Integrating AI into business operations isn’t easy. Here’s what enterprises can do…

Miles Everson • February 18, 2025

From the desk of Miles Everson:

Hello!

Welcome to today’s edition of  “Return Driven Strategy (RDS).”  I’m thrilled to share another insight with you in this article.

For those of you who are not yet familiar with this, RDS is a pyramid-shaped framework with 11 tenets and 3 foundations. When applied properly, these principles help businesses gain high levels of performance.

Let’s apply this framework in the context of AI adoption.

Ready to know more?

Keep reading below!

ANNOUNCEMENT

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Click  here  for full details (includes a FREE recommendation).




Integrating AI into business operations isn’t easy. Here’s what enterprises can do…

According to data from the  Federal Reserve Bank of St. Louis, generative artificial intelligence (AI) saw adoption rates of over 39% among Americans aged 18 to 64, two years after the public release of  OpenAI ’s ChatGPT.

Additionally, 28% of the same age group have reported using generative AI specifically for work.

Professionals in the fields of marketing, sales, customer service, product development, human resources, and finance are among those who possess high levels of expertise in the use of AI globally.

Even though adoption rates and proficiency in AI are high, there are businesses across the globe that still have doubts on fully embracing AI due to the costs associated and roadblocks associated with AI adoption.

Roadblocks

Aside from the financial cost of adopting generative AI tools, there are a few factors that prevent enterprises from fully integrating AI into their operations.

  • Data Leaks

    Since generative AI models need to be fed massive amounts of data, companies who run these tools face the risk of falling victim to data leaks, regardless of whether this happens by accident or a targeted attack by cybercriminals.

    Breaches can result in unauthorized access to sensitive information such as customer data and proprietary information that’s unique to an organization.

    There is also concern about internal data being used to train models made available to others. While a number of the enterprise solutions promise that this doesn’t happen, enterprises need to consider their liability and obligations to users and clients.

  • Employee Perception

    Another risk that’s preventing enterprises from adopting AI boils down to employee perception.

    Workers could interpret the adoption of AI tools or the onboarding of talent with AI skills as a sign of potential layoffs.

    While this concern is valid, both workers and organizations need to realize that there’s lots of work to go around, and at this point, AI exists to help users eliminate the mundane and low value portions of their job.

  • AI Hallucination

    Hallucination occurs when a generative AI chatbot, powered by a large language model (LLM), creates outputs that are nonexistent or totally inaccurate.

    Even though AI tools have seen massive strides in terms of output, there are still instances where AI algorithms will produce outputs that aren’t factual nor based on training data and the information given in user prompts.

Given these issues, how can enterprises fully embrace AI?

Part of the answer lies in bringing in workers (full-time or independent) who are proficient in leveraging generative AI tools.

Case in point: Workers who possess AI skills are more likely to know how to deal with issues such as hallucination.

Tech- and AI-savvy workers are also just as likely to make use of AI on a particular problem instead of treating it as a solution to every work-related task or challenge.

Last but not least, those proficient in AI are more likely to be knowledgeable on data security risks associated with AI use, putting them, and the organizations they are working for, in a position to mitigate those problems.

Leveraging AI to Prepare for the Future

In a world that’s being shaped yet again by another technological innovation, it’s crucial for businesses to start thinking about how they can integrate AI into various levels of their organizations despite the risk factors associated with it.

One of the ways an organization can mitigate the risk of leveraging AI is by hiring tech-savvy professionals who possess the knowledge and skills needed in the utilization of generative AI tools.

Professor Joel Litman  and  Dr. Mark L. Frigo  in the book,  “Driven”  emphasized the importance of identifying the services a firm needs based on its goals and engaging the right personnel in the achievement thereof.

As generative AI becomes better, it is reasonable to assume that adoption will become a necessity for businesses.

That’s why enterprise leaders shouldn’t set aside AI adoption and recruiting those who are proficient in the use of AI tools.

The more enterprises drag their feet on this, the higher the chances they will get left behind in the years to come.

If you’re looking to gain a better understanding of Return Driven Strategy and Career Driven Strategy, we highly recommend checking out  “Driven”  by Professor Litman and Dr. Frigo.

Click  here  to get your copy and learn how this framework can help you in your business strategies and ultimately, in ethically maximizing wealth for your firm.

Hope you found this week’s insights interesting and helpful.

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Stay tuned for next Tuesday’s Return Driven Strategy!

Are you familiar with the meaning of the word, “Delta”?

Learn more about  the importance of being vigilant to forces of change  in next week’s article!

Miles Everson

CEO of MBO Partners and former Global Advisory and Consulting CEO at PwC, Everson has worked with many of the world's largest and most prominent organizations, specializing in executive management. He helps companies balance growth, reduce risk, maximize return, and excel in strategic business priorities.


He is a sought-after public speaker and contributor and has been a case study for success from Harvard Business School.


Everson is a Certified Public Accountant, a member of the American Institute of Certified Public Accountants and Minnesota Society of Certified Public Accountants. He graduated from St. Cloud State University with a B.S. in Accounting.

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