AI Is Making Everyone More Productive. Who Is Coordinating All the Work?

No surprise that AI is making it easier for individuals to get more done. Employees can research, analyse information, produce content, develop software and complete tasks that previously required specialist support or collaboration.

But greater individual productivity doesn’t necessarily make an organisation more effective. More work is being produced and decisions are being made faster, yet the structure that keep teams aligned haven’t necessarily kept pace. The challenge is making sure that additional activity contributes to the same priorities rather than creating more work to coordinate later.

More Individual Output Doesn’t Mean Better Coordination

For most organisations, work depends on people understanding what others are doing, where responsibilities sit and which priorities matter. AI changes that balance. Someone who previously needed input from a designer, developer or analyst can now make progress independently.

That’s obviously valuable, but it also creates opportunities for work to overlap, decisions to conflict and teams to move in different directions without realising it. A marketing team might develop a new customer experience while a product team explores something similar. Different departments might build AI-assisted workflows around the same problem, using different information and assumptions.

Each team may be working efficiently, but the organisation could end up duplicating effort, creating inconsistent experiences or investing in solutions that don’t work well together.

When Roles Overlap, Who Owns the Decisions?

AI is making the boundaries between traditional roles less distinct. People can work across disciplines more easily, using AI to support tasks they might previously have handed to specialists.

That can improve collaboration, but it also raises questions about ownership. If more people can design a workflow, build a prototype or analyse information, who decides which approach should be taken forward? Who checks the quality of the work and makes sure it fits the wider business?

This is particularly relevant to AI-native product strategy. As we explored in What Happens When AI Can Build the Digital Product?, making something easier to produce doesn’t automatically make it worth producing. The same applies within an organisation: more people being able to do more work makes clear decision-making and accountability even more important.

When Work Happens Without the Conversation

There’s another consequence of employees becoming more independent. Some of the everyday interactions that help managers understand what’s happening may become less frequent.

An employee who was uncertain might once have asked their manager for clarification. Someone struggling with a problem might have involved a colleague. Those interactions weren’t always efficient, but they could reveal unclear instructions, competing priorities or assumptions that needed challenging.

Now employees can increasingly turn to AI for answers and make progress without involving anyone else. That can save time and help people work independently, but it can also mean managers see more completed work without knowing where people encountered difficulties or which assumptions shaped their decisions.

The answer isn’t to bring back unnecessary check-ins or require approval for every decision. It’s to maintain enough visibility to identify problems early, share useful learning and make sure individual work remains aligned with wider priorities.

AI Makes Organisational Alignment More Important

Businesses should benefit from people being able to work more independently and move through routine tasks faster. But that autonomy needs to sit within a clear understanding of what the organisation is trying to achieve, who owns key decisions and how work across teams fits together.

This applies to AI-native product design and AI-native product development, where faster experimentation still depends on consistent decisions about the product being built. Teams need to know which problems they’re solving, how their work connects to other parts of the product and who is responsible for deciding what happens next.

It also reflects a wider challenge for AI-native businesses. As we’ve discussed in An AI-Native Business Needs More Than Just AI Tools, the value of AI depends on how it changes the way a business operates, not simply how widely the technology is adopted.

AI can reduce the effort required to complete individual tasks, but it doesn’t remove the need for shared priorities, clear ownership and good decisions. As employees become more capable of working independently, organisations need to make sure that independence translates into progress towards common goals.

The advantage won’t come from maximising how much work each person can produce. It will come from helping people make better decisions, avoid duplicated effort and direct their increased capacity towards the work that matters most.