Was the Enterprise version of ChatGPT on your watchlist? The wait is over now!

Just after stirring a lot of imagination with its basic model as a chatbot interface or an advanced Plus model that provides better data analysis and Plugins for work, OpenAI brings to business leaders like you ChatGPT Enterprise with enterprise-grade security and privacy features.

It’s the same AI-powered chatbot app but with more advanced performance capabilities.

So much to unleash for business-focused operations actually is put forward by Generative AI or Generative Pre-trained Transformer or large language models (LLM) underpinning ChatGPT types of models.

In just eight months, Generative AI has captured a lot of attention, exhibiting promises to increase user productivity.

The most apparent and common scene among businesses is the AI arms race now.

Businesses want to adopt Generative AI essentially or, to some extent, to their enterprise workflows, make a change, and feel proud of being enthusiastic about the Generative AI wave.

If this has something in common with you, things be like this:

You finally have the technology work for you. You are on a spree to announce that your organization is a visionary to adopt Generative AI and make an impact for everyone connected.

What is significant here is to have enough data to customize ChatGPT Enterprise specific to your unique business use cases and drive business values.

But that’s just one piece of the puzzle.

What does it take to scale up Generative AI tools like ChatGPT Enterprise to bring you all the benefits you aim for your business processes and drive massive user productivity?

As the OpenAI's website claims, ChatGPT Enterprise can do a lot for business-focused functions for leaders with its unique features.

1. 4x faster than the standard GPT-4:

ChatGPT Enterprise gives consumers priority access to the GPT-4 model with a 32k token context window.

When you work with ChatGPT Enterprise, you can take advantage of a large context window, which performs twice as fast as the standard GPT-4 model to process 4X larger files or inputs.

• Process large text inputs or files of about 25,000 words in one go.

• Establish contact with recent conversations and gain context no matter the length.

• Get zero wait times for information processing.

2. Model scalability:

In the consumer version of ChatGPT, there is no way to add members in a bulk way and manage them.

Another concern is that the subscription is managed through emails, which needs to provide more flexibility to transfer the member access.

ChatGPT Enterprise has a solution to the previous problem. It allows consumers to add bulk members to the platform through a dedicated admin console and easy single sign-on. Also, it provides domain verification and dashboard analytics to visualize user participation and usage.

3. Enterprise-grade security:

OpenAI clarifies that its Enterprise version does not use consumer data to train its model.

All data communicated in the chat interface is encrypted in transit and at rest with AES-256 and TLS 1.2+ data. It means data passing through the internet won’t infringe the movement.

On top of it, ChatGPT Enterprise is SOC 2 compliant to ensure that it protects consumers' data while processing it and maintains privacy standards.

Employees can quickly get the information they need to work. As per information available on the ChatGPT Enterprise website, there are multiple use cases where employees can benefit from this enterprise tool.

However, the use cases are similar to those of consumer versions or the Plus models, and the task can be done more efficiently, accurately, and securely.

You have longer texts, and you want a condensed version of it. ChatGPT Enterprise, using its 4x larger context window, can process it faster and give you the most appropriate summarized version of the texts best suited for marketing teams to write follow-up emails for their clients or IT desk agents writing crisp responses for their users. Text summarization can be an efficient tool for any work that needs summarization for different purposes.

Imagine you need to create original content, say, business proposals, employee contracts, new project guidelines, and so forth. In a fast-paced workplace, this isn’t easy at all.

But with ChatGPT Enterprise’s 32,000-token context window, you can produce any length of content.

In relation to crafting a draft, ChatGPT Enterprise can be an easy help to make that information user-friendly. You can use the language translation feature to prepare a document in a specific language.

The language translation feature is also an excellent use case of ChatGPT Enterprise to help service desk agents work with foreign language-speaking people and provide a more flexible workplace for everyone.

Your software engineering team is fast at writing codes and enables you to bring a solution faster.

What’s more!

There is a quicker project iteration with more rapid tests using ChatGPT Enterprise.

Suppose all of the above isn’t your core objective, and you want a chatbot service to help your internal people collaborate effectively and find a way to retrieve information and work. In that case, ChatGPT Enterprise can have vast potential.

Things look so promising with all these vast possibilities lurking in the corner.

Isn’t it something that ChatGPT alone cannot bring you all those riches? Time is to consider some key factors.

Let’s say you have a budget to invest in a ChatGPT Enterprise. But what’s next?

It is essential to find out that ChatGPT surge's fantasies do not carry you out and explore the real benefits of your investment.

• Give a deep thought to the state of cloud adoption.

Before ChatGPT became a sensation, IoT, Cloud, and even Blockchain made quite a stir. But, they are still in their nascency. ChatGPT Enterprise or any similar model or infrastructure needs robust cloud architecture to scale up and unleash the expected business results.

