What Is Enterprise AI?

Generative AI models continue to astound, and business leaders have taken notice. Now CIOs are under pressure to put this new creative force to work. To help forward-looking CIOs and their teams answer the call for innovation, the tech industry is responding with a range of enterprise AI options. Your trusted vendor partners are updating business applications with AI-generated analytics and providing cloud infrastructures tailored for generative AI with compute power on tap and stringent data governance. They’re integrating enterprise data stores with large language models, so CIOs can take the lead and people across the organization can query business data as never before.

What Is Enterprise AI

Enterprise AI is the ongoing effort to bring quickly evolving generative artificial intelligence and related technologies to bear on mission-critical business workloads. The effort builds on successes using more limited AI systems and machine learning models for tasks such as anomaly detection, image recognition, and text analysis. While all these iterations have vastly improved the speed and efficiency of business operations, now, GenAI can do much more. As is demonstrated in near weekly, mind-bending updates, AI models can understand subtle verbal, written, or visual cues and use that input to create appropriate outputs, including text, graphics, computer code—even SQL queries.

Enterprise AI encompasses the work by business leaders to harness the immense potential of these new abilities by fine-tuning them on, or augmenting them with, their companies’ unique data and intellectual property. In that way, the AI model becomes familiar with the organization. From there, it can provide deeper insights and more reliably automate such tasks as providing customer service, personalizing marketing efforts, aiding in sales processes, and accelerating legal and risk management efforts.

For GenAI models to become enterprise AI, however, stringent conditions must be met. Is the infrastructure supporting a powerful large language model (LLM) that’s stable enough for mission-critical workloads and that has robust processing power, access controls, data security, and backup and recovery systems? Are people within the organization prepared to bring highly capable AI into daily operations?

Businesses looking to take advantage of GenAI (and who isn’t?) have many options. Perhaps the simplest path is to work with your business application vendors to introduce AI-infused modules into current workflows. Another option is to tap into GenAI services via APIs that enable the addition of features such as document summarization, data analysis, and chat to applications. Beyond that, a technology team can choose a GenAI model from an open-source and commercial model builder, bring it into a platform to train or augment, and then introduce it into production. This option requires a robust AI infrastructure.

In the end, the success of enterprise AI at any organization will come down to the ability to embed AI’s growing capabilities into a range of employee workflows, giving people new insights and helping them be more productive.

Key Takeaways

• Enterprise AI harnesses machine learning and the latest GenAI capabilities to solve business problems.
• Enterprise AI differs from consumer AI in that it relies on up-to-date business data and is trained to deliver outputs relevant to a particular enterprise.
• Enterprise AI systems often require integration with existing enterprise systems, with all the stringent data governance and high availability that entails.
• The promise of AI for enterprise customers has spawned a growing ecosystem of model builders, AI integrators, and hyperscale cloud services to support the burgeoning business opportunities.

How AI Is Being Used in Enterprises

Enterprise AI provides an array of functionality for employees and customers. Here are just a few examples.

• Application development: Improve developer productivity by using LLMs to write drafts of application code.
• Business operations: Provide wider access to organizational data through AI-enabled databases that support natural language prompts.
• Customer operations: Automate customer service interactions using GenAI models that understand a customer’s purchase history, past interactions, and native language.
• Marketing: Companies can achieve targeted outreach and customer nurture efforts on a massive scale using AI agents with access to buyer profiles and purchase histories.
• Risk and legal: Accelerate contract writing by having an AI assistant research and draft documents. Once approved, docs can be translated to multiple languages.
• Sales: AI-powered virtual sales representatives can gently and personably guide prospects through offerings during business transactions. AI can also help create personalized messaging for sales prospecting.
• Strategy and finance: Access powerful analytics for monitoring business performance—as well as competitors’ results—by analyzing data from internal, external, and public sources.

How Businesses Access Enterprise AI Now

Enterprise AI systems offer businesses a wide range of options for bringing GenAI into their operations.

AI embedded in enterprise applications is a solid, low-risk way for CIOs to show stakeholders just what GenAI can do to improve business operations. Enterprise application vendors such as SAP, Oracle, and Workday are surfacing AI-generated insights and workflows directly inside business applications, such as ERP, CRM, and HCM. Checking in with your key vendor partners is a great first step toward enterprise AI.

Augmenting a GenAI model with an array of business data is a competitive differentiator. Enterprises can now shop a range of open source and proprietary LLMs to find one that’s the right size and sophistication level for their needs. To make customization effective, organizations must have a platform that lets them fine-tune models and augment them with their own data. This can require a retrieval-augmented generation (RAG) implementation as well as a local vector database.

