Leaders are looking to productivity as the most immediate return on their AI investments. While there are pockets of progress, the impact hasn’t scaled. In some cases, the opposite is true. This is the AI productivity paradox: leaders expect AI to accelerate performance, yet people often feel busier, workflows are more fragmented, and the quality of output declines.
AI at work today is both overused and underused. Many people rely on AI for quick wins like summarizing, polishing, or generating slides from text. And those use cases generally work well. The problem is that they’re not the most valuable applications of AI, and they often create more content for others to consume—content that someone else will later use AI to summarize to digest. Meanwhile, higher-value use cases often fail because AI lacks the necessary context to move work forward, or the prompting isn’t skillful enough to deliver the right results. Over time, the need for elaborate prompting will diminish, but the importance of giving AI the right context will only grow.
Consider a familiar pattern: someone asks AI to “turn these bullets into a proposal,” then sends that output to their team. The next person asks AI to “summarize the key points” so they can skim the information. Both steps might feel efficient, but they don’t actually move the work forward. The AI-generated expansion adds volume without adding clarity. The summary compresses it back down, introducing even more signal loss along the way. The result is more content, more steps, and more work for everyone involved. These kinds of “quick wins” aren’t always wins. When AI is used simply because it’s easy, it often adds friction instead of value.
This imbalance has left most workplaces stuck in the messy middle. AI is in the mix, but not yet driving impact. People are using AI to make work faster, not better. To move work forward, leaders need to rethink how their teams use AI, from a tool that produces more to a partner that understands more.
2026 forecast
If people keep using AI only for quick wins, workplaces will face a new kind of productivity crisis. Words, documents, and messages will multiply across already overloaded channels, but the content inside them will become increasingly hollow. People will find the knowledge base article they were looking for, only to realize it’s beautifully formatted fluff. The more AI fills our systems with low-quality content, the harder it becomes to find the information that actually matters.
The future of productivity and the foundation of AI-native work depends on AI that truly understands the work it supports. AI must be built into the flow of work, with access to the organization’s knowledge bases, data, documents, and project trackers so that it understands its goals, priorities, and audiences. When AI carries that context forward, it can move beyond one-off task support to offer real partnership, helping people analyze, strategize, think more deeply, and make creative, well-informed decisions.
Right now, people must manually provide AI with context by defining tasks and goals, supplying background information, and providing the necessary nuance for accurate results. Doing this well requires translating organizational goals and context into clear direction for AI. It’s a shift from prompt engineering to goal engineering, where people focus on intent, outcomes, and constraints to get higher-quality results. But in the near future, AI-native tools that are deeply connected across tools and workflows will start to relieve this burden by bringing context to the person rather than the other way around. These AI systems will already understand the organization, remember the project, know what information matters most, and proactively offer support instead of waiting to be asked.
When that happens, AI stops crowding the workplace with more content and starts being used for higher-value work, such as deeper thinking, sharper communication, greater creativity, and better decision-making. The result isn’t just faster output; it’s smarter, more impactful work that moves the whole organization forward.
Action items for business leaders
To solve for the AI productivity paradox, organizations need to focus on context, not just capability. This means equipping people with the right training and tools to create higher-quality, more meaningful work.
- Find the gaps where AI is underused. Help people move beyond surface-level tasks and identify where AI could add strategic value by supporting innovation, critical thinking, and creative problem-solving.
- Train people to guide AI effectively. Help teams build goal-engineering skills. Train them on how to define problems clearly, provide relevant context, and articulate intent, desired outcomes, and constraints. AI’s value depends on the quality of the information it’s given and the clarity of the goals it’s working toward.
- Invest in AI that works with your organizational knowledge and context. Choose systems designed to integrate with your company’s data, tools, and workflows so outputs are factual, relevant, and aligned with real work. Generic AI doesn’t just produce generic results; it often produces wrong ones.
- Refocus productivity goals. Measure success not by the volume of output but by accomplishing real business goals. True productivity means less noise, clearer thinking, and a job well done.
When context and clarity meet smarter, native tools, AI stops contributing to the productivity paradox and starts solving it.
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.
