Feature
Editor's Note: This article has been updated to include new data and information.
Enterprise AI technology is intended to optimize a company’s systems from top to bottom. That technology is an integrated system of tools that can optimize everything from high-level decisions to mid-level office labor, the supply chain, inventory systems, product pricing, manufacturing systems and store layouts. Enterprise AI can also tap into a company’s data to answer customer questions.
When an organization builds an enterprise AI system, it typically collaborates with an AI developer to create tools, train those tools on the company’s data and build an intelligent, integrated system. These AI systems are powerful, often able to streamline operations, save labor and sort through massive amounts of data to improve the way that company does business.
When AI companies and experts discusses enterprise AI, they’re talking about huge data sets, large organizations, high-level decisions and a wide range of applications.
Table of Contents
- What Is Enterprise AI?
- How Does Enterprise AI Work?
- Key Enterprise AI Features
- Enterprise AI Benefits
- Why Are Companies Using Enterprise AI?
- 5 Enterprise AI Use Cases
- What Companies Are Adopting Enterprise AI?
- Top Enterprise AI Solutions
What Is Enterprise AI?
Enterprise AI is a blanket term for the use of artificial intelligence that helps large corporations automate and optimize operations. This AI is often deployed across the organization and integrated across departments. An AI capable of rapidly tapping into vast stores of corporate data, and making that data available to anyone who requires it, is a powerful tool. At a high level, it can enhance a leader’s ability to make decisions about the direction of the company. At the other end of the spectrum, it can answer common customer questions at all hours via a chatbot.
Once an AI is built and trained, it can work across various departments — finance, marketing, human resources, manufacturing, the supply chain and customer service — to optimize data access and streamline operations.
The AI technologies that enterprises use for these use cases include machine learning (ML), natural language processing (NLP), deep learning, computer vision and chatbots that engage with customers and employees to answer questions.
How Does Enterprise AI Work?
To tap the power of AI in the enterprise, it must first be integrated into business processes, data infrastructure and workflows. Integrating a complex group of technologies that will allow a company to experiment, develop and use AI is a complex process that requires significant AI skills. Once implemented, an enterprise AI can automate tasks, help make intelligent decisions and use data to improve operations.
Once a company identifies what it aims to do with AI and assesses the state of its data, the enterprise AI taps into huge amounts of data to learn, predict and analyze enterprise operations. This usually starts by tapping a deep learning model to teach the AI system to think about the data, see patterns in it and produce insights and predictions.
Once the AI has learned this process, it can gather data from unstructured sources such as customer interactions, transactions, internet of things (IoT) sensors, cameras and social media. It can also tap into company documents to learn, find patterns and make this knowledge readily accessible.
Once the AI collects the company data, it gathers it all together into data storage systems to centralize it. Then it cleans it and prepares it for analysis. Duplications are removed, errors are eliminated or corrected and the information is formatted.
After the data is prepped, the enterprise AI is trained on it. It learns about the company’s systems and customers, identifies patterns and readies itself to use the data to inform decisions, answer questions, identify inefficiencies and optimize processes.
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Key Enterprise AI Features
Enterprise AI products bring a range of tech features to an organization. Enterprise AI can automate, analyze and improve business operations and decisions because it is capable of quickly processing enormous amounts of data and learning from that data. It is also lightning fast. Research and data collection that might take a human hours or weeks can be accomplished by an AI in seconds.
Tapping into a company’s historical, customer and other data, enterprise AI can see patterns that are challenging even for data scientists to identify, quickly find answers to questions in a sea of data and surface the precise information people need, when they need it. It is not difficult to see why companies are motivated to tap into this technology. AI offers companies an enormous labor — and money — saving opportunity.
Here are some examples:
- Using natural language processing, companies can create always-on chatbots tied to real company technical data, product information, correspondence, contracts and more to quickly answer specific customer questions. These tools are fast and intelligent and can often handle the bulk of customer inquiries. This frees staff for higher-level work and allows lean teams to accomplish more work.
- By tapping historical sales, industry and customer data, enterprise AI can predict trends or anticipate how much demand there will be for a product or service. This allows companies to create targeted products and avoid expensive failures.
- By monitoring equipment failure trends, an AI can suggest maintenance for company equipment, thereby preventing failures, downtime and other logistical disasters.
- Computer vision can monitor security cameras, recognize people, perform quality inspections and enable robots to do work. This helps keep warehouses and other facilities more secure and operational.
- Machine learning can learn from data and use that knowledge to automate tasks, make decisions, predict anything from maintenance schedules to customer behavior and uncover patterns and insight.
Enterprise AI Benefits
Many organizations are already tapping into the benefits that enterprise AI can bring.
AI is often used to automate repetitive tasks, copying data from legacy systems to new ones, processing documents, summarizing data sets or creating copy for emails or other communications. This is often called robotic process automation or RPA.
