Broadly speaking, AI refers to computer systems that can perform problem-solving and decision-making tasks normally associated with human intelligence. These can include:
-Recognizing images and speech
-Making decisions
-Translating languages
-Providing recommendations
And more
Top 5 sectors using AI
1.LAW
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Various areas of AI in the legal sector can be discovered with adequate research and careful comprehension of the legal industry by IoT app development companies. The current Artificial Intelligence applications in the industry can be categorized into six main parts:
-DUE DILIGENCE: Lawyers use Artificial Intelligence tools to perform due diligence and uncover background information. In light of the current scenario, developers have opted to integrate a slew of new features, including agreement review, legal inquiry, and electronic media for this section of the industry.
-PROGNOSTICATION TECHNOLOGY: Artificial Intelligence (AI) aids in the generation of outcomes for legal investigations and agreement evaluations. This characteristic of AI programming appears to be extremely beneficial to legal firms and industries.
-LEGAL MECHANISM: Lawyers can obtain information points from prior or past instances using Artificial Intelligence technologies. They can also utilize this data to keep track of the judge’s instructions and forecasts. This technology is likely to become increasingly important on a global scale in the near future.
-DOCUMENTING MECHANISM: Different types of software arrangements are used in the legal industry to develop papers that aid in the collection of data and information. In the law firm industry, there are numerous documents that are useful. As a result, it is really beneficial.
-INTELLECTUAL POSSESSION: Artificial intelligence algorithms demonstrate lawyers how to examine massive IP files and extract meaning from a variety of attractive texts.
-ELECTRONIC RECEIPT: Lawyers used to make their own receipts for a long time. The billings of lawyers were turned electronic after AI software development technology was applied in these businesses.
- MARKETING & ADVERTISING
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AI marketing uses artificial intelligence technologies to make automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is often used in digital marketing efforts where speed is essential. AI marketing tools use data and customer profiles to learn how to best communicate with customers, then serve them tailored messages at the right time without intervention from marketing team members, ensuring maximum efficiency. For many of today’s digital marketers, AI is used to augment marketing teams or to perform more tactical tasks that require less human nuance.
AI Marketing Use Cases Include:
-Data Analysis: Collecting and sifting through large amounts of marketing data from various campaigns and programs that would otherwise have to be sorted manually.
-Natural Language Processing (NLP): Creating human-like language for content creation, customer service bots, experience personalization and more.
-Media Buying: Predicting the most effective ad and media placements for a business in order to reach their target audience and maximize marketing strategy ROI.
-Automated Decision-Making: AI marketing tools help a business to decide which marketing or business growth strategy they should use based on past data or outside data inputs.
-Content Generation: Writing both short and long pieces of content for a marketing strategy, such as video captions, email subject lines, web copy, blogs and more.
-Real-time Personalization: Changing a customer’s experience with a marketing asset such as a web page, social post or email to fit the customer’s past preferences to encourage a certain action, such as clicking a link, signing up for something or buying a product.
3.FINANCE
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1)Risk assessment
Can you use artificial intelligence to determine whether someone is eligible for a loan? Definitely. In fact, banks and apps are using machine learning algorithms to not only determine a person’s loan eligibility, but also provide personalized options, according to Towards Data Science. The advantage? AI isn’t biased and can make a determination on loan eligibility quickly and more accurately.
2)Risk management
Risk mitigation is always an important — yet ongoing challenge — in banking (and practically every other industry). Now, machine learning can help experts use data to “pinpoint trends, identify risks, conserve manpower and ensure better information for future planning,” according to Built In.
3)Fraud detection, management and prevention
Have you ever received a phone call from your credit card company after you’ve made several purchases? Thanks to artificial intelligence, fraud detection systems analyze a person’s buying behavior and trigger an alert if something seems out of the ordinary or contradicts your traditional spending patterns, according to Towards Data Science.
4)Credit decisions
Towards Data Science explains that artificial intelligence can quickly and more accurately assess a potential customer based on a variety of factors, including smartphone data (plus, machines aren’t biased.)
5)Financial advisory services
Looking to follow the latest financial trends? Interested in a portfolio review? Artificial intelligence algorithms can analyze a person’s portfolio (or the latest trends or most types of relevant financial information) so that you can receive the information you need as quickly as possible, according to Forbes.
