5 ways AI is showing promise as a decision-maker

5 ways AI is showing promise as a decision-maker

CIOs and others in the C-suite are already seeing payoffs from using AI to automate myriad types of business tasks and workflows. Now they’re eyeing a next-phase opportunity—relying on machine intelligence to handle complex decisions.

“If you look at the advances we have seen in AI, with the large amounts of data that large language models can process, we can safely hand off various decisions to machines,” says Prasad Ramakrishnan, CIO & SVP of IT at Freshworks. 

AI is becoming an integral part of decision-making for many different business functions – from finance to manufacturing to sales. Here’s a look at a few areas where it’s gaining influence.

Chatbot conversations and decisions

By some estimates, intelligent chatbots can already answer 80% of routine customer questions. This reduces costs while improving customer experience. Instead of waiting on hold or navigating through phone menus, customers can instantly get answers from a virtual agent that is far more engaging and knowledgeable than past generations of chatbots.

“Chatbots can come to your rescue with an answer derived from a knowledge base and know what type of tone to use when responding,” says Ramakrishnan.

Companies are now moving toward AI-powered decision-making in customer service—tapping into voice and sentiment analysis to automate complex processes such as recognizing customer intent and taking a recommended action to resolve it.

Sales optimization

In sales, AI can provide account reps with the information they need to close deals. An AI system can gather data from customer relationship management software, social media profiles, email interactions, and purchase histories to identify the candidates most likely to convert.

It can also factor in data specific to a sales prospect, such as whether the person has downloaded a resource or engaged with a particular email message. AI can then guide sales reps to follow up on the most promising prospects. 

“It can even feed into the sales narrative, prompting the rep to ask the right questions or use offers that have a higher propensity to appeal to a particular customer,” Ramakrishnan says. 

Outcomes are fed back into machine learning models to improve prediction accuracy continually.

Dynamic pricing

Airlines, ride-sharing services, and online retailers have long used dynamic pricing to adjust to changing market conditions. Utilities are an advanced use case: Power companies use sophisticated algorithms to set prices dynamically according to the volume of electricity generated by renewable energy sources and demand at different times of the day.

AI makes this capability available to any business. For example, a retailer could adjust prices on its website based on the visitor’s identity, inventory levels, and competitor prices. Hotels could dynamically adjust room rates based on traffic forecasts, weather conditions, and events in the area.

Supply chain logistics

Optimizing supply chains is a daunting task because of the number of variables involved. AI can help every step of the way. AI-generated “digital twins,” or virtual representations of physical assets or systems, can replicate live scenarios and predict breakdowns. 

AI analytics tools can assess supplier performance and capabilities to help companies choose the most reliable sources at the lowest cost; they can further streamline operations by using blockchain technology to execute smart contracts, in which transactions are automatically triggered when certain conditions are met.

Predictive maintenance

AI tools enable proactive maintenance approaches, using data analytics to detect anomalies in equipment and processes—such as the performance of jet engines—so they can be fixed before they fail. The benefits are twofold: Downtime is reduced when maintenance can be scheduled and performed without halting operations, and businesses can save money by avoiding unneeded maintenance. Deloitte estimates that predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers overall maintenance costs by 25%. 

Despite AI’s potential for enhanced decision-making capabilities, executives must carefully weigh serious risks. 

“AI engines are getting much smarter, but you don’t want to bank the future of your company on decisions being made by a bot,” says Ramakrishnan. “Make sure there is a human involved to check the quality of the results.”

For more insights about software’s critical role in modern business, visit The Works.

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