From “What and why it happened” to “What will happen,” predictive analytics has come a long way since its humble beginnings in the 1970s, so much so that it would constitute a market value of $22 billion in 2026.

On the other hand, Artificial Intelligence (AI) has been soaring through the ranks ever since it was officially recognized at a 1956 conference organized by Dartmouth College — although much of its prominence can be attributed to the IBM Deep Blue computer and the extremely influential IBM Watson. 

As much as these technologies enthrall, the synonymous and rather ambiguous use of the terminology (AI, ML, Predictive Analytics) gives way to confusion. Since both predictive analytics and AI are immensely critical to businesses, there is always a need to reflect upon their individual prowess and inextricable linkage. 

AI vs. Predictive Analytics

Before getting to the technicalities of this powerful combination, let’s outline some of the key differences between them. 

Definition: Predictive analytics pertains to learnings from historical data that a machine can use to make predictions about the future. Contrarily, AI is a more broad term that describes self-learning systems capable of learning from experience and autonomously improving themselves.

Process: Predictive analytics leverages preexisting data and transforms it into inferences that provide insight into future experiments. Conversely, AI involves learning, adapting, and identifying patterns via the iterative processing of conglomerated data.

Example of Predictive Analytics: A recommender system (usually employed on social media) recording user preferences, predicting their purchasing behavior, and providing recommendations on similar items. 

Example of AI: A self-driving car coupling real-time sensor data to machine learning algorithms to navigate a route while minimizing collisions.

Broadly speaking, predictive analytics is a subset of AI, which is why the interchangeable terminology is quite common. Nevertheless, better contextualization unearths aspects like those above that provide a coherent description of what each of these technologies actually entails.

When viewed as a combo, AI-based predictive analytics creates a powerhouse that’s capable of predicting the future with astounding accuracy and precision — a combination that can offer new levels of insight to predicting the outcomes of marketing campaigns, medical diagnoses, fraud detection, and several other complex business challenges.

Predictive Analytics Use Cases

  1. Predictive Analytics for Inventory Optimization

Manufacturing plants around the world are often faced with the problem of excess/insufficient inventory. Capgemini Research Institute’s 2020 supply chain report reveals how organizations have been struggling with inventory management ever since COVID-19 burst onto the scene. Almost 74% of businesses have reported a shortage of materials, and another 69% have complained about the stock imbalance. Not to mention that 74% have been agitated with the delayed shipments and the challenge in scaling the production as per needs. 

To that end, imagine a solution that could help forecast the stock levels, monitor the customer demands, and predict the demand spikes. Thanks to data science, predictive AI is voraciously employed to prevent supply chain disruptions by:

  1. Forecasting the future demand to see if the existing stock is sufficient to meet the market need or not.
  2. Estimating the production levels in order to balance the excessive inventory.
  3. Determining the best time of production to match the production levels to customer demands.
  4. Facilitating inter-inventory stock management through a holistic view of the supply chain’s functionality.
  1. Predictive Analytics in Asset Management

Asset management is a complex process that involves plenty of situational complexities, such as:

  1. Tracking and updating the assets throughout their lifecycles to ensure that no asset gets lost or is siphoned off
  2. Monitoring the asset performance to ensure minimal downtime and maximum output
  3. Predicting potential risk factors and letting people act on them in a time-sensitive manner, etc. 

It’s evident that predictive analytics has a hand in understanding the intricacies of these processes since it can help forecast potential problems before they happen, track where assets are located, and predict their locations if they run into a problem.

Doshi et al., from McKinsey & Company, outline how organizations are employing advanced predictive analytics and AI at the three stages of asset management:

  1. Asset Acquisition: Employing behavioral segmentation, facilitating data-driven prospecting, and predicting the associated productivity.
  2. Investment Management: Facilitating strategic sourcing, forecasting the supply chain risks, and making investment decisions.
  3. Asset Management: Facilitating operational control, reducing the total cost of ownership, and automating the entire asset management process.
  1. Predictive Analytics in Oil & Gas Industry

Scientific management has been an integral part of the oil & gas industry’s production networks. However, since the nature of oil & gas exploration is highly volatile, predicting the outcome of a well is extremely challenging. 

