How ChatGPT Can Help Your Business Earn More

These days, it’s almost impossible to go a day without seeing a headline about generative AI or ChatGPT. Suddenly AI is hot again and everyone wants to jump on the bandwagon. Entrepreneurs want to build AI companies, entrepreneurs want to introduce AI into their businesses, and investors want to invest in AI.

As an advocate for the power of Large Language Models (LLMs), I believe that generative AI has immense potential. These models have already demonstrated practical value in improving personal productivity. For example, I incorporated code generated by her LLM into my work and used his GPT-4 for proofreading this article.

Is generative AI the silver bullet for your business?

The pressing question now is how can companies, large and small, that are not involved in creating LLMs leverage the power of Gen AI to improve their bottom line?

Unfortunately, there is a disconnect between using LLM for personal productivity and for business benefit. As with any business software solution development, there is more than meets the eye. Using the example of Building a Chatbot Solution Using GPT-4, building just one chatbot could easily take months and cost millions of dollars.

This article outlines the challenges and opportunities for leveraging generational AI for business benefit, and provides a landscape of AI land for entrepreneurs, executives, and investors looking to unlock the value of technology in their businesses. reveal.

Expectations for AI in business

Technology is an integral part of today’s business. As companies adopt new technologies, they are expected to improve operational efficiency and improve business outcomes. Businesses, regardless of type, expect similar capabilities from AI.

On the other hand, business success does not depend solely on technology. Regardless of the emergence of tools like Gen AI and ChatGPT, well run companies will continue to thrive, while poorly run companies will still struggle.

Similar to implementing a business software solution, successful enterprise adoption of AI requires two key ingredients. Technology must work as expected to deliver tangible business value. Second, implementing organizations must know how to manage AI just like they manage other business operations for success. .

The Hype Cycle and Disillusionment of Generative AI

Like any new technology, Gen AI goes through Gartner’s Hype Cycle. With popular applications like ChatGPT raising public awareness of AI, we’re almost at the peak of inflated expectations. Eventually, there will be a “trough of disillusionment,” where interest wanes, experiments fail, and investments disappear.

The ‘Valley of Disillusionment’, which can be caused by several reasons such as immaturity of technology and mismatched applications, is a common generational phenomenon that can break the hearts of many entrepreneurs, business executives and investors. Here are two disenchantments of AI: Failure to recognize this disillusionment can lead to underestimating the real challenges of implementing technology in business and missing out on opportunities to make timely and wise AI investments.

One common disillusionment: Generative AI leveling the playing field

With millions of people relying on gen AI tools to perform a wide range of tasks, from accessing information to writing code, gen AI appears to be leveling the playing field for all businesses. is. Anyone can use it and English becomes the new programming language.

This is true for certain content creation use cases (marketing copywriting), but at the end of the day, gen AI focuses on natural language understanding (NLU) and natural language generation (NLG). Due to the nature of technology, tasks that require deep domain knowledge are challenging. For example, ChatGPT produced a medical paper that contained “significant inaccuracies” and failed the CFA exam.

Domain experts have deep knowledge, but may not be AI or IT savvy, or understand the inner workings of generational AI. For example, you may not know how to effectively prompt ChatGPT to get the desired result, let alone program your solution with AI APIs.

With rapid advances and intense competition in the AI ​​space, foundational LLMs are becoming more and more commoditized. The competitive advantage of an LLM-enabled business solution must lie in owning certain high-value proprietary data, developing domain-specific expertise, or elsewhere.

Incumbents are more likely to have already accumulated such domain-specific knowledge and expertise. Despite these benefits, there may also be legacy processes in place that hinder the rapid adoption of generational AI. Startups have the advantage of starting with a clean slate and harnessing the full power of technology, but they need to get their business off the ground quickly in order to acquire a significant repertoire of domain knowledge. Both face essentially the same underlying challenge.

A key challenge is enabling business domain experts to train and supervise AI while leveraging domain data and expertise without becoming experts. To address these challenges, see the key considerations below.

Key Considerations for Successful Generative AI Adoption

gen AI is a major advance in language understanding and generation technology, but it can’t do everything. It is important to take advantage of technology while avoiding its drawbacks. We highlight some key technical considerations for entrepreneurs, business owners, and investors looking to invest in artificial intelligence.

AI expertise: Gen AI is far from perfect. If you’re building an in-house solution, make sure you have in-house experts who truly understand the inner workings of AI and can always improve it if necessary. If you partner with an outside company to create your solution, make sure they have deep expertise to make the most of generational AI.

Software engineering expertise: Building a Gen AI solution is just like building any other software solution. It requires a dedicated engineering effort. If you build in-house solutions, you need advanced software engineering talent to build, maintain, and update those solutions. If you decide to work with an external company, let them do the heavy lifting (providing you with a no-code platform that makes it easy to build, maintain, and update your solution).

domain expertise: Building Gen AI solutions often requires capturing domain knowledge and customizing technology with that domain knowledge. Whether you build it in-house or work with an external partner, make sure you have the expertise not only to provide such knowledge in your solution, but also to know how to leverage that knowledge. . It is important for you (or your solution provider) to enable a non-IT expert to easily consume, customize, and maintain a Gen AI solution without coding or additional IT support from him. .

Take-out

As Gen AI continues to reshape the business landscape, it’s helpful to have an open mind about the technology. It is important to keep in mind the following points.

• Gen AI mainly solves language-related problems, but not all.
• Implementing a successful business solution is more than meets the eye.
• Gen AI does not benefit everyone equally. Recruit or partner with people with AI expertise and IT skills to harness the power of technology faster and more securely.

As entrepreneurs, business owners, and investors navigate the rapidly evolving world of artificial intelligence, they are faced with the challenges and opportunities involved, and who has the upper hand in leveraging technology to make quick decisions. , it is imperative to understand how to invest wisely in AI. Maximize your ROI.

Huahai Yang is the co-founder and CTO of Juji and the inventor of IBM Watson Personality Insights.