Enterprise AI Platform: A Comprehensive Guide

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What is Enterprise AI?

Enterprise AI is an umbrella term that describes the use of artificial intelligence (AI) technologies within businesses and organizations. It includes an array of AI-based tools, techniques, and platforms designed to enable organizations to gain actionable insights from their data, make informed decisions, optimize operational efficiency, and automate complex processes.

Enterprises want their own internal ChatGPT where employees can ask questions and get answers. But they do not want to share their data with public AI platforms. Sharing data with public AI platforms like ChatGPT poses several privacy and security risks for businesses. These platforms often require access to a large amount of data to train their models and improve their services. In the process, sensitive business information, confidential customer data, or intellectual property might be exposed to third-party entities, leading to potential data breaches or misuse. Once data is shared with a public AI platform, businesses may lose control over how and where it’s used. It’s possible that the data can be used to train AI models that are subsequently made available to competitors, inadvertently giving them insights into proprietary business operations. Additionally, in the absence of clear data usage policies, the platform might use the shared data for different purposes than originally intended, causing potential harm to the business.

To protect their internal data from being exposed to the public and competitors, companies need to build their own internal AI platform.

While AI technology has been around for a while, it’s only recently that we’ve started to see its adoption on an enterprise-wide scale. This shift is due to advancements in machine learning, deep learning, and other AI techniques, as well as the exponential increase in data generation.

Enterprise AI vs. Generative AI

While both Enterprise AI and Generative AI are branches of artificial intelligence, they serve different purposes and have different applications. Enterprise AI, as mentioned earlier, primarily focuses on leveraging AI techniques to solve business problems and optimize processes.

On the other hand, Generative AI, often associated with creative processes, is used to create new content. This could include generating texts, images, music, or even designs. Although Generative AI has potential applications in the business world (such as content creation and design), its primary usage differs from that of Enterprise AI.

Enterprise AI Use Cases

Organizations can use AI in a variety of ways to streamline operations, make better decisions, and service customers.

Automating Processes:

One of the most significant use cases of Enterprise AI is process automation. This could range from automating repetitive tasks like data entry and invoice processing to more complex procedures like supply chain management. With Robotic Process Automation (RPA) powered by AI, companies can reduce operational costs, increase productivity, and minimize human error. For instance, in the banking sector, AI can be used to automate credit decisions and compliance reporting, allowing employees to focus on more strategic tasks.

AI-Powered Search

Enterprise AI is also transforming search capabilities within organizations. AI-powered search or cognitive search leverages natural language processing (NLP) and machine learning to provide more relevant and personalized search results. These searches understand context, user behavior, and preferences to deliver the most relevant information. For example, an employee looking for specific contract details in a large organization can use AI-powered search to find the information quickly and efficiently, saving valuable time and resources.

Decision Making

Enterprise AI is revolutionizing decision-making processes by providing real-time, data-driven insights. AI algorithms can analyze vast amounts of data to uncover hidden patterns, trends, and correlations that can inform strategic business decisions. For example, an HR leader can ask questions about compensation or benefit trends to make better decisions.

Enterprise AI Chatbot

In customer service, AI-powered chatbots are becoming increasingly popular. These chatbots can handle multiple customer queries simultaneously, provide instant responses, and are available 24/7. They use natural language processing (NLP) to understand customer inquiries and machine learning to improve their responses over time. Businesses that deploy chatbots can provide superior customer service, thereby improving customer satisfaction and loyalty.

Enterprise AI has been instrumental in a variety of industries:

• Healthcare: AI is used for predictive analytics, patient triaging, and personalized treatment plans.
• Finance: AI helps in fraud detection, risk assessment, and algorithmic trading.
• Retail: AI assists in personalized marketing, demand forecasting, and inventory management.
• Manufacturing: AI aids in predictive maintenance, quality control, and supply chain optimization.

These are just a few examples; the possibilities with Enterprise AI are practically limitless.

Enterprise AI Benefits

Adopting an Enterprise AI platform brings numerous benefits:

• Efficiency: AI can automate repetitive tasks, freeing up time for more complex tasks.
• Accuracy: AI reduces human error in data analysis and decision-making processes.
• Predictive capabilities: AI can identify patterns and trends, allowing for better future predictions.
• Improved decision-making: AI enables data-driven decisions, providing a competitive edge.

