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Lately, it’s almost impossible to go a day without coming across creative AI headlines or ChatGPT. Suddenly, AI is hot again, and everyone wants to jump in: Entrepreneurs want to start an AI company, company executives want to. apply AI to their businessand investors who want to invest in AI.
As an advocate of the power of large language models (LLM), I believe that the AI gene holds great potential. These models have proven their practical value in enhancing personal productivity. For instance, I have incorporated code generated by LLMs in my work and even used GPT-4 to reread this article.
Is artificial intelligence the magic bullet for business?
The pressing question now is: How can businesses, whether small or large, not involved in LLM creation, leverage the power of AI genes to improve their bottom line?
Unfortunately, there is a gap between using an LLM for personal productivity versus business profitability. Like developing any business software solution, there is more to it than meets the eye. Just using the example of creating a chatbot solution with GPT-4, which could easily take months and cost millions of dollars to create just a single chatbot!
This section will outline challenges and opportunities to leverage AI genes for business benefit, revealing where AI is to entrepreneurs, corporate executives, and investors looking to tap into it. The value of technology for business.
AI Business Expectations
Technology is an integral part of business today. When a business adopts a new technology, they expect it to improve operational efficiency and deliver better business results. Businesses expect AI to do the same, regardless of the type.
On the other hand, the success of a business depends not only on technology. A well-run business will continue to prosper, and a poorly-managed business will still struggle, regardless of the arrival of the next generation of AI or tools like ChatGPT.
As with the implementation of any business software solution, successful business adoption of AI requires two essential components: The technology must work to deliver the expected specific business value. and the adopting organization must know how to manage AI, just like any other business to be successful.
Creative AI hype cycle and disillusionment
Like every new technology, AI generation is bound to go through Gartner’s Hype Cycle. With popular apps like ChatGPT enabling AI gene awareness for the masses, we’ve almost reached peak of inflated expectations. Soon, a “trough of disillusionment” will form as benefits dwindle, experiments fail, and investments are wiped out.
While “disillusionment” can be caused by a number of reasons, such as technological immaturity and inappropriate applications, here are two common disillusionments about next-generation AI that can dispel: broke the hearts of many entrepreneurs, corporate executives and investors. Without realizing these disillusionments, one may underestimate the real challenges of enterprise adoption of the technology or miss out on timely and prudent AI investments.
A common disillusionment: Creative AI levels the playing field
As millions of people are interacting with gen AI tools to perform a variety of tasks — from accessing information to writing code — it seems like gen AI levels the playing field for all businesses: Anyone can use it and English becomes the new programming language.
While this may be true for some content creation use cases (marketing copywriting), after all, the AI gene focuses on natural language understanding (NLU) and natural language generation. natural (NLG). Due to the nature of the technology, it struggles with tasks that require in-depth domain knowledge. For example, ChatGPT created a medical article with “significant inaccuracies” and failed the CFA exam.
While domain experts have in-depth knowledge, they may not be AI or IT savvy or understand the inner workings of AI generation. For example, they may not know how to effectively prompt ChatGPT to achieve the desired result, let alone use the AI API to program the solution.
The rapid advancement and fierce competition in the AI fields are also making basic LLMs increasingly a commodity. The competitive advantage of any LLM-enabled business solution will have to lie elsewhere, either possessing some highly valuable proprietary data or mastering some industry expertise. specific area.
Incumbents in businesses are more likely to have accumulated knowledge and expertise in that particular area. Despite such an advantage, they may also have legacy processes that hinder rapid adoption AI gene. Beginners have the benefit of starting from a clean medium to fully utilize the power of the technology, but they must start a business quickly to gain a significant pool of knowledge about the field. . Both face the same basic challenge.
The main challenge is allowing business professionals to train and monitor AI without requiring them to become experts while leveraging data or their domain expertise. See my key considerations below for tackling such a challenge.
Key considerations for successful adoption of generalized AI
While gene AI has remarkably advanced language generation and understanding technologies, it can’t do everything. It is important to take advantage of technology but avoid its shortcomings. I highlight some key technical considerations for entrepreneurs, corporate executives, and investors considering investing in AI generation.
AI expertise: Gen AI is far from perfect. If you decide to build solutions in-house, make sure you have in-house experts who really understand the inner workings of AI and can improve it whenever needed. If you decide to partner with outside companies to create solutions, make sure they have the deep expertise that can help you get the best out of AI generation.
Software engineering expertise: Building AI gen solutions is like building any other software solution. It requires dedicated engineering efforts. If you decide to build solutions in-house, you will need sophisticated software engineering talents to build, maintain, and update those solutions. If you decide to partner with outside companies, make sure they do the heavy lifting for you (providing you with a code-free platform for you to easily build, maintain, and update). update your solution).
domain expertise: Building AI gen solutions often requires importing domain knowledge and customizing the technology using that domain knowledge. Make sure you have the expertise the domain can deliver and know how to use that knowledge in a solution, whether you’re building it internally or collaborating with an external partner. It’s important for you (or your solution provider) to enable domain experts, who are often not IT experts, to easily import, customize, and maintain gen AI solutions. without coding or additional IT support.
As AI generation continues to reshape the business landscape, it is helpful to have an objective view of the technology. The important thing to remember is:
- Gen AI solves most language-related problems but not everything.
- Implementing a successful solution for business is more than satisfying to the eye.
- Gene 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, corporate executives, and investors navigate the rapidly evolving world of AI genes, it is essential to understand the challenges and opportunities involved, who has an edge to take advantage of. Use technology and quick decision making and prudent investment in AI to maximize ROI.
Huahai Yang is the Co-Founder and CTO of pregnant and the inventor of IBM Watson Personal Insights.
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