No cloud infrastructure = no scale-up. Here’s why?

It’s essentially significant that to build your customized workflows, you must have appropriate and enough data for your domain-specific work. There’s no denying about that.

However, data gathering and storing needs a robust data structure for further processes like data analyzing and cleaning and integrating the data storage with the cloud and the Generative AI model.

Evaluate what’s your cloud readiness strategy. If you don’t have it yet, you can prioritize it. Well, that’s all at the expense of enormous costs.

• Cloud platform migration is a strain on bottom-line expenses.

Big players such as AWS, Google, Microsoft, and Nvidia are gaining enough transactions to deploy and scale Generative AI models.

There are young players, too. But preferences always go with the known players rather than with someone about whom you have very little knowledge.

No matter who you want to build a vendor relationship with, a new or established cloud deployment platform, vendor lock-in could become your primary challenge.

A switch between an existing platform and something more robust or cost-effective like that of Databricks or Huggingface, be ready to pay for the data that dwells in the existing vendors’ cloud platform.

• Do you have the right AI team?

What about the skilled talent? There is a talent shortage who can work with LLM and implement necessary functions to the infrastructure. Even if they are available, it is a pinch in your pocket.

On top of it, ChatGPT Enterprise is an open-source platform, enabling you to customize its underlying infrastructure. However, working across ChatGPT Enterprise infrastructure is more challenging if one has limited LLM or ChatGPT environment knowledge. Unfortunately, the customization you need may be short of expectations.

As you discover your business cases, you will soon find that customization could be more flexible with ChatGPT Enterprise. That’s another blow for you.

Calculate an initial state of costs associated—with model investment, capturing the right talent, and cloud infrastructure maintenance.

• Legal implications

When it is about data governance, it is challenging.

ChatGPT says they provide Enterprise-grade data security, meaning you will be tempted to provide personal data inside the infrastructure, assuming the architecture will not learn from users' inputs and train the algorithms.

However, global data regulators expressed concerns over Generative AI usage and the collection of users' data.

The European Union and the Federal Trade Commission are considering ChatGPT's commercial use concerning the probability of the violation of data privacy and public safety, as well as the increase of bias.

In a similar context of exposing public data to the ChatGPT platform, Italy's regulatory agency, Garante, banned it, citing that OpenAI exposes public data and allows other users to view the chatbot's conversation titles.

Italy also put a fine of 20 million euros or 4% of its global revenue later, though Italy lifted a ban.

Other EU countries, such as Brussels, Ireland, and European countries, including France and Germany, showed interest in discovering more about ChatGPT risks using Italy's research work and followed suit.

Regulators believe that AI is here to simplify our jobs and not take control of human intelligence.

Sophie Hackford, an advisor at John Deere, said, "We need to be thinking about it very carefully now, and we need to be acting on that now, from a regulation perspective."

Based on all of these perspectives, if AI is unregulated, it can raise some risks for data governance and inflict your business reputation if any violation happens on the legal front.

Legal implications may be more unsatisfactory in the future.

AI models tend to self-learn on AI-generated internet content, overlapping the training corpus built with human-generated content. So, models learn from wrong information and generate information to cause workplace discrimination, such as bias or exposing private data to third-party service providers, to further increase the risk of cybersecurity threats.

The penalty for a data breach is not hidden from anyone.

If you risk it, you can foresee the consequences well.

Let’s say you do not have any essential investment budgets for your enterprise workflows. Does it mean your Generative AI ambitions will not soar?

Enterprise leaders look to maximize the productivity of their employees in the best possible way to unleash maximum benefits on the service delivery, customer experience, and growth side. What essentially do you need to do to attain these ambitions?

Conversational AI, in combination with a large language model,is the answer to your quest to join the Generative AI race and drive real business values.

A workplace conversational AI platform is significant enough to facilitate many low-priority work in a much more streamlined and automated manner. We refer to them as low-priority work. The fact, however, is that they consume the essential time of the service desk agents and prevent them from addressing more critical requests across all business functions.

Say a user constantly faces a sync issue between his desktop system and cloud infrastructure; it is a massive productivity constraint for any business and a critical issue that seeks immediate help.

However, if agents are engrossed with common requests, they need more time for the critical issues.

To gain the power of large language models, you can get hold of a conversational AI platform, which is an efficient tool for applying Generative AI features to knowledge discovery and enabling them to retrieve the correct information.

So, when your employees have accurate and contextual information, they are fast at their work.

Generative AI and conversational AI, when combined, ramp up the self-service capability to a few notches high.

Use cases:

• Automate and streamline employee tasks.

