Leadership Must Pick the Right Generative AI Wave

Do you feel like a surfer staring at endless waves as you contemplate the ocean of business leadership possibilities from generative AI?

You’re not alone. The latest iterations of generative AI, from ChatGPT to GitHub Copilot to Stable Diffusion and others, have leadership contemplating three options for their next move. You can 1. stick your head in the sand; 2. try to invest and capture value from every aspect of generative AI; or 3. insightfully select the right options that serve your customers, improve your operations and make best use of your personnel.

Unless you have unlimited capital and resources, I recommend Option 3.

The Pitfalls of Ignoring or Overinvesting in Generative AI

In Option 1, John notes that “My Dad started this business 40 years ago without generative AI. I’ve run it the last 20 years without generative AI, and I can run it another 30 years.”

That will lead to ruin. Eventually, the tide will come in. John’s business will drown. I give John 5 years max.

Option 2 looks enticing, and even I have to contain my excitement at the possibilities. A recent McKinsey analysis, “The economic potential of generative AI: The next productivity frontier,” examined 63 use cases and concluded that generative AI could add $2.6 trillion to $4.4 trillion in value annually. And generative AI can play in any sector – the McKinsey report detailed use cases in retail and consumer packaged goods, banking, pharmaceuticals and medical products.

But that wave likely leads to a wipeout as well.

Susan may think her organization can handle everything, but few enterprises can afford to hire enough people and throw enough money at the massive quantities of computational power required to train generative AI with the however many billions of parameters necessary to tailor it to every aspect of your operations. And today’s high interest rates preclude going into massive debt.

OpenAI spent a staggering amount of money to develop ChatGPT, and the world’s most famous large language model costs a bundle to operate. Estimates for OpenAI’s daily spend are all over the map, from $100,000 a day to $700,000 a day. And the company may have lost a staggering $540 million last year, including $89.31 million on staff.

Earlier this year, Microsoft confirmed it has invested billions in OpenAI. Unless Susan’s business can tap into some of the billions of dollars venture capital is pouring into generative AI, her enterprise will burn through its cash reserves before seeing enough benefits.

Focusing on High-Impact Areas

After all, if you look closely at the McKinsey report, 75% of generative AI’s total impact will come in customer operations, marketing and sales, software engineering, and research and development. The potential for generative AI in manufacturing and supply chain functions is much lower.

Supply chain and manufacturing still will benefit from AI, according to the report, but that benefit will come from “numerical and optimization applications that were the main value drivers for previous applications of AI.”

So Felicia, who picked Option 3 for her consumer packaged goods firm and concentrated on automating key functions in customer service, marketing and sales, will be the likely winner.

For leaders, the right strategy is to transform your business into an AI enterprise. The danger is staying onshore or trying to ride every wave you see. I think 50% of businesses will be like John, 40% like Susan and 10% like Felicia. Insightful Leaders will follow Felicia’s path and select the most lucrative generative AI waves to ride. Make sure your organization is on the right wave.