What to Know About Generative AI in Corporate Workplaces
Opinions expressed by Entrepreneur contributors are their own.
Companies of all sizes have become accustomed to using predictive AI to achieve a range of outcomes, such as anticipating risk, developing new products and forecasting buying behaviors. However, many enterprises are struggling to figure out how to realistically incorporate generative AI into their operations. It poses many advantages, of course, but it’s also fraught with fear and uncertainty.
Perhaps because of that, only 12% of IT decision-makers recently surveyed by Enterprise Technology Research, as reported by the Wall Street Journal, said they plan to use OpenAI technology — creator of the most popular generative AI tool, ChatGPT. Yet, the global generative AI market is expected to reach $111 billion by 2030, per Acumen Research and Consulting.
With all the buzz around it and advancements in the technology, there’s little doubt that generative AI is going to be an asset across industries as widespread as healthcare, insurance and logistics. However, it’s a newer solution. As such, businesses and their leadership teams are only starting to determine how best to leverage it to its fullest — and safest — degree.
This leaves corporate leaders at a crossroads. Many want to bring generative AI solutions in-house. Some — particularly those at enterprise-level corporations — have even put a budget behind this desire. They want to access this emerging technology in the most efficient ways possible. I believe the easiest way to make that happen is for businesses to join forces with AI-based startups.
Related: The Secret to How Businesses Can Fully Harness the Power of AI
Attributes, advantages and areas of concern around generative AI
Because of its continual learning capacity, generative AI might well be described as creative AI. That is, it can create content that didn’t exist before. While that’s exciting, it’s brought about much discussion on how to handle its downsides, such as inaccuracies. Generative AI isn’t able to identify or self-correct when it gets things wrong or even pushes out content that’s inappropriate or biased.
Another overarching problem with generative AI concerns data. Because it’s trained on vast amounts of data, it may produce content that violates intellectual property rights. What is the law around generative AI content that leans heavily on existing content? It’s a fine line between unique expression and plagiarism, and the laws haven’t quite caught up to where that line lies.
In addition, vertical, industry-specific solutions with unique data libraries, rather than general generative AI models, provide the most applicable answers but can be costly. Accessing the vast amounts of data needed to produce accurate insights is expensive, and the computing power required to do so is highly demanding and unsustainable in terms of expense. However, Microsoft seems to be exploring collaborations with AMD to lower computing costs, and potential software technologies could reduce computing consumption.
Of course, generative AI is far from being all negatives and no positives. Due to its transformative nature as a technology, it could become a tool for sector disruption, helping companies save time and resources and improve their decision-making.
In my view, I see generative AI as a value-added tool that’s only going to become more capable and intelligent. New models are emerging that could address the issues of cost by using smaller data sets, but it will take a few years for new models to evolve to a stage where they are affordable and user-friendly enough for practical applications. At present, generative AI is most effective when used in conjunction with human input. Human intervention fosters consideration of different perspectives and minimizes ethical and flawed data risks.
Take ChatGPT, for example. The quality of its output and answers depends on the quality of the input and human intelligence involved. To get high-quality answers, content and results from ChatGPT, human users must take active roles in the process to create feedback loops. Otherwise, ChatGPT (and similar generative AI solutions) is interesting but not reliable or holistically useful.
Related: The Top Fears and Dangers of Generative AI — and What to Do About Them
Collaboration: Key to bringing generative AI solutions into corporate settings
Collaboration between startups and corporate enterprises can be the game-changing factor across the entire generative AI landscape. Not only do partnerships allow founders to explore various options and even work with different model providers, but they also lower the barriers for companies to access generative AI. It also produces more interest in open-source model ecosystems. With open-source contributions, there can be a collective and effective effort to push generative AI’s boundaries, challenge dominant AI players and drive down costs. Ultimately, it fuels a positive
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