Why Healthcare Organizations Should Be Ready to Make Big AI Decisions

Most healthcare organizations are still approaching artificial Intelligence (AI) adoption as if it were traditional software—something they select, integrate, standardize on, and then expect to behave predictably for years. 

But AI doesn’t work that way. Models evolve. Outputs drift. Economics change. And what performs well today may behave very differently tomorrow, even when nothing in their own environment has changed. 

Unfortunately, for the 88 percent of organizations that are already using AI in at least one business function within their enterprise, this mismatch between expectations and reality is becoming a serious operational risk.  Both market volatility and the volatility of AI technology drive the need for quick, data-driven decisions to continuously adopt the best, most impactful AI solutions. 

Constantly changing capabilities 

Organizations across numerous industries – from healthcare to financial services to insurance, and even government – are finding that employees are embracing AI to increase efficiency and become more effective in their jobs. This has led organizations to enable AI (grudgingly or not) and apply it across numerous business units and functions. 

However, the organizations embracing AI may be in for a bit of a surprise.  

Unlike traditional software applications, which are based on code that consistently performs the same function the same way every time it is run, AI is constantly learning and evolving. Using an AI agent or model might yield the same response 10 times, but produce something completely different on iterations 11-20. Worse, subsequent models might not function the same way or as effectively for their use case as the original models. 

This lack of determinism and repeatability can be problematic for organizations that rely on AI for critical business functions.  

If a government agency is using AI to answer eligibility questions for citizen services, or a financial services company is relying on an AI model to automatically qualify prospects for a particular service or product, or if a retailer is using AI to interact and answer customer questions online, they want those responses to always be accurate, repeatable, and consistent.

Due to rapid iterations and advancements in the AI industry, these organizations may find that the AI model or agent they’ve standardized on and leveraged across their organization has been updated or replaced by a newer model. That newer model may no longer interact or respond the way they need and want it to, leaving users with a new AI model that operates in a way that is more expensive, less useful, and misaligned to their operations. 

While the technological differences between AI and other, more traditional, software applications can certainly create volatility, other unique factors in the AI industry are adding to the uncertainty. Some of them involve how AI companies are currently operating and doing business. 

Today, the large AI companies are focused on improving their models, increasing the capability of their solutions, and driving as much adoption as possible. Candidly, they’re not really focused on turning a profit – they’re mostly focused on growing their product and user base with the record investments they’re receiving from other large technology companies, and deep pocket investors. 

At some point, these companies will have to turn their expanding user base into profits, and that could mean massive increases in license and token costs. If the cost of an AI model or agent becomes too high for an organization, they may also have to shift to a different AI solution. 

Ultimately, volatility in the existing AI marketplace could force organizations to make critical decisions about which AI agents and models to use. They may even find themselves having to make those decisions quickly to restore essential business processes that they’ve built around AI solutions. 

Making informed decisions 

The problem with having to make important decisions about AI models in this volatile market is a lack of data. Many AI solution providers provide little information about their solutions and how they work.  

Worse, they don’t give users the ability to run their data through the model and test its capabilities with the frequency necessary to make essential business decisions. As we discussed, AI models and agents could return the same response 10 times, but something different on the 11th-20th time. If organizations can’t run their data through that agent and test its capabilities 20 times, they may not have a clear, comprehensive understanding of how that AI model or agent will perform in their business. 

Thankfully, there is a new generation of AI enablement and governance tools dedicated to providing this kind of data to organizations so that they can make rapid, informed AI decisions. 

Companies like Waapeaai are working to pull back the curtain and empower organizations with the data they need on AI models and agents so they can see how they will truly perform when applied within their organizations. This includes allowing organizations to analyze how newer, better, or more cost-effective models and agents operate in comparison to previous versions and iterations. 

These solutions effectively allow users to run the new and old models against their organization’s data and AI usage activity over time and compare their performance, costs, and fit to their purposes. 

Today, there are many AI models and solutions available from numerous different AI companies. Each of these models may perform better than the others at one specific task. AI management and governance tools enable running these solutions head-to-head, assessing their strengths and weaknesses, and making better, more informed AI decisions as the market evolves. 

Unfortunately, many companies embracing AI within their organizations are still approaching these solutions the same way they did more traditional software applications. But AI is not like the software that came before. The volatility inherent in AI agents and the shifting AI landscape and business models create a situation in which organizations will have to change AI agents and providers on the fly. When that time comes, they need the data and information necessary to make rapid, informed decisions.

The author, Apel Tato, is the Chief Executive Officer at Waapeaai, which helps organizations navigate their AI Journey at a time of unprecedented technical innovation, sky-high expectations, and uncertain outcomes.