What Is Enterprise AI? A Comprehensive Guide

Among the general public, awareness about AI technologies is at an all-time high. Everyone is familiar with consumer AI apps such as Siri, Alexa, and ChatGPT. These apps have made AI a household topic of discussion. They’ve helped many people with directions, grocery shopping, and knowledge retrieval.

But this is AI for the individual. Deploying generative AI for enterprises, securely and at scale, is a more difficult undertaking. However, businesses cannot afford to neglect the recent advances in AI. For many enterprises, there is no choice in the matter. The imperative is to grow with AI — or lose.

Historically, the strict requirements of enterprise AI made implementation slow and cumbersome. But recent advancements have unlocked the full potential of generative AI for large companies. Now let’s take a look at where enterprise AI is today.

What is Enterprise AI?

Before we go any further, let’s define what we mean by enterprise AI. As a general definition, enterprise AI refers to the AI technologies and deployments used by large businesses and organizations. More specifically, it is how organizations apply AI solutions to complex business challenges.

Enterprise AI typically focuses on solving specific business use cases, such as document analysis, customer success chatbots, and physician assistants. Sectors such as healthcare, finance, and education are rapidly adopting enterprise AI solutions to fuel essential business operations.

Enterprise AI encompasses a wide range of technologies, from generative AI, to natural language processing, to robotic process automation (RPA). The total predicted market size of enterprise AI is expected to top $560 billion in 2034.

Consumer AI focuses on providing engaging and useful user experiences around personal productivity. The apps we listed in the introduction are examples of consumer AI products, built for daily use cases around shopping, scheduling, and accessing information for individuals.

Enterprise AI requires deep customization to let developers and IT teams implement the exact solutions they need. At the same time, enterprise AI also needs to provide the same capabilities for non-technical stakeholders. Additionally, enterprise AI requires stringent security, privacy, governance, and compliance standards.

Enterprise AI: Core Tech Stack

Now that we have a working definition of enterprise AI, let’s take a look at a typical tech stack for such a deployment. Each organization will initiate its own unique implementation, but these are some of the typical components.

IT Infrastructure

Enterprise AI requires an array of IT infrastructure such as servers, physical storage devices, and networking equipment. For this, organizations often harness cloud computing services, such as Azure, AWS, or Google Cloud Platform. Companies might also use on-premise hardware, for reasons such as security.

This IT infrastructure handles backend AI processes, manages data, storage, and general computing requirements. Generally, enterprise AI requires a high volume of compute for both training and inference. This may require upgrades to current contracts or purchasing more hardware.

Large Language Model (LLM)

Large Language Models (LLMs) are the core of enterprise AI systems. Their transformer models (such as GPT, BERT or multimodal) and weights (the trained parameters) determine their intelligence, capabilities, and compute requirements. While you can develop and train models from zero, you can also fine-tune an LLM with a specialized dataset, making it more suitable for a number of specific tasks.

There are several cloud platforms that help train, host, and deploy AI models. Each major cloud provider has an AI suite that can create instanced versions of popular LLMs. They come with an API to connect them to your internal systems. Some of these cloud platforms include Azure AI, Amazon Bedrock, and Vertex AI.

Data Pipeline

High-quality data is vital for AI, whether for training and fine-tuning a model, or for using a chat interface to plan a marketing campaign. This is why creating a robust data pipeline is so important. There are several stages here, including connectivity, preparation, indexing, and retrieval:

• Connectivity: Enterprise AI must connect with all data sources, whether Sharepoint, AWS S3, relational databases, SaaS accounts, or other relevant third-party systems.
• Data preparation: Before it can be used, data needs to be processed. This involves parsing, extracting text from scanned documents (OCR), deduplication, and normalization.
• Indexing: To save time and computing resources, indexing is fundamental to group data that’s frequently accessed together. Since the data will be ingested into AI systems, it’s helpful to store it in vector databases such as Weaviate.io or in hybrid setups that support structured and unstructured data.
• Retrieval: Efficient data retrieval using semantic search, keyword search, vector search, and query transformation help models get the most pertinent information for each request. That increases the accuracy and quality of the models. That describes the concept of retrieval-augmented generation (RAG).

