Generative AI has clearly made an impression on the world in the past few months with the release of large language models like ChatGPT. So what’s the buzz around Generative AI?
Generative AI has several uses inside and outside the enterprise that I would love to discuss in this article.
Generative AI Explained and Noteworthy Statistics
In simple terminology, generative AI is a class of artificial intelligence models that can create new data, such as images, music, or text, that resembles and often expands upon the patterns present in the training data it was trained on. ChatGPT’s case contains content from across the web in many different file types, such as audio, text, video, images, 3D models, code, and so on.
The model learns patterns from this data and data that is input by the 100 million users the system accounted for in the beginning of January 2023 – tallying about 13 million unique visitors per day in the same month. Check out the stats for yourself!
Generative AI is starting to revolutionize industries, which will continue far into the future, all thanks to deep learning algorithms.
Mindbreeze defines deep learning as,
“Deep learning is a subset of machine learning and artificial intelligence. The best way to look at deep learning is to think of it as “human imitation,” as deep learning aims to copy the methods in which the human brain receives and interprets knowledge. Because deep learning is not linear, it requires a large number of datasets to analyze and interpret information accurately and adequately.”
By ChatGPT showing the world what is achievable regarding Generative AI, enterprises want to get involved with the wide-ranging possibilities of machine learning algorithms to optimize business operations and employee productivity.
Exploring Business Use Cases of Generative AI
Generative AI can assist workers with tasks in ways we have never seen before if the model is trained from company data. The strict datasets in which models are trained can allow companies to leverage Generative AI in safe and accurate matters with ultimate context to specific projects.
Understanding the use cases and pain points you want to solve is essential to justifying ROI, so beware of jumping into the hype too quickly.
However, the capabilities are undoubtedly remarkable, so let’s explore some of the many business use cases used today and those that will benefit in the future.
Market Demands and R&D – Understanding the market is critical to developing the types of products the world seeks and what the competition is working towards. Using market intelligence, generative AI can help pinpoint which features of your product need innovation – improving the speed of research and development efforts. What are the current trends in customer behavior? Automated analysis of datasets can spot these trends and improve product design and product engineering.
Customer Experience, Marketing, and Sales – Customer experience is relevant to every brand. How enterprises connect with potential and existing clients is invaluable to new and recurring business. Approaching customer experience with personalized and unique experiences is a tactic we preach at Mindbreeze and to companies we work with on their customer experience efforts. Tailoring content and marketing campaigns are vital to engaging with customers most effectively. Salespeople and support teams can also leverage content created by generative AI to approach customer support tickets and business development outreach strategically.
Legal – The amount of data existing from legal documents related to cases, reports, regulatory documents, jurisdictions, and the vast amount of information from external data sources is seemingly endless. Lawyers and legal departments have complex questions daily and inputting their queries into large language models can help answer them. In addition, lawyers and policymakers can use generative AI to draft and revise legal documentation.
With all the above key areas plus more having valuable use cases for generative AI and deep learning, workforces need to take caution.
Verifying models are trained on company data and unavailable to the public is paramount for safe enterprise use. The advances in AI over the last decade and beyond are truly remarkable and show their monumental impact daily. Like all decisions, especially those related to confidential data, companies need to assess solutions they plan to use to protect and safeguard their information.
Final Thoughts
And remember, just because a model is impressive does not mean it is secure. After implementing caution in your decision-making on your generative AI needs, I recommend getting started and bringing your business processes to a whole new level.
