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.
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.
Deploying ChatGPT can lead to swift and accurate first-level support, enhancing the user experience.
From marketing copy to reports, ChatGPT can assist teams in generating content efficiently.
With the right integration, ChatGPT can help process and interpret vast amounts of data, providing concise insights.
Some common gotchas to cover when implementing Generative AI in a company are.
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.
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.
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.
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.
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:
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.