Application Layer

All the components we’ve listed thus far focus on the backend of enterprise AI. But on the frontend, there needs to be a set of applications and interfaces to let users trigger actions, see results, and work with data.

These include chatbots, virtual assistants, AI-enhanced search engines, task automation workflows triggered by user input, or solutions for report-building and decision-making assistance.

In this context, solutions like Stack AI provide pre-made interfaces that are ready to share across your organization. No-code interface builders with API connectors offer a more customized way of building apps and products to leverage infrastructure capabilities without the hassle.

Governance

Governance is also a crucial component of enterprise AI systems. The ability to control permissions, data integrity, and other functions is essential for enterprise companies that require strict security and compliance guarantees. Here are some of the governance capabilities of enterprise AI:

• Role-based access control (RBAC): RBAC ensures teams have access to the minimum viable information to be effective, while protecting business and user data.
• Performance monitoring: This keeps track of latency, throughput, and response times to ensure the models stay within operational requirements.
• Drift detection: In circumstances where users interact with AI in an evolving way, the active configuration might degrade in quality over time. To protect against this, many enterprise AI solutions employ drift detection.
• Explainability: This reveals the reasoning process from user input to AI output. Explainability builds user trust, while meeting regulatory and auditing requirements.
• Guardrails: Many enterprise AI systems employ guardrail features to prevent hallucinations, data misuse, or inappropriate outputs.
• PII masking: Masking PII keeps AI within compliance bounds by anonymizing sensitive information.

Investing in systems that promote better governance ensures AI implementation can scale and remain sustainable in the future, while protecting users, augmenting security, and fulfilling auditing requirements.

Enterprise AI: Advantages & Disadvantages

Enterprise AI offers a number of advantages to businesses, but it also comes with drawbacks. Here’s an overview of all the different angles enterprises should consider before implementing AI.

Advantages

• Enhanced Decision-Making: LLMs have advanced reasoning capabilities and problem solving skills, allowing enterprises to use them to handle complex business problems.
• Automation and Operational Efficiency: AI’s capacity to work with unstructured data, along with added reasoning and decision-making capabilities, enables enhanced automations.
• 24/7 Availability: Now that AI agents can operate call centers, perform medical work, and many other intelligent tasks, the potential for 24/7 availability is a reality.
• Personalized Customer Experiences: With generative AI and big data, you can use data to personalize the customer experience in all your channels.
• Innovation and agility: Brainstorm ideas for products, services, or business improvements, and learn how they could be implemented.

Disadvantages

• Data quality optimization: AI responses are only as good as the source data used as reference. Adding the highest-quality data — as well as the most relevant for each task — requires constant optimization work.
• Integration with current systems: Connecting AI-enabled systems to other existing infrastructure is complex from a technical and engineering standpoint, as well as from a privacy and security angle.
• Skill gaps in the workforce: Training teams to drive the most value out of these tools is time-consuming and expensive.
• Ethics, bias, and fairness: Due to the data used to train AI models, they end up making biased decisions.

These are some of the main advantages and disadvantages a company might encounter when adopting enterprise AI. But they will depend on your specific use case, and what you are trying to solve for.

Enterprise AI: Implementation Strategies

There are a number of ways that large companies implement enterprise AI. Developing in-house means you’ll hire talent, provision infrastructure, and build AI technology for each solution end-to-end.

However, software engineers, developers, data scientists, and machine learning professionals all command high salaries, making it costly to staff your team. Also, developing each solution will take a considerable amount of time.

AI and automation agencies are an alternative to hiring in-house. Due to their experience with previous clients, you can take advantage of productized offerings that can solve a wide range of problems in the short- to medium-term.

