Predictive analytics has become integral to how many businesses operate and make strategic decisions. Powered by artificial intelligence (AI) and machine learning algorithms, predictive analytics aims to make empirical predictions about future outcomes and events based on historical data.
As AI advances, predictive analytics is becoming more sophisticated and accurate. Businesses across various industries increasingly adopt AI-powered predictive analytics solutions to forecast trends, model different scenarios, mitigate risks, and identify new opportunities.
In this post, we’ll explore how AI transforms predictive analytics and look at some key ways businesses leverage these capabilities to effectively “look into the future” and stay ahead of the curve.
The Evolution of Predictive Analytics with AI
Predictive analytics is not new – businesses have been analyzing data to anticipate future outcomes for decades. However, AI and machine learning have expanded the scope and scale at which predictive analytics can be effectively leveraged. Here are some of the key advantages AI offers:
More Data, Better Predictions
AI algorithms are extremely adept at processing large, complex datasets with hundreds or thousands of variables. The more high-quality, relevant data these models can analyze, the more accurate and precise the predictions.
Advanced Analytics and Modeling
AI expands the predictive modelling and analytical techniques that can be utilized beyond conventional linear or logistic regression. Machine learning algorithms like random forests, neural networks, and deep learning can uncover non-linear relationships and patterns that would be impossible to model with traditional analytical approaches.
With techniques like online or incremental learning, AI models continuously adapt and improve accuracy as they process new data. This allows the predictions to become more precise over time.
Granular and Individualized Predictions
AI allows highly segmented and customized predictive analytics across narrow data slices, individual customer behaviours, or precise scenarios. This micro-targeting provides tactical advantages.
Various “what-if” scenarios can be played out using AI to model future versions and understand probable outcomes. This allows businesses to develop contingency plans. AI transforms predictive analytics from snapshots based on backwards-looking data to dynamic, forward-looking strategic capabilities that generate actionable insights.
Current Applications of AI Predictive Analytics
Many industries and functions are already exploring and deploying AI predictive analytics at scale to plan more strategically for the future. Here are some major practical uses of this capability:
1. Predictive Sales Forecasting
AI-driven predictive sales forecasting has become a must-have capability for consumer businesses. Sales leaders can accurately predict revenue trends weeks or months in advance by analyzing hundreds of signals from within (e.g., pipeline health) and outside the organization (e.g., market and competitive shifts). This enables them to course-correct strategically if needed.
2. Supply Chain Forecasting
By modelling risk factors by building multi-tier supply chain predictive models, businesses can anticipate disruptions to raw material supply, inventory, or fulfilment capacity. This allows them to build in buffers and redundancies. Such resilience is invaluable during times of uncertainty like the COVID pandemic or global conflicts.
3. Financial Forecasting
AI helps banks and lending companies score loan and credit risks more accurately in the financial industry by predicting variability in income, repayment behaviour, and economic factors. Portfolio managers leverage predictive analytics to construct optimal portfolios, and wealth managers use it to align investment strategies to evolving client needs.
4. Healthcare Analytics
Both at individual patient and population levels, predictive healthcare analytics is enabling earlier interventions and improved outcomes. The risk of hospital readmission, the likelihood of post-surgery complications, the expected response to a drug, the risk of chronic illnesses, and even sepsis-related mortality in ICUs can now be reliably predicted using AI.
5. Customer Churn Models
Predicting the likelihood that a customer will cancel or fail to renew a subscription is vital to retaining revenue for subscription-based services. By analyzing usage patterns, purchase history, support tickets, and other data – customer churn models can pinpoint the highest-risk subscribers so preventive retention incentives can be offered.
These examples demonstrate that AI-powered predictive analytics has expansive applications for forecasting trends, emergent events, and outcomes that can impact operational efficiency, risk exposure, and profitability.
The Future of Predictive Analytics
As AI capabilities mature, predictive analytics systems will become ubiquitous across industries. Shortly, we can expect predictive intelligence to power most decisions – from budgeting talent planning to logistics flow optimization and dynamic pricing.
Eventually, predictive analytics may evolve into “predictive enterprises” – organizations use their enterprise data internally and contextual signals from the outside world to continuously align operations and re-align them to the anticipated future state.
The Bottom Line
The capabilities to predict what lies ahead are accelerating rapidly. As ethical oversight and governance combined with technological advances, predictive analytics may fundamentally evolve entire industries, business models, work, and society. The full implications remain, but the promise is a more strategically predictive, data-driven future where risks can be contained before they escalate. Opportunities are sensed early enough to harness their potential.