6)Trading
Since artificial intelligence is used to analyze patterns within large data sets, it’s no surprise that it’s often used in trading. As Built In explains, AI-powered computers can sift through data faster than humans, which expedites the entire process and saves large chunks of time.
7)Managing finances/personalized banking
Chatbots and virtual assistants have reduced (and in some cases eliminated) the need to spend time on the phone waiting to speak with a customer service representative. Now, thanks to technology and AI, customers can check their balance, schedule payments, look up account activity, ask questions with a virtual assistant and receive personalized banking advice whenever it’s most convenient, according to Towards Data Science.
8)Preventing cyberattacks
Consumers want to be reassured that banks and financial institutions will keep their money and personal information as safe and secure as possible, and artificial intelligence can help. It’s estimated that up to 95% of cloud breaches are caused by human error. Artificial intelligence can boost company security by analyzing and determining normal data patterns and trends, and alerting companies of discrepancies or unusual activity.
9)Better predict and assess loan risks
As Forbes explains, artificial intelligence can analyze a customer’s spending patterns and actions, which can predict loan borrowing behavior. This is also important in areas around the world where people have smartphones and other means of connection and communication but may not have traditional credit. Forbes gives this example: A loan applicant can download an app and the lender would use it to analyze the person’s “digital footprint” — which includes social media use, browsing history and more in order to build a more complete picture.
10)Enabling 24/7 customer interactions
Thanks to artificial intelligence and the prevalence of virtual assistants and chatbots, customers can ask questions at all hours of the day (and night!) and don’t have to wait to speak with a person.”It’s always about making the human interaction more efficient, because in many of these cases, there’s still a customer service rep,” says Rob Thomas, senior vice president of IBM’s Cloud and Data Platform, in a recent Yahoo! Finance video. “But AI is making them more productive, making them better at solving the problem.” This means “virtual assistants can respond to customer needs with minimal employee input,” according to AI News. “A straightforward means of increasing productivity, the time and effort spent on generic customer queries is reduced, freeing up teams to focus on longer-term projects that drive innovation across the business.”
11)Reducing the need for repetitive work/process automation
AI can automate repetitive mundane, time-consuming tasks, such as reviewing documents or pulling information from applications, which will free up employees to tackle other projects.
12)Reducing false positives and human error
People make mistakes, and human error is an unfortunate reality. In the financial services industry, 94% of surveyed IT professionals said they aren’t confident that their employees, consultants and partners can safely protect customer data. Thankfully, artificial intelligence can help reduce false positives and human error.
13)Ability to execute tasks of any length
Artificial intelligence has the ability to scale, meaning that you can use this type of advanced technology for short- or long-term projects.
14)Making smart underwriting decisions
AI solutions are helping banks and lenders “make smarter underwriting decisions” when it comes to the approval process for loans and credit cards, according to Built In. This is done by using a variety of factors that paint a more accurate picture of those who may be traditionally underserved.
15)Save money
Every item previously mentioned on this list can contribute to increased revenue. By automating tasks, you free up employees to take on additional responsibilities instead of hiring more personnel. Virtual assistants and 24/7 chatbots create a more positive customer service experience, and using AI to help determine whether someone qualifies for a loan typically means finding those with good credit who won’t default.
4.RETAIL & CUSTOMER SERVICE
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Today’s dynamic retail industry is built on a new covenant of data-driven retail experiences and heightened consumer expectations. But delivering a personalized shopping experience at scale — that is relevant and valuable — is no easy feat for retailers. As digital and physical purchasing channels blend together, the retailers that are able to innovate their retail channels will set themselves apart as market leaders.
So, what exactly does that look like? Here are some examples of how AI in retail is reshaping the entire industry.
Inventory Management – AI in retail is creating better demand forecasting. By mining insights from marketplace, consumer, and competitor data, AI business intelligence tools forecast industry shifts and make proactive changes to a company’s marketing, merchandising, and business strategies. This also impacts supply chain planning, as well as pricing and promotional planning.
Adaptive Homepage – Mobile and digital portals are recognizing customers and customizing the e-retail experience to reflect their current context, previous purchases, and shopping behavior. AI systems constantly evolve a user’s digital experience to create hyper-relevant displays for every interaction.
