While artificial intelligence (AI) is transforming industries around the globe, many businesses—both large and small—have yet to realize how this technology can benefit their own operations. Start by thinking beyond chatbots and customized marketing.
Data management and analytics can play a critical role in making organizations more efficient, drive visibility, and accelerate growth.
Leveraging AI can gain actionable insights and operational efficiencies that result in a significant edge with a reasonable payoff model.
Natural language processing
Natural language processing (NLP) allows businesses to build their data into viable business solutions. NLP uses vary greatly, but here are a few examples of useful applications.
Sentiment analysis
Understanding the sentiment in data is used to monitor and analyze customer feedback and harness social media trends.
Document processing
Analyzing text provides semantic search and reasoning and automates manual data entry and processing. It extracts data from various documents from PDFs, images, and scanned documents like invoices, financial statements, and compliance reports allowing companies to automate this information.
Chatbots and virtual assistants
These tools interact with customers providing requested information. Chatbots and virtual assistants handle customer queries, solve support issues, and provide product recommendations consistently and in real-time.
Text classification
Text classification and topic modeling automatically classifies and organizes text data. It assigns texts to predefined categories and is used to sort emails, articles, customer reviews, and other types of text data.
Entity recognition and extraction
This is a process of identifying and classifying named entities in text and can be used for advanced text extraction and text mining tasks.
Natural language generation
This tool automatically generates text from data to creates reports and provide text summaries and descriptions.
Predictive analytics
Predictive analytics is a field of data science that uses historical data and machine learning algorithms to make predictions about future events and unlock the value in data. It identifies trends, predicts demand, and helps make pricing and marketing decisions.
Demand prediction
Predictive models for demand prediction forecast future demand for products or services, optimize inventory levels, and make better decisions about pricing and marketing strategies.
Churn prediction
Custom predictive models identify residents at risk of moving out. With this, management can target at-risk residents with personalized offers and benefits to nudge them to stay.
Predictive maintenance
Machine learning (ML)-driven predictive maintenance solutions can detect potential equipment failures before they happen. By using predictive analytics, you can avoid costly downtime and improve the efficiency of your operations.
Computer vision
Custom image and video analysis can be tailor-built to a business’s specific goals. Algorithms and data processing can help deliver deeper insights into data and optimize processes for maximum efficiency. Examples of computer vision applications include:
Image analysis and segmentation
Algorithms for image analysis and segmentation can automatically detect and classify entities in images, extract specific features from images, and develop biometrics systems for facial recognition.
Object detection, tracking, labeling
Custom computer vision automatically detects, tracks, and labels objects in images and videos. Object tracking helps businesses identify and analyze the movement of specific targets over time to aid in surveillance, build intelligent systems for activity recognition and develop traffic monitoring systems.
Visual search
Visual search powers recommendation engines, search engines and product catalogs. Using deep learning techniques, image recognition models can be trained to accurately identify unique images or even search through large databases to find similar objects based on visual similarity.
Intelligent text recognition
Intelligent character recognition solutions automatically recognize text or handwritten characters in images and videos. This can be used to develop systems for intelligent document processing tools, to build optical character recognition engines for scanned documents or to search for specific text in videos.
Image generation with GANs
Using generative adversarial networks (GANs) to develop custom image generators is helpful for producing unique images or even realistic-looking video sequences. Various applications include data augmentation, art generation, and marketing automation.
Recommendation systems
Tailored recommendation systems can help companies better understand customer preferences and then suggest the appropriate products or services. Based on deep learning algorithms and employing natural language processing, image recognition and other machine learning techniques deliver highly accurate recommendations. Through these actionable insights into prospect and resident behavior, companies are able to create targeted campaigns targeting customer engagement and satisfaction.
Recruitment
Intelligent recruitment and job-pairing solutions can help employers find the best candidate for a job. The system works by analyzing the profile of the job and the profile and resumes of the potential candidates, then uses machine learning algorithms to identify the best potential matches for the job based on experience and qualifications. The result is a faster connection of employer to the most qualified candidate.
News and information delivery
Recommendation frameworks apply data from previous user engagement to tailor content to the preferences of each unique user. Engaging content is automatically curated specifically to their profile. The same insights into user behavior, preferences, and trends can better target audiences with other types of content and offerings.
Online shopping
Improve resident experience by providing personalized recommendations based on past user activities and preferences. The system can be integrated into existing value add offers and use data mining and machine learning algorithms to track and analyze resident behavior to generate appropriate product, service and content recommendations. Personalized offers and promotions to residents and prospects will further boost engagement.
Data has become the biggest asset for business and—when processed properly—can be greatly monetized. Some of a company’s most pressing and strategic questions are often answered with the data itself. A robust data strategy and road map includes rapid prototyping and tool evaluation, data acquisition planning, data quality measurement and an understanding of local data compliance requirements.
Data transformation and connectivity
Considerations include data scraping ad extraction, data matching across systems, scalable data processing and storage as well as extract-transform-load functions. When the latest technologies, such as AI, ML and deep learning are applied to data available from internal and external sources, a powerhouse for strategic decision-making is created.
Data analytics strategy
The approach is often multifaceted with components including platform selection, AI/ML model application, comprehensive dashboard delivery for decision-making, and product development considerations like cloud data/AI platforms, as well as automated data integration. AI and analytics play a vital role in making organizations more efficient, bringing visibility, and accelerating growth. With it, organizations can gain an informational edge using data and actionable insights.
Digital transformation is top of mind for all businesses, from start-ups to Fortune 500 groups. Tech continues to advance at a breakneck pace, as innovations such as blockchain, AI, machine learning, deep learning, advanced analytics, internet of things, talk technology, virtual reality, and augmented reality make the news with breakthroughs and new applications.
It’s critical to define the technology roadmap that meets your business goals—one that improves operational productivity through workflow digitization, process automation, and enterprise integration.
One that helps you understand disruptive technology and identifies opportunities for your business.
