science with linguistics, psychology, and philosophy. Tomorrow’s software teams won’t just need engineers; they’ll need experts who understand how humans think, communicate, and interact with technology. 🎯 Taking ActionFor Decision Makers:
For Software Creators:
📚 Dive Deeper
Resources
Luis J. Salazar Founder | AI4SP Sources: Our insights are based on data from over 250M data points from individuals and organizations who used our AI-powered tools, participated in our panels and research sessions, or attended our workshops and keynotes. 📣 Feel free to use this data in your communications, citing "AI4SP" and linking to AI4SP.org. 📬 If this email was forwarded to you and you'd like to receive our bi-weekly AI insights directly, click here to subscribe: https://ai4sp.org/60 |