Implementing Generative AI for Business Success

GenAI is being implemented by a variety of companies, but the overall number of companies is in the minority. But some of those early adopters are illustrating how to do it right.

Mary E. Shacklett, President of Transworld Data

February 1, 2024

5 Min Read
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Cagkan Sayin via Alamy Stock

In 2023, Statista reported that at least 30% of firms in several sectors were using generative AI, and that at least 15% of three other industries said they had adopted GenAI

Those that said they were using GenAI included 37% of surveyed marketing and advertising firms, 35% of technology companies, 30% of consulting practices, 19% of education institutions, 16% of accounting firms, and 15% of healthcare organizations were using generative AI.

Now that we are in the new year, corporate adoption rates are set to rise in 2024, although most companies are in exploratory stages, and have yet to determine their strategic direction with GenAI.

What GenAI offers is a way to auto-generate text, images, or other media based upon patterns and structures on which AI is trained. It then can generate data that has similar characteristics. You see it on your smart phone when, unrequested by you, you see “albums” of photographs from your photo library that are auto assembled for you to commemorate memories, or seasons of the year.  You experience GenAI on auto-calls as you interact with linguistically trained robots that attempt to answer your questions.

These GenAI renderings have received mixed reviews from participants. For the CIOs now being asked by their boards and CEOs to implement GenAI, the reaction is mixed, too,

Related:CES Panel: AI Strategies for More Transportation Transformation

One reason for the cautionary approach is that CIOs are wary of technology pitfalls. They want to avoid false starts and failures. This is why GenAI is being eased into corporate settings, and not aggressively adopted.

CIOs are also looking at early GenAI implementation successes and failures because they want to start their companies' generative AI journeys on a firm footing, and with a handful of best practices.

Just what do we know about GenAI and how well it is working for businesses?

Successful GenAI Use Cases

Marketing and advertising companies are the biggest users of generative AI today, and for good reason: They can easily see how GenAI can aid their businesses, and the tech is working for them.

At Nextvoo, an SMB that sells videoconferencing gear, the company built a customer base of over 2,600 customers in six months by training and then using generative AI to analyze text and images. Out of this automated research, the GenAI identified high-quality customer prospects who were most likely to purchase the company’s products. This helped to generate $20 million in sales.

In publishing, companies have been using generative AI to predict the most likely “next best seller” book topics. They use GenAI predictive analytics for research of topics, titles and reader reactions.

Related:What the NYT Case Against OpenAI, Microsoft Could Mean for AI and Its Users

At AAA auto services, the response time for member emergency automobile calls was reduced by 10%, thanks to the use of voice- and text-based generative AI that streamlined calls and automated the automobile emergency response process.

Why did these use cases work?

In all those instances, companies had a specific business problem to which they wanted to apply GenAI -- a problem whose success could clearly be measured in tangible metrics.

These companies initiated their use cases in areas of the company where the risks of a deleterious outcome were low if the GenAI didn’t work. If the worst case scenario would have been that they would have had to continue to develop sales prospects on their own, or they would continue to research where the next best-selling book would be “by hand,” or they would have to live with slightly longer response times to car distress calls. Not using GenAI was not going to break their company.

GenAI False Start Stories

In August 2023, Gannett publications launched GenAI for content generation in its news, and several stories were printed with errors and poorly written content. The articles were widely mocked on social media.

Related:Feasting on High-Quality AI Data

In the legal profession, an attorney got himself in trouble when he used ChatGPT to research case law to support his case, only to find (along with the judge) that six of the cases he cited did not exist.

In December 2023, the New York Times sued OpenAI and Microsoft for alleged copyright infringement. The Times claimed that OpenAI and Microsoft had used OpenAI to mine NY Times content and stylistic elements. That case is now pending, with the arguments of copyright infringement versus fair use hanging in the balance.

How did these GenAI false starts happen?

Companies overestimated the maturity of GenAI. The business rules for these GenAI projects failed to adequately vet content for verity and linguistic correctness. It is unclear what the standard of accuracy was for these projects, but what is clear is that the accuracy wasn’t good enough. Quality assurance, iterative testing and business rule refinement needed more time, and projects were given the “go-head” for launch into production before they were ready.

In other cases, such as the NY Times lawsuit for copyright infringement, legal and regulatory guardrails were inadequate. Legal precedents and law making always lag technology. Consequently, it’s smart to consult with attorneys, auditors, and regulators on any potential legal or ethical “guardrail” issues that could arise from a GenAI project before you install it.


Ensuring that a GenAI project fulfills its business mission, that it will produce accurate results, and that it will operate within legal, regulatory, and ethical guidelines are major challenges for CIOs as more GenAI gets rolled out.

However, we do know four things:

  • It’s best to start GenAI projects on smaller, achievable, and measurable business goals that everyone in the business sees value in.

  • Iterative QA testing, continuous AI model refinement for accuracy, etc., must be diligently and continuously followed.

  • AI projects should be initiated in areas of the company where a GenAI false start or failure won’t be catastrophic.

  • As much attention should be placed on installing legal and regulatory guardrails as on ensuring the accuracy and relevance of GenAI output.

About the Author(s)

Mary E. Shacklett

President of Transworld Data

Mary E. Shacklett is an internationally recognized technology commentator and President of Transworld Data, a marketing and technology services firm. Prior to founding her own company, she was Vice President of Product Research and Software Development for Summit Information Systems, a computer software company; and Vice President of Strategic Planning and Technology at FSI International, a multinational manufacturer in the semiconductor industry.

Mary has business experience in Europe, Japan, and the Pacific Rim. She has a BS degree from the University of Wisconsin and an MA from the University of Southern California, where she taught for several years. She is listed in Who's Who Worldwide and in Who's Who in the Computer Industry.

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