AI’s ability to quickly sift through huge datasets for answers to specific questions is a huge boon to corporate decision makers who use it find market trends, predict customer behavior and find ways to make fast, informed decisions about the direction of the company.
AI’s ability to find patterns in huge data sets gives it a unique ability when it comes to optimizing systems. For example, enterprise AI may be used to streamline the supply chain using logistical and geographic data that is too complex for humans to easily comprehend. Enterprise AI may also be used to improve the layout efficiency of a factory floor or to manage the robots doing the factory work.
It may seem counterintuitive, but AI can help companies offer highly personalized attention to customers. With unrelenting attention to detail and the ability to see patterns quickly in vast amounts of data, AI can target customer needs and desires — even in small geographies or specific markets — that humans might never notice. This allows companies to efficiently build the products customers want.
In a world where security risks are exploding at the speed of AI, companies need a way to fight back. AI can aid security and risk management, offering companies tools that can fight AI fire with AI fire and keep networks safer.
AI can generate almost any type of written communication, whether it's emails, marketing copy or software code, it is fast, accurate, detail-oriented and able to draw from the details it learned from studying company data.
Why Are Companies Using Enterprise AI?
Companies are eager to implement enterprise AI because it can speed up and optimize everything from supply chain management to fraud detection and customer relationship management (CRM). It can save labor costs. It is, according to one Forrester report, “a strategic business asset for transforming operating models.”
Though the benefits are potentially huge, the cost and effort to implement AI at enterprise scale can be significant. Most respondents to the Forrester survey said they are struggling to prepare their business data and that they face significant challenges around people’s understanding of AI and ability to trust and use it. Ethics and privacy issues are another stumbling block.
- “Significant opportunity for innovation and competitive advantage lies in applying AI to re-think how businesses operate and deliver dramatic improvements in how companies engage customers,” C3.ai claimed in a report. AI can help companies make better use of their workforce, streamline operations, automate order systems and anticipate customer demand.
- Enterprise AI can improve operational efficiency by taking over routine tasks — such as data entry, moving files around and processing invoices — to lift the load from human team members while eliminating errors and making work happen quickly. It can even identify where and how business processes are cumbersome or slow and suggest improvements.
- Because enterprise AI is fast at processing huge amounts of data, it can bring the intelligence of data science to all corners of the operation. This intelligence can help teams — from marketing to sales to maintenance — make better decisions and predict trends and the need for services. And it can offer this insight to team members via a natural language tool that is easy to access. This can help the company drive innovation and improve profits and efficiency.
- Enlisting enterprise AI on the customer service team can quickly improve the speed and personalization of a company’s client interactions, which can help build customer loyalty and satisfaction. Either by offering 24/7 service through a chatbot or by automating communications, a customer service team can do much more with much less human attention.
- Enterprise AI can improve a company’s security and compliance by watching for data breaches, managing risk and monitoring software and security compliance. As hackers increasingly rely on AI and bots to attack networks, AI is an increasingly necessary threat response.
- Enterprise AI can also do quick, data-driven environmental impact studies, suggesting ways companies can conserve energy, improve logistics and reduce waste.
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5 Enterprise AI Use Cases
Enterprise AI can be used across the company — from the C-Suite to the delivery truck — for a wide range of functions.
Here are some examples:
- Personalized Marketing: Enterprise AI tools can quickly analyze customer and market data to help marketers target their messaging more accurately. A well-trained generative AI tool can also be used to quickly craft personalized messaging that understands customer preferences and behaviors and uses the voice and data of the brand to keep messaging accurate and cohesive.
- Cybersecurity Threat Detection: Enterprise AI is fast and effective at detecting malicious behavior because it can use machine learning to recognize when a behavior falls outside the norm. It allows threat detection software to think like a hacker. And because it’s a machine, it can recognize a large number of threats per minute. It can use the knowledge it gains from watching a network — and what is attacking it — to make suggestions about how to protect it. And it can respond with a speed that no human can approach when it is responding to a known threat.
- Better Customer Service: Enterprise AI chatbots can quickly answer customer questions at any time of day. The chatbots can access a company’s own knowledge bank quickly, use natural language processing to understand the customer’s questions and provide answers to those questions that are detailed, accurate and up to date. It can do all of this with the voice and personality the company wants to portray. It can also identify when a question requires human intervention and escalate it to the right people.
- Research and Development: Enterprise AI can access vast data sets, run simulations to predict outcomes and cut the time and resources necessary for product development while reducing the waste of failed efforts. In the life sciences and chemical industries, AI can accelerate the process of developing new drugs and materials by quickly running simulations that cut weeks or months from the development process.
- Faster Software Development: AI offers fast, accurate coding. And increasingly, software developers are using it to debug, improve and write software code. This use case will likely grow as more non-technical companies offer software enhancements or tools to their offerings.