Enterprise AI Challenges

Despite the immense potential of Enterprise AI, it’s not without challenges:

• Data privacy: Ensuring the privacy and security of sensitive data is paramount.
• Bias in AI: AI models can unintentionally reinforce existing biases.
• Lack of skilled personnel: Implementing AI requires specialized knowledge and skills.
• Integration with existing systems: Seamless integration with current infrastructure can be complex.

For many organizations, the challenge is balancing security and compliance needs with the compute necessary to run generative AI at scale.

Steps to Set Up an Enterprise AI Platform

Setting up an Enterprise AI platform may seem daunting, but breaking it down into steps can simplify the process:

1. Capture All of The Data

Data is the fuel that powers AI. It’s important to capture all relevant data that can be used for training AI models. This could be structured data like databases or unstructured data like text files, emails, or images.

2. Label It

Labeling data involves assigning meaningful tags or labels to the data points. This is a crucial step for supervised learning, a common AI training technique.

3. Store the Data

Once the data is captured and labeled, it should be securely stored. Depending on the scale of the operation, data can be stored on-premises, in the cloud, or in a hybrid cloud model.

On-Premises Infrastructure: An on-premises infrastructure involves hosting the AI infrastructure, including servers, storage, and network hardware, within the company’s physical location. This model offers maximum control over the data and the infrastructure. Companies can customize the setup to meet their specific needs and ensure the highest level of security and compliance. However, it requires significant upfront investment and ongoing maintenance. It is ideal for companies with high-security requirements and those that can afford to manage and maintain the infrastructure.

Cloud-Based Infrastructure: In the cloud-based model, the AI infrastructure is hosted on the servers of a third-party cloud service provider. This model eliminates the need for substantial upfront investment and reduces the burden of maintenance as the cloud service provider manages the infrastructure. It offers the flexibility to scale the infrastructure up or down based on demand. Furthermore, cloud service providers offer various AI tools and services, which businesses can leverage to accelerate their AI initiatives. However, data security and privacy could be potential concerns.

Hybrid Infrastructure: A hybrid infrastructure combines on-premises and cloud-based infrastructure. It allows businesses to keep sensitive data and critical AI workloads on-premises for security and compliance reasons, while leveraging the cloud for less sensitive, scalable workloads. This model provides a balance of control, flexibility, and scalability. However, it can be complex to manage and may require sophisticated IT capabilities.

Most companies host systems in their own data centers, typically for privacy and security reasons. Private cloud deployments could be an option for companies with strict compliance requirements that prohibit sending data to external parties.

For certain use cases, lower latency could also be a strong argument in favor of on-premise deployments.

The amount of data that comes in and the inference that needs to happen, almost in real time, puts a lot of strain on the systems. In many cases there is just not enough bandwidth to send the data into the cloud, and that’s why you need on-premise systems to be able to properly do enterprise AI at scale and speed.

4. Get an AI Model

Various AI models are readily available, each with different strengths and uses. It is critical to choose a model that best fits your company’s needs. A service called Huggy Face has a immense library for natural language processing applications and its platform allows users to share machine learning models and datasets.

Microsoft recently introduced Azure ChatGPT, which is a Private version of ChatGPT tailored for Enterprises. It is a free Azure app with built-in guarantees around the privacy of your data.

In addition, Microsoft launched Azure AI Studio, a new capability within the Azure OpenAI Service that lets customers combine a model like OpenAI’s ChatGPT with their own data — whether text or images — and build a chat assistant or another type of app that “reasons over” the private data.

Microsoft defines a “copilot” as a chatbot app that uses AI, typically text-generating or image-generating AI, to assist with tasks like writing a sales pitch or generating images for a presentation. The company has created several such apps, such as Bing Chat. But its AI-powered copilots can’t necessarily draw on a company’s proprietary data to perform tasks — unlike copilots created through Azure AI Studio.

5. Customize the Model

Lastly, you must customize the model for specific use cases. This could mean adjusting parameters, selecting the right inferences, and choosing the right hardware.

A promising option for enterprises is to customize a baseline model to fit their needs and workflows. Open source models can train on their own data to create a model more closely aligned to their use case.

Conclusion

Enterprise AI is transforming the way businesses operate, bringing a plethora of benefits while also posing challenges. Adopting enterprise generative AI safely and effectively will require looking closely at each business’s use cases and risks. Everyone’s excited about these generative models now. But it does require a deep evaluation.