CIOs make important decisions almost every day. Ensuring that their actions reach logically, intellectually and decisively is essential to long-term business success. When is this Predictive Artificial Intelligence It will become an essential tool.
Predictive AI uses statistical analysis and machine learning to identify patterns, predict behavior, and predict future events. Nowadays, more and more CIOs are using this technology to predict future outcomes, causality, exposure to risks and other important decisions.
Predictive AI includes cutting-edge approaches from classic statistical methods Deep learning model, says Holly Wiberg, an assistant professor of operations research and public policy at Carnegie Mellon University's Heinz College. “There is no model that is best for all cases. The right tools depend on use cases, available data, decision stakes, and other factors.”
For example, models that predict the risk of death for hospitalized patients in a 24-hour period have different outcomes than models that predict whether customers will purchase on retail sites, explains Wiberg. “These diverse use cases have different performance metrics and interpretability needs of interest.”
Predictive AI will help you move from rearview mirror to windshield, says Damu Bashyam, CIO and innovation officer at Berkadia, a commercial real estate and mortgage company. “In commercial real estate, there are many signals, including macroeconomic trends, demographics, real estate performance, capital markets and more,” he said. “AI can work early, allocate resources better and manage risk with more confidence, bringing them together to show what could happen next.”
Connecting predictions to downstream decisions
How well will it go? AI Model Peter Mottram, leader in enterprise data and analytics practices at consulting firm Protiviti, says we can predict results will be directly correlated with our ability to find meaningful patterns of data that cannot be seen by the human eye.
Ultimately, the key to leveraging predictive AI is to link prediction tasks to downstream decisions, says Wiberg. She points out that predictions have no business value in themselves. That value comes from the actions it informs. “As organizations consider integrating predictive AI tools, leaders must start with use cases. What business problems are, what insights do they want to gather from the data, what actions will inform them to resolve the problem?” Predictive models often combine with other quantitative frameworks such as optimization and simulation to model a wider system and link predictions to decisions. “This system-level view ensures that the tool solves the right problems and maximizes the impact of your organization.”
First Step
Yogesh Joshi, Senior Vice President of Global Product Platform at the consumer credit reporting agency Transunion, emphasizes that the first step is to identify clear business issues that can benefit from forecasting or pattern recognition.
Next, select use cases such as demand forecasting, customer termination, fraud detection, and more. Next, select the appropriate tool. “Platforms like Azure ML, Datarobot and Amazon Sagemaker provide access point of entry,” says Joshi. Finally, build a cross-functional team that includes data scientists, domain experts and decision makers.
Industry-wide forecast AI applicability
Predictions are ubiquitous across different domains, whether they forecast customer demand for consumer products, hospitalized patients census or rush hour traffic in the metro system, Wiberg says. “Predictions are a classic problem, but the recent developments in AI have enabled more data-driven and real-time predictions, leveraging multiple data streams.” These predictions inform resource allocations, including supply strategies and capacity management, as well as other downstream decisions.
Starting small and smart, I recommend Judge Erolin, CTO of software engineering services company BaireSdev. Find a single use case with measurable results to use as a pilot, such as using cloud infrastructure or predicting customer termination. “Use it to build internal trust and demonstrate value,” he advises. “There's no need to overhaul your system or drop six numbers into the software,” Erolin points out that many CIOs will succeed by integrating predictive models into existing business intelligence tools or partnering with vendors that provide plug-and-play prediction capabilities.
Possible pitfalls
When interpreting predictive AI models, it is important to recognize that predictive functions do not imply causality. “The ability to forecast future demand cannot necessarily be changed to increase future demand,” Wiberg said.
Performance monitoring is also important. “Model performance can drop 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 surveillance strategies to identify “model drifts” and take corrective actions when necessary.
Final Thoughts
Predictive AI is not a silver bullet – it's a tool that enhances human decision-making, but it doesn't replace it, says Joshi. “Success depends on aligning AI initiatives with business goals, promoting a data-driven culture and ensuring ethical and responsible use,” he explains. “If done correctly, it can be a transformative force.”
Mottram warns, do not advance perfection. “Predictive AI is a new hot area, bringing the first benefits that people in the game include talent, competitive insights, and cost-saving promises that allow them to invest in more AI solutions or improve the CIO's ecosystem.”