What Companies Are Adopting Enterprise AI?
Various companies across industries — retail, banking, healthcare and more — are tapping into the potential and power of enterprise AI.
Retail
The retail industry is a heavy user of enterprise AI. Faced with huge logistics problems and an army of customers with questions, AI is a valued member of the team.
Walmart uses AI to create a more efficient supply chain, reduce emissions and manage the logistics of packing trucks, planning deliveries, getting goods to its stores on time and optimizing the movement of goods to and from retail locations. The company’s route planning tools became so effective that Walmart spun the tool out as a product that it sells to other companies.
Target uses enterprise AI to improve customer experience and pricing strategies, as well as optimize its supply chain and manage inventory. It also offers a guest-centric AI solution to help store customers find what they need quickly and easily.
Food retailers use AI to stay ahead of rapidly changing food trends and accurately deliver fresh foods to where it will be wanted, thereby minimizing spoilage and decreasing delivery times.
Clothing retailers use AI to discover where their brand, sizes and styles are in demand so that they can serve every customer, regardless of geography or demographic.
Banking
Because banks are a target for fraud, and AI is fast and effective when it comes to detecting and responding to malicious actors, they rely on enterprise AI for risk management and fraud detection. They also uses enterprise AI for forecasting and customer service.
J.P. Morgan has used an AI-powered large language model to validate payments and detect fraud for years. It also uses AI to offer a better customer service experience. The speed and accuracy of the AI reduced account validation rejection rates by 15 to 20 percent.
Bank of America calls its AI-powered virtual assistant Erica. She uses natural language processing to listen to and respond to customer queries. She can also warn customers when their spending habits are potentially leading to a zero balance, alert customers to a change in credit rating and remind people when payments are due. Her anomaly detection software lets her flag purchases that might be fraudulent to protect customers.
Valley Bank tapped an AI to help it detect money laundering crimes. Its AI tool, Tara, helped cut anti-money laundering false positives by 65%. This kind of anomaly detection is a laborious process when humans do it. But predictive analytics helped the fraud team quickly comb through millions of transactions to find problems.
Healthcare
The healthcare industry is data heavy. It uses AI, across the board, to assist with everything from drug research to improving health outcomes to managing vast amounts of data.
Drug companies, such as Pfizer, use enterprise AI to speed drug discovery, optimize clinical trials and streamline the drug development process. The technology is also used to predict drug efficacy, manage the vast stores of data involved in developing and testing a drug and more.
Healthcare providers like the Mayo Clinic use enterprise AI to speed the results of preventative care screenings, identify patients at risk for illness and lift the workload from doctors by helping them write clinical notes and stay abreast of medical advances.
Hospitals and other healthcare facilities also use AI to enhance patient care experiences, improve health outcomes and reduce costs.
Manufacturing
Enterprise AI is a great tool for industries that have large manufacturing, warehousing or assembly machinery, as it can predict maintenance to reduce downtime and assist with design by creating digital versions of ideas.
Toyota, for example, uses enterprise AI to help car designers work smarter, helping them incorporate complex engineering constraints, such as drag and chassis dimensions, which affect the way the car handles and uses fuel, into the design process.
GM uses enterprise AI to predict trends, detect potential malfunctions in manufacturing and design autonomous cars.
AI can also help manufacturing companies with quality control inspections and to optimize the robots working on the factory floor.
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Top Enterprise AI Solutions
If you’re looking for enterprise AI to solve difficult problems in your industry, the leading AI companies provide tools and expertise to help.
- IBM Watson offers a range of enterprise industry-specific AI tools. The company began development of its AI, Watson, way back in 2006 in a research project intended to push the boundaries of AI. The product is named after IBM’s founder.
- AWS offers a set of pre-trained enterprise AI services that can be integrated with applications to create AI solutions capable of offering personalized recommendations, improving safety and security and increasing customer engagement. The SageMaker suite of tools has everything a company needs to build, train and deploy a machine learning model.
- Microsoft Azure AI offers a number of AI tools for the enterprise, such as for machine learning, AI-powered applications, decision making support, computer vision, natural language processing and more. The tools integrate with other Microsoft products and with other frameworks.
- Oracle AI offers a comprehensive set of AI solutions through its Oracle Cloud services. These cloud-based AI applications — OCI Language, OCI Vision, OCI Speech, OCI Anomaly Detection and OCI Forecasting — can be used for various purposes and across departments.
- Google is a leader in AI research, data analytics, natural language processing and computer vision. The company’s DeepMind division has developed several pioneering AI systems.
- SAP offers the SAP AI Core and SAP AI Foundation for analytics, automation and conversational AI tools.
- C3.ai offers tools for manufacturing, oil and gas, utilities and healthcare. The C3 AI Suite for enterprise is largely focused on predictive maintenance, supply chain optimization and CRM.
