Enterprise ChatGPT – how to implement Generative AI in your company.

Aug 23, 2023 2:18:25 PM | Enterprise ChatGPT – how to implement Generative AI in your company.

Aligning company goals and client data privacy.

With any powerful technology, implementation comes with great reasonability.

Generative AI is no different to any previous major technology advance – requiring companies to perform a complex balance of achieving Operational Efficiencies against Security and Compliance.

Dentons, the world’s larges global law firm is to launch a proprietary version of ChatGPT that will empower its lawyers to apply generative artificial intelligence on active client matters. Its FleetAI shows how an enterprise is able to roll out Generative AI to all its employees while managing risk.

Likewise PricewaterhouseCoopers is investing heavily in developing Generative AI to automate aspects of its tax, audit and consulting services. Its first pilot is called ChatPWC and addresses the security concerns with Shadow GPT that we discussed last week.

However, as with most IT, you don’t need to spend billions building your own complex solution, rather in the same way you utilise Xero, Sage or QuickBooks today, with a basic understanding of the issues and the correct software platform you can begin to implement Generative AI in your company securely and with an immediate cost benefit.

What is Generative AI?

Generative AI refers to machine learning models that create new data resembling the data they're trained on. These models can produce text (i.e., Chat GPT), images (i.e., DALL.E2) GPT), or Speech to Text (i.e. Whisper). Typical Enterprise use cases include.

Customer Support

Deploying ChatGPT can lead to swift and accurate first-level support, enhancing the user experience.

Content Creation

From marketing copy to reports, ChatGPT can assist teams in generating content efficiently.

Data Analysis

With the right integration, ChatGPT can help process and interpret vast amounts of data, providing concise insights.

Enterprise Generative AI Gotchas 

Some common gotchas to cover when implementing Generative AI in a company are. 

Clearly Define Objectives

Before diving headfirst into implementation, understand your enterprise’s unique requirements. Are you aiming to improve customer interactions? Or do you want to enhance internal communication? By pinpointing objectives, you can tailor the AI's training and integration effectively.

Data Security & Compliance

Generative AI, by design, assimilates and learns from data. In the enterprise context, this could involve sensitive information. Hence, ensure that your AI integration adheres to data protection norms and industry-specific compliance standards. Consider deploying the model in a closed environment, especially if you're dealing with highly confidential data.

Iterative Training

One size doesn’t fit all. To maximize the benefits of ChatGPT, invest time in finetuning the model for your specific use-case. Continuous feedback loops, where the AI learns from its interactions and corrections, are paramount to optimize performance.

Integration with Existing Systems

Generative AI can provide maximum value when seamlessly integrated with existing enterprise tools – be it CRM systems, content management platforms (i.e. SharePoint), or data analytics tools. Consider building middleware or utilizing APIs to connect ChatGPT with your current infrastructure.

Client Documents & Data Ingestion Separation

For generative AI, separating client documents from data ingestion is crucial. This ensures the AI trains without biases or privacy breaches. By keeping client data distinct, you maintain trust, uphold data privacy standards, and ensure that generated content remains neutral and independent of specific client influences.

User Training

While ChatGPT and similar models are designed to be intuitive, it's essential to train your staff. Familiarize them with the system's capabilities, potential pitfalls, and best practices to ensure they leverage the technology effectively.

Monitor & Optimize

Post-implementation, actively monitor how the AI is being utilised. Are there recurring issues? Is it meeting its defined objectives? Use this feedback to make necessary adjustments, be it in training, integration, or user access.

Plan for Scalability

As with any digital solution, plan for the future. Ensure that your implementation can scale up as your enterprise grows and as the AI model itself evolves. Regular updates and adaptation to newer versions will keep your enterprise at the forefront of innovation.

Ethical Considerations

Generative AI, given its capabilities, comes with ethical implications. From unintentional bias in responses to the potential for misuse in content creation, enterprises need to establish guidelines and checks to ensure ethical utilization.

The Enterprise Copilot

Generative AI offers impressive opportunities for companies to automate many mundane tasks for its employees, freeing them to work on final reviews, customer interaction and other high-level work – a Copilot for your Company

And when implemented thoughtfully, it can revolutionise how a business operates and interacts, both internally and externally.

Our Advisory & Corporate Finance Copilot

At M&A Deal Platform, working with our partners Microsoft and Open AI, we have developed a Copilot to support and automate Advisory & Corporate Finance Services.

One example of our Copilot is the Business Plan wizard, that uses a structured modular approach to creating a Business Plan, that is integrated with Azure Open AI for rapid first draft content creation.  Together they reduce Business Plan creation from days and hours to minutes.

Another example is our Secure ChatGPT, an private interface to Azure Open AI that provides access to your own instance of Open AI and provides an additional level of controlled generative AI into your existing business processes.

Our Secure ChatGPT means your prompts (inputs) and completions (outputs), your embeddings, and your training data:

  • are NOT available to other customers or competitors.
  • are NOT available to OpenAI.
  • are NOT used to improve OpenAI models.
  • are NOT used to improve any Microsoft or 3rd party products or services.
  • are NOT used for automatically improving Azure OpenAI models for your use in your resource.
And your fine-tuned Azure OpenAI models are available exclusively for your benifit.

Next Steps

If you interested in learning more, why not book a demo of our software platform and we can discuss how it could be used to automate your practice workflows.

Book a Demo


James Ruthven

Written By: James Ruthven

James Ruthven is a technology leader with 25+ years of success in leading organisations in change and growth, delivering solutions that transform customer experience.