Artificial Intelligence (AI) technologies, from Natural Language Generation to Speech and Image Recognition, are marching forward into a new era. AI adoption in business goes far beyond automation and replacing manual jobs – the global AI market is predicted to snowball, reaching around 126 billion U.S. dollars by 2025, while the number of AI use cases will be growing exponentially. If you are wondering what the proven ways to adopt AI for business growth are, look through these 5 common use cases.
Customers are becoming more demanding – they expect an instant resolution and value the quality of the service they receive. To satisfy this need, businesses need to look for new ways to enhance customer service:
The addition of AI chatbots for customer service might be a dubious solution since an average chatbot can’t imitate human emotions, and automated replies aren’t always accurate and aligned with customer expectations. However, chatbots leave room for improvement, and their intelligent use benefits businesses that leverage chatbots to enhance the first line of support and respond to frequently asked questions. Chatbots can help create a helpdesk, provide 24/7 availability, personalized response, and multilingual capabilities.
Using chatbots in combination with human communication can be the perfect solution. Conversational AI, for instance, answers simple questions, which constitute nearly 50% of the needs of most outreach, and humans then take on the conversations in more sophisticated cases. Chatbots can also serve as customer concierges that update contact information, place an order, or schedule a service call in the blink of an eye, without the need to wait on hold.
- Customer identification or predictive personalization
The customer activity supplemented by information on demographics and personal preferences enables adopting customer identification or predictive personalization. For example, Netflix came up with an AI-powered algorithm that creates individual recommendations and improves user experience. Thanks to this strategy, the company can save 1 billion U.S. dollars a year in terms of customer retention.
- Biometrics, face and voice recognition, language and emotion analysis, intent prediction solutions
Biometric hardware and software that involve face or voice recognition may serve for authentication and identification. Let’s consider an example. Working in the field of banking, companies aim to improve customers’ experience through seamless onboarding. They can simplify the process and allow customers to upload pictures of ID and a selfie instead of using manual data insertion for this need.
Language and emotion analysis improve user experience during interaction with assistants from the call centre as well. An application based on language analysis reads the mood of the client and determines the vulnerable tone in the voice, thus enabling assistants from call centres to identify calls that pose risk and assess customer feedback.
As for intent prediction solutions, they can be used by businesses to analyze clients’ requests and actions, increase customers’ satisfaction, reduce costs, and improve employee engagement.
Taking customer service to a whole new level through the implementation of AI solutions may be perceived as an easy target, yet trying to come up with a relevant AI initiative often causes businesses to face challenges. The devil’s always in the details and lack of data for AI model training and the lack of a mature team often leads to failure. To improve the quality of customer service and gain a competitive advantage, businesses should look for reliable machine learning experts with relevant expertise.
Nowadays, content writing processes are established with a focus on writers or subject-matter experts who generate new ideas, though this focus will shift towards AI in the future. Diversity fuels innovation and AI can be advantageous to plan, create, distribute, and analyze content.
The application of AI in content marketing can be classified in several areas:
- Proofreading and grammar check (tools like Grammarly or Hemingway)
- Analysis of keywords, ideas, and trends (tools like StoryBase or Answer The Public)
- Content distribution with personalized recommendations for users
- Content performance (tools that assess the effectiveness of marketing campaigns)
- Topic generation aligned to trends
Artificial Intelligence for marketing also implies obtaining insights with predictive analysis, advertising automation, sales personalization, and price optimization (to adjust prices, AI analyzes customer activity, emerging trends, competitor’s prices, and other factors).
AI is being put to good use for social media where it’s applied to create better content, manage its distribution, and measure social media ROI. Facebook already uses AI tools for newsfeed customization. AI analyzes the content and decides whether you as a user might be interested in it, taking into account your preferences, existing connections, and other factors. Because the ocean of data in Facebook is almost boundless, coping with its analysis for further newsfeed customization is impossible without AI technology.
To focus on strategic goals, the Human Resources department can automate routine tasks with the help of AI tools. These tools simplify recruiting and talent management, boost development programs, employee engagement and internal mobility, and automate administrative tasks.
Before diving into AI use cases in HR optimization, let’s cast a glance at statistics. According to Gartner, in 2022, 31% of all workers worldwide will be remote (a mix of hybrid and fully remote). And AI tools may facilitate recruitment and training of employees, enhancing their experience.
Tasks carried out by the HR department with AI application include:
- Screening of databases to find the suitable candidates
- Interview arrangement
- Resolving issues related to applicant queries
- Customized onboarding
- Coverage of the employees’ training needs
- Leveraging workforce data for prediction, employee performance, and making informed decisions
Artificial intelligence also enables optimization of the internal communication process with the help of team chat software, virtual meeting rooms, and chatbots.
The enterprise environment is a key risk area for cyberattacks. AI may be adopted into defence strategies to predict risks and identify threats. The cybersecurity landscape is quite challenging these days and companies should incorporate measures to prevent such risks as phishing, social engineering attacks, and voice phishing.
Machine Learning techniques are applied to automate threats and weak point detection. AI systems empower cybersecurity, and their integration has the following benefits:
- Efficient mapping and preventing new threats
- Battling bots through the analysis of behavioral patterns
- Possibility to process massive chunks of data
- Literate vulnerability management
- Accurate threat forecasting
- Shortening the response time
- Different authentication approaches (CAPTCHA, fingerprints scanning, facial recognition)
Concerning the aforementioned approaches to handle authentication, enterprises usually implement an AI biometric verification system with such security methods as iris scanning, facial recognition, fingerprint identification, and voice verification.
Companies that intend to operate at the cutting edge of technologies should consider AI-powered analytics to obtain insights for making decisions. AI analyzes data and provides businesses with clear recommendations that are taken into account by CEOs and managers while solving strategic challenges.
Business Intelligence tools collect and parse data to create useful reports. According to Techjury, by 2023, over 33% of large-sized businesses will practice decision intelligence. Enterprises are intensively investing in cloud-based BI solutions to deal with structured and unstructured data, and AI helps to find connections between these data structures.
In summary, the influence of AI on the business is inevitable because, unlike traditional software, it broadens the scope of decision-making and has a positive impact on the width and depth of analysis. Large amounts of clear data and intensification of processes provide a promising perspective for the future of AI technologies in business.