3 Ways to Optimize AI in Your Company

With the rapid expansion of AI into every facet of every industry, it is becoming more crucial than ever to understand what artificial intelligence is and how it should be applied throughout one's business...

This is, of course, the running theme of Wall Street and Corporate America these days, as billions (soon to be trillions) are being spent to become part of the AI game. So let's first break down what the current picture looks like for companies adopting AI into their respective operations and who is "winning" versus "losing"

AI Winners

The AI picture has a clear-cut picture of which companies are winning and which are losing. The clearest winner of the AI boom has been, as you might have guessed, Nvidia.

Make no mistake, Nvidia's explosion of profit and growth is very real. In fact, the profits of all AI infrastructure supporters have experienced substantial growth since ChatGPT's introduction three years ago.

The infrastructure supporters are the companies supporting the underlying hardware that allows Generative AI to work. These companies include not only chipmakers, but also cloud providers such as Amazon Web Services, Oracle, and Microsoft Azure, that can provide high powered computational machines capable of training (and hosting) Large Language Models (LLMs) for your every business need.

AI Losers

However, the AI users have been the largest losers of the AI Boom thus far. AI users, in this instance, are companies attempting to utilize Generative AI and LLMs to enhance their business.

What is more interesting is that one of the largest losers of the AI race is currently none other than... OpenAI itself. While we are still in the early stages of what value Generative AI can bring to the world, the fact is that OpenAI is losing a lot of money with little hope of turning a profit any time soon.

A study developed by Michigan Institute of Technology recently showed that 95% of companies that implemented a Generative AI Pilot failed to realize a return on such investment.

AI is a Tool, Not a Silver Bullet

What this tells us is that if a company simply throws AI into its operations mix, there is a 95% chance these efforts will underwhelm or flat out fail. However, the 5% of companies that are utilizing Generative AI correctly are experiencing significant growth and efficiency in their businesses.

These are the top 3 ways a company can utilize AI to ensure it remains in that unique 5% of winners.

Be Customer Value-Centric, Not AI-Centric

The largest fallacy seen in the adoption of Generative AI is becoming AI-centric at the expense of customer value. In other words, businesses are frantically pursuing AI adoption based on the idea that LLMs automatically improve efficiency and overall business performance upon integration.

This has proven to be ill-advised when done apart from business value planning and consideration of end user experience. An example of such an error can be seen with the payment company Klarna's AI integration efforts.

Klarna took an "all-in" approach to AI adoption, which ended up reducing customer agent headcount heavily between 2023 and 2024. The company claimed the AI Chatbots could do the work of 700 agents.

However, by 2025, Klarna (by its own admission) saw a significant drop in customer satisfaction and slowly re-hired humans as customers heavily preferred human interaction over the AI agents.

Companies that implement AI without proper thought or for shortcut gains will find themselves with high costs and customer dissatisfaction (and even employee distrust).AI should not be implemented for the sake of AI, and certainly not to simply make investors happy.

Generative AI is a tool that can be applied in many correct and incorrect ways. Before one throws AI into one's workflow, one must first analyze the end user and ensure the integration improves their experience.

In order to understand experience, one needs to understand the boundaries, limits, and use cases of LLMs.

Understand the Boundaries of LLMs

Large Language Models (LLMs) like GPT-4 and Claude are powerful, but their strength lies within defined boundaries. These models are not conscious thinkers or data analyzers—they are pattern recognition systems trained to predict the next most probable word based on billions of examples from the internet. They do not “know” facts; they reproduce learned relationships between tokens.

This distinction is crucial. When businesses deploy AI expecting human-like judgment or reasoning, they set themselves up for disappointment. LLMs excel at language generation, summarization, and structured data synthesis, but they are poor at logical consistency, real-time data retrieval, and factual reliability without assistance from other systems.

For instance, if an AI model is used to summarize quarterly financials, it will do so based only on the text it’s given—not live market data. Similarly, when used in customer service, it can mimic empathy but will not truly “understand” customer frustration or urgency. This is why organizations must clearly define the boundaries of what an AI system can and cannot decide, particularly in regulated or high-stakes industries.

Understanding these boundaries prevents over-reliance and misuse. The most effective companies are those that treat AI as a co-pilot, not an autopilot—using human expertise to frame, validate, and refine AI outputs.

Reduce Hallucinations through RAG Models

One of the most effective ways to increase LLM reliability is by integrating them with Retrieval-Augmented Generation (RAG) systems. In essence, RAG combines two components: an external knowledge retriever and a language generator. Before producing an answer, the retriever searches a curated database, documentation, or knowledge graph to find the most relevant information. The LLM then uses this retrieved context to generate a grounded, accurate response.

This process significantly reduces hallucinations—the confident but false outputs that plague standalone language models. For example, instead of relying solely on its training data, a RAG-enabled AI customer assistant can pull answers directly from verified company FAQs, policy documents, or manuals before responding. Similarly, an AI analyst can cite and summarize actual SEC filings rather than fabricating financial figures.

The result is a hybrid system that combines the precision of databases with the fluency of generative models, transforming AI from a creative guesser into a reliable assistant. Companies adopting RAG architectures often experience both improved output accuracy and higher user trust, as every response can be transparently sourced.

When designing AI-driven workflows, the golden rule is this: the closer your model is tied to verifiable data, the more valuable it becomes. RAG is not a mere technical feature—it is the cornerstone of scalable, trustworthy AI adoption.

By applying these three concepts, any company can put itself in that 5% group of companies that are optimizing AI to the fullest. As models become more accessible, it is becoming increasingly important for business leaders to understand the application of LLMs and, most importantly, keep an optimal user experience as the primary goal when applying Artificial Intelligence to new as well as existing workflows