How to Implement AI at Your Company

Everyone is talking about the impact of AI, but how to best use it at your company, especially in a way that doesn’t make your employees feel replaceable, can be tricky.

May 2, 2023
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3
min read

Photo by Hitesh Choudhary on Unsplash

Wharton professor Ethan Mollick says that companies can’t wait to try out ChatGPT. I have a lot of concerns about the implications of AI (see The Path to Hell Is Paved with AI and ChatGPT is Just a Calculator . . . Crossed with a Nuclear Reactor), but for better or worse he’s right. Companies are going to be implementing AI (e.g., ChatGPT, Microsoft copilot) and you can’t be left behind if it is a game changer. Here’s how to begin to experiment with it at your company in a cost-effective way.

First, understand that there are many risks and unknowns, a few of which are listed below.

  • Copyright and other IP issues abound.
  • Is it even legal to train LLMs (large language models) on the content of others? (When the copyright was given, this was not an expected use.)
  • What happens to the data you enter? What are the privacy considerations for your proprietary data? Can the AI use that as part of its model in the future?
  • How do you know if the AI is giving wrong information?
  • AI might not have the same ethical boundaries you do.

Creating some online ads probably won’t touch many of these concerns. On the other hand, analysis of a company report with customer data in it very well could.

With that in mind, I’d recommend the following steps.

1. Create an AI trial board

This should include at least one person from each department. If you don’t have a legal department you may want to run your plans by legal counsel. This is a trial, so you don’t want too much overhead, but there do need to be guardrails given the concerns above. Use your judgment, e.g., regulated industries like financial or health services should proceed with more caution than say an online parody website.  

Example of limitations might be:

  • Don’t include any customer data in requests.
  • All AI output can only be used internally; or any output must be approved by a manager before being shared externally.
  • Review the output for correctness before using; when you use it you are responsible for what it says even if it was generated by the AI.

2. Create basic training

Not everyone will know what these tools are. Create a basic training video explaining how to access them (use them)–but not how to make use of them. This is an important distinction. If you give examples of how to use them, you’ll bias people towards using the tools in a certain way. You want to provide the minimum guidance possible for using them. Show the mechanics but don’t give ideas about ways it can be used. Nevertheless, be sure to be explicit about the limitations of use.

3. Commit to companywide, dedicated time

Pick a week and say to the whole company, "Over the next four weeks you are expected to spend X hours trying to use <tool>.” I’d recommend around 8-10 hours (which is about 2 hours a week). Some may choose to spend a few hours one afternoon; others may try it at different times throughout the week. The different types of engagement will lead to different innovation opportunities. Setting specific time requirements and recognizing that this comes at the cost of other work, shows that you are serious about the employees trying it. The time boxing limits applicability.

4.Collect results

Have everyone share examples in some central location, such as a shared folder. It may be the output from the AI, or a writeup of what was done and why it was helpful, or maybe a video of how it was being used. These results will all be reviewed.

5. Review and Propose

The AI trial board can cherry pick good ideas and create proposals that individual teams can pilot over the coming months. Importantly these are proposals; they should be reviewed more carefully given the risks above.

Whether you try the process above or a different version the key is to find balance between the need to not be left behind but also to proceed with caution.

There’s a final step. Suppose you do discover there’s a safe way to significantly increase the productivity of a team. It may be tempting to lay off people given the productivity boost. Resist this impulse. No doubt AI will have an impact on labor down the road, but you don’t want to make sudden decisions for two reasons.

First, it sends a signal to the rest of the company that they should be cautious about finding significant productivity improvements. That’s the opposite of what you want. Second, productivity improvements in one area often lead to new work in another. Over the coming months see what the team does with the newfound extra capacity. Even if you find you are now overstaffed in this area, before laying people off, see if other departments need help; you’ve got employees who already know your business, don’t just cast them aside, especially given the coming rapid changes AI may bring across other departments. Doing so not only shortsells your future but it sends a signal for employees to keep things inefficient.

Like electricity, plastics, computers, and the internet before, AI offers the potential for significant productivity changes to your organization. Deliberate experimentation is important since no one, not even the inventors of the tool, can fully predict what impact it may have. Equally important is to work with your employees and be clear that this tool is there to help them, not replace them. The nature of their work may change, and that can be scary, so it’s important to send clear signals that while the job may change, their employment will not and that the company will upskill and retrain as needed.

By
Mark A. Herschberg
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