IS ARTIFICIAL INTELLIGENCE IN BUSINESS, THE FUTURE?

Why should businesses adopt AI?

How does machine learning and business intelligence benefit business?

Business intelligence, artificial or otherwise, is all about data. Accurate, real-time data provides granular insights into how a business is performing, helps decision makers to understand trends better and respond to a rapidly changing business environment to achieve competitive advantage.

But while business intelligence uses basic calculations to provide answers, machine learning (ML) supercharges business intelligence. ML applications use predictive and cognitive analytics to identify trends and patterns from complex data sets and mathematical models to make predictions beyond the scope of human analysis.

Marketing is a leader in the application of business intelligence models augmented by machine learning tools – tools like Google Analytics, which has led to more effective advertising budget usage through targeted ad placement, realistic sales predictions and better planning.

ML applications are empowering business across a wide range of sectors, supporting faster decision making, increased productivity and operational quality and allowing proactive responses to predicted challenges – reducing costs associated with playing catch-up.

• Buyer behavior models can provide deeper insights into the customer journey, allowing sales processes to be improved and marketing to be optimised through more effective campaigns. Predictions based on new trends can help to build customer acquisition models and reduce attrition rates.
• Applying AI to customer relationship management (CRM) systems replaces intensive manual updates into a self-updating, auto-correcting system.
• Customer segmentation activities can be improved by creating tailored marketing programs, matching products and services with demand, highlighting hidden relationships and dependencies and foreseeing customer behaviors.
• Cyber threats and fraud can be predicted and losses prevented by targeting threats and vulnerabilities before they occur.
• In finance, predictive analytics models can forecast financial performance, evaluate investment risks and optimise financial portfolios.
• Risk assessment applications can predict the impact of decisions such as whether an acquisition or operational change will be profitable
• In healthcare, applications are being used to optimise expenditure and deliver better treatments by mining huge volumes of data to predict health outcomes and identify relationships between diagnosis, medical treatments, risk factors and patient outcomes.

Bringing AI to the C-Suite

Despite the obvious advantages, Dr Richard George, Chief Data Scientist at Faethm AI, says that machine learning for business applications has been difficult to implement.

“AI and ML provide a lot of untapped potential for businesses although tools have not yet made it to C Suite decision-making.

“When you don’t understand what machine learning can do for your business, how data is managed or manipulated, or you can’t verify the accuracy of the models being used, it can be difficult to justify investing in the technology,” he says.

He advises teams considering whether a machine learning application is right for their business to begin by rating the importance of the decision they’re trying to make.

“For an autonomous car, you want 100 percent accuracy when it comes to predictions about pedestrian or animal movements that could cause collisions. For sales, a model that is 90 percent accurate is probably acceptable.”

Models that are now coming into ML applications for business are at a point of accuracy that meets business needs, he says. Models are available to support strategic decisions such whether to enter a new market or predict the success of an acquisition. In HR, tools like faethm are being used in decision support, including predicting how planned innovations will impact jobs.