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\u2019re eyeing a next-phase opportunity\u2014relying on machine intelligence to handle complex decisions.\n\n\u201cIf 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,\u201d says Prasad Ramakrishnan, CIO & SVP of IT at Freshworks. \n\nAI is becoming an integral part of decision-making for many different business functions \u2013 from finance to manufacturing to sales. Here\u2019s a look at a few areas where it\u2019s gaining influence.\n\nChatbot conversations and decisions\n\nBy 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.\n\n\u201cChatbots can come to your rescue with an answer derived from a knowledge base and know what type of tone to use when responding,\u201d says Ramakrishnan.\n\nCompanies are now moving toward AI-powered decision-making in customer service\u2014tapping into voice and sentiment analysis to automate complex processes such as recognizing customer intent and taking a recommended action to resolve it.\n\nSales optimization\n\nIn 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.\n\nIt 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. \n\n\u201cIt 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,\u201d Ramakrishnan says. \n\nOutcomes are fed back into machine learning models to improve prediction accuracy continually.\n\nDynamic pricing\n\nAirlines, 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.\n\nAI makes this capability available to any business. For example, a retailer could adjust prices on its website based on the visitor\u2019s identity, inventory levels, and competitor prices. Hotels could dynamically adjust room rates based on traffic forecasts, weather conditions, and events in the area.\n\nSupply chain logistics\n\nOptimizing supply chains is a daunting task because of the number of variables involved. AI can help every step of the way. AI-generated \u201cdigital twins,\u201d or virtual representations of physical assets or systems, can replicate live scenarios and predict breakdowns. \n\nAI 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.\n\nPredictive maintenance\n\nAI tools enable proactive maintenance approaches, using data analytics to detect anomalies in equipment and processes\u2014such as the performance of jet engines\u2014so 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%. \n\nDespite AI\u2019s potential for enhanced decision-making capabilities, executives must carefully weigh serious risks. \n\n\u201cAI engines are getting much smarter, but you don\u2019t want to bank the future of your company on decisions being made by a bot,\u201d says Ramakrishnan. \u201cMake sure there is a human involved to check the quality of the results.\u201d\n\nFor more insights about software\u2019s critical role in modern business, visit\u00a0The Works.