Ready to get started?
Pre-built bots and app workflows for FREE
Start on our free plan, and scale up as you grow.
Very few could deny they haven’t interacted with ChatGPT after its release. Just within months of introduction to the world, it attracted over a million users.
Trained with about 75 billion parameters, the pre-trained natural language transformer inside ChatGPT generates versatile and unique content indistinguishable from what humans can produce due to its expanding capabilities to process billions of data instantly in generating new content.
Perceived as revolutionary to transform thousands of industry workflow processes, ChatGPT slowly nudged industry leaders like CIOs or CEOs towards applying Generative language properties 一, the usual identity for ChatGPT-like AI features across enterprise use cases, and leverage untapped business values.
Chris Bedi, chief digital information officer at ServiceNow, predicts that every industry will embed Generative AI in their enterprise workflows. CIOs and IT leaders should work on their Generative AI strategies.
Gartner predicted that Generative AI is more like a general-purpose technology with a similar impact to that of steam engines and the internet.
It wouldn’t be wrong to say that Generative AI will become mainstream soon. But, GenAI adoption for enterprise workflows is not without challenges. As a Generative AI-based chatbot service provider, we would love to share the pros and cons of Generative AI so that you know its benefits and limitations.
Enterprise Generative AI refers to applying large language models or generative AI properties to enterprise applications or workflows.
Deploying a Generative AI model into enterprise applications aims to augment process efficiency, boost employee productivity, and offer fast service delivery to customers by facilitating domain-specific information discovery.
Some examples of Generative AI in Enterprises involve many use cases for different applications.
When applied to ERP solutions, Generative AI can help produce an accurate financial forecast using historical data, inventory optimization for proper supply chain management, or working capital optimization through advanced contract management.
Embedding Generative AI into CRM can enable marketing leaders to summarize sales documents or extract key information from them to help send real-time follow-up messages and ensure customer retention.
The more straightforward application of Generative AI in enterprises is to improve employee communications and enhance user experience through real-time response generation and problem resolution with conversational AIchatbots.
In the enterprise context, conversational AI platforms layered with Generative AI deliver multiple high-yielding problem-solving use cases, enabling enterprise leaders to combat workplace productivity issues and user frustration while improving customer experience and expediting business outcomes.
Discovering Generative AI pros and cons from this perspective is essential to stay ahead of the risk curve and better use LLM properties for enterprise workflows.
Generative AI offers significant benefits for industries of any type despite its limitations.
Based on what Generative AI can create, it certainly contributes to massive business benefits.
Generative AI can be diverse and versatile by accessing domain-specific datasets. For example, a Generative AI model can be integrated with enterprise-related CRM data, company website, chatbot conversation history, ERP, and other internal sources to build its huge database.
Connected with the enterprise knowledge base, it can improve its search results to deliver enriched output that helps solve specific workflow problems.
With Generative AI, information is accurate and just in time rather than perplexing, allowing users to complete their tasks at scale with little to no human intervention.
Say a user needs help with a VPN connectivity issue. Ideally, what happens with rule-based AI systems is that they suggest a resolution by referring to multiple links to the VPN articles. As expected, reading through these articles is quite annoying.
If there is an issue with the VPN protocol, an LLM model will help troubleshoot it by suggesting the following instructions,
This is perhaps useful for reducing MTTR and offering a real-time solution to users who get back to work with minimal disruption to their productivity level.
All businesses have one thing in common 一 how to achieve operational efficiency, which focuses on improving resource utilization.
AI-powered workflow automation aligns with business prospects, but Generative AI is way ahead to give more business value by simplifying and removing extra steps in processes.
Even with AI-powered automation, if a user in his initial days at the office wants to know the process of raising a request for a new office laptop, the workflow automation may involve several steps. For example,
However, Generative AI provides sophisticated workflow automation that gives straightforward responses and the faster ability to accomplish a task.
For the same employee issue, a Generative AI solution can help allocate a laptop to the right candidate without requiring a step to fill out a form, seek the manager’s approval, etc. Instead, a manager may receive a copy to track asset allocation.
In this process, the roles of HR, managers, and IT personnel are dramatically reduced, improving end results.
Enterprise has a diverse range of complex activities. Product development, supply chain and logistics, finance management, marketing, and many other enterprise services need process efficiency.
Generative AI provides context and real-time accomplishment of these activities to a greater length.
Say a company seeks to launch a KYC or customer verification product. From code generation to product testing, Generative AI makes effective strides for a faster product launch with fewer efforts.
Working capital management is another example of Generative AI helping enterprises save time and accelerate process efficiency. A dunning message is compelling if a finance team needs to collect dues ahead of time.
By providing context to Gen AI, the sales team can craft compelling messages for a dozen dealers with accurate context for each account and drive cash flow.
Generating unique ideas for marketing copies or summarizing longer texts of a deal document eases human effort and augments business communications.
Generative AI understands users’ input and provides answers based on the context. LLM-powered chatbots provide the right essence for what users want to find through personalizing answers and increasing user engagement.