Expanding use of AI services from cloud providers is a popular option as well. For years, cloud vendors have offered AI and ML models for operations such as anomaly detection and computer vision. These AI services let developers add machine learning to apps without slowing application development and can often be custom trained for more accurate and relevant results.

Companies can also access cloud platforms designed for training GenAI and ML models. A growing list of cloud-based platforms that let enterprises design and launch their own AI and ML implementations foster collaboration among businesspeople, data scientists, and data managers as they identify and customize GenAI models—or even build, train, deploy, and manage new, sophisticated ML models using popular open source frameworks.

Another area where cloud providers excel is infrastructure: Deep learning is the most compute-hungry system most enterprises have ever run. As a result, they’re looking for cloud infrastructures that possess the GPUs required to train and deliver GenAI. These services also benefit from the cloud’s elasticity and usage-based pricing, to help lower the cost of AI.

Governments and other organizations may require tight controls over where and how AI technologies and associated data are deployed; the policies and personnel used to operate the AI technologies; and the processes and systems in place to protect the data. Large cloud vendors are ramping up sovereign cloud and even sovereign AI options across the globe.

Finally, independent software vendors (ISVs) can help bring GenAI expertise to enterprise customers in industries such as manufacturing, retail, law, construction and many others.

Bottom line, companies that want to put enterprise AI to work don’t have to go it alone.

Consumer vs Enterprise AI

While consumer AI and enterprise AI offer some of the same basic features, consumer AI focuses on personal experiences and entertainment, while enterprise AI addresses business challenges and serves to improve efficiency.

Let’s look at the differences in more detail.

Consumer AI

You’ll find consumer AI powering popular virtual assistants such as Siri, Alexa, or Google Assistant, where it helps with voice searches, smart home automation, or personalized recommendations for music or movies. Consumer AI is most often trained on a broad cross section of public data, and consumer AI applications are generally designed to handle individual user interactions. While these systems are built for scalability to accommodate millions of users, the complexity of tasks is often limited to personal needs and supplemented with personal data, such as voice recordings, location information, or browsing history.

Enterprise AI

Enterprise AI is developed for businesses and organizations such as government entities or healthcare providers, with the aim of improving operational efficiency, decision-making, and productivity. Enterprise AI solutions often rely on integration with existing enterprise systems and may require those who implement them to understand complex algorithms and machine learning models. By its nature, enterprise AI often works with sensitive data related to business operations, customer information, or proprietary knowledge, calling for robust security measures that safeguard this data from unauthorized access or breaches. Common applications of enterprise AI include customer service chatbots, data analytics tools, and supply chain optimization systems.

Get Enterprise AI Your Way with Oracle

Oracle’s goal is to help you gain value from GenAI in a way that’s seamless to your organization. To do that, Oracle Cloud Infrastructure (OCI) embeds GenAI across every layer of the tech stack.

You can access AI-generated insights from within your Oracle applications, or use APIs to update any application with AI services for tasks like anomaly detection or document summarization. At the database level, Oracle Database 23ai and Heatwave MySQL combine the power of LLMs and RAG with enterprise data stores, enabling employees to query knowledgebases using natural language prompts.

Finally, OCI provides access to proprietary and open source generative LLMs so you can select the model suited for your needs, and then run it on an infrastructure designed to handle the most demanding AI workloads.

Once GenAI hit the public consciousness in late 2022, it didn’t take long for enterprise leaders to see the potential value for their businesses. Now, there are a wide selection of ways those visionaries can bring GenAI to their operations.

As companies figure out how to use and gain value from GenAI, employees will benefit from new tools for workflow management, more broadly available analytics, and more. Enterprise AI will also trickle into the consumer services you use when you’re banking, travel, dining, and shopping. The enterprise transformation has just begun. Where it takes us depends on business leaders boldly going into the AI-enabled future.

Enterprise AI FAQs

What is the difference between consumer AI and enterprise AI?

Consumer AI is delivered by popular chatbots, such as Siri or Alexa, and websites such as Google and Perplexity AI, and deals with widely available public information. Enterprise AI is often drawing from company-specific, local, often sensitive data stores and is geared towards driving productivity and efficiency gains.

What is enterprise GenAI?

Enterprise GenAI is the work done by businesses to harness GenAI models to improve operations. This can be done by using LLMs to improve developer productivity, adding AI-generated insights within business applications, or using LLMs to help employees across the company query knowledge stores using natural language prompts.

How big is the enterprise AI market?

The analyst consensus is that the market for enterprise AI services was about US$24 billion in 2023. The market is currently difficult to assess, however, because AI needs clean data sources, so enterprise AI often looks like an extension of the digital transformation that’s been underway for nearly a decade. That said, the consensus is for 10x growth by 2032, when the market for enterprise AI services will have grown to over US$340 billion.