Whether you run a marketing agency, offer SaaS products, sell digital services, or provide consulting, this approach can inject fresh life into your outreach campaigns. This isn’t just a theoretical idea—it’s a replicable blueprint for growth used by some of the smartest business owners scaling to seven- and eight-figure revenues in 2025.

Let’s dive deep into this AI-driven lead generation playbook.

Step 1: Understand the Power of AI in Lead Generation

The digital marketing ecosystem has changed dramatically with the rise of generative AI. ChatGPT, OpenAI’s flagship conversational model, is no longer just a chatbot—it’s a Swiss army knife for entrepreneurs. While most users focus on ChatGPT for content creation, few realize it can also structure messy, unorganized web data into business-grade, outreach-ready formats.

The key insight here is that ChatGPT can help you automate and organize a vital step in your business development: converting raw internet data into clean, structured lead lists. When paired with manual sourcing techniques from search engines and directories, this becomes a virtually limitless method to harvest leads—without spending a dime.

Step 2: Sourcing Leads from Google and Yellow Pages

The process starts with smart manual data collection. Here’s how to mine gold from everyday search tools:

Using Google Smart Searches

Start by targeting your niche. For example, if you’re offering services to real estate professionals, use Google with a query like:

real estate + gmail.com

This search syntax pulls up public listings with email addresses associated with real estate agents or firms. Scroll through the results, and you’ll find dozens—if not hundreds—of leads with names, company info, and contact details.

Pro tip: Use various location modifiers (e.g., “real estate Dallas gmail.com”) to broaden your reach and generate geographically segmented leads.

Using Yellow Pages for Localized Results

Yellow Pages is still one of the most effective free directories for contact details. Just type in a niche and location (e.g., “Real Estate, Nashville”) and comb through the listings. Clicking into each business profile often reveals phone numbers, email addresses, and websites.

This data can be gathered using simple copy-paste actions and stored in a Google Doc. Don’t worry if it looks messy at this point — ChatGPT will take care of the cleanup.

Step 3: Mass Data Collection Without Spending a Penny

Once you’ve run multiple searches on Google and Yellow Pages, start copying and pasting the search result data into a single Google Doc. This part can feel chaotic — names mixed with emails, phone numbers with addresses — but that’s okay. You’re building raw input for AI to refine.

Repeat the process for as many niches or regions as you want. The more variations of your search terms, the more targeted your list becomes. You’re essentially crowd-sourcing contact data using the public internet — and the volume is only limited by your time.

Step 4: Organizing and Structuring Data with ChatGPT

Here’s where the magic happens.

With all your copied data dumped into a Google Doc, open ChatGPT and use a structured prompt like:

Then paste the messy data directly below the prompt and hit enter.

ChatGPT will parse the text, extract structured data, remove duplicates, and return a table in CSV format. You’ll now have a professional-looking lead list that can be opened in Excel or Google Sheets. This process takes minutes compared to hours of manual formatting.

Remember to manually verify and clean the final file, especially for edge cases like incorrect LinkedIn links or placeholders in wrong columns.

Step 5: Download and Deploy Your Lead List

Once ChatGPT has generated the structured CSV file, download it for use across your outreach platforms. This formatted data is now your lead generation fuel.

Here are a few platforms where you can use your lead list:

• Cold Email Tools: Instantly, Smartlead, Lemlist
• LinkedIn Automation: Clay, PhantomBuster
• Instagram DMs: Cold DMs tools for automated messaging
• Phone Outreach: Close.com or VoIP-based cold calling tools
• CRM Import: Salesforce, HubSpot, Pipedrive

You now have a full-fledged pipeline of leads ready to be nurtured, messaged, or called.

Step 6: Crafting Outreach That Converts

Leads are only as valuable as the outreach strategy that follows. The effectiveness of your communication determines whether these leads become clients.

Here’s a tried-and-true cold email framework that works across industries:

Components of a High-Converting Outreach Message:

• Personalization: Mention their name, company, or work.
• Clarity: Get to the point quickly.
• Value Offering: Highlight a clear benefit, not a sales pitch.
• Authority: Show proof of your past success (e.g., helped a similar client grow from $15M to $23M).
• CTA: Ask if they’d like a free resource, not if they want to buy.
• Brevity: Keep it under 100 words for better response rates.

