While AI pilots are skyrocketing across organizations, the return on those investments remains underwhelming. According to MIT NANDA’s State of AI in Business 2025 study, 95% of organizations are getting zero return from their AI pilots. The 5% that do succeed share one defining trait: They integrate AI deeply into high-value workflows.
That’s where most companies are falling short. It’s not that people aren’t using AI; if anything, they’re using it far more than leaders realize. McKinsey’s Superagency in the Workplace report found that employees use AI tools nearly three times more frequently than their managers think. The problem is where and how that AI use happens. Most employees rely on generic, consumer-grade chatbots that live outside their normal workflows. They have to switch tabs, copy and paste context, and manually “ask” for help.
This break in workflow is the first productivity killer. The second is that these standalone tools have no understanding of what you’re working on. They’re disconnected from your systems, your data, and your goals. Without access to the right context—whether that’s a customer note in Salesforce, a relevant Slack message, or a previous project document—AI can’t deliver guidance that’s tailored, accurate, or actionable. The result is a lot of AI activity, but very little impact.
AI that drives real productivity looks different. It is embedded into the flow of work, where it can draw from real context and act in sync with the tools people already use. When AI works where people already work, it eliminates friction and compounds productivity instead of fragmenting it.
We’ve seen this pattern before. Superhuman Mail was built on the recognition that email was the biggest productivity problem hiding in plain sight. We spend more time in email than in any other work app. Despite that investment, we often reply late or miss messages entirely, which slows down deals, deadlines, and decisions. The issue wasn’t effort; it was friction. Email hadn’t evolved in decades, and professionals were losing hours every day to context switching and overload. We solved that by rebuilding email to eliminate the time and focus tax, not by asking people to change how they worked.
The same dynamic is playing out with AI today. AI isn’t struggling to drive results because it lacks enthusiasm or potential; it’s because it’s sitting outside of how people actually work.
2026 forecast
The companies that will see measurable impact from AI aren’t the ones just mandating more AI use. They’re the ones integrating it into the natural flow of work, meeting people where they already work instead of asking them to work around the AI.
The biggest friction today is that employees have to remember to use AI, and know how to use it correctly. They have to choose which chatbot to go to, craft a good prompt, and then move the output back into their real workflow. This “AI detour” model limits impact. The first wave of value will come from AI-native productivity platforms that are ubiquitous, proactive, and connected. Real productivity will come from AI that works everywhere you do, anticipates needs without being asked, and understands context across your data and systems.
Ubiquity is important because it eliminates the “AI detour” model by integrating AI into where employees already work. But ubiquity is just the beginning. The real transformation will come from proactive AI: agents that help without being asked. Instead of waiting for prompts, AI will know what you need and when you need it, drawing on signals from your tools, habits, and preferences.
Imagine getting a Slack message from your manager asking you to schedule a QBR. Before you can even open your calendar, your AI surfaces available times, recent sales metrics from Salesforce, and last quarter’s deck. Or imagine opening your inbox in the morning to find every email you’ve received already paired with a draft reply, some even sent automatically once you’ve built enough trust in the system. It’s the same principle that made Grammarly so powerful: Instead of waiting for you to ask for help, Grammarly proactively improves what’s in front of you. Extending that principle across every workflow through ubiquitous, proactive, integrated AI-native workflows will transform isolated AI activity into meaningful, measurable impact.
Action items for business leaders
Turning zero-return AI pilots into AI programs with measurable impact requires more than enthusiasm; it demands thoughtful AI integration. Leaders should focus on reducing friction, building trust, and integrating AI where work already happens.
- Build toward ubiquity. Don’t give employees more tools to visit; instead, bring AI to where they already work. Identify how they use generic chatbots today and integrate that value into existing systems. Choose AI-native tools that reduce context switching and keep teams in flow.
- Prioritize proactivity. AI’s value shouldn’t depend on how well someone can write a prompt. Choose tools that guide employees, where prompts enhance the experience rather than unlock it, and surface help when it’s needed.
- Connect the context. AI shouldn’t create new silos but break down old ones. Link tools so AI can draw from shared systems like CRMs and project trackers and deliver guidance rooted in real organizational knowledge.
- Redefine AI readiness. Go beyond prompt training. Equip employees with AI-native tools embedded in their daily workflows and teach them how to collaborate with these systems naturally and impactfully.
Integration is where AI’s promise becomes performance. Once AI lives inside the flow of work, it lays the groundwork for rethinking the very workflows that power modern organizations.
This is just one trend shaping the foundation of AI-native work. Explore all three in the 2026 AI Shortlist: 3 Trends Defining the Next Era of AI-Native Productivity.
