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Businesses of all sizes have grown accustomed to using predictive AI to accomplish a variety of things, such as anticipating risk, developing new products, and predicting buying behavior. However, many companies struggle to figure out how to realistically incorporate generative AI into their operations. Of course it has many advantages, but it is also fraught with fear and uncertainty.
Perhaps for this reason, 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. However, according to Acumen Research and Consulting, the global market for generative AI is projected to reach $111 billion by 2030.
With all the hype surrounding it and technological advances, there is no doubt that generative AI will become an asset in widespread industries such as healthcare, insurance and logistics. However, it’s a newer solution. Therefore, companies and their management teams are just starting to determine how best to make the most of it and in the most secure way.
This leaves business leaders at a crossroads. Many want to bring Generative AI solutions in-house. Some, particularly those at the corporate level, have even set aside a budget to support this desire. They want to access this emerging technology as efficiently as possible. I believe the easiest way to make this happen is for companies to join forces with AI-powered startups.
Related: The secret to how businesses can fully harness the power of AI
Generative AI attributes, benefits, and areas of concern
Because of its ability to learn continuously, generative AI could be described as creative AI. That is, it can create content that didn’t exist before. While it’s exciting, it’s given rise to a lot of discussion about how to handle its downsides, such as inaccuracies. Generative AI is unable to identify or self-correct when something is wrong or even eliminate inappropriate or distorted content.
Another general problem with generative AI is data. Because it is trained on large amounts of data, it could produce content that infringes intellectual property rights. What is the Generative AI content law that relies heavily on existing content? The line between unique expression and plagiarism is a thin one, and laws haven’t quite caught up to that line yet.
Additionally, vertical and industry-specific solutions with unique data libraries, rather than general generative AI models, provide the most applicable answers but can be costly. Accessing the large amounts of data needed to produce accurate insights is expensive, and the computing power required to do so is very demanding and unaffordable. However, Microsoft appears to be exploring partnerships with AMD to reduce computing costs, and potential software technologies could reduce computing consumption.
Of course, generative AI is far from all bad and none good. Thanks to its transformative nature as a technology, it could become an industry revolutionizing tool, helping companies save time and resources and improve decision-making.
From my point of view, I see Generative AI as a value-added tool that will become increasingly capable and intelligent. New models are emerging that could solve cost problems using smaller datasets, but it will take a few years for new models to evolve to a stage where they are affordable and easy enough to use for practical applications. At present, generative AI is most effective when used in conjunction with human input. Human intervention encourages the consideration of different perspectives and minimizes the ethical and erroneous risks associated with the data.
Take ChatGPT, for example. The quality of the results and responses depends on the quality of the inputs and the human intelligence involved. To get high-quality responses, content, and results from ChatGPT, human users need to take active roles in the process to create feedback loops. Otherwise, ChatGPT (and similar generative AI solutions) are attractive but not reliable or holistically useful.
Related: The main fears and dangers of generative AI and what to do about it
Collaboration: Fundamental to bring generative AI solutions into enterprise environments
The collaboration between startups and companies can represent a turning point in the entire panorama of generative artificial intelligence. Not only do partnerships allow founders to explore various options and even work with different model vendors, but they also reduce the barriers that prevent companies from accessing generative AI. It also produces increased interest in open source model ecosystems. With open source contributions, there can be a collective and effective effort to push the boundaries of generative AI, challenge the dominant AI players, and reduce costs. Ultimately, it nurtures a positive innovation environment for both the startup and the collaborating company.
Collaboration offers another opportunity: Generative AI solution companies and startups can focus on implementation and adoption rather than investing in more fundamental systems. Such a partnership will entice large companies to integrate generative AI into their workflows, making it less complicated for the startup to explore faster and potentially attract more investors for future developments.
That said, businesses aren’t just going to partner with a generative AI startup without any consideration. Keep these things in mind to streamline and inform your decision-making when you collaborate:
1. The CIO and CTO must feel comfortable with the solution
Right now, CIOs and CTOs are in a state of panic. Why? They are under pressure from their boards to understand the implications of generative AI because it accesses sensitive data. As a result, while partnering with a startup is a perfect way to train and retrain a generative AI model with industry-specific inputs to ensure accuracy and consistency, it can feel like a liability risk.
To help the CIO and CTO feel comfortable, talk about what data security measures are or could be in place. This could include data encryption solutions and secure learning techniques. Once these measures are established, key players in your company are likely to be more confident in implementing generative AI internally. Remember: Most CIOs and CTOs understand that Generative AI will require domain knowledge and access to unique industry data libraries. They simply want to avoid an infringement that could put your brand in an unwanted spotlight.
Related: Generative AI: The Rising Kid in Boot Block
2. Employees will need to learn how to use generative AI effectively
If you want employees to step in and implement generative AI to increase competitive advantage, you need to make it happen. This means much more than simply implementing generative AI applications. It means explaining best practices around the use of technology and data regulation. There are currently extensive discussions on data regulation, so your team will need to stay up to date.
Providing employees with the most up-to-date information on the regulation of data use and processing, not to mention concerns about data ownership, is critical. The more they know, the more they can control the use of generative AI and mitigate problems.
Generative AI is having a huge success around the world right now, especially with the release of ChatGPT last year. Even if it’s still in its infancy, companies like yours can move forward by partnering with startups that develop generative AI models and applications. You just need to do some due diligence to make sure you get the full benefits of Generative AI and avoid preventable bottlenecks.
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