Benefits of Using Artificial Intelligence in Decision Making
Over the years, AI's predictive accuracy has improved immensely. With more accurate models, human stakeholders can rely on AI to make informed decisions with great confidence. AI in decision making has far-reaching advantages throughout the business lifecycle.
Business automation: AI-driven automation is at the forefront of the Fourth Industrial Revolution. Businesses can save a lot of time by automating tedious repetitive tasks. All major industries are applying automation to their production, marketing, resource allocation and sales processes. Predicting supply and demand can optimize revenue streams.
Automated systems are less prone to human biases and unintentional errors. Can reduce operating expenses by reducing dependence on human labor.
Standardize information: Quality AI products need valuable data to achieve desired results. Businesses collect raw unstructured information from a variety of sources, including public datasets, scraping the internet, internal workflow, conducting market research, and sourcing leads.
``If there's one thing we can do to save hundreds of billions of dollars every year, it's to start with data schema standardization,' Secondmind CEO Vishal Chatrath`
In the old days 🙂 (about five years ago), this raw information was processed and analyzed manually. With AI, all information is processed automatically. Different AI techniques can standardize information regardless of the data source. AI models can quickly adapt to various types of information to draw relevant conclusions.
Opinion mining: Sentiment analysis or opinion mining is the ultimate tool to get inside the minds of customers. Advertisers use online search preferences, blog posts, surveys, comments, emails, tweets, and all other user-related activity to research what the customer wants. This information helps them improve customer satisfaction and relationships.
AI has given powerful Natural Language Processing (NLP) models that can perform sentiment analysis on any type of information. NLP models have become better at understanding human emotions. Using artificial intelligence, brands can mine social media and deliver more personalized product experiences by listening to their customers' needs in real time.
Customer relationship management (CRM): CRM systems are vital for the efficient management of a company's suppliers, customers and employees. AI-powered CRM tools can deliver accurate sales forecasts and support managers in making informed strategic decisions.
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They can detect patterns in sales data, predict lead scoring and customer churn rate, allowing businesses to take important steps to prevent any churn. CRM data can be leveraged by AI-powered virtual assistants who can perform tedious office tasks and field customer queries.
High ROI and better decisions: Standardized data, optimized business processes, automated market sentiment analysis, and AI-powered CRMs enable businesses to make better decisions. AI can minimize the percentage of incorrect decisions and reduce overall costs, promising high returns on investment. If any aspect of the business changes, AI can capture it in real-time and help decision makers optimize the supply and demand pipeline.
Artificial Intelligence-Based Decision Making in Various Industries
Businesses rely on data-backed AI to support human decision-makers, benefiting both producers and consumers. Let's analyze some industries where AI is driving decision-making.
Food and Agriculture: The survival of all life on Earth depends on a constant supply of quality food products. AI applications can help us achieve food security, promote sustainable food production, and ultimately end world hunger. The Food and Agriculture Organization (FAO) uses artificial intelligence, machine learning and satellite imagery to monitor agricultural stress such as drought and drought. They developed a mobile app called FAMEWS that can monitor the spread of 20 plant pests and limit the destruction of crops. Their AI can also intelligently manage agricultural water supply. All these developments contribute to the sustainable growth of this sector.
Healthcare: Image recognition and image segmentation are powerful tools enabled by Machine Learning with extensive use cases in healthcare. Detection of various cancers and medical abnormalities in X-rays and CT scans is now possible with ML. InferVision uses AI and ML to examine thousands of CT scans to detect signs of cancer, empowering radiology staff to make more accurate decisions.
Banking and Financial Services: Banks use AI-powered fraud detection systems to process loan applications. One such product, developed by underwrite.ai, analyzes loan applications from businesses to predict their credit risk. They use AI techniques such as Genetic Algorithm (GA) along with Machine Learning and Big Data to analyze credit portfolios.
Travel and Hospitality: Artificial intelligence is solving various problems faced by the ravel industry, such as providing personalized and automated customer support experience and predicting travel disruptions. Hopper helps travelers make better travel decisions with up to 95% accuracy by predicting the best time to book and purchase tickets. Utrip is an AI recommendation system for a personalized travel experience with an industry-proven increase in bookings. Julie, an AI-powered assistant from Amtrak, can make train reservations and answer more than five million inquiries a year.
Logistics and Transportation: Smooth supply chain operations rely heavily on modern logistics and transportation. IoT and AI are disrupting this industry with smart applications. KeepTruckin is an AI-based fleet management system that ensures driver safety, provides GPS tracking and smart routing, and reduces fuel cost. AI dashcams use Computer Vision to detect and prevent unsafe driving incidents and provide real-time alerts to drivers.
Manufacturing: Artificial intelligence also finds its usefulness in manufacturing industries. Automotive manufacturer Volvo uses ML-based techniques to monitor the performance of its cars in hazardous conditions. Various sensors are installed in the car that collect different driving variables. ML allows them to evaluate the security of their vehicles.
Marketing: Various products like Albert, Gumgum, and Wordstream effectively use AI and Machine Learning to design optimized ad campaigns and write dynamic ad copy.
Retail and E-commerce: Retail and E-commerce are the sectors best suited for AI disruption. From demand forecasting to automated inventory management, AI is helping retailers create better strategies. Intel uses artificial intelligence technologies to control product availability using smart shelves. Smart self-checkout systems can detect missing or unreadable barcodes, and contactless kiosks can recognize speech and gestures to interact with customers. Another product, MakerSights, uses artificial intelligence to make informed decisions throughout a product's lifecycle. Helps retail brands increase margins and predict best-selling products.
Real Estate: Real estate price prediction is the most common application of artificial intelligence in real estate. AI is making an impact on nearly every aspect of the real estate industry. Zillow is an AI-driven personalized property recommendation system. It also connects agents with serious buyers by analyzing buyer information and interaction. Skyline AI and other products like Proportunity collect various data points to predict real estate prices.
Entertainment and Gaming: Artificial intelligence is driving the entertainment industry by keeping users glued to a screen for several hours. Games like Dota, Overwatch, and Counter-Strike use AI extensively as coaching agents to train players. AI-based games make it harder for human players to win. Streaming services use AI to recommend content based on the user's interests. Lifelike fashion models created by GAN will transform the fashion industry. An AI-based tool like SyncWords can automatically generate and translate captions for real-time videos and pre-recorded sessions.
Energy: The energy sector is using AI for anomaly detection in various equipment and power plants. Sensors collect real-time status of machines and AI