AI-powered predictive analytics is a game-changer in the oil & gas industry that can help with understanding reservoir and riskiest wells, better exploring, drilling more effectively, predicting the fate of exploration wells, determining the best time to pull out rigs/equipment from oil & gas fields, etc.

More profoundly, predictive AI is equipped for:

  1. Ensuring Human Safety: Predicting the possible incidents such as blowout, well fire, and reduced pressure to prevent them from occurring in the first place.
  2. Predictive Maintenance: Forecasting the equipment failure and possibilities of downtime, issue early warnings, and even maintain the schedules in line.
  3. Predicting Quality: Reducing the need to reprocess the samples, eliminating the quality issues, and avoiding production disasters.

A viable solution for achieving the above is UptimeAI Expert, which helps predict performance degradation and performance upsets while consistently advancing the system’s energy efficiency. 

  1. Predictive Analytics in Pharma

COVID-19 has very well elucidated the vitality of an effective healthcare R&D infrastructure in place. The “effectiveness” of the same, as it turns out, is attributed to the sophistication of the technology being employed. 

In the pharmaceutical industry, predictive analytics has assumed a much bigger picture with the advent of AI. The combo helps with:

  1. Streamlining Clinical Trials: Facilitating and optimizing the conduct of clinical trials, reducing time to market, and offering a better medical treatment.
  2. Providing Patient Insights: Making treatment decisions based on patient history and clinical outcomes and offering customized treatments.
  3. Developing Drug R&D Strategies: Facilitating the R&D via unstructured data and big data, avoiding costly clinical trials, and diagnosing diseases early.
  4. Assessing the Risks: Forecasting the probabilities of adverse outcomes in terms of costs and/or treatment cycles, etc., and preventing manufacturing glitches.
  5. Advancing Multi-channel Promotion: Maximizing the impact of the campaigns by intently targeting the appropriate target audience.

FAQs:

  1. How can predictive analytics improve performance measurement?

One of the benefits of having predictive analytics in place is that it can help with systematically understanding the performance metrics. With better insights into what drives the outcomes, it becomes possible to effectively change behaviors that lead to desired outcomes. 

For instance, predictive AI can link company data together in order to generate insights about company performance and then measure it against industry benchmarks or focus areas. These insights would allow companies to better understand their strengths and weaknesses as well as figure out any deviation whatsoever and make decisions accordingly.

  1. Is predictive analytics machine learning

Predictive analytics relies on predictive modeling, which gives way to the use of machine learning algorithms.

  1. What is the difference between predictive and prescriptive analytics?

Predictive analytics provides insights into what outcomes or events may happen in the future. This could include predicting customer behavior, the severity of malware attacks, or events that unfold within a system. Prescriptive analytics, on the other hand, offers advice on how to change or optimize the system to make desired changes happen.

  1. What are the new trends in AI and predictive analytics?

Some of the most interesting things regarding trendy areas pertain to how AI blends with predictive analytics and offers new perspectives on market opportunities. Here are some trends that we’re seeing:

  1. Analytical platforms that integrate with AI and machine learning models can work in tandem to identify causal relationships.
  2. Analytics backed by neural networks and deep learning algorithms, which can provide high-resolution outputs.
  3. Predictive analytics focused on one specific vertical, rather than offering a “one-size fits all” approach.
  4. Predictive analytics is applied to all types of data sets, including time-series and unstructured data, rather than only statistics and structured data.

Conclusion

AI & predictive analytics solutions optimize processes and resources for enterprises while reducing costs and improving efficiency. Given their remarkable ability to process copious amounts of data quickly and identify patterns, while validating them from past experience. AI & predictive analytics together hence can unlock a host of possibilities for industries and see a widespread adoption together.

Want to check how you can leverage AI & Predictive Analytics to make your plant monitoring proactive and next level efficient? Book a demo with our team here.