Despite the explosion of Generative AI (GAI) tools for both personal as well as business use many companies remain in the dark over how to implement and use GAI.

ChatGPT was certainly the launching pad for the current explosion of Generative AI (GAI) tools for both personal as well as business use, and yet, more than a year after the launch of ChatGPT, companies are still facing the same question when they first considered the technology: How do they actually go about putting it into business use? Many companies have simply discovered that generative AI tools like LLMs, while impressive, aren’t exactly “plug and play.”

While the “fear of missing out” fervor of the GenAI craze has seemingly calmed, many companies are still facing the same questions they were when it started: How can they take advantage of the promised cost savings and substantial efficiency gains that GenAI allegedly offers? How do they actually go about putting it into business use?

According to the Harvard Business Review, many companies are still grappling with how to implement machine learning and “traditional” AI into the business to enhance data analytics and improve efficiency, let alone take on the additional complexities of GAI and LLM solutions. GAI can do a lot of things that traditional AI cannot and vice versa.

ChatGPT may be able to write a 5,000-word report in no time, but it cannot, for example, currently, do many other tasks, like extracting and classifying driver’s license data, that traditional AI can do easily in a matter of seconds. As such, companies need to think deeply about which business cases might be appropriate to find the benefits of GenAI. Navigating through traditional AI is like steaming through choppy waters with a state-of-the-art but somewhat cumbersome vessel, and GenAI adds more tonnage, power, and an even more turbulent sea. A company still unsteady with the former will, of course, struggle with the latter.

The Business Review also points out that many companies continue to struggle with the long term implications of GAI. such as the long-term costs and the impacts of current and future regulation — are still uncertain.

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How BigRio Helps Bring LLM and Advanced AI Solutions to All Industries

As a company dedicated to helping companies implement GAI solutions we have seen first hand all of the issues that the Harvard Business Review has pointed out – and even more challenges. The primary challenge is to think through on how to build a Gen AI infrastructure that everone in the business can use across functions like HR, Legal, Finance, Sales, Marketing etc.. then identify the developers of the GAI tools that fit the needs of your business. Now, you can opt to utilize existing models developed by big tech companies like Microsoft, Google, or OpenAI – or you could building your own.

Of course, it is much easier to use an off-the-shelf LLM solution; however, while these “open source” GAI/LLM solutions like ChatGPT have gained significant attention across various fields they are limited by their need to be non-specific in scope and ability. Your business is unique shouldn’t GAI solution be as well?

What if you could build a “Custom” GAI model for your organization’s unique targets, domain and needs from the ground up? You can, with BigRio’s Help!

Creating a custom large language model from scratch requires extensive resources, the expertise of AI developers and data scientists, the MLOps team, and computational power. It involves training the model on massive datasets, fine-tuning it through multiple iterations, and optimizing its performance. This process demands substantial time, expertise, and computational resources, including high-performance hardware and storage systems. The good news is that the BigRio team can offer you all of the above and more!

BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery, originally in the field of diagnostics and research. We have recently been focusing our efforts on supporting startups and developing our own solutions that use LLMs and GAI to improve those areas of healthcare as well as in direct patient interactions and customer relationship management.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.