How CIOs Use Predictive AI to Make Better Decisions

CIOs make important decisions almost every day. Ensuring that their actions are reached logically, intelligently, and definitively is vital to long-term business success. This is when predictive artificial intelligence becomes an essential tool.

Predictive AI uses statistical analysis and machine learning to identify patterns, anticipate behaviors, and forecast upcoming events. A growing number of CIOs now use the technology to forecast likely future outcomes, causation, risk exposure and other key decisions.

Predictive AI encompasses a wide range of approaches, from classical statistical methods to state-of-the-art deep learning models, says Holly Wiberg, assistant professor of operations research and public policy at Carnegie Mellon University’s Heinz College. “There’s no one best model in all cases; the right tool depends on the use case, available data, decision stakes, and other factors.”

For example, models that predict a hospitalized patient’s 24-hour mortality risk have different consequences than models that predict whether a customer will make a purchase on a retail site, Wiberg explains. “These diverse use cases have different performance metrics of interest, different needs for interpretability, and more.”

Predictive AI helps us move from the rearview mirror to the windshield, says Damu Bashyam, CIO and innovation officer at Berkadia, a commercial real estate and mortgage firm. “In commercial real estate, there are many signals, including macroeconomic trends, demographics, property performance, and capital markets,” he states. “AI pulls those together to show what’s likely next, so we can act earlier, allocate resources better, and manage risk with more confidence.”

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Tying Predictions to Downstream Decisions

How well an AI model can predict an outcome will correlate directly to its ability to spot meaningful patterns in data that are impossible to see with the human eye, says Peter Mottram, leader of the enterprise data and analytics practice at Protiviti, a consulting firm.

Ultimately, the key to leveraging predictive AI is to tie the predictive task to downstream decisions, Wiberg says. She notes that a prediction doesn’t have business value on its own — its value comes from what actions it informs. “As organizations consider integrating predictive AI tools, leaders must start from the use case: what is the business problem, what insights do they hope to glean from data, and what actions will that inform to solve the problem?” Predictive models are often coupled with other quantitative frameworks, such as optimization and simulation, to model the broader system and tie predictions to decisions. “This system-level view ensures that tools solve the right problem to maximize organizational impact.”

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First Steps

Yogesh Joshi, senior vice president, global product platforms, at consumer credit reporting agency TransUnion, emphasizes that the first step is identifying a clear business problem that could benefit from forecasting or pattern recognition.. “Then assess your data readiness, ensuring that you have clean, relevant historical data.”

Next, choose a use case, such as demand forecasting, customer churn, or fraud detection. Then, select the right tools. “Platforms like Azure ML, DataRobot, or Amazon SageMaker offer accessible entry points,” Joshi says. Finally, build a cross-functional team, including data scientists, domain experts, and decision-makers.

Applicability of Predictive AI Across Industries 

Predictions are ubiquitous across various domains, whether forecasting customer demand for consumer products, inpatient census at a hospital, or rush-hour traffic in a subway system, Wiberg says. “While forecasting is a classical problem, recent developments in AI have enabled more data-driven and real-time predictions, leveraging multiple data streams.” These forecasts inform resource allocation, including supply strategy and capacity management, as well as other downstream decisions.

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Start small and smart, recommends Justice Erolin, CTO at software engineering services firm BairesDev. Find a single use case with measurable outcomes to use as a pilot, such as forecasting cloud infrastructure usage or predicting customer churn. “Use that to build internal trust and demonstrate value,” he advises. “You don’t need to overhaul your systems or drop six figures on software right away.” Erolin notes that many CIOs succeed by integrating predictive models into existing business intelligence tools or partnering with vendors that provide plug-and-play predictive features.

Possible Pitfalls

When interpreting predictive AI models, it’s important to recognize that predictive features do not imply causation. “A feature that’s predictive of future demand cannot necessarily be modified to increase future demand,” Wiberg says.

Performance monitoring is also critical. “Model performance can degrade over time for several reasons, including shifts in underlying predictive features, changes in user behavior, and external system shocks.” She adds that organizations should develop proactive monitoring strategies to identify ‘model drift’ and to take corrective action when necessary.

Final Thoughts

Predictive AI isn’t a silver bullet — it’s a tool that enhances human decision-making, but it doesn’t replace it, Joshi says. “Success depends on aligning AI initiatives with business goals, fostering a data-driven culture, and ensuring ethical and responsible use,” he explains. “When done right, it can be a transformative force.”

Don’t let perfection get in the way of progress, Mottram warns. “Predictive AI is a new, hot area, and those in the game will get the first advantages, including talent, competitive  insights, and the promise of cost saving that can be invested into more AI solutions or improving the CIO’s ecosystem.”