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Global artificial intelligence investment surged from $12.75 million in 2015 to $93.5 billion in 2021, and the market is projected to reach $422.37 billion by 2028. Digital transformation projects and the expansion of public cloud computing will continue to drive this growth, along with net-new AI capabilities.
AI’s focus now turns toward Generative AI, such as ChatGPT. Generative AI refers to machine learning algorithms that can form new meaning from unstructured or structured input, such as text, images, audio, video, code, and other forms of content.
Generative AI tools include DeepMind’s Alpha Code (GoogleLab), OpenAI’s ChatGPT, GPT-3.5, DALL-E, MidJourney, Jasper, and Stable Diffusion. This list will grow as more venture capital money is pushed into this space.
Indeed, over $2B has already been invested in Generative AI, which is just a type of AI, up 425% since 2020, according to the Financial Times. This makes up only a fraction of today’s AI market; Generative AI presently accounts for only 1% of all data produced.
But Generative AI is growing. Gartner predicts that it will account for 10% of all data produced by 2025. “Data produced” means valuable output from an AI engine that can be leveraged for any number of business purposes.
Also see: Top AI Software
Does Generative AI Produce Business Value?
However, many are asking the right question at this point: Does Generative AI produce business value?
First, let’s understand that this technology is white hot. Everyone’s weighing in on the subject, including eWeek, McKinsey, Microsoft, Accenture The New York Times, and OpenAI. This feels like cloud computing in 2008 when the technology first started to pick up steam and a great deal of hype.
Yes, the hype is beneficial for an emerging technology market. Or for this derivative of existing technology. AI as a concept has been around since the 1950s. Even though AI became much better over the years, and certainly much cheaper, the primary concept of “AI” remains the same.
However, previous iterations of AI could not generate “new meaning” from existing data, and net-new creativity was not one of its strong suits. But that’s where Generative AI can shine.
In dealing with the business use cases for Generative AI, it’s essential to understand the realities beyond the hype. Even if the AI system can find new meaning from a large amount of meaningless data, that does not automatically justify its use as a functional business solution.
An often-cited application of Generative AI is its ability to create original works of art, music, and literature. If you’re interested, you can find stories that have been written by Generative AI systems. Today I suspect Ph.D. candidates are more interested in these types of applications than most businesses.
Also see: AI vs. ML: Artificial Intelligence and Machine Learning
The Many Possibilities of Generative AI
But let’s open our minds and consider the strengths that Generative AI can bring.
Design Applications
Generative AI product design has the potential to create net-new designs that could have tremendous value, in part because Generative AI can consider a design’s functionality in conjunction with current market demands. As we’ve discovered over the years, many businesses find it hard to do both.
Marketing
Marketing also comes to mind. Generative AI can create and direct advertisements and marketing campaigns focusing on desired outcomes. For example, tell your Generative AI system that you want sales of a specific product to increase in Europe by 20 percent within a specific timeframe. This is not the first time that marketing has dabbled in AI magic, but Generative AI’s ability to consider many more data points and create yet-to-be-defined innovative answers could make Generative AI’s ultimate business case.
However, we also must consider the impact of everyone having fully optimized marketing, which by itself will tend to change markets based upon the changing response to an intelligently targeted marketplace. In other words, we will get better at creating demand, but the market will adjust its behavior based on our ability to do better marketing. It’s a downside of an upside.
Healthcare
Healthcare presents another high-value use case. While AI is no stranger to diagnostic support and clinical systems, today’s typical system only looks at a few dimensions, which makes them difficult to build. For example, they understand that a particular medication may solve a chronic problem such as high blood pressure, but they don’t consider the health downsides of that drug’s long-term use on a specific body type.
An AI system that evaluates massive amounts of medical data would be invaluable, such as health outcome data for millions of patients over the last 40 years. The traditional AI knowledge engines you would need to set up are complex and challenging to maintain. Generative AI has the potential to make setup and maintenance easier and the resulting knowledge much more valuable and impactful.
There are many other good use cases. For instance, after watching pre-built supply chain management systems fail during the pandemic, I know that holistic intelligence provided by Generative AI has the potential to solve many supply chain problems. But not all.
Beware of Over-Hype
We must remember the core issue of over-hyping anything. We then expect too much magical value to fall from the magic technology tree. I’ve read hundreds of articles that call Generative AI the next Big Thing, which it won’t be.
At best, it’s a solid step in the right direction with strong enabling technology that will provide a foundation for good business systems. Unless you have an unlimited R&D budget with no expectations of an ROI, you still need a well-understood business case to justify its use.
Also see: The Future of Artificial Intelligence