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April 19, 2024
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What is Enterprise AI? Definition, Platforms, Examples & More

You’ve probably often wondered, “What is enterprise AI? How can it help me improve my operations?” Don’t worry, this guide aims to answer all your questions. 

Enterprise AI refers to software solutions that employ new technologies like artificial intelligence and machine learning to streamline business processes and drive better decision-making. 

With the enterprise AI market predicted to grow in the future (up to $204.79 billion in the next five years) now’s the best time to invest in this technology. 

In this article, you will learn everything about what enterprise AI is, including its advantages and disadvantages, and discover some of the best solutions on the market. 

Let’s dig in! 

What is Enterprise AI?

Enterprise AI is an artificial intelligence and machine learning–based solution that aims to enhance your business functions, such as data collection and analysis.

Simply put, enterprise AI takes all your company information and makes the most out of it, presenting it in a format that’s easy to understand by anyone. As a result, you experience a boost in employee productivity and streamline routine tasks through automation, which enhances your customer service and supply chain workflows, for example. 

Enterprise AI doesn’t work alone, however. It employs different technologies to solve challenges, including natural language processing (NLP), machine learning, and computer vision. 

But is enterprise AI that different from regular AI? For the answer to this question, follow us in the next section. 

What’s the difference between enterprise AI and regular AI?

The main difference between enterprise AI and regular AI is its purpose. 

Enterprise AI’s focus is on solving complex problems within large companies. Its main goal is to automate business processes and generate valuable and actionable insights from large volumes of data.

Moreover, it’s highly customizable to meet the specific needs of a business, involving custom algorithms, data models, and even integrations with other existing tools.

Regular AI, on the other hand, has a more general purpose without being highly customizable. You can’t tailor it to a specific use case or industry and it aims to perform specific tasks, such as generating images or coming up with ideas. 

In conclusion, enterprise AI works to solve business-related problems, while regular AI is more general. 

To drive this point further, let’s look at some examples of enterprise AI. 

What are Some Examples of Enterprise AI?

Enterprise AI is super versatile. For example, you can enhance your customer experience using AI-powered chatbots and other assistants, to handle user inquiries and tickets. It can go as far as optimizing cybersecurity and helping businesses make data-driven decisions. 

But let’s go through some of the best examples and use cases of enterprise AI in more detail: 

  • Supply chain management: Enterprise AI helps providers in the supply chain industry reduce costs and increase their ability to respond to sudden changes in demand. How? By spotting potential bottlenecks and optimizing inventory. 
  • Personalized marketing: Since enterprise AI analyzes preferences and behaviors, it can create more targeted and tailored recommendations or advertisements. 
  • Enterprise AI search: Enterprise search refers to an employee’s ability to search across their company’s internal knowledge and quickly find what they’re looking for. There are plenty of enterprise AI search tools out there, but one of the best ones is Guru. This solution connects all your apps, chats, and docs in a single source of truth. And gives you the responses you need without sorting through irrelevant results. 
  • Human resources management: Enterprise AI automates many HR tasks like onboarding new hires, screening resumes, and matching candidates. It also helps identify new training opportunities and create the best retention strategy. 

This is not all — an enterprise AI search platform like Guru has nice features such as AI-powered answers that deliver accurate info (also with sources). With Guru, employees can find everything they desire without leaving Slack, their browser, or the Guru app. 

Still, should you really use enterprise AI? Like any other tool, enterprise AI has its advantages and disadvantages. Let’s look over them. 

What are the Advantages and Disadvantages of Enterprise AI?

Enterprise AI software can change the way you run your business through automation — and take it to new heights. 

But where there’s good, there’s also bad. 

Let’s look at both and uncover enterprise AI’s advantages and disadvantages. 

Key advantages of enterprise AI

Here are enterprise AI’s most essential benefits: 

  • Streamlines operations: This is the main purpose of this tool and it does so by automating routine tasks, improving your workflows, and aiding your digital transformation. As a result, you’ll experience an increase in productivity and efficiency. 
  • Improves security: Since enterprise AI handles big data, it should have security best practices and measures in place to protect it from breaches. Good enterprise AI tools have efficient ways to detect and respond to threats. 
  • Manages data better: Enterprise AI eats data for breakfast. It’s able to analyze large volumes of information quickly, sort it, and help your decision-making process. 
  • Boosts customer relationships: Nearly 64% of business owners think that AI will improve customer relationships. And it does! Through AI chatbots, people can discover relevant information quickly. In turn, it enhances their experience and strengthens relationships. 

Now, let’s go over to the dark side. 

