That’s an eye-catching title! This article discusses what it considers to be five myths about generative AI, see if you agree with them!

The article from Knowledge at Wharton (Five Myths About Generative AI That Leaders Should Know, written by Scott A. Snyder and Sophia Velastegui and available here), identifies (wait for it!) five myths about generative AI that the authors think you should know. Below are the five myths, with my comments as to whether I agree with them or not:

Myth #1: Gen AI Tools Are Available for Free or Minimal Cost

The authors discuss how, for enterprises with thousands of employees, the per user costs of GPT-4 ($20/month), Microsoft’s suite via Copilot ($30/month), or Google Gemini Advanced ($29/month) can really add up. That’s true; however, not every organization is a huge enterprise. Not to mention, there are many implementations of gen AI tools – including into platforms that organizations may already be using – and the costs can range widely for the use of those tools from negligible to expensive (many are still undetermined).

BOTTOM LINE: To use a lawyer’s favorite phrase, “it depends”. I don’t think you can classify this as a myth if it’s not false across the board.

Myth #2: AI Always Improves Human Performance

The authors point to a recent BCG study, which had mixed results on performance improvement with GenAI, including that “consultants using GPT-4 for business problem-solving showed a 23% decrease in performance compared to the control group, with less-experienced workers performing even worse.” But it’s what the authors say here that I think is key:

“The key factor for success when working with gen AI lies in training employees in essential soft skills such as critical thinking, judgment, empathy, and bias detection to effectively task models and evaluate their results.”

BOTTOM LINE: I couldn’t agree more. Gen AI isn’t guaranteed to improve human performance. Like any other technology, it’s a combination of people, process and technology that leads to performance improvements.

Myth #3: Building the Gen AI Model Is the Hardest Part of Implementation

The authors point out that “customizing pre-trained models — such as ChatGPT, Gemini, Claude, or Llama — to specific company use cases and datasets requires significant effort”. But they also note that “as much as 50% of the effort in successfully deploying new AI models goes beyond technical implementation — it involves transforming the way people work. This requires significant effort in changing management to ensure that employees are prepared for and receptive to the adoption of AI solutions.”

While I agree that transforming the way people work can be a significant cost for implementing GenAI (and often exceeds the cost of customizing the model), I think that’s not always true. There are a million workflows out there, and some are easier to integrate GenAI into than others.

BOTTOM LINE: Had they said “always” the hardest part of implementation, I would agree. Transforming the way people work is often the harder part, but not always.

Myth #4: You Can Wait and See How Gen AI Plays Out Before Making a Move

The authors note that “companies might be tempted to run a few pilots and adopt a wait-and-see approach before making broader investments”, but that history shows us that companies who wait find themselves caught off guard by disruption (using examples like JCPenney and Circuit City being “crushed” by Amazon and Nokia and RIM/BlackBerry faltering against the iPhone and app store model). I like the analogies in that they show how companies who failed to embrace new technologies quickly enough either failed outright or became largely irrelevant.

BOTTOM LINE: I agree that companies can’t wait – AI is continually evolving in terms of its capabilities. If you’re standing still, you’re falling behind.

Myth #5: Investing in Gen AI Will Automatically Give You a Competitive Advantage

The authors note that “as gen AI technologies become more widely accessible, competitors can quickly catch up [to early adopters] by using similar tools and approaches”. They add: “To maintain a sustainable advantage in the market, organizations must realize that merely deploying gen AI technology is not enough. They must continuously innovate, differentiate, and evolve their gen AI strategies to outpace competitors.” Well put.

BOTTOM LINE: Couldn’t agree more. In fact, I would go so far as to say the opposite is true: Failing to invest in GenAI will automatically put you at a competitive disadvantage!

Check out their article here for more analysis.

So, what do you think? Do you agree with all five myths about generative AI? Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by my employer, my partners or my clients. eDiscovery Today is made available solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscovery Today should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.