Example:

Hi [Name],
I noticed [Company Name] has been helping people in [Location] find their dream homes. We’ve helped over 100 real estate firms increase revenue with lead generation strategies. Our latest client grew from $15M to $23M in just a year. I’ve put together a free guide on how to replicate these results—would you like me to send it over?

You’re not selling — you’re offering value and prompting engagement. The same structure applies to DMs, calls, or even direct mail.

Step 7: Scale and Automate Your Pipeline

With ChatGPT formatting your data and outreach tools automating delivery, your lead generation becomes a scalable engine. You can repeat the entire cycle weekly or monthly depending on your bandwidth and market size.

To go further:

• Train a virtual assistant to do steps 1–3.
• Use Zapier or Make to integrate leads directly into your CRM.
• Use AI tools to A/B test subject lines, messages, and CTAs.
• Schedule follow-ups automatically based on engagement metrics.

This isn’t just a lead hack — it’s a long-term growth engine.

Final Thoughts: Why This Method Works in 2025

The biggest obstacle to business growth is not a lack of opportunity, but the cost of acquiring attention. This AI-powered lead generation model flips that paradigm — giving you warm, high-quality leads without a marketing budget.

Here’s why this strategy is future-proof:

• It relies on evergreen platforms (Google, Yellow Pages).
• It leverages generative AI to save time and boost accuracy.
• It creates a direct path from search to CRM-ready leads.
• It allows hyper-personalized outreach at scale.

Whether you’re a solo founder or run a 10-person team, this system can power your sales pipeline for years to come.

The post How to generate leads using ChatGPT in 2025 appeared first on RoboticsBiz.

ChatGPT is a large language model (LLM), representing a significant advance in AI. Even though the average person may understand that AI is designed to mimic human behavior – whether beating a grandmaster in chess, creating art, or answering customer queries – they may not realize the complexity of the technology that makes this possible.

Let’s dive deep into what LLMs are and how they shape our interactions with AI technology.

Understanding Large Language Models

A large language model is a form of AI that leverages machine learning algorithms to understand, generate, and respond in natural human language.

As per the name, LLMs use enormous amounts of data, often from the internet, to learn and understand patterns in our language – the linguistic structures, colloquialisms, nuances, and even the emotional context of words. They can then synthetically reproduce this language in a way that appears quite human.

Looking Back at How Large Language Models Developed

The inception of LLMs can be traced back to the early 1960s with the development of Eliza, one of the first chatbots ever created. Although relatively rudimentary, Eliza was groundbreaking at the time as it simulated conversation by recognizing and rehashing certain predetermined phrases.

However, it wasn’t until the deep learning boom of the 2010s that these models began to use massive amounts of data and computing power to achieve far greater fluency and comprehension.

MongoDB’s look at large language models explored the introduction of transformer architecture in 2017, which marked a turning point in the history of AI. Transformer models read entire sentences at once, enabling them to better understand the overall context within human language.

Transformers set the groundwork that eventually enabled the development of AI, like OpenAI’s GPT, one of the most powerful LLMs today.

How Large Language Models Work

In essence, large language models work by predicting the likelihood of a word following a given set of words. Using probability and statistics, an AI model like GPT-3 can predict all the possible combinations of words that could follow, scaling it according to their potential occurrence within its trained data.

Whenever the model anticipates the next word in a sentence, it doesn’t just look at the previous couple of words. Instead, it considers the entire sequence it has seen so far. This is known as the “context window” of a language model. The larger the context window, the more effective and accurate the model’s predictions and outcomes.

Internally, LLMs, like transformer models, use ‘attention mechanisms’ to discern which input parts are important when generating an output. This helps model intricate relationships between words and their position in the sentence.

Use Cases of Large Language Models

LLMs are finding immense utility in varied fields due to their ability to understand natural language.

As detailed in RoboticsBiz’s previous posts about chatbots, one of the biggest use cases of LLMs is in customer service. Chatbots powered with LLMs can handle customer inquiries and complaints and deliver personalized service. Enterprises are also using LLMs to sift through enormous volumes of data and extract useful insights in market research, social sentiment analysis, and more.

