Why Every Business Needs AI Guidelines in 2023 | Entrepreneur

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What do Wells Fargo, Apple, iHeartRadio and Northrup Grumman have in common? Outside of being household names, they’ve heavily restricted the internal use of AI tools like Bard and ChatGPT. Some call it reactionary or short-sighted. Others — especially risk-averse business leaders — are silently applauding and taking notes.

So who’s right about AI use in the workplace? It depends. The exactly same generative AI tool could offer incredible benefits while also having use cases that are unethical and legally risky.

Taking an “it depends” approach requires an AI framework that your organization can use to assess the value, impact and risk of AI tools.

What is an AI framework?

An AI framework is a set of rules a company can align itself with as it determines potential use cases for AI within its businesses.

There are three main components to an AI framework businesses should consider:

The contextual pillars that you will use to assess the internal and external use of AI and its impact on your business
The business strategies, goals and objectives that AI could impact
How those strategies, goals and objectives intersect with your contextual pillars

To help you construct your own AI framework, we’ll quickly cover why each of these areas matters.

Step 1: Identify contextual pillars

There are several ways AI could impact your business. Identify which pillars are the most likely to matter to your business, then align your strategies, goals, and objectives around those pillars as you assess both impacts and use cases.

You might be wondering where to start. Check out Torrens University ‘s AI framework — it looks at the business implications of AI from an ethical standpoint. Not all of the recommended contextual pillars (or dimensions) will be relevant for your business, but the following should be relevant to almost any company:

Privacy
Fairness
Transparency
Diversity

How employees use or respond to AI tools could come with legal and ethical consequences, especially as it relates to privacy (information you share with AI or allow AI to access), fairness (known biases with AI, which we discuss later), transparency (allowing both employees and customers to know where and how AI is being used) and diversity (another area where known and inherent biases in AI could have negative consequences).

When most people think of data privacy, they think of personal data, but AI frameworks also need to consider corporate data. In April 2023, Samsung experienced a data leak because employees shared sensitive company information with Chat GPT. Samsung’s situation shows you why AI frameworks are essential and how the misuse of AI can impact internal stakeholders.

Step 2: Consider business strategy, goals and objectives

Let’s say your software development team has set a goal of hiring ten more developers. The applications start rolling in, and they’re all eerily perfect. After running the resumes and cover letters through an AI detector, you find out why: practically all your applicants are using AI to write their resumes and cover letters.

This situation will likely impact your hiring strategy. But it’s an external situation, so you’re left with learning how to handle AI in a way that could significantly impact the quality of your hiring.

Do you only hire individuals whose resumes aren’t AI written?
Do you ignore it and potentially hire someone who isn’t qualified?
Do you even use AI writing detectors in the future since they’re known for false positives?

Whether you like it or not, AI will impact the objectives, goals and strategies you have in place across your business. This is why you should identify the areas of your business where AI could have an impact, then align your framework to those areas.

Step 3: Identify how context and business strategies overlap with AI usage

Many companies are finding that AI is a minefield. Many of the dangers and benefits have been well known, even before the current explosion of generative AI tools like ChatGPT.

If you’re using AI, what would be the impact if your organization was liable for an unfortunate outcome resulting from inherent AI biases? That type of risk is why Amazon ditched an AI-driven recruiting tool in 2018 after it showed significant bias against female candidates.

Again, it depends. Conversely, studies show that AI can boost employee performance and productivity. That’s especially the case for new workers, where AI can help them learn their tasks much faster.

Your default may be to wait it out and see what happens. However, AI is becoming unavoidable. Statistica says, “Businesses that begin using AI early will find it easier to reap the benefits.”

Take a proactive approach right now by developing an AI framework. Standardizing your approach will significantly reduce your risks while giving you more flexibility to use emerging AI technologies in ways that positively influence your business.

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