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.
📊 The AI Value Reality Check
When 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:
- Super users report productivity gains equivalent to 20+ hours per week
- These gains come from orchestrating 5-10 specialized AI tools, not a single solution
- The most significant impact is in specific roles where current AI excels:
| Role Category | Forecasted Jobs Reduction/Hiring Freeze |
|---|---|
| Customer Service & Support | 25-35% |
| Content Creation & Translation | 25-35% |
| Digital Marketing & Lead Gen | 20-25% |
| Administrative & Paralegal | 20-25% |
| Software Development & QA | 10-15% 15-25%[1] |
| Data & Business Intelligence | 15-20% |
| Financial Operations & Analysis | 15-20% |
[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 ROI
Despite enormous potential, three key factors limit organizational AI ROI:
1. First-Generation Enterprise AI Tools Learnings
The 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.
| Solution Type | Overall User Satisfaction | Task-Specific Satisfaction | 3-Month Retention Rate |
|---|---|---|---|
| Enterprise AI Tools (Copilot, Gemini, etc.) | 41% (flat for 3 months) | 70-75% for specific tasks like meeting summaries | 20% remain active |
| Native AI Applications | 78-80% | 78-80% | 75% remain active |
💡 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 Challenges
Our data shows tangible differences between implementation approaches:
| Implementation Approach | 3-Month Active Usage |
|---|---|
| Adding AI to existing workflows | 20% remain engaged |
| Reimagining workflows around AI | 75% remain engaged |
3. Widespread Skills Gaps
As 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.
🧩 ROI and Productivity Leaks
Our 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:
- Better work-life balance
- Higher quality and more creative output
- Learning and professional development
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 Team
The most successful AI adopters aren’t pursuing a single universal assistant—they’re assembling teams of specialized AI tools:
- Super users consistently leverage 5-10 different AI tools
- Almost all super users simultaneously use multiple AI assistants (ChatGPT, Claude, and Perplexity.AI as the leading ones)
- 10% of super users are now exploring agentic AI with limited autonomy
- They create “adversarial” setups where one assistant checks another’s work
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:
- Each AI “team member” has specific strengths and limitations
- The combined capabilities address a broader range of needs
- Human orchestration provides quality control and strategic direction
- The team evolves as needs and capabilities change
🎙️ 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
| Step | Action | Details |
|---|---|---|
| 1 | Map your ideal AI team structure |
• What capabilities do you need?
• Which specialized tools address each need?
• How will they work together?
|
| 2 | Audit current AI investments |
• Identify overlapping capabilities and identify gaps
• Measure actual results vs. licenses
|
| 3 | Reimagine core workflows |
• Don’t automate existing processes—reimagine them
• Start with high-value, repetitive tasks
• Seek guided experiences for non-technical users
|
| 4 | Establish balanced metrics |
• Include qualitative measures alongside time savings
• Track adoption, retention, and productivity
• Understand productivity leaks as insights
|
| 5 | Shift your organizational mindset |
• From controlling AI to orchestrating it
• From single-solution thinking to a portfolio approach
• From immediate ROI to transformation potential
|
📚 Resources
- Digital Skills Compass: Free assessment in 7 languages at skills.ai4sp.org
- AI ROI Calculator for the UK: Simulate potential returns at uk.roicalc.ai
- Workshops & Training: Book sessions for your team
- Complete Research: Request our detailed findings
Luis J. Salazar
Founder | AI4SP
Sources:
Our insights are based on +250 million 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.
