How to Achieve the Promise of Generative AI

Why Generative AI Pilots Fail

Generative AI is a powerful tool, but our ability to get significant value from it continues to be stymied. No killer apps have emerged. Enterprises have done countless pilots, yet very few get into production. Our research says somewhere between 70 and 90% fail. That’s a horrendous failure rate.

Why the Generative AI Pilots Fail

Generative AI is indeed a useful tool. The pilots fail at an astronomical rate because enterprises are not putting the tools to work properly. They don’t assign tasks with a meaningful and significant ROI.

Stop Searching for the Killer App

There is no big diamond in the ring. When there is one, it doesn’t seem justifiable given the enormous effort required to redo the digital core.

Where Generative AI HAS Made a Difference

The pilots that did go into production actually had compelling results. Studying these pilots demonstrates what it takes to get a disruptive and significant return from generative AI, the shorter commute.

Infusing AI into Business Processes

For example, generative AI has been successful in the audit function. Radically improving the audit process did not involve a killer app. Instead, the auditors reframed the problem. You get an investable process when you reframe the problem into: How do I infuse the tech stack with AI?

The Successful Generative AI Pilots

These companies purchased access to a CMM. They had to rearchitect their cloud systems so they produced more reliable data. This included moving their legacy mainframe systems into the cloud. They had to retire technical debt and close the white spaces between their different technology solutions so that the rest could work seamlessly.

The Three Towers of Work

The successful generative AI pilots received significant investment across three distinct towers.

• Technology. These companies purchased access to a CMM. They had to rearchitect their cloud systems so they produced more reliable data.
• Data. Their tech stack needed to get better and more reliable. This required data cleansing.
• Business Process Engineering. This is often the most overlooked element. The companies with successful pilots had to change their operations team. They had to alter how they interacted with the workflow.

Handsome Rewards When Done Right

Deloitte and Ernst & Young used generative AI to completely transform their audit function. For example, fraud detection substantially improved. They need fewer people to achieve far better results.

The Generative AI Journey Is Neither Short Nor Cheap

These journeys are neither short nor cheap. Currently, there’s a standard one-to-one ratio between implementing an app and the effort for tech services to implement it. These AI implementations are one-to-12:50 to 20. Much more investment in the organization is required.

Conclusion

In my opinion, you should not be looking for the 100 places to add AI across all your business functions. Instead, select one or two areas where you think generative AI can make a substantial difference…and be willing to make the sizable investment across all three towers of work to actually get the benefits ChapGTP originally promised. And stop searching for that killer app!

FAQs

Q: What is the main challenge with generative AI pilots?
A: The main challenge is that enterprises are not putting the tools to work properly, leading to a high failure rate.

Q: What is the key to successful generative AI implementation?
A: The key is to reframe the problem and focus on infusing AI into business processes, not just searching for a killer app.

Q: What are the three towers of work that are required for successful generative AI implementation?
A: The three towers are technology, data, and business process engineering.

Q: Why is the generative AI journey neither short nor cheap?
A: The journey requires significant investment across all three towers of work, leading to a longer and more costly process.