Everyday tasks such as VPN settings, password resets, account unlocks, and user provisions are familiar to the IT service desk.

With automated app workflows built upon knowledge base FAQs or determined dialog flows, your employees can instantly get help and solve a problem.

• Facilitate more critical tasks.

A live agent handover can easily address printer issues unresolved by tier 0 or tier 1. An agent readily understands the issue and provides help with past and present conversation history. As a result, employee productivity increases through instant and real-time responses.

• Provide work updates instantly.

For a particular department, say a DevOps team handling a cloud migration project, if a manager is unavailable in the middle of the project, a conversational AI can be effective enough to help anyone associated with the assignment get the latest updates on the project progress and move ahead.

• Deliver non-IT support

The effectiveness of integrating large language models with conversational AI facilitates non-IT task management. For example, you can quickly redefine HR support, such as employee onboarding and PTO inquiry.

Say an employee wants to query about leave balances; a conversational AI with LLM embedded inside it can deliver only a straightforward answer.

Employees no longer need to work with links to the HRMS to navigate through a list of holidays taken and remaining balances. They can see a figure that displays the number of leaves remaining.

• Derive advanced data analytics.

Generative AI and conversational AI platforms fetch more real-time data across bot activity, including issues resolved through self-serve, tickets handled by agents, and a number of unresolved tickets, etc. The advanced data is efficient in making improvements to the conversation flows and addressing more issues seamlessly.

Benefits:

• Proactive service desk support

Conversational AI with the power of LLM can help you achieve the proactive service desk status. You can fetch data to find root cause analysis and efficiently solve the repetitive problem.

• Increase in user productivity

The flexibility of knowledge discovery, enabling more efficient self-service, boosts employee efficiency and productivity for the internal workforce, including service desk agents.

• User experience

Employees can solve their usual productivity problems at scale. There are fewer frictions in getting the correct information to solve day-to-day workplace issues and resume work more steadily.

• Cost efficiency

LLM-powered conversational AI solves problems more efficiently, meaning enterprises can have fewer backlogs of service desk requests, which reduces agent utilization and consequently reduces costs for bottom-line expenses.

Workativ conversational AI embeds the large language model capability inside its no-code chatbot platform, which is flexible and convenient for employees to solve workplace problems efficiently.

Workative simplifies the knowledge search using Generative AI properties and helps your employees find information at scale to solve low-priority issues efficiently.

When you have Workativ, you can have the power of conversational AI and LLM together, allowing you to apply use cases specific to your business needs and gain all the benefits as stated in the above section.

So, how does Workativ make it easy for you to drive your Generative AI initiative without all the cost required to build custom solutions with ChatGPT Enterprise?

• Ease of customization

Workativ is a no-code conversational AI platform. Setting up your chatbot with LLM integrations is relatively easy for everyone in your organization. It does not require a steep learning curve of exceptional abilities with coding, which is a must for customization with the ChatGPT Enterprise ecosystem. Using our help doc, you can achieve easy deployment.

• No developer cost involved

Since ours is a no-code platform, you have no liabilities to hire a specialist with niches around AI. It is easy for any non-technical person to configure dialog flows inside the chatbot, test, and push live.

• Zero total cost of ownership

As with a custom solution, you aim to build with ChatGPT Enterprise, you must take care of TCO. As discussed earlier, the costs involve the charges for cloud platforms, Generative AI model, data storage, and search UI. Since it is a subscription-based model, you must bear the recurring costs as long as you use these services.

In contrast with the burden of TCO, our SaaS-based platform, Workativ, offers a straightforward pricing model. So, pay for what you use.

No cost is included for DB, LLM models, computational platforms, etc.

• Generative AI security

Workativ provides Enterprise-level security and compliance requirements, including GDPR, HIPAA, and industry-specific standards.

With that, security does not rest upon you. Workativ ensures the responsible use of LLMs without the risk of exposing your data to the infrastructure and causing any security breaches.

However, checking your training data to eliminate the risk of exposure to PII information or any confidential company data is always desirable.

ChatGPT Enterprise indeed holds a lot of promises for enterprises to achieve their ambitious business goals.

Businesses of many types have expressed keen interest in applying Generative AI abilities to their various business functions. Many leading organizations across healthcare, food and beverages, life sciences, and media houses have built their solutions and use them in their unique use cases.

So far, so good. But can these businesses drive absolute business values with their investment? This is something hidden.

So, if you set high expectations for Generative AI or ChatGPT Enterprise, consider the above facts. It is a challenging iteration. It is time-consuming and may take years to leverage the real value for your money.

For a quick start with your Generative AI project, though, Workativ can be transformative and offer great workplace transformation opportunities.

To know more about your scope for enterprise business use cases, schedule a demo today.