Sarah the Gardener
Imagine Sarah is a gardener who depends on knowing when to water her plants. Over the past few weeks, she has noticed that whenever her plants’ leaves start drooping in the late afternoon, the soil is dry. Based on this, Sarah concludes that drooping leaves are a sign her plants need water, and she begins using this observation to determine when it’s time to water them. Predictive AI reaches similar conclusions, but by analysing thousands of factors (instead of just the dryness of the soil) and decades’ worth of data (instead of just several months).

How does predictive AI work?
In statistics, having more data generally leads to more accurate analysis. For example, an opinion poll must have a minimum number of respondents to be considered reliable, and scientific studies often need to be repeated multiple times to achieve statistical significance.

Similarly, predictive AI relies on access to large datasets, often referred to as “big data”. The more data it has, the more accurate its predictions tend to be. While an opinion poll might include responses from a few thousand people, a predictive AI model could analyse thousands or even millions of past opinion polls to forecast trends in public opinion or upcoming elections.

Machine Learning
Machine learning is a subset of AI that involves training a computer program to recognise patterns without human intervention. For instance, with sufficient data on financial transactions, a machine learning model could learn to identify patterns that distinguish between fraudulent activities and legitimate transactions. Or, by using a substantial collection of images depicting various fruits at different stages of ripeness, the model could learn to identify a ‘ripe’ apple and correlate it with optimal harvesting times based on ideal growing conditions, such as when temperature and rainfall exceed specific thresholds.

In predictive AI, machine learning algorithms analyse large data sets autonomously, uncovering patterns and generating predictions based on those insights without requiring human oversight.

Identifying Patterns
Just as Sarah the gardener learned to associate drooping leaves with dry soil, predictive AI identifies patterns in data. However, unlike Sarah, who observed a single pattern, predictive AI can analyse hundreds or even thousands of variables to detect trends that could signal future events.

Predictive AI plays a crucial role in advanced digital transformation, offering a wide range of applications that enhance decision-making and operational efficiency. Knowing what might happen in the future, even with some uncertainty, can give businesses a significant advantage. Some of the potential scenarios include:

1. Trend Forecasting for Product Development

• Predictive AI can analyse vast amounts of market data, consumer preferences and emerging technologies to forecast future trends. This helps companies innovate by developing products and services that meet evolving consumer demands, giving them a competitive edge in rapidly changing markets.

2. Workflow Bottlenecks

• By continuously monitoring project workflows and dependencies, predictive AI can identify areas where tasks are likely to bottleneck due to delays in preceding activities. Recognising these bottlenecks early helps project managers adjust timelines and reassign resources to keep the project on track.

3. Optimising Development Timelines

• By analysing past project data, predictive AI can identify patterns in development processes, such as common delays or resource constraints. This allows project managers to predict potential bottlenecks and optimise development timelines, improving overall project efficiency and reducing time to market.

4. User Experience (UX) Design Improvements

• Predictive AI can forecast how users are likely to interact with a digital solution based on historical user interaction data. This allows designers to anticipate potential UX issues and make iterative improvements, ensuring a smoother and more intuitive user experience upon launch.

5. Service-Level Agreement (SLA) Monitoring

• Predictive AI can continuously monitor service performance against SLAs and forecast potential breaches based on current trends. This enables service control teams to take preventive action, such as reallocating resources or adjusting service priorities, to ensure compliance with agreed-upon performance standards.

6. ROI Forecasting

• Predictive AI can analyse historical financial data, market trends and project performance metrics to forecast the potential return on investment for ongoing and future initiatives. This allows businesses to prioritise projects or investments that are more likely to yield positive outcomes, ensuring resources are allocated to value-generating activities.