The unprecedented potential of artificial intelligence (AI) and predictive analytics have permeated into every field and are rapidly transforming industry verticals.
AI and predictive analytics can help improve efficiency and make way for faster decision-making. Traditionally, organisations detect problems as and when they appear, attempt to resolve them and then restore services back online. Now organisations can break free of this reactive approach and become proactive when it comes to detecting and resolving issues.
These insights, provided by AI and predictive analytics, are remarkably different from traditional analytics. They are more accurate and detailed than manually-generated predictive insights, as they use large volumes of historical data to distinguish patterns from white noise, and churn out predictions based on those patterns. Since AI bots are programmed to continuously learn and adapt, they improve and make more accurate predictions over time.
Let us delve deeper into the many ways AI and predictive analytics is levelling up industries.
Maintenance activities on servers or networks are known to cause disruptions in network or application availability, leading to an increase in alarms during maintenance periods. Applying predictive algorithms to maintenance can help foresee when applications or networks are likely to go down in the future.
AI and predictive analysis also help organisations monitor applications in real-time and foresee potential failures. This can help them plan software and security updates in a manner that’s least disruptive to business services, as well as quickly assemble alternate servers or networks to share the load during downtime.
Leveraging AI in resource allocation can improve desk service processes, making way for faster request resolutions and better compliance. AI algorithms are better equipped to analyse, diagnose, and suggest resource requirements so that organisations can make faster and more efficient resource management decisions.
For instance, organisations can plan resource requirements by mapping incoming requests against several factors, such as geography (remote offices and city offices), time (busiest hours and off-hours), day (weekdays and weekends), or seasonal changes (holidays and vacations).
The asset life cycle is rife with wasted resources. Each stage of the asset life cycle is known to include a lot of unnecessary expenditure that could have been saved by leveraging AI and predictive analytics.
A typical scenario is over-procurement and underutilisation of hardware and software assets. AI can prevent such waste and aid in intelligent decision-making at each stage of the asset life cycle. In the purchase cycle, it can help organisations make smart purchase decisions by investing in assets that have a longer life cycle but relatively lower costs. It can also help plan asset purchases to ensure that assets spend minimal time on storage shelves.
AI and predictive analytics in asset management can also help reduce spending by giving organisations a clear picture of asset retention costs.
Anomaly detection, with the help of AI and predictive analytics, is at the forefront of innovation and advancement. This process would analyse the data, and pinpoint towards anything unusual, in terms of operations or expectations. It could help brands predict whether a certain campaign succeeds, if a video will go viral and the degree of engagement from the audience.
By using anomaly detection, one can deduce what worked in their favour and what did not. It can help determine if a prospective client will become a customer or if they will walk away.
Furthermore, AI and predictive analytics can help bridge the gap between customers and organisations, where the latter can understand their customers’ behavioural patterns and eliminate hurdles that push them away.
Although it might seem inevitable that such powerful business tools will be adopted en masse, the reality is more nuanced than that. While organisations want tools for smarter, faster decision-making, there is a tight wire trapeze act to follow when balancing data, people, and technology when transforming a business to an AI-driven predictive analytics model.
Maintaining the best data practices, as well as focusing on combining the powers of machine learning and predictive analytics is the only way for organisations to keep themselves relevant and effective.
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