Forecasting has never been an easy business. Even as we’re able to draw on more and more data about the world around us, our ability to reliably guess what the future holds is easily skewed by our biases, hunches and emotions.
Now, thankfully, artificial intelligence (AI) is helping us to address that weakness. While we’ve heard plenty about how AI is reshaping the world of work, much of that discussion has centred on its capacity to create – research pieces, visual assets, written content and the like. Whilst that element of AI is invaluable, we shouldn’t overlook the many other ways it can help us.
Artificial intelligence has, crucially, enabled machine learning. By that we mean the ability of digital devices and software to improve responses over time based on previous scenarios they’ve encountered.
This combination of growing data sets, AI and machine learning has gifted us predictive analytics: analysis which not only answers the question of what’s happened, but also what’s most likely to happen next.
Predictive analytics plays a crucial role in addressing various societal challenges. By using this technology, we can make data-driven decisions that lead to more efficient and effective outcomes across a range of sectors.
Policymakers can leverage these tools to address existing sustainability, social and energy issues, whilst gaining a sense of which new challenges might be coming down the track.
These kinds of predictions involve crunching unfathomably large data sets and divergent possibilities to provide unique insight. For policymakers, then, predictive analytics shouldn’t be seen as a mere consideration, it should be seen as essential. By using AI-driven insights, we can anticipate market trends, optimise supply chains, and enhance product development strategies.
What does that look like in practice?
In the world of utilities, it looks like providers optimising energy use in homes and buildings, identifying peak demand, reducing waste and lowering bills. For Scottish Water it means analysing the water cycle to better understand water demand and, in doing so, ensure efficient usage and prevention of shortages.
Predictive analytics can also help to keep our transport systems moving by anticipating where congestion or disruption may occur, and offering strategies to avoid hold-ups. That kind of analysis can be extended to support in other vital services, for instance in predicting where and when waste collections are needed or in helping to improve emergency response times.
When it comes to protecting our health, busy professionals and physicians already benefit from AI use to improve the speed of illness detection, treatment delivery and patient flow. Innovations rolled out at NHS Grampian are just one recent example of this. Elsewhere, councils like Barnsley have used AI to transform social care and increase productivity.
And these are just a few selected examples of where AI-driven solutions can help us make smarter, data-informed decisions that enhance daily life, conserve resources, and improve overall well-being.
By learning from these use cases, policymakers can utilise predictive analytics to address a whole range of challenges facing communities. When this insight-driven policymaking is matched with similar approaches across the commercial world, AI and machine learning can become forces for positive change right across society.
In order for that positive change to be realised, enforcement of high standards and guidelines in this rapidly evolving space is a must.
The tools needed to end our predilection for unreliable forecasts are already with us, and it’s vital that they’re shared far and wide. Yes, predictive analytics is very much about predicting the future, but no one should have to wait until tomorrow for what AI can help achieve today.
For more examples of how AI is helping the UK achieve more, visit the Microsoft AI Hub