Generative AI for the Enterprise

With the surge in generative AI, enterprises in all fields are looking for ways to adopt this technology. It has unique abilities to push modern technology boundaries to new heights.

However, the main consideration for adopting generative ai for enterprise environments is whether to use cloud-based solutions, abstracted services, or build your own and how that choice affects your organization’s data privacy.

What is Generative AI?

Generative AI refers to a subset of AI systems that are algorithms capable of creating new content, be it text, images, or other data.

Generative AI relies on generative models such as:  

• Large Language Models (LLMs)
• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs)
• Diffusion Models
• Text-to-Image Models
• Text-to-Speech (TTS) Models
• Music Generation Models

Each model works with different data to produce new content for a specific use case. Each models is also either private or open-source.

Private models are accessed through an API as a service for a subscription fee or license, such as Google DeepMind’s AlphaFold, Microsoft Azure, IBM Watson, and OpenAI (which started as open-source but has since become private). They may offer quicker adaptation options, but there are issues related to security and privacy, as well as high costs when scaling usage.

Open-source models are open for any organization's internal usage. Examples include Mistral 7B, Llama 2, and Falcon 2. These models require on-premises hardware to run in local data centers or private cloud environments, but they are more cost-efficient in the long term. Additionally, open-source models provide enhanced security and privacy protection as the data never leaves your premises.

What Is Unique About Generative AI?

Generative AI stands out as it can autonomously generate new content, solutions, and insights from any structured or unstructured data. It enables users to ask natural questions from the data and get insights quickly without performing a time-consuming search, data analysis, or interpretation.

For example, you can quickly debug complex industrial equipment based on your internal confidential materials, such as CAD drawings and manuals, or automate processes, such as purchasing and tendering specific to your organization.

But how can you integrate generative AI into existing company infrastructure? How can you combine it with internal and external data sources? How can you avoid data leaks to the cloud?

Many enterprises are actively working to overcome these issues because emerging technologies like generative AI can uncover new business opportunities, a once-in-a-decade opportunity.

Generative AI Terms

Understanding key terms is essential for leveraging generative AI in the enterprise context:

• Artificial intelligence: These are computer algorithms that perform human tasks such as learning and making decisions that are time and resource-consuming.
• Machine learning: This is just one approach within the AI field that focuses on statistical algorithms, which can learn from data and make predictions.
• Generative AI: This is a subset of AI powered by foundational machine learning models that can generate new content or data from trained data.
• Training data: The process of feeding any data to AI models so that the models can learn patterns and produce results similar to the data they have access to.
• Edge AI: This means deploying AI models close to where the data is produced, such as smartphones, IoT, telecom towers, or any other device to process real-time data with higher accuracy and access it faster by IT departments, employees, or customers.
• Cloud AI: This refers to when you deploy and run AI models on any cloud platform such as Google Cloud or Microsoft Azure. This option may provide easy access to the resources and AI capabilities, but in reality, have a higher cost than the on-premises option.
• API-based AI: This is when you integrate Generative AI capability into applications using an API. Simple and easy, but expensive.
• Private cloud: This is when only certain cloud-based computing is allocated to one user without sharing it with others.
• On-premises AI: This means deploying open-source models in your local data center or private cloud environment for cost efficiency, security, and data privacy.
• Hybrid Cloud: A computing environment that combines on-premises infrastructure with cloud services.
• Microservices: A software architecture style where applications are composed of small, independent services.

How Generative AI Transforms Enterprises?

Enterprise generative AI can significantly revolutionize business operations by offering tools that enable more efficient decision-making and innovative problem-solving.

1. Develop faster and more efficient approaches for new innovations.
2. Automate manual tasks that take hours.
3. Remove the complexity of finding answers to problems from multiple data and data sources.
4. Connect applications, internal tools, and pipelines to provide insights on how to make better decision-making.
5. Develop faster and more efficient approaches for innovations.

All these sound promising and great for any enterprise, but is it really that simple? I guess you know the answer.

For enterprise-wide transformation and adoption, where there are thousands of employees, hundreds of cross-functional teams, and applications and tools, unifying everything is far from simple.

Why On-Premises and Proprietary Data Matter Most for Enterprises

The most valuable data enterprises have is proprietary data stored in their local data centers. This data is the core intellectual property, such as recipes, software code, or design blueprints. Such data should never leave the internal network, especially not in the public cloud. How do you then use generative AI? 

In the public cloud or abstracted services, you can easily create prototypes and scale to production, but this often comes with high costs and data privacy issues. Even if the providers specify, that they do not train from your data, can you ever be 100% sure?

There are off-the-shelf products and co-pilots also widely available, but what happens if everyone uses the same solutions? There is no creativity and no growth. Nor do these solutions consider a company’s way of doing business. 

The solution to the problem lies in on-premises AI development. You combine open-source generative AI models and sensitive data within the local data centers and internal infrastructure. That enables generative AI capability even for the most demanding data scenarios.

What Does an Enterprise Need to Consider When Adopting AI?

Adopting generative for enterprise involves several considerations:

• Data privacy and security: Protect sensitive data, by using on-premises or hybrid deployments.
• Skills and capabilities: Employees should have the skills to leverage AI and secure data effectively.
• User management: The capability to integrate with enterprise logins such as LDAP, Microsoft Azure, or Okta.
• A zero-trust security model: Any workload deployed should be isolated and not communicate with other unless specifically configured.
• IP risks: Using API-based AI solutions can put your data at risk of leaking into the black box. Do you trust them with your data?
• Hardware: Will you run applications on API-based models for quicker deployment but with the risk of data leaks and high fees, or on-premises for security and cost efficiency? Or perhaps hybrid deployment is the solution! 
• Software Infrastructure: You need modern software infrastructure that supports enterprise-grade workloads, and is scalable and secure.

The Role of Generative AI in Shaping Future Enterprises

The future of generative AI in enterprise continues to evolve and have deeper integration day by day.

At ConfidentialMind, we believe generative AI in is not going anywhere, quite the opposite. Organizations will integrate it into every aspect of their business, data, tools, and strategies. We are not saying it will be an easy journey. There is still a lot of unknown. 

Where is the value of generative AI for enterprises? What ROI can you expect? Some even suggest it is FOMO rather than real revenue generation opportunities. If competitors focus on AI, what if they are right? What happens then? No one wants to stay behind. Every enterprise is in a race to find the biggest value, and some have discovered it.

For example, Klarna recently replaced 700 customer support contractors with AI agents, estimating it will drive a $40 million profit improvement for the company in 2024. Who is next?

Conclusion

Generative AI holds much potential for enterprises across various sectors but does not come without challenges. It can create new possibilities for faster, better, and more efficient decision-making, but also has concerns such as vendor lock-ins, high operation costs, and data privacy issues.

On-premises, secure generative AI development is here to eliminate these issues and allow enterprises with confidential data to have the same capabilities as API-based solutions but with zero trust security and reduced costs.