Key disadvantages of enterprise AI

Here are the disadvantages of using enterprise AI: 

  • Requires careful consideration: Unfortunately, you can’t just say you want to implement the tool and that’s it. This decision requires careful consideration as well as management. 
  • Ethical concerns: If not designed properly, the enterprise AI tool will raise ethical concerns about its use. Many people feel like the data generated by AI isn’t accurate, but they also have concerns about the use of sensitive information. 
  • Data privacy and security issues: Even if the tool you select has the best security measures in place, incidents happen, which can impact your reputation and sow mistrust. 

In the next part of this guide, let’s focus more on enterprise AI platforms. But before we get to the best tools to use, let’s define the concept. 

What are Enterprise AI Platforms?

Enterprise AI platforms or enterprise AI software represent a technology that acts as a data management solution for large enterprises. It uses machine learning models to increase operational efficiency by analyzing structured and unstructured data from various sources and making sense of it. 

Or rather, turning it into actionable insights that improve your decision-making. 

In a nutshell, it’s a unified central database equipped with all the necessary tools for analyzing large datasets in order to create a dynamic business and reduce operational costs. 

With the definition out of the way, let’s talk about the top 10 enterprise AI tools currently available on the market. 

10 Top Enterprise AI Platforms to Know

If you’re ready to invest in enterprise AI platforms, you’re in luck. We did the research for you and created a list of the best AI solutions you can check out right after finishing reading this guide. 

Dive in! 

1. Guru

Guru is not just very good AI enterprise software; it’s also an employee intranet hub and a centralized knowledge base. 

But today, we’re gonna focus on Guru’s enterprise search ability that gives you instant answers right in your workflow. And makes you avoid sorting through the many irrelevant search results other tools provide. 

In a nutshell, Guru has a secure AI-powered search that delivers relevant info based on location, previously asked questions, and more. “Search function provides easy access to … the updated source of information we need to get our job done. Everything is in one place!” 

Moreover, with its built-in natural language answers, you’ll stop searching aimlessly for the answers you seek since you’ll get the best results for your inquiries.

But we think this on-demand demo video will explain everything better. 

2. Databricks

Databricks is a cloud-based data intelligence platform that uses data science, predictive analytics, and generative AI to manage huge volumes of information. 

What appeals about Databricks? Well, people like that the platform is easy to use. Moreover, “The platform scales extremely well and really helps build the data and AI ecosystem within large enterprises.” 

With this tool, you can develop enterprise AI applications using your data that’s safe and private. Moreover, by using natural language, you empower people in your team to discover new actionable insights that’ll make your processes more efficient. 

Source: G2

3. H2O Driverless AI

H2O Driverless AI is a tool that uses automation and fast computing power from GPUs to drive optimization, innovation, and efficiency. 

DAI assists greatly in the automation of our services. It greatly streamlines the process of feature selection, generation, model construction, model testing, and ensemble building and deployment.” 

Which means that this tool empowers data scientists to work on AI projects faster and more efficiently through key machine-learning tasks and process automation. 

Moreover, it has advanced functionalities such as data visualization, feature engineering, and low-latency deployment. 

Source: Capterra

4. IBM Watson Studio

IBM Watson Studio provides a data science and machine learning AI model that helps enterprises accelerate their digital transformation. 

This AI system offers data governance capabilities, meaning that you can mitigate drift and bias better. Moreover, it simplifies experimentation and deployment, improves model development, and speeds up data preparation. 

IBM Watson Studio also has open-source tools and drag-and-drop features that allow people to collaborate on the same platform, which in turn boosts productivity.

The integration with the existing APIs and flexibility [of the] deployment of instances in various environments are the best features of IBM Watson Studio.” 

Source: G2

5. Dataiku

Dataiku offers an intuitive platform for everyday AI, which empowers your business to build projects faster and work in a safe, protected environment.

Overall, Dataiku connects your people, workflows, and technologies in one centralized place, allowing data experts to collaborate efficiently and manage risk better. 

It saves me a lot of time when cleaning data and mixing different languages and technologies such as SQL, Python, and R in the same project.” 

Dataiku also integrates with generative AI services, including OpenAI’s ChatGPT, AWS, Hugging Face, etc. 

Source: Capterra

6. DataRobot

DataRobot is a machine learning platform with AI capabilities that allow you to build accurate predictive models and bring them all together in a single place.

This tool has a highly scalable framework, meaning that it’s able to monitor and analyze performance at scale with real-time alerts and valuable insights. This results in continuous improvement and optimization for your business, as you can clearly see your profitability and performance. 