Workativ’s Generative AI chatbot personalizes answers for routine employee support needs and improves user experiences.
Generative AI indeed enhances user experience both for enterprise customers and internal employees.
Multiple use cases of Generative AI have a diverse range of user benefits.
Say a customer has problems with a particular brand of headphones. When he contacts customer support, the customer support agent can implement the ‘Brainstorming’ use case in a Generative AI solution to surface relevant ideas to help him troubleshoot the problem.
He sends the user prompts in the Gen AI model to command it to provide ideas. This is how it looks in the behind-the-scene environment below:
By looking at the factual responses from the model, the customer support agent can guide the customer and fix the problem.
Isn’t it transformative for the customer and the agent who can easily solve the issue with much less effort?
The customer is happy, and so is the agent, by closing the ticket in one go.
Generative AI encourages bottom-line savings.
With your people using Generative AI to summarize texts, extract key information from sales documents or customer emails, generate unique responses in a question-answer ecosystem, and more, labor costs go down.
Besides, generating new ideas or writing new content from scratch saves time for activities such as writing a contract paper for a new deal, crafting marketing materials for publication on social channels, and modifying a text into different forms.
For example, if you want to create an FAQ list for a common problem related to a password reset, Generative AI can provide the correct lists to add to the knowledge articles.
It is faster and simpler to create new product designs that have ever existed using Generative AI. As a result, a product becomes viable in a shorter period due to the convenience of effortless conceptualization, allocation of appropriate resources such as techniques and technologies, and streamlining the testing process.
For example, Generative AI can be used to create new software products, drugs, supply chain solutions, and more.
Enterprises with ample financial resources and AI advancements can drive excellent revenue opportunities.
Pros and cons of Generative AI in Enterprise can have leaders solidifying their strategies. As you are aware of the pros of Generative AI, now learn the cons of Generative AI.
There are significant risks associated with Generative AI. Generative or large language models are built upon massive publicly available datasets. To restrict violation of GDPR or other statutory laws, it is essential to check the risks of Generative AI. It is mandatory to be aware of the risks and take appropriate steps to reduce them.
Generative AI models are trained on billions of datasets encompassing the intellectual properties of artists, developers, or creators. If businesses or vendors use AI-generated works and claim them as unique content without giving enough credit to the original creators, it is a violation of copyrights.
Mitigating the risk: Before training the Generative AI model, the enterprise must ensure they comply with laws for acquiring intellectual property data by licensing, compensating, or sharing revenues for the said properties.
Faulty algorithms or flawed data sampling used to train Generative AI models can reflect biased behavior. As a result, the business outcomes can be discriminative if training data contains biased human decisions.
Mitigating the risk: Improved prediction capability in AI models effectively parses traditional human decision-making and disregards variables that fail to provide accurate end results.
Generative AI can generate only those with training data inside its architecture. Anything that does not exist cannot be mimicked or produced. For example, if there is a unique problem, Generative AI fails to solve it.
Mitigating the risk: Self-learning is a way to learn, adapt to unique challenges, and offer solutions. However, this sometimes does not meet expectations. At the same time, Generative AI helps augment human creativity by providing useful data to analyze and produce a solution.
Generative AI capability is restricted to generating content but is not designed to provide a real-time solution to a problem. For example, it cannot offer a solution for a customer who wants to fix his broken LCD monitor or book a new computer monitor.
Mitigating the risk: When connected with a conversational AI platform, generative AI can leverage contextual awareness and improve intent detection to solve a user problem.
Attackers can shoot ransomware or malware attacks by taking control over its underlying database architecture, which intrigues a user to reveal his personal information or company data.
Mitigating the risk: Implementing good data governance is imperative. This means enterprises must ensure that Gen AI model training is done using hygienic or sanitized data. In addition, human oversight or supervision is necessary to detect any anomaly in the model behavior.
To learn more about mitigating Gen AI risks and leveraging it to its fullest potential, read our blog “Best Security Practices to Use LLMs.”
Workativ provides outstanding Generative AI offerings for its conversational AI chatbots to facilitate seamless HR and IT support.
Workativ offers Hybrid NLU for response enrichment and augments workplace automation for every type of enterprise use case. It also ensures that enterprises that leverage our chatbot solution can comply with security laws like GDPR and HIPPA to protect personal data and eliminate workplace bias.
Workativ follows robust security practices to ensure uninterrupted services to its user base. Our solutions can improve user experience by solving user issues and giving the ability to offer end-to-end customer services.
If you look forward to implementing Generative AI properties in your enterprise chatbot, feel free to connect with sales experts at Workativ.
Gartner predicts that Generative AI will be at the core of encouraging about 15% of new application development by 2027.Though this AI adopts a risky phenomenon, Generative AI has endless possibilities that hold significant promises to transform every aspect of enterprise work.
The ability to make complex things simple and solve problems is the ultimate that we can expect to thrive.
Responsible application of Generative AI produces ethical business outcomes without risk. It depends on how actively and responsibly we train Gen AI models and apply them to use cases at their best.
To learn more about unique Gen AI use cases for your enterprise chatbot, ask Workativ today.