Global investment in artificial intelligence grew from $12.75 million in 2015 to $93.5 billion in 2021, and the market is forecast to reach $422.37 billion by 2028. Digital transformation projects and the spread of public cloud computing will continue to drive this growth, along with net new AI capabilities.
The AI focus is now turning to 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 pours into this space.
Indeed, more than $2 billion has already been invested in Generative AI, which is just one type of AI, a 425% increase since 2020, according to Financial Times. This makes up only a fraction of today’s AI market; Generative artificial intelligence currently accounts for only 1% of all generated data.
But Generative AI is growing. Gartner predicts that it will account for 10% of all generated data by 2025. “Generated data” means valuable output from an AI engine that can be leveraged for a number of business purposes.
Also see: Best AI Software
Does generative artificial intelligence create business value?
However, many are now asking the real question: Does Generative AI produce business value?
First, let’s understand that this technology is red hot. Everyone is covering this topic, including eWeek, McKinsey, Microsoft, Accenture, The New York Times, and OpenAI. This looks like cloud computing in 2008 when the technology was just starting to take off and there was a lot of hype.
Yes, hype is good for an emerging tech market. Or for this derivative of existing technology. AI as a concept has been around since the 1950s. Although artificial intelligence has gotten 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 the network’s new creativity was not one of its strong points. But that’s where Generative AI can shine.
In solving Generative AI business use cases, it is essential to understand the reality beyond the hype. Even if an AI system can find new meaning from a large amount of meaningless data, this does not automatically justify its use as a functional business solution.
An often mentioned application of Generative AI is its ability to create original works of art, music and literature. If you are interested, you can find stories written by Generative AI systems. Today I have doubts about Ph.D. candidates are more interested in these types of applications than most companies.
Also see: AI vs ML: Artificial Intelligence and Machine Learning
The many possibilities of generative artificial intelligence
But let’s open our minds and consider the strengths that Generative AI can bring.
Generative AI product design has the potential to create entirely new designs that could have enormous value, in part because Generative AI can consider design functionality combined with current market demands. As we’ve discovered over the years, many companies find it difficult to do both.
Marketing also comes to mind. Generative artificial intelligence can create and direct advertisements and marketing campaigns focused on desired results. For example, tell your Generative AI system that you want sales of a certain product to increase in Europe by 20 percent within a certain time frame. This isn’t the first time marketing has dabbled in the magic of AI, but Generative AI’s ability to take into account many more data points and create innovative responses that have yet to be defined could make Generative AI the ultimate business case.
However, we also have to consider the impact of everyone having fully optimized marketing, which will itself tend to change the market based on the changing response to the intelligent target market. In other words, we will get better at creating demand, but the market will adjust its behavior based on our ability to market better. That’s the downside of the advantage.
Health care represents another high-value case. Although artificial intelligence is no stranger to diagnostic support and clinical systems, today’s typical system only looks at a few dimensions, making them difficult to build. For example, they understand that a certain drug can solve a chronic problem such as high blood pressure, but they do not consider the health disadvantages of long-term use of that drug on a certain body type.
An AI system that evaluates vast amounts of medical data would be invaluable, such as health outcomes data for millions of patients over the past 40 years. The traditional AI knowledge mechanisms you should deploy are complex and demanding to maintain. Generative AI has the potential to make deployment and maintenance easier, and the resulting knowledge much more valuable and impactful.
There are many other good use cases. For example, after watching pre-built supply chain management systems fail during the pandemic, I know that the holistic intelligence provided by Generative AI has the potential to solve many supply chain problems. But not all.
Beware of excessive advertising
We must remember the key issue of over-advertising anything. Then we expect too much magical value to fall from the magical technology tree. I’ve read hundreds of articles calling Generative AI the next big thing, which it won’t be.
At best, it’s a solid step in the right direction with strong technology that will provide the foundation for good business systems. Unless you have an unlimited R&D budget with no expectation of ROI, you still need a well-understood business case to justify its use.
Also see: The Future of Artificial Intelligence