Enterprise AI
In this article, we will examine what is enterprise AI, and what are the different types, benefits, and use cases of it.
Raido Linde
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November 12 2024
Enterprise AI is when large organizations adopt artificial intelligence in their processes. With a wide range of AI solutions available, teams can analyze large amounts of data across existing internal and external enterprise systems. This enables development of applications to understand to optimize business operations, automate tedious tasks, and create advanced analytics. With a wide range of AI solutions available
There are many enterprise AI types, but many overlap with each other. Some parts of one system may provide capabilities to other systems and so on. Here are the key enterprise AI types we will cover in this article:
Machine learning is a branch of the artificial intelligence field, featuring statistical models capable of learning patterns from any structured or unstructured data.
Natural language processing (NLP) is another field of AI, that provides computers capabilities to decode and understand.
Generative AI is a subbranch of machine learning that uses NLP reasoning to understand human language and create content that is close to human.
High cloud costs: AI requires a lot of processing power, which results in unbearable overheads for companies using cloud solutions. Many are starting to realize this and looking for more cost-efficient solutions.
Data protection: Protecting internal secrets and customer personal information is the highest priority for all businesses. With AI, your data is at risk of leaking into the wrong hands, especially if using private models.
Development complexity: Developing and integrating these AI systems with existing enterprise infrastructure and hundreds of data sources is complicated.
Skill cost: there is so big demand for AI engineers, which has skyrocketed their salaries. For example, Tesla pays up to 1$ million dollars per year for AI engineers to avoid them leaving for competitors.
Scalability issues: Designing AI solutions that can handle ever-increasing flow of data and enterprise-wide workflows
Enterprise AI has completely changed how large companies and organizations use technology to their advantage. Let's take a closer look at some of the benefits business AI provides.
Generative AI adoption has been one of the main reasons for the explosion of enterprise AI. Below here are three key benefits that generative AI for enterprises provides:
Enterprises are still in the early stages of the adoption of enterprise AI. Many are completing their first POCs and transitioning into production. This transition is growing at a rapid pace.
Three main ways businesses use enterprise AI in 2025 are:
AI agents are artificial intelligence systems or programs that can perform actions autonomously on behalf of employees or other systems.
Developing AI-based applications for internal or external (customers or clients) use cases. For example, rag-based chatbots, troubleshooters, internal private chat, and much more.
Adding AI functions to existing applications is mostly done to modernize these softwares. It is achieved by connecting AI backend to the application via an API .
AI is used by all industries to find solutions to problems that other technologies haven’t been able to succeed with. Below here are real enterprise AI use cases by different companies and organizations from various industries:
Each day there are more use cases for AI. But because each business is different so is their use of AI. As a result, in the next section, we will discuss how can your organization get started with AI and what to look out for.
Choose the right AI solution: Decide between building the systems yourself or selecting enterprise AI platforms to get started instantly. More about this in the next section.
Define a clear objective: Identify your business goals for using AI. For example, improve customer service, automate certain tasks, or make domain-specific solutions for clients.
Choose high ROI use case: Gather 10 possible use cases and analyze their impact. Choose what provides the quickest return on your investment.
Ensure data privacy: Use open-source models that run on your own hardware or private cloud environment. This way you can secure your data and be compliant with data privacy regulations such as GDPR, local AI Acts, or government mandates.
Monitor the results: Constantly monitor the performance of your systems to assess their alignment with your goal.
Provide continuous training: Train your employees to use AI systems. For example, make 10 examples of how a certain application works best. This reduces employees having to learn the system and improves the overall user satisfaction.
In-house AI development is ideal for organizations that have the necessary capacity such as AI engineers and want to have full control over the technology regardless of the cost and time it takes to build the underlying infrastructure.
Partnering with an AI company is more suited for organizations that need to implement AI quickly, don't have the resources to build an AI team, or are looking for a solution with additional expertise.
“Even the largest enterprises that have the internal capacity to build these systems, choose to partner with AI companies like ConfidentialMind to be able to get started faster,” says Markku Räsänen, CEO of ConfidentialMind. He adds, “Some companies have built certain components of enterprise AI systems internally, but need additional features to bring their innovations to the market. By partnering with the right AI company, they have considerably reduced their time to market and development costs“
Enterprise AI has completely changed how large companies and organizations use AI technology to their advantage. For example, they can use it to optimize workflows, reduce overheads, improve resource allocation, and much more. But setting up these systems comes with a hard choice - Whether you choose to build the AI solution internally or partner with an AI company. Regardless of your choice, the adoption of AI in the enterprise is not only crucial but mandatory to stay ahead in today's highly competitive world.
