For some companies, AI is a threat to a long established business model, but for others it’s a revolutionary technology that will allow them to disrupt competitors and grow market share. But one thing is becoming clear: the companies that don’t embrace AI will be outcompeted by those that are working more efficiently and productively by strategically embracing the functionality enabled by artificial intelligence developments.

AI is already transforming work and experiences in specific instances, and it seems likely that it’s only going to continue to change the way we work and the experiences we expect going forward. Processes ranging from client service to coding are already being rewritten around AI functionality. Executives demand that delivery speed increase and time to market decrease. Consumers expect customer experiences to be seamless and personalized.

Shareholders expect companies to implement AI and demonstrate meaningful improvements to core business outcomes. Companies that are viewed as being at risk of disruption due to AI (or companies that are viewed as failing to adopt AI) are being punished in the capital markets.

With stock prices under pressure and shareholders pushing companies to adopt AI, we are seeing companies rush to incorporate AI into their strategies. Executives feel the need to promote AI adoption and publish ambitious plans that illustrate AI positively impacting the bottom line in the near term. They feel the need to prove to shareholders and the financial news media that they understand AI and will be leveraging it to press their advantages and improve the pace of innovation. Rather than being one of the legacy companies that gets disrupted by a tech-forward competitor, they will become a thriving new AI-enabled business.

But claiming that AI will be a tailwind for the business and actually using AI effectively are two different matters. And so far, it seems that legacy companies are struggling to achieve noticeable improvements to the bottom line by using AI.

Beyond a few obvious use cases (e.g., coding, chatbots added to customer experiences), it may not be obvious how AI can be utilized for the company’s benefit; how particular products, services, operations, or processes can be optimized and improved with the addition of AI. And adding to the difficulty is the fact that many executive teams seem to lack a fundamental understanding of what AI is, its capabilities, its limitations, and how to properly think about it within the context of their businesses.

As many executive teams indiscriminately attempt to retrofit their established business models and legacy companies with AI, a lack of understanding is resulting in a flawed approach to AI utilization. Feeling the pressure to prove to shareholders that they are embracing AI and staving off disruption, leadership teams are standing up AI governance, forming AI vendor relationships, attempting to broadly increase AI fluency among their employees, and searching for any and every opportunity to implement AI.

Many companies are turning to employee AI adoption and usage statistics as an easily measurable proxy to check the box of using AI. But AI usage is not a strategy and indiscriminate AI usage may negatively impact the bottom line rather than helping the company. In fact, several early examples show AI yielding no ROI or even negatively impacting the bottom line:

• A widely-cited MIT study released in 2025, “STATE OF AI IN BUSINESS 2025,”  showed that 95% of companies experienced no return from their utilization of AI.
• A story in May of 2026 about an unnamed company that spent $500 million in a single month on Anthropic’s Claude AI platform also generated a lot of conversation. The company had failed to set AI usage limits for employees.
• Uber revealed that it had used up its full year AI budget by April of 2026 and had introduced employee spending limits on AI tools. 

So it seems that for many companies, AI has at best no impact and at worst is an expense driver. And without a structured approach that clearly understands where AI fits into the business model, companies will never be able to effectively utilize AI. All nonstrategic approaches to AI will result in a misallocation of time, money, and other resources.

In order to effectively use AI and derive the maximum potential benefit, it’s important to recognize what AI really is, how it can be used to promote positive business outcomes, and where it fits within a business’s strategy.

One conceptual tool that may help companies to properly use AI is what I call the organizational alignment stack: a hierarchy of business defining concepts that can help illustrate where AI belongs and the proper way to think about incorporating AI within a business. 

One thing that’s important to call out here is that there is significant work that must be done to define the business before AI even enters the picture. Obviously, a company needs to have a clearly defined Vision, Mission, and  overarching Strategy, but before AI is considered, a company should also define its business unit goals, strategy, objectives, and even the roadmap to future state. AI becomes useful after the big picture brainwork that defines and directs the business has been completed, and the team is activating on the day-to-day work of execution. AI can augment human brainwork and can serve as a useful tool in defining the business and its strategy, but AI is most properly seen as supplementary tooling.

For a legacy business, many of these organizational elements are likely already defined, so the organizational alignment stack is most valuable in understanding where AI actually fits as a contributor to business outcomes. Once the particular business objectives are well defined, the question of using AI becomes far more concrete.

Some of the most valuable questions to ask when determining when and how to use AI (and what AI tools to use) aren’t even directly AI focused at all. The questions the business should be asking are: “What do we need to do to achieve our objectives?,” “What problems do we need to solve to meet our goals?” It’s the answer to these questions that informs when, where, and how AI can best be used. If the answer to “what is needed?” or “what solves the problems we’re facing?” is an AI tool or capability, then that is a clear use case for AI.

For example, maybe a company wants to increase user retention on a complex software product with a large number of features accessed through non-intuitive user workflows. AI could be used to solve the user experience problem, perhaps through embedding an LLM-enabled chatbot that leverages existing documentation to prompt the user through certain workflows depending on the user’s goals, but it’s possible that AI isn’t even the best answer to the problem of user retention, and the best solution is a redesign of the user experience that doesn’t require AI at all.

Or consider another example of a company that wants to improve sales. A potential AI solution could be an AI Sales Development Representative seeking to increase leads through automated cold outreach and AI-enabled lead qualification, but maybe the company just needs to better define its unique value proposition and train its salespeople to communicate it to existing prospects.

These examples might seem trivial or obvious, but hopefully they illustrate the fact that AI is an enabler and a tool, not a strategy.

So, to recap: AI is a transformative technology, and companies must use it or risk falling behind. However, it’s not always obvious how to best use AI to achieve strategic business objectives. Thus, in order to maximize effectiveness and return on investment, companies must seek to strategically use AI by understanding its proper role in the business and how it can be used to achieve the company’s goals. AI is a tool not a strategy.