Although every business is different, even those in completely separate industries face some of the same long-standing problems. In recent years, artificial intelligence has become the technology that’s well-positioned to solve many of these business challenges.
Let’s look at five key challenges businesses face and how AI-powered solutions from specific companies are addressing those obstacles.
1. Fraud
Handling more digital and mobile transactions gives customers what they want. However, it may also give criminals what they want — that is, an opportunity to grab sensitive personal and financial data. With stronger consumer expectations about transaction speed, companies are struggling to meet demand while ensuring each transaction is scanned for potential fraud.
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AI has become the only technology solution fast enough to help companies process such speedy transactions. For example, companies like Sift Science and Feedzai leverage AI and machine learning algorithms to sort and assess data in a matter of seconds. As a result, these companies have vastly decreased fraud, spammers and a wide range of financial crimes. Other companies such as PoshMark, Door Dash and others have been able to reduce fraudulent transactions, chargebacks and customer spamming.
2. Customer support
Thanks to the immediacy that accompanies the digital marketplace, the customer experience has become a vital part of every company’s success. Today’s companies might deliver faster transactions, but they still struggle with round-the-clock customer support.
AI is stepping in to help companies offer responsive customer support across multiple channels, even without a human being to handle customer inquiries. For example, Agara is helping B2C companies adopt AI-enabled support for an enhanced customer experience. Customers enjoy having a human-like, real-time voice that can quickly answer their questions with informed responses.
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AI is the only solution that can react to customer queries as they speak while simultaneously traversing a company’s complex software grid to offer tips and assistance to operators in real-time.
Similarly, companies like Verint Next IT deliver intelligent virtual assistants (IVAs) and enterprise chatbots. Verint’s approach is different because it offers a virtual agent to talk to the clients, while Agara keeps the operators but gives them AI tools. This type of technology often lead to faster, more effective resolutions for customers, which in turn builds brand reputation and customer loyalty.
3. Personalization
While customers might like the convenience of shopping online and with their mobile devices, they still want brands to see them as individuals and provide personalized interactions. With a much larger customer base and without the connection of face-to-face, in-store transactions, companies are struggling with how to personalize each experience.
Amazon was one of the first to use AI to create personalized recommendations based on past orders. That feature was just the beginning of what AI-powered solutions are now able to do. For example, Persado uses AI to personalize marketing messages based on the technology’s continual learning processes to assess formatting, word positioning, word choices and more.
Dynamic Yield takes it one step further by using AI to determine how personalization can be added throughout the customer journey. This means studying, processing and segmenting for behavioral messaging, targeting and retargeting and recommendations. Case studies show that the use of AI to improve personalization has yielded increased conversions and revenue.
4. Data analysis
The increase in data is beneficial, but it’s still a challenge to structure and usefully mine all this information. Although AI has become a major part of data analysis in the last decade, organizing that data is still a complex undertaking.
DataRobot uses AI to help AI. Implementing a technology it invented known as automated machine learning (AutoML), the company has figured out how to automate part of the process of developing machine learning and AI applications, including those for data analysis. Data and software engineers, as well as analytics experts, can quickly build effective data analysis models to improve their AI-powered data analysis processes.
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5. Productivity
Companies that want to get the most out of their workforce and processes focus on working smarter for enhanced productivity. Once again, AI can provide a better solution.
Appnomic calls itself a “self-healing” enterprise and takes a proactive approach to solve the challenge of continuity of business applications. They use AI for predicting and preventing IT issues before they turn into problems that impact productivity. The company has applied its solution to a wide range of industries, from financial to retail to manufacturing.
Without AI’s prediction capabilities, businesses would need to both fix the problem as well as any damage that’s been made. AI keeps the IT department from firefighting and helps them do their jobs better.
Many industries, such as insurance, financial services and healthcare, suffer from legacy processes that can decrease productivity in the digital age. That’s the main issue that AI-driven Vidado plans to fix. Using AI, the company can help these industries speed up their digital transformations by transforming paper processes into automated digital processes. Greater efficiency means increased productivity and reduced expenses.
AI to the rescue
Thanks to these AI-enabled solutions, age-old business challenges are finally being addressed in an effective way. In the process, organizations can satisfy customers, secure transactions, improve audience and customer interactions, better manage data and become more productive.
It’s important to note that AI is not a silver bullet. Appnomic, for instance, detects IT issues with AI, but relies on automated scripts to solve them (or alert an operator). It’s the art of knowing when and how to use AI that makes these solutions useful.
Original post: https://www.entrepreneur.com/article/347626
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