The platform absolutely brute-forces through popular and niche modeling techniques with your data, presents you with a ton of metrics afterward … and even has deployment options.

Source: Capterra

7. Amazon SageMaker

Amazon SageMaker provides a machine learning service for improving your workflows and offers a way to quickly build and train ML models at scale. 

With this tool and its no-code interfaces, you can streamline your data management initiatives and find the best way to organize large volumes of structured and unstructured data. 

What can you do with Amazon SageMaker? Well, you can build, train, and deploy ML models using pipelines, profilers, debuggers, and more in one integrated development environment. 

Moreover, you get simplified access controls and transparency over all your ML projects.

SageMaker makes it very easy to train and deploy models.” 

Source: Capterra

8. Google Cloud Vertex AI

Vertex AI is Google Cloud’s very own enterprise AI platform that helps companies build and deploy machine learning models at a faster pace. 

This tool helps you build and use generative AI for rapid prototyping, as well as testing models. It has a range of features, such as AutoML, MLOps, model deployment, and custom model training.

Furthermore, Vertex has forecasting for predicting patterns based on data, and a method for summarizing, classifying, and extracting valuable info from the knowledge you gather. 

The facilitation of unified analytics is a treat in itself, but the [...] excitement and experimentation is where transformation is brewed.” 

Source: Google

9. C3 AI

C3 AI is an enterprise AI solution that businesses can use to accelerate digital transformation by analyzing multiple datasets effectively. 

C3 AI has multiple uses. 

One, you can build and deploy enterprise AI apps that unlock better business value. Two, it acts like a unified knowledge source that enables companies to unlock valuable insights through an intuitive chat interface and search. 

Moreover, you have quick access to relevant insights across all existing tools and systems so you can make better data-driven decisions in the future.  


10. Microsoft Azure

Microsoft Azure is last on our list, but still a relevant cloud-computing tool that makes your organization more efficient.

Azure has plenty of capabilities, such as edge, on-site, hybrid, and multicloud, allowing you the flexibility to innovate from anywhere and modernize your IT infrastructure. 

Azure has it all and is extendable by you or thousands of developers, thus, regardless of your payload, Azure [has] you covered from small static websites to planet-scale systems.” 

What is the Future of Enterprise AI?

According to PwC, AI technology could generate $15.7 trillion in revenue by 2030, boosting the GDPR of local economies by 26%. 

Moreover, Mordor Intelligence says that the digitalization of large-scale companies is here, characterized by advanced digital technologies such as the Internet of Things (IoT), blockchain technology, and edge computing. 

The integration with other technologies will enhance enterprise AI’s ability to analyze enormous amounts of data in real time more efficiently. In addition, it’ll bolster security and transparency, which increases the trust in artificial intelligence solutions. 

Overall, the enterprise AI market will continue to grow, reaching $270.06 billion by 2032

The Bottom Line

Has this article shed light on this topic? Does it answer this question: “What is enterprise AI?” 

We hope it does. We worked hard to give you all the facts and make this new technology not so scary. 

If you want to get to know enterprise AI up close and personal, try Guru! 

This tool will make you forget traditional search and make it infinitely better. In no time, you’ll say ‘goodbye’ to context-switching and access all your data directly in your workflow.

Don’t be shy — sign up to Guru for free

Key takeaways 🔑🥡🍕

What is enterprise AI?

Enterprise AI refers to artificial intelligence solutions designed specifically for large-scale organizations and businesses. These AI systems are built to handle complex tasks, process vast amounts of data, and integrate seamlessly with existing enterprise software, while prioritizing data security, privacy, and customization to meet the unique needs of each organization. Enterprise AI is typically more expensive than regular AI and comes with dedicated support and maintenance services to ensure smooth implementation and operation within the enterprise environment.

What's the difference between regular AI and enterprise AI?

The main difference between regular AI and enterprise AI lies in their scale, integration, and customization. Enterprise AI is designed to handle large-scale, complex tasks and datasets within an organization, seamlessly integrating with existing enterprise systems while prioritizing data security and privacy. In contrast, regular AI may offer more generic, consumer-oriented solutions with limited scalability and integration capabilities.

What are some enterprise AI use cases?

Enterprise AI has various use cases, such as fraud detection in financial institutions, predictive maintenance in manufacturing, and intelligent customer service chatbots in large-scale customer support operations. Other applications include supply chain optimization, risk management, and personalized marketing campaigns based on customer data analysis. Enterprise AI can also be used for automating repetitive tasks, improving decision-making processes, and enhancing overall operational efficiency within an organization.

Written by
Mark Whelan
Search everything, get answers anywhere with Guru.

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