How ChatGPT can help your business make more money

Lately it has become almost impossible to go by a day without encountering headlines about Generative AI or ChatGPT. Suddenly, AI has become red-hot again, and everyone wants to jump on the bandwagon: entrepreneurs want to start an AI company, business leaders want to adopt AI for their businesses, and investors want to invest in AI.

As an advocate of the power of large language models (LLMs), I believe that gene AI has enormous potential. These models have already proven their practical value in improving personal productivity. For example, I’ve incorporated code generated by LLMs into my work and even used GPT-4 to proofread this article.

Is generative AI a panacea for business?

The pressing question now is: how can companies, small or large, that are not involved in creating LLMs, take advantage of the power of gen AI to improve their bottom line?

Unfortunately, there is a gap between using LLMs for personal productivity gains versus for business gains. Like developing any business software solution, there is much more to it than meets the eye. If we just use the example of creating a chatbot solution with GPT-4, it could easily take months cost millions of dollars to create just one chatbot!

This piece outlines the challenges and opportunities of leveraging gen AI for business profits, revealing the layout of the AI ​​land for entrepreneurs, business leaders and investors looking to unlock the technology’s value for business.

Business expectations of AI

Technology is an integral part of business today. When a company adopts a new technology, it expects it to improve operational efficiency and deliver better business results. Businesses expect AI to do the same, regardless of its type.

On the other hand, the success of a company does not depend on technology alone. A well-run business will continue to thrive and a poorly-run business will still struggle, regardless of the rise of gen AI or tools like ChatGPT.

As with implementing any business software solution, successful business adoption of AI requires two essential ingredients: the technology must perform to deliver concrete business value as expected and the adoption organization must know how to manage AI just like managing any other business activities for success .

Generative AI hype cycle and disillusionment

Like any new technology, gen AI is bound to go through a Gartner Hype Cycle. With popular applications like ChatGPT activating the awareness of gen AI for the masses, we are almost at the peak of inflated expectations. Soon the “valley of disillusionment” will begin as interests decline, experiments fail and investments are wiped out.

While the “valley of disillusionment” could be due to a variety of reasons, such as technology immaturity and ill-suited applications, below are two common disillusionments of gen AI that could break the hearts of many entrepreneurs, corporate executives and investors. Without acknowledging these disillusions, one could either underestimate the practical challenges involved in adopting the technology for businesses or miss the opportunity to make timely and prudent AI investments.

A common disillusionment: generative AI is leveling the playing field

As millions of people interact with Gen AI tools to perform a wide variety of tasks – from accessing information to writing code – it seems that Gen AI is leveling the playing field for any business: anyone can do it. and English becomes the new programming language.

While this may be true for certain use cases for content creation (marketing copywriting), after all, gen AI focuses on natural language understanding (NLU) and natural language generation (NLG). Given the nature of the technology, it struggles with tasks that require in-depth domain knowledge. For example, ChatGPT generated a medical article with “significant inaccuracies” and failed a CFA exam.

While domain experts have in-depth knowledge, they may not be very savvy with AI or IT or understand the inner workings of gen AI. For example, they may not know how to effectively nudge ChatGPT to get the desired results, not to mention use AI API to program a solution.

Due to the rapid advancement and intense competition in the field of AI, the basic LLMs are also becoming more and more commodities. The competitive advantage of any LLM-based business solution should lie elsewhere, either in having some high-value proprietary data or mastering some domain-specific expertise.

Established companies have previously built up such domain-specific knowledge and expertise. While they have such an advantage, they may also have outdated processes that hinder the rapid adoption of gen AI. The startups have the advantage of starting with a clean slate to fully harness the power of the technology, but they need to get things off the ground quickly to acquire a crucial repertoire of domain knowledge. Both essentially face the same fundamental challenge.

The main challenge is to enable experts in the business domain to train and mentor AI without having to become experts while taking advantage of their domain data or expertise. See my main considerations below for taking on such a challenge.

Key considerations for the successful adoption of generative AI

While gen AI has significantly advanced language understanding and generation technologies, it can’t do everything. It is important to take advantage of the technology but avoid its shortcomings. I highlight several key technical considerations for entrepreneurs, business leaders, and investors considering investing in gen AI.

AI expertise: Gen AI is far from perfect. If you decide to build in-house solutions, make sure you have in-house experts who really understand the inner workings of AI and can improve it when needed. If you decide to partner with third-party companies to create solutions, make sure the companies have deep expertise that can help you get the most out of Gen AI.

Software engineering expertise: Building gen AI solutions is just like building any other software solution. It requires special technical efforts. If you decide to build in-house solutions, you need advanced software engineering talents to build, maintain, and update those solutions. If you decide to work with third-party firms, make sure they do the heavy lifting for you (providing you with a no-code platform where you can easily build, maintain, and update your solution).

Domain Expertise: Building gen AI solutions often requires the incorporation of domain knowledge and adaptation of the technology using such domain knowledge. Make sure you have domain expertise that can provide such knowledge and know how to use it in a solution, whether you’re building in-house or collaborating with an outside partner. It is critical for you (or your solution provider) to enable domain experts, who are often not IT experts, to easily adopt, customize and maintain gen AI solutions without coding or additional IT -support.

Take away food

As gen AI continues to reshape the business landscape, an unbiased view of this technology is helpful. It is important to remember the following:

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

As entrepreneurs, business leaders and investors navigate the rapidly evolving world of gen AI, it is essential to understand the associated challenges and opportunities, who has the upper hand to benefit from the technology, and how to decide quickly and invest wisely in AI to maximize ROI.

Huahai Yang is co-founder and CTO of juji and an inventor of IBM Watson Personality Insights.