generative AI for business

Generative AI for Business: Unlock the Potential of Generative AI

In recent times, it has become virtually impossible to spend a day without reading headlines about generative AI or ChatGPT. Suddenly, AI has regained its limelight, and everyone is eager to join the bandwagon. Entrepreneurs are looking to start AI companies, corporate executives are keen to adopt generative AI for business, and investors are actively seeking opportunities to invest in AI ventures.

The Magic Bullet for Business: Is Generative AI the Answer?

Now, the burning question arises: How can businesses, both big and small, not directly involved in LLM creation, leverage generative AI to improve their bottom line?

Unfortunately, there exists a significant gap between LLMs for personal productivity gains and business profits. As with any other business software solution, there is more to it than meets the eye. GPT-4 could easily take months and cost millions of dollars to create a single chatbot solution!

This article outlines the challenges and opportunities involved in harnessing generative AI for business success. It will provide a comprehensive overview of the AI landscape for entrepreneurs, corporate executives, and investors. This is eager to unlock the true value of this transformative technology for their businesses.

Managing AI Business Expectations

Technology plays an integral role in most businesses. When an enterprise adopts cutting-edge technology, it expects operational efficiency to improve business outcomes. The same holds true for AI and its rules of application.

However, it is important to note that business success does not solely depend on technology. A well-managed business will thrive, while a poorly managed one will struggle, irrespective of generative AI or tools like ChatGPT.

Successful AI adoption in business requires two crucial elements. Firstly, the technology must deliver tangible business value as anticipated. Secondly, the organization must possess the necessary expertise to effectively manage AI, like other core business operations.

The Generative AI Hype Cycle and Reality Check

As with any upcoming technology, generative AI is bound to experience a Gartner hype cycle. With popular applications like ChatGPT leading to the widespread awareness of generative AI, we are approaching a peak of inflated expectations. However, this will be followed by the inevitable “trough of disillusionment” as interest wanes, experiments fail, and investments are wiped out.

The trough of disillusionment may arise due to various factors, including technological immaturity and ill-suited applications. Here, we will discuss two common disillusionments that may shatter entrepreneurs’ hopes. Failing to recognize these challenges may underestimate the practical hurdles associated with adopting generative AI for business purposes. This may result in missing out on timely and prudent AI investments.

Generative AI Levels the Playing Field

As millions of individuals interact with generative AI tools to perform a wide range of tasks, from accessing information to writing code, it may seem that generative AI levels the playing field for every business. It gives anyone the ability to use AI, with English becoming the new programming language.

While this may hold true for certain content creation use cases, such as marketing copywriting, it is important to remember that generative AI primarily focuses on natural language understanding (NLU) and natural language generation (NLG). Due to the nature of the technology, it struggles with tasks that require deep domain knowledge. As an example, ChatGPT generated a medical article with “significant inaccuracies” and failed the CFA exam.

Domain experts possess in-depth knowledge, but they may lack AI or IT expertise, as well as an understanding of how generative AI works. They may not know how to effectively prompt ChatGPT to achieve the desired results, let alone use AI APIs to program a solution.

The rapid advancement and intense competition in AI have also turned foundational LLMs into commodities. Therefore, the competitive advantage of any LLM-enabled business solution must lie elsewhere, either in possession of high-value proprietary data or in mastering domain-specific expertise.

Incumbents in established businesses are more likely to have accumulated such domain-specific knowledge and expertise. However, while enjoying this advantage, they may also have legacy processes in place that hinder generative AI adoption. On the other hand, startups have the advantage of starting from scratch to fully harness technology power. Yet, they must quickly establish their business to acquire a critical repertoire of domain knowledge. Ultimately, both incumbents and startups face the same fundamental challenge.

The key challenge lies in enabling business domain experts to train and supervise AI without becoming AI experts themselves. This is done while leveraging their domain data and expertise. Below, I will outline key considerations to address this challenge.

Essential Considerations for Generative AI Adoption

While generative AI has made significant advancements in language understanding and generation technologies, it is important to recognize its limitations and avoid potential pitfalls. Investing in generative AI requires several key technical considerations for entrepreneurs, executives, and investors.

1. AI Expertise

Generative AI is far from perfect. If you decide to develop in-house solutions, ensure that you have internal experts who possess a deep understanding of AI and can enhance the technology when necessary. Alternatively, if you choose to partner with external firms for solution development, ensure that they possess the requisite expertise to help you derive the maximum benefit from generative AI.

2. Software Engineering Expertise

Building generative AI solutions are like developing any software solution. It requires dedicated engineering efforts. If you opt for in-house development, you will need skilled software engineers to build, maintain, and update the solutions. Conversely, if you collaborate with external firms, ensure that they handle the heavy lifting for you. This is done by providing a no-code platform that facilitates easy development, maintenance, and updating of your solution.

3. Domain Expertise

Constructing generative AI solutions often entails incorporating domain knowledge and customizing the technology accordingly. It is essential to have domain experts who can contribute their knowledge and know-how to the solution, regardless of whether you choose in-house development or collaboration with an external partner. Enabling domain experts, who may not possess extensive IT expertise, to easily ingest, customize, and maintain generative AI solutions without coding or additional IT support is critical.


In conclusion, generative AI continues to reshape the business landscape. Maintaining an unbiased perspective on this technology is crucial. It is important to remember the following: Generative AI primarily solves language-related problems but not every problem imaginable. Implementing a successful AI solution for a business extends beyond surface-level considerations. Generative AI does not offer equal benefits to everyone. Recruit or partner with individuals with AI expertise and IT skills to effectively harness.


How is generative AI used in business?

Generative AI, also known as generative adversarial networks (GANs), is increasingly being used in various ways to benefit businesses. Here are some common applications:
1. Content creation
2. Image and Video Synthesis
3. Design and Creativity
4. Data Augmentation
5. Chatbots and Virtual Assistants
6. Simulation and Forecasting
7. Personalization and Recommendation Systems

How AI can be used to improve business strategy?

AI can significantly improve business strategy in various ways by providing data-driven insights, enhancing decision-making processes, and optimizing operations. Here are some key ways AI can be used to improve business strategy:
1. Data Analysis and Insights
2. Predictive Analytics
3. Customer Segmentation and Personalization
4. Competitive Intelligence
5. Pricing Optimization

what is an example of generative AI?

The most common example of generative AI nowadays most people are using is ChatGPT and DALLE.

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