robot, technology, future, futuristic, business, economy, business, money, dollar, bill, high tech, cyber, cyber technology, data, artificial intelligence, 3D, metal, blue background, studio, science, sci fi, hand, gesture, robotic, tech, illustration, innovation, shiny, chrome, silver, wires, concept, creative
getty
In 2025, the boardroom’s call to action was clear, adopt AI or be at the back of the pack. Over the last two years, companies have spent an estimated billions on the adoption of AI with the goal of achieving transformational efficiency and unprecedented growth. As we begin 2026, a harsh reality has settled in throughout the enterprise. Simply having AI does not equate to making money with AI. If your ROI for AI is not appearing on your bottom line, you’re not alone. This issue will require a new approach to measuring success, which will move beyond simply measuring the number of companies adopting AI to measuring tangible return on investment. A large and increasing value gap now exists between the trillions of dollars being spent on AI infrastructure and the tangible return on investment appearing on the bottom line.
“This is the AI monetization crisis. This crisis was created by a basic error in how the economics of AI will work rather than an error in technology itself. For nearly two decades we have understood how to create economic value through SaaS (software as a service) subscriptions based on the number of “seats” or users. However, with the creation of a new consumption model, usage, this very successful way of creating economic value through software has been replaced by a very unpredictable one.”
The Collapse of the Old Model
For years, the cost of enterprise software has been directly proportional to employee numbers. An organization that has 500 employees will buy 500 software licenses which allows for very accurate and predictable financial projections. However, AI doesn’t operate on a user-by-user basis. Instead, it operates on a computation basis, charging by each token, API call, or inference cycle. This transition from employee-based pricing models to activity-based costing models is producing what we’ve called a “black box” invoicing problem for the finance department of many organizations. An individual employee may generate $1,000 in model costs in just a single day of work, but an automated AI agent may generate millions in customer interaction processing without being able to be assigned a single “seat”.
The uncertainty surrounding these costs is central to the nature of the crisis. Because of this, when the cost of services are obscure and volatile, it is very difficult to develop an appropriate long-term business model based upon them. Therefore, establishing a defined framework by which to measure the return on their investment in AI is extremely important. Approximately nine in ten (90 percent) CFOs at major corporations are currently utilizing or are planning to utilize AI, however, they are finding that financial operability, not technical preparedness, is the most significant obstacle to achieving successful utilization of AI within their organizations. Ironically, the tools designed to offer frictionless intelligence are creating massive new impediments to productivity in the most operationally driven portion of the organization.
A New AI Economics Is Emerging
Business leaders can now focus on closing the value gap with AI Economics, an approach that goes beyond tracking the adoption of AI, and is focused on the tangible financial returns generated by activities driven by AI. The key aspect of this change is to transition from purchasing technology, to purchasing results. While the concept of outcome based (or value-based) pricing has been presented as a potential answer to how to fairly price the use of AI models, it is still quite a barrier to implement it in practice. For example, a recent article in Forbes points out that it is almost impossible to isolate the objective value that a particular AI model produces in a large and complex business process. Therefore, fair pricing for both vendors and buyers is a problem at this time.
This has created a challenging environment for AI sellers who have significant computing costs, they are forced to pass on high variable use charges to customers who are hesitant to buy an unknown ROI path at an unknown price. As a result, there is a stalemate which stymies innovation and keeps organizations from fully benefiting from their AI investment.
AI Pricing Reality Check: Old SaaS Vs. AI Usage Bills
Institute of Business AI
Three Steps to Bridge the AI Value Gap
To begin addressing the challenges of monetizing AI, business owners and executives need to develop an innovative approach to value creation, as well as have some form of financial control. The following are three actions that may be taken by business owners and executives that would allow them to begin closing the gap between what they perceive as AI expenses, and the true business benefits of using AI.
1. Request Radical Transparency From Your Vendor
The time has come for business leaders to no longer accept opaque, use based invoices for AI expenses. Business leaders must request that AI vendors provide transparent, understandable and predictable pricing models for their tools. In addition, AI vendors should also provide detailed reporting of the AI expense per business activity and outcome. Prior to signing a contract with an AI vendor, it is essential to request cost calculators, budget caps and pricing tiered models for the AI tool or service that matches your business objectives, not simply the vendor’s computation overhead. If the vendor cannot demonstrate to your financial team how their AI tool will generate revenue for your company, then the vendor’s AI tool is not suitable for your business needs.
2. Start with Problems, Not with Tools
The number one error in creating an AI strategy is identifying an exciting new AI technology and then attempting to identify a challenge to be solved by that technology. Rather than doing this, start by evaluating the most significant bottlenecks, cost centers or revenue opportunities in your company. Are there large volumes of customer service tickets being generated? Does it take an extended amount of time to get a new product from conception through to launch? Once you’ve identified a specific, quantifiable business problem, you can begin to analyze how various AI technologies are able to address this single problem while providing a clear ROI. According to a recent study conducted by Digital Applied, marketing was found to be an area where small businesses experience the highest returns when using AI, given the fact that many marketing-related tasks are high-volume, repetitive and involve working with language.
3. Build an Internal ROI Culture
The first step to empowering your employees to think about AI like investors is to ensure that before you approve any new AI project that there is a very basic business plan that outlines the costs of implementing the new project, the estimated financial ROI and the KPIs that will measure the success of the new project. This is not required to be an extremely complex or detailed financial model, if you are unable to estimate the number of hours saved using a particular tool, then multiply the number of hours saved by a blended hourly employee rate and you will have your ROI. In fact, a small business with annual revenue of $500,000, allocating 1-3% of its revenue for technology, and carving out 20-30% of that allocation for AI experimentation can create a budget for innovation while ensuring that the company’s financial stability is maintained, according to a recent study.
The “AI Monetization Crisis” represents a necessary correction in the evolution of enterprise technology. It is a shift from the “hype of adoption” to the “reality of accountability.” In this new age, the organizations that will thrive will not be those that have implemented AI technology, but rather the organizations that have successfully monetized their AI technology.