Why Is ChatGPT Not The Answer To Your Enterprise Conversational AI Needs?

ChatGPT does not offer API access to integrate with the internal enterprise systems. The inability simply discourages customers or internal users from using a conversational mechanism. As a result, the pre-trained model like that of ChatGPT falls short of enough knowledge needed to communicate with the backend systems like CRM, ERP, ITSM platforms, Enterprise Service Desk, and others to help proceed with real-time response and feedback.

This certainly prevents ChatGPT from absorbing the capability of conversational AI models and scales up enterprise workflows that can help resolve problems in real-time or help with a number of tasks such as,

• Placing orders and fetching order history

• Helping in transactions

• Following up with future or prospective customers

• Closing a deal or retrieving product information

• Revealing chat history or transferring a call to a live agent for IT support teams

• Reporting fraud and resolving issues

With that said, every department can leverage conversational AI platforms, from marketing to IT and operations to finance, to drive meaningful business results through human-like interactions.

Note: It is true that ChatGPT Plus comes with API. But, this feature is only used to offer product recommendations and suggestions that aim to improve the shopping experience for shoppers on e-commerce platforms. It is more of working on the personalization experience side. Shopify and Instacart are early adopters of this API access.

On the other hand, Azure OpenAI service makes it easy for enterprise integration with ChatGPT combining Azure Cognitive Search. But, this process involves high computing costs, with developers supervising the training session strictly to enable constant API connection with the external database.

With no-code conversational AI platforms, the enterprise does not need to take all these tough iterations. Enterprise deployment and design are easy and fast.

Enterprise operations need current data to help teams respond and move business processes at scale. But with ChatGPT as an enterprise chat interface, accessing the pre-trained core language model and tweaking its existing database can be challenging.

So, developers are unable to customize its data model to enable it to perform and manage enterprise-wide tasks, such as

• Printer issues

• Account Unlock

• Password resets

• Device or apps provisioning

• Accounts payable and receivable

Also, it is evident that ChatGPT has 2021 cutoff knowledge and also is not connected to the internet. The lack of current data poses risks to the enterprise's operational efficiency.

The conversational AI platform allows ease of customization for virtual assistants by communicating with its underlying language model to allow for functionalities as per existing or future task patterns.

The ChatGPT interface examines and produces answers based on prompts across its large database. So, it is easy to manipulate its capacity and ask it to generate that is unethical and biased. Or use the platform to divulge significantly important information about internal work processes. This can be damaging to the organizational reputation.

With a little cautious step as you tailor instructions for the ChatGPT through MLChat meta-data, it is possible to define prompts more clearly and avoid the chances of mitigating the chances of prompt attacks.

Although the standard ChatGPT misses this capability, it is fast to append prompts using ChatML available through a higher level of API access.

The whole process isn’t just complicated but needs robust control over prompts. On the other hand, conversational AI platforms eliminate the probability of ambiguity across the workplace or business environment.

LLMs like ChatGPT have their own database and can store data that is being shared. But, while using the chat engine to generate and automate content production for emails or meeting minutes, it is likely to get overwhelmed and expose company data, personal identification information (PII), or other company data to OpenAI’s external database.

The challenge is that once the data is shared, it is impossible to retrieve or delete it. The recent Samsung incident highlighted major data security lapses in the ChatGPT model.

An appropriate safety guardrail must be in place to prevent security threats while continuous robust supervision is necessary.

Conversational AI platforms are designed to maintain robust security for enterprise data and prevent any kind of security vulnerabilities.

ChatGPT is a ‘black box’ solution. It means an LLMs-powered AI chat solution like ChatGPT produces output that lacks visibility. It is because there is a lack of data transparency and accountability, which makes it difficult for humans to understand how different parameters and algorithms combine to generate those outputs.

ChatGPT will likely make mistakes, and users who possess little to no domain expertise to interact with ChatGPT-like interfaces will produce misinformation without any traces of references or citations that could verify the truthfulness of the data.

One major reason why ChatGPT produces ‘black box’ responses is that it can’t think and judge from real-world experiences. So, the response it produces cannot be altered.

On the contrary, Conversational AI platforms can improve search results by allowing internal and external users to provide appropriate search terms and continue the conversation flow with the interface, such as self-service chatbots.

Simultaneously, enterprise leaders can easily fetch chatbot analytics data to find where it goes wrong, which specific issue was tough to handle, and many other issues to find a proper remedy to mitigate and easily handle future cases.

The standard ChatGPT is not as huge as the commercial tier features regarding context capacity. In order to harness the enterprise-level benefits of ChatGPT(mainly GPT 3.5 turbo), CIOs or CTOs need to shore up large budgets to spend over huge compute infrastructure such as hardware or software costs, including the budget to run Azure in the backend.

It is also important to note that dedicated capacity for a longer context limit does not ensure that GPT 3.5 turbo will mitigate the chances of toxicity. It means you need to have someone to supervise the content generation and ensure its veracity.

Another concern is the additional operational costs. This is because enterprises must look at ways to keep the ChatGPT 3.5 model running and operational at all times, which further necessitates hiring a team of expert engineers and developers for configuration, installation, and ongoing maintenance.

If conversational capabilities are a key priority to improve enterprise-wide operations, AI-powered virtual assistants are better at solving the current problems for CTOs or CIOs as they look to minimize operational costs in the high time of recession. They are no-code cloud-native platforms that can easily integrate with the on-prem infrastructure through API or work as an independent model for your enterprise needs.

Workativ chatbot builder harnesses the power of LLMs or generative AI, the underlying technology in ChatGPT making, to expedite knowledge article search.

With the chatbot builder, it is easy and fast to upload your knowledge articles no matter what. You can choose to upload knowledge articles for IT support, HR support, and Marketing to the folder to the database of the chatbot interface.

ChatGPT helps eliminate the need to train the chatbot model. Instead, it accelerates user search performance using Knowledge AI capability and retrieves the right specific knowledge article to solve real-time issues.

It is evident that creating dialog for a pre-trained chatbot takes a lot of time for copywriters, which also needs document approval before releasing the conversations to the live environment.

By integrating ChatGPT-like interfaces or LLMs into the chatbot builder, Workativ makes it possible to automatically generate conversations with intent extraction for various use cases like,

• Account unlock

• Password resets

• Device provisioning, etc

As a result, Workativ expedites faster time to market for enterprise-wide use cases and alleviate the challenges faced by customers or internal users in the IT or HR department.

One more effective use case of ChatGPT-powered chatbot builder is that Workativ enables developers to easily draft messages for various use cases. With the power of sensing the next sequence of search queries, it can generate email messages rapidly and allow for publication instantly. This makes the send-and-receive messages effective and useful for end-users as it requires minimal reviews for edits and fixes.