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Implementing AI: The initial steps

ChatGPT was launched in 2022. In the beginning, it was mainly individual employees who adopted the tool, sometimes secretly using the tool to enhance their own productivity.

These days, surveys show that large firms are starting to adopt ChatGPT and other similar tools in a more official and systematic fashion.

Where should they begin? How do you estimate the potential gains from implementing AI in different areas, and, more broadly, gain a better understanding of the organizational implications?

This is a question we have been discussing in Reconfig over the last few months internally as well as with some of our partners (consultants who are helping clients implement AI) and external experts.

As a result, we recently launched a new set of features that complement the existing capability of the tool to support organization design processes.

The new features are based on three key insights.

 

Getting the unit of analysis correct is critical

The first relates to the “unit of analysis.” We see that some consultants start with an analysis of jobs (or “job families”) to estimate the potential productivity gains from automation or augmentation.

Instead, we focus on activities or work processes. This is because a single job may consist of a number of different activities, only some of which can be automated, and most of those can only be partially automated. Or vice versa, the automation of a single activity may have an impact on multiple jobs. So Reconfig’s key unit of analysis is the activity with a drill-down into specific tasks within the activity.

 

Internal company data are needed to get actionable insights

Second, to estimate the potential gains, there are two main approaches. The first is to look at external data (There are studies that estimate the augmentation and automation potential of AI for generic job categories and/or activities.) The other approach is to use internal data about the time that is spent on different activities and derive scenarios for productivity gains given different levels of augmentation and automation.

We decided to rely on both: First, we use large language models (LLM) to analyse the activities of the organization and produce well reasoned estimates for the likely percentage improvement of generative AI in each activity. These estimates should be reviewed by managers and internal experts and adjusted based on their knowledge and experience.

Then, Reconfig combines these estimates with the activity profiles of the employees, which state how much time each employee is spending on each activity. These data are then used to provide a detailed breakdown of the potential productivity gain from generative AI per employee, per team and for the organization as a whole.

 

When to move from generative AI tools to agents

Third, organizations will need to move beyond the generic language models such as ChatGPT to realize the potential of AI. We agree with industry observers that the next phase will consist of building domain-specific systems that in some cases will fully automate processes. The most typical example is an AI agent that is trained specifically for a clearly defined and targeted role in the organization.

You can think of an AI Agent as having a really efficient remote employee that you can only reach digitally. You ask your agent to perform a task, it gets working on it and will query you when input or verification is needed. You then sign off on the final result before the task is completed and you can use the result. In this manner, the agents could even be placed on your organization chart as a valuable part of your team.

 

How we can help

We would be happy to give you a demo. During the demo, we will show you how you can use Reconfig to get tailored recommendations for implementing AI agents and to evaluate the effects of the changes at the individual, team and organizational level.