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Rapid advancements in artificial intelligence (AI) have created a new frontier in business innovation. Significant progress in computing power, data storage and algorithms have enabled the development of more sophisticated AI systems. Improved software deployment processes like containerization and orchestration will advance AI and machine learning (ML) applications in both reach and scope. For example, now developers can build, deploy and manage AI/ML workloads in a scalable and efficient manner with generally available solutions like Red Hat OpenShift Data Science and NVIDIA AI-ready Enterprise Platform.

All these technological improvements have made AI more accessible and practical in various fields. Among AI’s disciplines, generative AI is the catalyst that empowers businesses to create, iterate and optimize solutions to complex problems. The growing interest in generative AI provides an excellent opportunity to explore its potential and offer insights into its capabilities for transformative business applications.

What is generative AI?

Generative AI is a branch of AI that enables machines to create original content, such as images, texts and music. Unlike traditional AI systems that rely on predefined rules or explicit data patterns, generative AI leverages complex neural networks to learn from vast datasets and autonomously generate outputs.

What can generative AI do for business applications?

Generative AI holds immense promise for unlocking creativity across various industries. Businesses can leverage generative AI to augment human creativity and accelerate innovation by driving operational efficiency, creating engaging marketing campaigns, detecting fraud or generating realistic virtual agents. With generative AI applications and the correct data, companies can explore more possibilities, minimize risk, optimize production and automate tasks, leading to breakthrough solutions and cost savings.

Personalized customer experiences

Customer personalization is a cornerstone of successful businesses. Generative AI can play a pivotal role by analyzing vast customer data to help business leaders understand preferences, behaviors and trends. With this knowledge, companies can dynamically generate personalized recommendations, targeted advertisements and tailored experiences, ultimately fostering more robust customer engagement and loyalty.

Generative AI is commonly used to develop virtual assistants and chatbots that can interact autonomously with customers, handle inquiries and provide support. The business application of virtual assistants has been around for quite some time. For example, Watson Assistant was released in July 2016 and is used today in customer service, marketing and human resources. AI-powered virtual assistants can improve customer service, automate routine tasks and enhance user experience.

Streamlining operations and efficiency

Generative AI can drive operational efficiencies by automating time-consuming and repetitive tasks. From generating automated reports and optimizing supply chain management to predictive maintenance and anomaly detection, businesses can leverage generative AI to streamline operations, reduce costs and improve overall efficiency. 

For example, Ansible Lightspeed (technical preview) will help developers create Ansible Playbook automations more efficiently using generative AI with IBM Watson Code Assistant. By automating mundane tasks, employees can focus on higher-value activities, thereby fostering organizational productivity and innovation.

Enhancing decision-making

Generative AI can be a valuable tool for data-driven decision-making. Businesses can generate alternative scenarios, test hypotheses and make predictions by leveraging historical data and running simulations. Generative AI can analyze vast amounts of data, identify patterns and generate forecasts or simulations to aid in decision-making processes. It can provide valuable insights, optimize operations and support strategic planning. 

For example, Atomiton predicts energy demand for manufacturers and identifies optimized ways to run energy-consuming machines on the production floor to reduce costs. This decision-making capability empowers business leaders to explore various outcomes, assess risks and optimize strategies across a wide variety of industries.

Preserving privacy and security

Data privacy and security are critical for all businesses, especially those in the areas of healthcare and finance. Generative AI offers a privacy-preserving approach by generating synthetic data that maintains the statistical properties of the original dataset while ensuring individual privacy. This approach enables data sharing and collaboration while safeguarding sensitive information. 

Fraud detection and cybersecurity

Generative AI can help identify and prevent fraudulent activities by analyzing data patterns, anomalies and potential threats. It can enhance security systems, detect vulnerabilities and mitigate risks. Certified operators from Red Hat partners such as Dynatrace, CrowdStrike and many others harness AI, albeit in different ways, to detect fraud and ensure cloud operations remain secure.

Generative AI is a transformational shift in business applications. By embracing generative AI, businesses can tap into unprecedented creativity, deliver personalized experiences, streamline and secure operations, enhance decision-making and foster innovation.

Learn more about Red Hat's AI/ML partner ecosystem and dive deeper by exploring the certified applications Red Hat AI/ML ecosystem catalog.

About the author

Adam Wealand's experience includes marketing, social psychology, artificial intelligence, data visualization, and infusing the voice of the customer into products. Wealand joined Red Hat in July 2021 and previously worked at organizations ranging from small startups to large enterprises. He holds an MBA from Duke's Fuqua School of Business and enjoys mountain biking all around Northern California.

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