In education, LLMs are being used to create intelligent tutoring systems that can provide personalized feedback and instruction to students. Our article on ‘AI and Robotics in Education’ talks more about this. Meanwhile, medical practitioners leverage LLMs to extract and synthesize information from medical literature and patient records, aiding in diagnostics and treatment plans.

Advanced AI models like GPT-4 also allow developers to create applications using natural language, opening the coding field to a wider range of people.

The Limitations of Large Language Models

Large language models have immense potential but are not without their disadvantages. Current LLMs are inherently limited by the data quality they are trained on. The AI will echo this bias in its outputs if the data is biased or inaccurate.

Additionally, while these models can mimic human-like conversation, they don’t truly understand language like humans do. Their responses are based on patterns in data rather than conscious understanding. This often results in incorrect or nonsensical outputs, even if the prose is grammatically correct.

Finally, the ethical implications and potential misuse of these powerful language models are ongoing research and debate topics. For instance, the unmonitored use of such technology might facilitate the diffusion of deepfake content or false information. A VOA report detailed the devastating results of a deepfake video scam wherein a Hong Kong company lost $26 million. Therefore, it is imperative to implement and adhere to stringent use guidelines.

Final Word

Giant strides have been made in developing AI technology over the last decade, with large language models leading the way. They represent an important milestone in the AI journey, and as technology progresses, they will continue to be refined and optimized. Grand View Research predicts the AI market size to grow to $1,811.8 billion by 2030.

Yet, as with any powerful technology, the ethical and security implications must also be carefully navigated. The opportunity to shape this outcome is among the many exciting challenges facing AI enthusiasts and professionals today.

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1. Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots by Michael McTear

This comprehensive guide delves into the evolution of CAI, exploring its transition from theoretical concept to practical reality through advancements in AI and deep learning. The book examines three primary approaches to dialogue system creation: rule-based, data-driven statistical, and neural dialogue systems. It also explores performance assessment, future research challenges, and the social and ethical implications of CAI.

2. Conversational AI: Chatbots that Work by Andrew Freed

This practical guide equips readers with the skills to design and build effective AI-powered chatbots for customer support and various conversational tasks. It covers essential aspects like sourcing training data, evaluating performance, crafting human-like dialogues, and building sophisticated voice assistants capable of handling complex interactions.

3. Conversational AI with Rasa: Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots by Xiaoquan Kong, Guan Wang, Alan Nichol

This book empowers developers to create robust chatbots using the Rasa framework, leveraging open-source NLP and machine learning technologies. It guides readers through constructing, configuring, training, and deploying diverse chatbots, exploring form-based dialogue management, response selectors, knowledge base actions, and advanced techniques. Additionally, it delves into customization, conversation-driven development methodologies, and bot evaluation and improvement.

4. The Definitive Guide to Conversational AI with Dialogflow and Google Cloud: Build Advanced Enterprise Chatbots, Voice, and Telephony Agents on Google Cloud by Lee Boonstra

This guide serves as a roadmap for those looking to leverage Google Cloud and Dialogflow for building enterprise-grade conversational agents. It covers fundamental principles, advanced techniques, building multilingual chatbots, orchestrating sub-chatbots, and crafting voice bots. It culminates with advanced use cases like fulfillment capabilities, custom integrations, chatbot security, and creating personalized voice platforms.

5. Microsoft Conversational AI Platform for Developers: End-to-End Chatbot Development from Planning to Deployment by Stephan Bisser

This book caters to developers and proficient users interested in creating chatbots using Microsoft’s Conversational AI Platform. Assuming basic coding knowledge, it offers a step-by-step guide, covering collaborative chatbot development from inception to deployment and evaluation. It equips readers with the skills to translate business requirements into actionable specifications and utilize tools like Bot Framework Composer.

6. ChatGPT Millionaire Mindset: Transforming Your Wealth with Conversational AI by Shu Chen Hou

This book combines the concepts of mindset development and CAI, aiming to transform your approach to wealth creation. It delves into the millionaire mindset’s principles and introduces CAI applications in finance management, investment guidance, and entrepreneurship coaching. While the focus is not purely on technical aspects of CAI, it explores its potential for financial empowerment.

