Category: RPA

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AI-Powered Experiences
Connect | Automate | Scale

Over the last few years, Generative AI has played a significant role across organizations. It is also very  interesting that just 2% expect few to no barriers to bringing Generative AI into their organization.

In IT, change is the only constant. We migrated to the cloud, we’re managing an explosion of customer data, and we’re starting to automate our processes. We expect this AI inflection point more nervous than other big waves of innovation. To manage these inflection points it is very important to streamline our AI journey.

Our first priority is to unlock our data and make it discoverable. We need to create new experiences to unlock your data, from anywhere, and to make it discoverable. This includes on-premises, hybrid/cloud data, as well as data in any format, including structured and unstructured data. Integration/APIs help you to build a framework to unlock data across all of your disparate systems.

Since data is everywhere and sources are spread across your organization. It is a human-centric task. To mitigate these human-centric tasks we need to create workflows & automate manual tasks across structured and unstructured data with minimal coding. This can be achieved by leveraging APIs, data cloud, and automation tools like RPA and IDP.

Next, we talked about the importance of building securely. With a backlog of ongoing projects, we need a way to scale the use of these API building blocks across the business, with security and governance. We need a way to protect and implement security policies across every API in your digital space before you launch your next application, like an e-commerce platform or even a mobile app. Universal API Management allows us to bring security and governance to any API.

And finally, we need just one more piece – an AI model. AI model interacts with LLMs via an API. As we make our inventory data discoverable, composable, and automated – we can build those experiences using AI models. when we bring these technologies together with an LLM, we can create intelligent AI-driven experiences. We can implement predictive and generative capabilities by using discoverable and consumable data via APIs.

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ChatGPT: A Intro & Company Use-Case

The internet is full of buzz about the new AI based chatbot, chatGPT. ChatGPT reminds me of the early days of  google, how google came and changed our internet search forever. We were using lycos search engine but google gave a new definition of search engine. Similarly I am seeing chatGPT is trying to define our search which is based on AI and AI models. It is coming as a new disruptive technology. Suddenly google is looking like old school.

Generative Pretrained Transformer 3 (GPT-3)  from OpenAI, is the main component for Jasper.ai and other cloud based content writing, chatbot and machine learning applications. GPT-3 was first publicly released by OpenAI on June 11, 2020.  GPT-3 is based on the concept of natural language processing (NLP) tasks and “generative pretraining”, which involves predicting the next token in a context of up to 2,048 tokens. 

GPT-3 is based on Large language models (LLMs). Large language models (LLMs) are AI tools that can read, summarize, and translate text. They can predict words and craft sentences that reflect how humans write and speak.Three popular and powerful large language models include Microsoft ’s Turing NLG, DeepMind’s Gopher, and OpenAI ’s GPT-3.

ChatGPT was first publicly released by OpenAI on November 30, 2022 based on the GPT-3 framework. Initially developed as part of the GPT-3 research program, ChatGPT was built on top of the powerful GPT-3.5 language model to specifically address natural language processing tasks that involve customer service chat interactions.

OpenAI’s Chat GPT3 has demonstrated the capability of performing professional tasks such as writing software code and preparing legal documents. It has also shown a remarkable ability to automate some of the skills of highly compensated knowledge workers in general. ChatGPT has immense potential for ecommerce customer experience automation. ChatGPT allows customers to personalized shopping and fully automated 24 x 7 customer service on-demand.

In spite of chatGPT buzzwords, ability to content writing and customer service on-demand, I am little careful to use this technology for my business. I tested a few use-cases in chatGPT. It is working fine with some simple use-case and problem solving. But as soon as I added a few more variables to my problem, the chatGPT response was not correct.

Here is screenshot from ChatGPT for my problem and solution from chatGPT

The problem shown above chatGPT directly calculated from equation and response came as 5 min.

In chatGPT’s response it is not calculating a person’s waiting time in the queue. 

So from above question right answer would be

Average Waiting Time = Average Processing Time x Utilization / (1-Utilization).

Average Waiting Time = 5 x (5/6) / (1 – 5/6) = 25 minutes

So, the correct answer is 25 minutes waiting in line. If we add the 5 minutes at the kiosk, we obtain a total of 30 minutes.

So from the above issue, I would like to highlight a few points if your company is trying to implement any ChatGPT solution.

  1. Does the ChatGPT AI model is configured based on your company use case?
  2. Do you have enough historical data to run and test AI based chatGPT LLM models?
  3. ChatGPT runs on the big model like LLM model. Big models incur a big cost, and LLM are expensive.
  4. Since ChatGPT runs on a big model (LLM), ChatGPT  needs to overcome performance constraints.

Keep an eye out for GPT-4, which may be released as early as the first half of 2023. This next generation of GPT may be better at their results and more realistic. 

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RPA, BOTS, AI and API

In today’s competitive markets, industries face many challenges in order to remain successful. These include staying ahead of the competition, understanding customers need and preferences, and providing a high level of service that will make customers happy.

Here are few challenges for current industries

  1. Resolve customer issues ASAP
  2. Collect and qualify customer information
  3. Easily connect to business process
  4. Enable business new features quickly.

In current business requirements 90% of organizations see increased demand for automation from business teams, due to that 95% of IT leaders are prioritizing automation. 

Automation is a critical component of digital transformation and business success. Robotic Process Automation (RPA) bots are at the forefront of this revolution, providing businesses with an automated solution to optimize their processes while improving customer experiences. RPA bots can be used in many areas such as data entry, document processing and workflow management; they automate repetitive tasks that would otherwise take up valuable resources from human employees. This automation not only increases efficiency but also reduces costs associated with manual labor, allowing companies to focus on more pressing issues like innovation or collaboration between departments. By utilizing intelligent bots powered by artificial intelligence (AI), companies can further streamline operations and provide customers with immediate feedback on requests or inquiries in real time without manual intervention from employees. Additionally, natural language processing (NLP) capabilities allow chatbots used in websites or apps to respond quickly and accurately when communicating with customers.

Using NLP, Bots can decipher specific sentences or words customers type and associate them to an intent. NLP provides insights by analyzing past chat transcripts to identify common customer utterances or phrases (such as order status, account information, password reset, logging an issue, etc.) that the Bot can use to take action. A predictive model for bots to understand intent and take action called intent model. The intent model is made up of intents and utterances.

APIs , NLP and AI are the essential components for Bots. Once an intent model from NLP identifies action then Bots call APIs. APIs help to execute tasks from the backend system for Bots. Suppose if users are looking for order status from bots and APIs are not responding on time it will fail the whole Bots purpose. So APIs are one of the key components for Bots.

APIs streamline Bots tasks and automated any process/tasks for any team. Bots and APIs empower business and IT teams to collaborate with ease and break silos in every step of their automation journey. Enable end-to-end automation at scale Reuse and compose RPA securely.