As with every previous AI era, the benefits promised to the enterprise from the application of generative AI will likely be lower than those that will be achieved. To succeed, enterprises must avoid the pitfalls of the previous AI eras. Over my 40-year career in enterprise AI, I understood the types of and reasons for these difficulties and what enterprises must do to avoid repeating them as they apply generative AI. I share them below.
Over the past forty years, and prior to the introduction of Large Language Models (LLMs), AI went through three eras:
1. Expert Systems era in the 80s,
2. Machine Learning era in the 90s,
3. Deep Learning era that started in 2006.
During the first era, I was developing AI technology for the enterprise. During the second era, I was running organizations and startups that offered machine learning products. And for the past twenty years, I’ve been funding private companies that create cutting-edge enterprise AI solutions. The hype about generative AI is growing dangerously. You are actively experimenting with generative AI because you see it as an enabler to higher employee productivity and cost savings in areas such as customer support, marketing and sales, office operations, design, and engineering. But you must understand the pitfalls from the previous eras to successfully harness and deliver generative AI’s enterprise potential.
Each past AI era produced important successes. Digital Equipment Corporation’s XCON expert system was a success of the Expert Systems era, HNC’s Falcon system was a success of the Machine Learning era, and DeepMind’s (Google) AlphaFold was a success of the Deep Learning era. But each of these eras was also associated with a hype cycle. The hype cycles associated with the first two were followed by multiyear “winters” that were caused by underachieving what had been promised. The underachievement took several forms. In some cases, successful prototypes that addressed important problems could not scale to production-grade systems. In others, promised features proved prohibitively expensive to develop, or could not even be developed regardless of cost. And in many cases AI-based solutions were developed successfully by the enterprise itself or third parties but for problems that were unimportant.
