The 9 Rights for Human–Agent Performance

AI agents are becoming part of everyday work, the result will be an inevitable uplift in productivity and performance, right? Well, there is no guarantee that this will happen.

Why not? Well, maybe because either (a) the technology or (b) the people using it fall short. Or perhaps both, specifically the integration of people and technology.

Leaders and teams in some of the world’s biggest and best organizations have been using a framework, called ‘the 9 Rights’ to optimize ways of working in their organizations. In particular it has helped them to cope with the shift to hybrid working and the drive for greater productivity and efficiency.

Increasingly, leaders are discovering how the framework can also be used to support human-agent performance.

Here is an overview of the framework – please contact us if you would like to explore it in action via our powerful 5 minute app.

An Agent-Enabled 9 Rights Framework

Here is a visual summary of the 9 Rights framework:

The work is not simply “automate tasks”; it is rethink workflows, roles, decision points, handoffs, and value creation. Moreover, training people on individual tools may have a short shelf life.

Rather we need to equip our leaders to ask: What problem are we solving, and where does AI genuinely improve the work? Hence the value of a framework, such as the 9 Rights.

Let’s explore some of the detail behind the 9 Rights Framework as it applies to working with AI:

The right people and agents, with the right roles and decision rights, doing the right work, in the right context, at the right time and in the right way, with the right resources, measures and incentives—turning inputs and activities into the right outputs, outcomes and longer-term impact.

Re-thinking Productivity and Performance

Output was traditionally the means of measuring productivity, in particular the quality and quantity of output. But AI is calling this approach into question.

The volume and speed of AI output continue to astonish. But when do the volume and speed of output become a problem rather than a solution? At what point does AI outpace people in terms of their ability to guide, control or even check the work? Take AI slop, for example.

Gotten any AI-generated slop from your colleagues recently? It adds to our work—and our frustration too. Recent research highlights how speedily generated but poorly curated AI output transfers rather than eliminates work. The result is a ‘shadow workload’ borne by those who must quietly clean things up.

However, if the way we measure productivity does not change, organizations may end up rewarding those who create more output with AI than they check. And checking AI output isn’t easy because it comes so fast and often gives the impression that it does not need to be checked. Moreover, multi-agent working means multitasking on steroids—and it can be exhausting.

But while the focus is on output, the process starts with the input. AI isn’t going to make our data-quality challenges disappear; rather, it can amplify them.

One of the biggest risks in the AI era is confusing activity with value. An agent may complete thousands of tasks and generate large volumes of output without improving business performance. That is why it is important to understand the full chain of results with respect to AI: inputs, activities, outputs, outcomes and impact.

AI productivity is not the amount of work produced by AI-enabled individuals. It is the additional value created by a well-designed human–AI system, says the HBR.

From AI Deployment to Performance Design

The 9 Rights shift the conversation from “Where can we deploy agents?” to a more important question:

How to design the conditions required for people and agents to perform well together?

That turns AI adoption from a technology initiative into a practical exercise in work design, governance, accountability and performance.

The 9 Rights framework—or 9+1, as the number of variables expands to reflect the complexity of the work—is part of a broader performance framework that includes a balanced scorecard, dynamic alignment and ways of interacting within teams. We are already beginning to see the impact of AI on these factors too.

Take collaboration, for example. Increasingly, executives are using AI not just to do the work, but also to do their thinking. They are using it as an adviser, counsellor and coach—often in preference to collaborating or having conversations with others.

Our Pitstop to Perform app enables leaders and teams to quickly assess performance regardless of stage of adoption of AI. Contact us to find out more.