Dynamic Outreach – Advanced CRM and marketing systems learn a consumer’s behaviors and preferences through repeated interactions to develop a detailed shopper profile and utilize this information to deliver proactive and personalized outbound marketing — tailored recommendations, rewards, or content.
Interactive Chat – Building interactive chat programs is a great way to utilize AI technologies while improving customer service and engagement in the retail industry. These bots use AI and machine learning to converse with customers, answer common questions, and direct them to helpful answers and outcomes. In turn, these bots collect valuable customer data that can be used to inform future business decisions.
Visual Curation – Algorithmic engines translate real-world browsing behaviors into digital retail opportunities by allowing customers to discover new or related products using image-based search and analysis — curating recommendations based on aesthetic and similarity.
Guided Discovery – As customers look to build confidence in a purchase decision, automated assistants can help narrow down the selection by recommending products based on shoppers’ needs, preferences, and fit.
Conversational Support – AI-supported conversational assistants use natural language processing to help shoppers effortlessly navigate questions, FAQs or troubleshooting and redirect to a human expert when necessary — improving the customer experience by offering on-demand, always-available support while streamlining staffing.
Personalization & Customer Insights – Intelligent retail spaces recognize shoppers and adapt in-store product displays, pricing, and service through biometric recognition to reflect customer profiles, loyalty accounts or unlocked rewards and promotions — creating a custom shopping experience for each visitor, at scale. Stores are also using AI and advanced algorithms to understand what a customer might be interested in based on things like demographic data, social media behavior, and purchase patterns. Using this data, they can further improve the shopping experience and personalized service, both online and in stores.
Emotional Response – By recognizing and interpreting facial, biometric, and audio cues, AI interfaces can identity shoppers’ in-the-moment emotions, reactions or mindset and deliver appropriate products, recommendations or support — ensuring that a retail engagement doesn’t miss its mark.
Customer Engagement – Using IoT-enabled technologies to interact with customers, retailers can gain valuable insights on consumer behavior preferences without ever directly interacting with them. Take the Kodisoft interactive tablet for example – Kodisoft developed a tablet to be used in the restaurant setting for customers to use to browse menus, order, and play games. Supported by the IoT Hub and machine learning, this tablet has leveraged consumer data and behavior trends, allowing companies to increase engagement and success with customers.
Operational Optimization – AI-supported logistics management systems adjust a retailer’s inventory, staffing, distribution, and delivery schemes in real-time to create the most efficient supply and fulfillment chains, while meeting customers’ expectations for high-quality, immediate access and support.
Responsive R&D – Deep learning algorithms collect and interpret customer feedback and sentiment, as well as purchasing data, to support next-generation product and service designs that better satisfy customer preferences or fulfill unmet needs in the marketplace.
Demand Forecasting – Mining insights from marketplace, consumer, and competitor data, AI business intelligence tools forecast industry shifts and make proactive changes to a company’s marketing, merchandising, and business strategies.
Customized Selections – Taking customer service to the next level, many retailers are using AI to help them provide unique, personalized experiences for customers. And, there’s big money in providing such services.
5.HEALTHCARE
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Artificial intelligence (AI) and machine learning solutions are transforming the way healthcare is being delivered. Health organizations have accumulated vast data sets in the form of health records and images, population data, claims data and clinical trial data. AI technologies are well suited to analyze this data and uncover patterns and insights that humans could not find on their own. With deep learning from AI, healthcare organizations can use algorithms to help them make better business and clinical decisions and improve the quality of the experiences they provide.
AI in healthcare use case: Natural language processing
When subject matter experts help train AI algorithms to detect and categorize certain data patterns that reflect how language is actually used in their part of the health industry, this natural language processing (NLP) enables the algorithm to isolate meaningful data. This helps decision makers find the information they need to make informed care or business decisions quickly.
Healthcare payers
For healthcare payers, this NLP capability can take the form of a virtual agent using conversational AI to help connect health plan members with personalized answers at scale.
Government health and human service professionals
For government health and human service professionals, a case worker can use AI solutions to quickly mine case notes for key concepts and concerns to support an individual’s care.
Clinical operations and data managers
Clinical operations and data managers executing clinical trials can use AI functionality to accelerate searches and validation of medical coding, which can help reduce the cycle time to start, amend, and manage clinical studies.