These six books offer diverse perspectives and practical insights into the fascinating world of Conversational AI. Whether you’re a seasoned developer, a curious entrepreneur, or simply interested in understanding this evolving technology, these resources can provide valuable knowledge and equip you to navigate the future of human-computer interactions.

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The integration of generative AI into the legal profession has the potential to be transformative, automating certain tasks and augmenting attorney capabilities. However, before integrating these technologies into litigation strategies, legal practitioners must consider crucial factors to ensure ethical, practical, and professional use.

This article presents the top five considerations, which underscore the critical need for balancing the advantages of AI with the ethical responsibilities and intellectual rigor that define the practice of law. By doing so, attorneys can harness the power of generative AI to enhance their practice while upholding the principles that underpin the legal profession.

1. Safeguarding Confidentiality

The cornerstone of legal practice is confidentiality. Attorneys have a fiduciary duty to protect their client’s sensitive information. When utilizing generative AI, the paramount consideration is safeguarding client data. The input of confidential, privileged, or proprietary information into AI platforms could compromise the attorney-client privilege and expose confidential information to unintended parties. This breach may erode trust and result in legal and ethical repercussions.

2. Navigating Hallucinations

The term “hallucination” might sound outlandish in the context of AI. Still, it refers to the phenomena where AI-generated responses may be inaccurate or fabricated due to a lack of information. Legal professionals relying solely on these responses without due diligence risk basing their arguments on misinformation. A false sense of accuracy can lead to unfavorable outcomes, emphasizing the importance of independently verifying information generated by AI systems.

3. Balancing Trust and Verification

Generative AI models like ChatGPT are trained on vast datasets, including those from the internet, which inherently carry biases and inaccuracies. Consequently, relying solely on AI outputs without scrutinizing them for accuracy can lead to misguided legal strategies. Attorneys should adopt a cautious approach by incorporating AI insights while subjecting them to rigorous validation, thereby maintaining the integrity of their arguments and advice.

4. Meeting Client Needs Effectively

Generative AI offers a promising resource for attorneys to streamline their casework. However, it’s essential to recognize the limitations of these systems. While they excel at predicting the next word in a sentence, they lack the nuanced comprehension and interpretation capabilities that human attorneys possess. As such, relying exclusively on AI-generated content might fall short of addressing complex client requirements. Legal professionals must blend AI assistance with their expertise to provide comprehensive solutions.

5. Addressing Plagiarism Challenges

The allure of AI for drafting legal documents is undeniable, yet caution is warranted. Generative AI platforms generate content without explicitly indicating their sources. This raises the specter of inadvertent plagiarism if generated material resembles existing sources. Employing generative AI to expedite document creation should involve thorough verification to ensure originality, mitigating potential legal and ethical ramifications.

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Designed to understand and respond to human language with remarkable proficiency, ChatGPT leverages a deep learning technique known as the Transformer, which enables it to generate human-like text based on the input it receives.

ChatGPT’s ability to generate contextually relevant responses, maintain a conversation, and provide meaningful and informative insights. Under its advanced language capabilities, it can read and understand documents, answer questions about them, and even translate text into different languages. Moreover, its ability to learn from past interactions allows it to continually enhance its responses, thus improving its efficiency and accuracy over time.

One sector where ChatGPT’s potential for disruption is particularly pronounced is banking. Its ability to process vast data rapidly and accurately can enhance efficiency, improve customer experience, and even mitigate risks across various banking activities, including retail, commercial, and investment banking.

The potential for AI-powered disruption is tremendous in the banking sector, where accuracy, efficiency, and compliance are paramount. From streamlining Know Your Customer (KYC) procedures during customer onboarding to providing personalized financial advice, from monitoring regulatory changes to detecting fraudulent activities, ChatGPT is poised to revolutionize traditional banking practices.

Let’s look at some of the key use cases of ChatGPT in banking and financial services.

