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Enterprise AI, Explained

by | Mar 6, 2024

What is Enterprise AI (Artificial Intelligence)?

Enterprise AI is a subset of artificial intelligence focused on driving business value in a large-scale organization. Businesses that harness the advantage of the power of enterprise AI applications, organizations can experience significant advances in their value chain operations. This will result in improved business agility, efficiency, and profitability, allowing for better decision-making at a quicker rate while improving worker productivity.

Recent breakthrough innovations in AI, intelligent automation, and machine learning have dramatically changed the landscape of enterprise AI and increased the necessity for quick adoption. Businesses must now incorporate this new technology or risk being left behind.

Image show the definition of enterprise AI.

Regular AI vs Enterprise AI

The major difference between enterprise AI and regular AI is its purpose: enterprise AI focuses on resolving specific, high-value use cases at a large scale. Large organizations regularly interact with hundreds of applications and produce enormous amounts of data. Due to the size and scale of the data it made enterprise AI is uniquely positioned to consume large amounts of data. This can often require companies to upgrade their tech stack to handle the massive flow of information necessary to power these models.

Enterprise AI is also distinguished from traditional AI by its ability to integrate with existing enterprise systems and tools such as Salesforce, ServiceNow, and Workday. It also typically features an API that allows it to interact seamlessly with other components of an organization’s technology stack, making it easier to scale. The value of enterprise AI lies not only in the power of the modal but in the amount of data it has access to. This is why Capacity has invested in creating over 150 native integrations connecting data across all business parts.

Enterprise AI Solutions

The possibilities for incorporating enterprise AI into your business are boundless, but let us explore three particularly effective solutions from which any company can benefit.

Image showing three enterprise AI solutions.

Answer questions

Enterprise AI solutions should take on most of the grunt work your team is bogged down with daily. Relieving teams of specific steps in processes can add a lot of value to the day-to-day experience. Without AI, these processes could include reaching out to a team member, dealing with a data platform and datasets, managing data silos, logging into one or multiple systems, and manually searching through an intranet.

Real-time support automation platform Capacity can seamlessly connect an organization’s tech stack via app integrations, deep learning, cloud computing, computer vision, conversational interfaces, and machine learning models. Rather than an employee searching an intranet for knowledge, asking a coworker, submitting a helpdesk ticket, or diving into a cloud drive, the platform’s enterprise AI capabilities make it possible for an employee to access accurate information instantly by asking a real-world question via a chatbot.

Capacity features a conversational interface that is equipped with a knowledge base and helpdesk that connects to an organization’s cloud drive, documents, and mission-critical apps via API integrations. It can intelligently find and answer employees’ most commonly asked questions in seconds.

illustration of capacity offering a user the choice of "yes" or "no" to the question "would you recommend this product to a friend" as well as a response from capacity saying "great, how would you rate your overall experience with us today" and then four stars out of five selected

Get Started with AI

Capacity’s customized AI Assessments can help:

  • Identify opportunities for using AI
  • Prioritize use cases that fit your goals
  • Build an action plan for implementation

Automate processes

In addition to eliminating specific steps or tasks via efficiencies, an enterprise AI platform can also complete certain steps via automation and optimization. This is where much of the fear comes from when adding AI to the enterprise. While AI applications can complete repetitive tasks via algorithms and integrations, there is no real substitute for strategy and problem-solving. The tasks that AI projects can and should alleviate are those that slow down team members from doing important tasks. This includes sending emails, scheduling meetings, setting reminders, extracting data from documents and white papers, and much more.

Capacity features a workflow builder that enables teams to create digital representations of physical processes with low-code technology. When teammates develop workflows for different processes, they can assign tasks to themselves, their teammates, and Capacity to complete.

Using enterprise AI to automate tasks can:

  • Save valuable time
  • Improve decision-making and use cases
  • Eliminate room for human error
  • Enable employees to apply more critical thinking to their work

This technology benefits business leaders in all industries, including financial services, retail, healthcare, life sciences, supply chain management, and data science.

Build solutions

Though this article has mentioned this benefit in the last two sections as a secondary outcome, it is worth mentioning for the third time. Enterprise AI empowers employees with extra time to focus on things only a human can do, such as communicating and strategizing to build solutions to solve problems.

While extra time to solve business processes and brainstorm solutions is a great amenity to have on your side, Capacity can supply you with tools to build solutions. Capacity’s developer platform enables teammates to create integrations to proprietary apps and systems with a low-code solution, step-by-step instructions, clear descriptions, and a user-friendly interface. There’s no need for data scientists or engineers.

Image showing enterprise AI platforms.

Top 3 Enterprise AI Platforms


Capacity is one of your best options if you’re in the market for an AI-powered enterprise solution. Not only does it leverage natural language processing (NLP) to deliver answers and save time quickly, but it also gives developers room to customize integrations and create their automated tools, so the possibilities are endless! Customers and employees experience enhanced satisfaction thanks to Capacity’s powerful yet easy-to-use functionalities.


Another popular platform for enterprise AI is MindBridge Ai Auditor, which specializes in audit automation by automating data analysis processes with analytics models. It offers advanced analytics capabilities, including predictive forecasting and anomaly detection, and support for NLP-based chatbot development. Additionally, MindBridge allows users to easily monitor their models in production and track user activity within the platform.

Microsoft Azure

Lastly, Microsoft’s Azure Machine Learning platform provides an end-to-end solution for developing, deploying, and managing AI applications at scale. Its wide range of tools includes built-in ML algorithms that can be used to quickly build models from existing data sets or trained from scratch using custom datasets. Additionally, the platform integrates with many popular cloud services such as Azure Databricks, Cognitive Services, and Power BI, making it easy to take advantage of various resources across the Microsoft stack.

Challenges associated with adopting Enterprise AI 

Despite the apparent advantages of adopting enterprise AI, there are several challenges associated with its implementation. Firstly, most enterprises need in-house expertise and resources to effectively develop and manage these applications. As a result, they must either hire dedicated teams or rely on external vendors to take care of the technical aspects such as deployment, monitoring, and maintenance.

Additionally, compatibility issues can make incorporating enterprise AI into existing systems easier. If a system is not designed with AI in mind, it may not have the flexibility to integrate new tools or adjust existing ones accordingly. This could lead to compatibility issues when attempting to create an ecosystem that includes both traditional and advanced technologies. Finally, enterprises may also face regulatory obstacles depending on their industry – for instance, financial service providers must adhere to stringent data handling rules, which could limit their ability to participate in advanced analytics projects.

Final Thoughts

In conclusion, incorporating enterprise AI into existing processes can effectively improve efficiency and accuracy. However, it is vital to understand the challenges associated with its implementation, such as lack of in-house expertise, compatibility issues, and potential regulatory hurdles, before committing to a project. It is crucial to choose the right enterprise AI partner to guide you through the process of implementation.

If you want to integrate enterprise AI into your business, request a demo below and see what Capacity can do for you!

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