AI in 60 Seconds 🚀 - AI ROI: Measure What Matters
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AI ROI: Measure What MattersMar 13, 2025 Seven of ten executives report board pressure to demonstrate AI ROI, and we’re incorrectly measuring AI’s impact. Just as the PC revolution took a decade to show measurable productivity gains, today’s AI transformation requires a fundamental shift in assessing value. Two parallel AI economies are emerging: a highly productive but fragmented “Shadow AI” ecosystem driven by employees and underwhelming enterprise implementations constrained by traditional metrics. Successful organizations aren’t just deploying AI—they’re building complementary teams of specialized AI tools and reimagining workflows from the ground up. 🎧 Dive deeper into this week’s insights in our “AI in 60 Seconds—The 10-Min Podcast” version. Available on Apple Podcast, Spotify, or your favorite app. 📊 The AI Value Reality CheckWhen Vinod Khosla of Khosla Ventures forecasted in October 2024, “About 80% of the work involved in 80% of jobs across the economy can be automated over time,” he painted a compelling vision of AI’s potential – See here his WSJ Interview. Our global AI tracker confirms we’re on this trajectory—but with important nuances:
[1] Combined effect of AI and outsourcing to India, Eastern Europe, and Latin America reaches 15-25%, as AI helps address language barriers while building on remote work successes. These impacts aren’t hypothetical—they’re already unfolding and are expected to intensify over the next 18 months, reshaping the workforce as we know it. 🚨 Three Critical Barriers to AI ROIDespite enormous potential, three key factors limit organizational AI ROI: 1. First-Generation Enterprise AI Tools LearningsThe first generation of generative AI features applied to existing enterprise tools emerged as a side companion and always optional feature. In retrospect, this approach shows low traction and results, and a story emerges for software creators: rethink the workflows and embed AI as a behind-the-scenes element that results in tangible business or individual value.
💡 When users rate enterprise AI tools like Copilot or Gemini on overall experience, satisfaction hovers in the low 40s. Yet when we ask the same users about their satisfaction with how Copilot or Gemini complete specific tasks (like meeting summarization), ratings jump to 70-75%, matching native AI tools. The problem isn’t the underlying technology—it’s a combination of inflated marketing claims creating unrealistic expectations and design that adds AI as a disconnected feature rather than reimagining the workflow. In some use cases, Enterprise tools leave users staring at empty prompt boxes, while native AI applications guide users through familiar workflows enhanced by AI. 2. Workflow Friction & Implementation ChallengesOur data shows tangible differences between implementation approaches:
3. Widespread Skills GapsAs discussed in our AI Success in 2025 Article and Podcast, most organizations lack the AI literacy to fully leverage even essential AI tools. Powerful tools become expensive chat toys without prompt engineering skills and AI awareness. 💼 Enterprise vs. Niche: The Productivity-Risk TradeoffOur analysis of 180,000 AI use cases shows a critical insight that undermines traditional ROI calculations: 72% of time saved by AI doesn’t convert to additional throughput on average, with this “productivity leak” ranging from 40% to 80%, depending on the role. Instead, this time flows to:
Creative roles like marketing and design sit at the high end of the spectrum, with nearly 80% of saved time reinvested in quality and innovation, hence “leaked.” Meanwhile, process-oriented functions like customer support, operations, and accounting show leaks closer to 40%, with more saved time directly increasing throughput. Explore the expected ranges for your company with our online AI ROI Calculator. This “productivity leak” isn’t a flaw—it’s a feature of how humans naturally optimize their work when given new tools. But it wreaks havoc on simplistic ROI calculations that assume every hour saved translates to an hour of additional work output. 📣 Coming Next: In our next edition, we’ll explore the productivity leak phenomenon in depth, providing role-specific insights and strategies for measuring the true ROI of “leaked” productivity. A straightforward and actionable ROI direction:Our research shows that CTOs struggle to identify ideal pilot users for tools like M365 Copilot. Imagine if marketing took a practical approach: Start by giving access to team members spending 15+ hours weekly in meetings—Copilot can save them 5 hours per month on summarization and follow-up. That would give them a measurable target for immediate ROI. Instead, enterprise AI marketing promises sweeping transformation without acknowledging the learning curve, workflow disruption, or realistic adoption timeline. ⚙️ The Path Forward: Building Your AI TeamThe most successful AI adopters aren’t pursuing a single universal assistant—they’re assembling teams of specialized AI tools:
This portfolio approach mirrors how we build human teams—specialists with complementary skills working together under human orchestration. The key breakthrough is thinking about AI tools as team members rather than features:
🎙️ Behind the Curtain: In our 10-minute companion podcast, we share stories from founders who’ve built seven-figure companies in under 24 months with AI at the helm. Listen to their anecdotes on selecting, training, and scaling digital teams. Plus, we’ll give you a peek into how we tackle these challenges at AI4SP! 🔮 One More Thing…You don’t measure your human team members purely on hours worked. Why would you measure your AI team that way? Forward-thinking leaders are creating new frameworks for assessing AI value that focuses on transformation rather than simple time savings. These frameworks track quality improvements, productivity gains, novel insights generated, and knowledge accessibility. Let’s realize that adding AI to old workflows is like putting a jet engine on a bicycle: a powerful technology constrained by infrastructure never designed to support it.
🚀 Taking Action: From Tools to Teams
📚 Resources
Luis J. Salazar Founder | AI4SP Sources: Our insights are based on over 250 million data points from individuals and organizations that 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 |