There is usually consulting and training included or available as add-ons, useful to navigate the technological landscape and upskilling your workforce. The number of AI solutions depends on the output the agency can handle, with more comprehensive solutions costing substantially more.

Lastly, there’s AI software that your teams can use to build solutions by themselves. The offering is wide, including code, low code, and no code options. Most take care of the infrastructure side. Instead, they offer pre-built tools to build logic, connect data, and run LLMs from customized interfaces.

The main advantages of this approach are that you can empower front-line employees to build solutions: after all, they’re the ones dealing with the problem day-in, day-out. With security handled from the backend, these teams can build, iterate, expand, and distribute tools across teams.

Enterprise AI: Trends & Predictions

Many companies are innovating rapidly in the enterprise AI space. Other companies are implementing AI in novel ways. Let’s take a look at a number of the major trends in the enterprise AI space.

• AI Agent Technology: Instead of a turn-based interaction where the user goes first and the AI goes next, AI agents can make a plan of action based on a prompt and iterate until a solution is complete. This added autonomy opens the field for human-in-the-loop settings, possibly replacing a human and AI copilot.
• Improved models: Improved models such as OpenAI’s o3 currently in preview, which sources argue has reached artificial general intelligence (AGI). Beyond this benchmark, artificial super intelligence (ASI) could open a path for more automation, new products, and services.
• Establishment of multimodal models: Enterprise AI will accept and generate any kind of data type: text, images, audio, video, or a combination of all.
• No-code reaches widespread adoption: More users will be able to build custom-made solutions and products without requiring technical skills. This will allow AI products to expand horizontally across an organization, allowing non-technical teams to build their own domain-specific apps.
• Rise of small language models (SLMs): Small language models (SLMs) will enable edge AI computing, personal AI models, efficient task-specific models, and a range of consumer AI products and services.
• Commodification of AI infrastructure: As prices continue to drop for training, fine-tuning, inference, and RAG, AI infrastructure will become commodified. This lowers the cost of launching, training, and expanding AI solutions.

As enterprise AI continues to expand, these are some of the trends that might continue to emerge in the near future.

Stack AI: The Enterprise AI Platform for Builders

For enterprises looking for a way to apply AI to their business operations, the list of requirements is long. But Stack AI is a solution that can solve for many of the use cases enterprises encounter.

Stack AI is an enterprise AI platform that empowers your teams to build generative AI tools to solve business use cases and automate work. Stack AI provides a visual canvas where you can drag-and-drop nodes, representing inputs, outputs, LLMs, and connected knowledge bases.

You can use any LLM among all leading providers — like OpenAI, Anthropic, Google, and Meta — and choose the best model for each task. When building specialized solutions, you can also leverage integrations with AI infrastructure such as Hugging Face (community models), Cerebras (fastest inference and training services), or Azure AI (containerized, HIPAA-compliant deployment of OpenAI models).

Stack AI connects to popular enterprise-grade software such as Microsoft Sharepoint, AWS S3, Salesforce, or HubSpot, among many others. As you connect your data sources, you can organize them into knowledge bases inside Stack AI.

Stack AI comes with pre-built templates for popular use cases. Chatbots, RFP response agents, and contract analyzers are just some of the templates available. Stack AI also maintains HIPAA, GDPR, and SOC compliance, required standards for many industries to ensure privacy and security.

With these certifications, enterprise teams can build secure and scalable AI agents that can solve for their business use cases.

Enterprise AI: A Rapidly Evolving Space

The enterprise AI space is expanding rapidly, as new solutions begin to introduce the speed, scalability, and security large organizations need to power business operations.

No-code builder tools such as Stack AI allow teams in enterprise companies across healthcare, finance, and other verticals to build domain-specific AI apps while maintaining the necessary security and compliance.

Sign up for Stack AI today to start building no-code generative AI apps that you can share with your team.