The latest technological innovation is driven by AI, positioning businesses to lead a new era of productivity. Unlike traditional software, AI enables rapid execution and efficiency, which is crucial in today’s fast-paced, interconnected world. With its ability to accelerate processes, AI plays a pivotal role in driving digital transformation for enterprises by enhancing both processing speed and time to market. We have listed the most popular AI use cases for enterprises.
Most Popular AI Use Cases for Enterprises
1. Cloud pricing optimization
As organizations increasingly move towards cloud computing environments and focus on delivering cloud-native applications AI turns out as an important solution for cloud cost optimization. Enterprises like Airbnb and Dropbox leverage AI to optimize cloud costs towards their AWS services. Companies leverage AI to optimize their cloud spend by identifying cloud usage patterns. AI enables them to detect anomalies in cloud usage, uncovering opportunities for saving, and recommend more cost-effective resources for use.
2. Conversational AI
Conversational AI tools such as chatbots and voice assistants are in vogue since the launch of ChatGPT. Interactive AI Assistants powered by human language understanding capabilities make technology accessible to the no-code employees. Natural language instructions can do wonders around their workflows. Conversational AI in enterprises helps to automate customer support, or patient information collection leaving the agents free for high value work. Pentagon Credit Union (PenFed) have significantly reduced customer wait time and load on their customer service representatives leveraging Conversational AI to answer customer FAQs.
3. Uptime/reliability optimization
Uninterrupted service uptime is crucial to the e-commerce, online trading, realtime analytics service companies for trust building. AI constantly scans systems, networks, and processes for inefficiencies, potential disruptions, and to identify any looming threats surpassing human efficiency. Utilizing AI enables companies that offer round-the-clock digital services to spot problems before they start and safeguards from crashing, hacking, and human error.
4. Predictive maintenance
Predictive maintenance, a sub technology of AI finds significant implications in machinery maintenance especially in the energy and manufacturing sectors. GE analyzes data from aircraft engine to track problems and schedule maintenance and improve efficiency. DC water reviews CCTV footage of sewer pipes with an AI tool to predict their tentative time of maintenance.
5. Customer service operations
AI manages customer service interactions and provide personalized product recommendations based on customer behavior. GenAI chatbots address customer complaints with natural and empathetic tone enhancing customer satisfaction. Intelligent chatbots listens to patient’s history and even offers preliminary first aid suggestions or prepare diet charts.
McKinsey reports that around 67% of millennials “expect real-time customer service,” and 75% of customers expect a “consistent cross-channel service experience.”
6. Personalization
Whether it is browsing through an e-commerce store or an OTT platform customers prefer personalization. AI personalization utilizes data, customer engagement, deep learning, natural language processing, machine learning, and more to curate highly tailored experiences to end-users and customers. AI has helped companies deliver products and content to targeted audiences, ensuring that every app or service you use is personally tailored to your unique interests. Amazon prompts on what item needs to be brought and Spotify suggests a new singer based on your favorite genre.
7. AIOps (AI IT operations management)
AIOps tools are gaining popularity as enterprises aim to enable IT operations autonomously. Organizations leverage AIOps for anomaly detection or deviations from normal behavior. With AIOps tools organization improves IT predictability and ensures faster resolution of IT problems. AIOps brings together cross-functional teams to manage FinOps, improve sustainability, improve CI/CD, strengthen system resilience, and eliminate tool sprawl. Delta Airlines leverage AIOps to create a “full-scale digital simulation environment for its global operation,” to enable employees take critical decisions based on hypothetical outcomes created from operational datapoints.
8. Process automation
AI combined with RPA creates IA (Intelligent Automation) a critical tool in automating time-consuming rule based processes. By automating processes, organizations can free up employees to work on high-value projects. Atlantic Health System implemented process automation to streamline prior authorization workflows, responding to the increased workload caused by the COVID-19 pandemic. Johnson & Johnson combines RPA, machine learning, AI, and task mining to identify and automate complex cross-departmental processes. Similarly, AT&T has used process automation since 2015 to reduce manual data entry, evolving to streamline multiple workflows across the company.
9. Financial reporting and accounting
AI significantly reduces the burden of manual tasks in financial reporting. GenAI can automatically produce standardized financial reports such as income statements and balance sheets, ensuring consistency and precision. NLP technologies enable users to interact with financial data using plain language queries. AI excels in analyzing large volumes of financial data, offering predictive analytics that helps organizations forecast future trends based on historical data. Intuit uses AI to enhance financial planning, with over 730 million AI-driven consumer interactions annually and 58 billion machine learning predictions daily. Through its GenOS (Generative AI Operating System ) platform, it implements financial LLMs focused on tax, accounting, and cash flow management.
10. Recruiting/hiring
AI automates repetitive tasks such as resume screening, interview scheduling, and initial candidate communication. This automation allows recruiters to focus on more strategic activities, such as building relationships with candidates and making informed hiring decisions. AI technologies improve candidate sourcing by analyzing data from various online platforms, including social media and job boards. AI has the potential to reduce unconscious bias in recruitment by providing objective assessments based on skills and qualifications rather than subjective criteria. By standardizing evaluations, AI can help create a more diverse and inclusive workforce. Unilever partnered with Pymetrics to create an AI-powered platform that assesses over 1.8 million job applicants annually. The system uses natural language processing and body language analysis to evaluate candidates during video interviews, analyzing their responses, expressions, and movements.
Conclusion
Incorporating AI into business operations drives efficiency, reduces costs, and enhances customer satisfaction. From personalized customer experiences to optimized operation and advanced financial reporting, AI is transforming industries across the board.