Retail Banking and Wealth Management

ChatGPT can revolutionize processes within retail banking and wealth management in several ways. First, this advanced language model can be used for comprehensive due diligence during the Know Your Customer (KYC) process and customer onboarding. Quickly and accurately sifting through vast data can significantly streamline these procedures, enhance customer experience, and ensure regulatory compliance.

Furthermore, ChatGPT can offer rapid and effective solutions to common customer inquiries. Capable of learning from many past interactions, the system can provide quick resolutions to simple issues, improving customer satisfaction and freeing up valuable time for customer service representatives.

ChatGPT can deliver personalized financial advice tailored to individual client’s investment objectives and risk appetite in wealth management. Analyzing historical data, current market trends, and the specific details of a client’s financial situation can suggest optimized investment strategies, ultimately guiding clients toward achieving their financial goals.

Cards and Payments

Within the domain of cards and payments, ChatGPT also promises significant improvements. For credit profile evaluations, it can analyze an individual’s financial history, thereby providing a detailed creditworthiness assessment. This aids in efficient decision-making concerning credit limits, interest rates, and loan approvals.

Moreover, ChatGPT can be instrumental in resolving merchant disputes. Effectively parsing transactional data and identifying inconsistencies can expedite the dispute resolution process, minimizing operational costs and customer dissatisfaction.

Perhaps most importantly, ChatGPT can flag potential fraudulent payments and money laundering attempts. By scrutinizing transactional attributes for anomalies, it can alert the appropriate authorities, thereby enhancing the security of financial systems and protecting customers from fraud.

Commercial and Investment Banking

ChatGPT can ensure regulatory compliance in commercial and investment banking by reviewing transactions for potential violations. With its capacity for processing vast amounts of data, it can efficiently spot irregularities, thereby averting hefty penalties for non-compliance.

Additionally, ChatGPT can help in monitoring the regulatory landscape. Keeping track of new regulations, amendments, and updates can inform banks in real time about crucial regulatory changes, ensuring they adapt their practices accordingly.

Also, by sampling regulatory reports, ChatGPT can detect any lapses or misrepresentations of data submitted to regulators. This proactive approach enhances accuracy, credibility, and the bank’s reputation.

Community Banking, Commercial Lending, and Investment Banking

In the arenas of community banking, commercial lending, and investment banking, ChatGPT brings about several promising use cases. For instance, it can assist with loan underwriting and risk assessment for retail loans. Evaluating borrowers’ financial data can predict their repayment capacity, helping banks make informed lending decisions.

Furthermore, ChatGPT can determine the creditworthiness of new businesses without a credit history. Assessing a business’s financials, market position, and industry dynamics can generate a detailed risk profile, providing valuable insights for lenders.

Regarding feedback, ChatGPT can provide constructive input on lending decision outcomes. Learning from previous decisions and their outcomes can help banks optimize their decision-making process, reducing financial risk and enhancing profitability.

Lastly, ChatGPT can generate stress test scenarios and risk profiles for investment portfolios in investment banking. Simulating various market conditions and their potential impact on portfolios can assist in risk management, ensuring the longevity and success of investment banking operations.

In conclusion, ChatGPT holds immense potential to revolutionize traditional practices across the banking sector. By leveraging its capabilities, banks can enhance efficiency, improve customer service, ensure compliance, and optimize decision-making processes, signaling a new era in banking.

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Traditional contact centers have long recognized the advantages of automating repetitive customer interactions to save costs and improve efficiency. However, using Interactive Voice Response (IVR) systems and conventional chatbots often resulted in impersonal and mechanical interactions, reducing customer satisfaction. Customers have consistently preferred human interaction, especially when dealing with complex issues.

ChatGPT addresses this challenge by introducing generative AI, enabling chatbots to engage in more empathetic, intuitive, and human-like customer conversations. By leveraging this advanced technology, contact centers can create a more personalized and natural customer experience, leading to improved outcomes such as higher customer satisfaction, increased customer retention, and, ultimately, business growth.

Another critical aspect that ChatGPT can revolutionize is the process of summarizing customer interactions. Currently, agents spend valuable time summarizing each interaction, a task essential for creating personalized experiences and ensuring efficient complaint resolution. However, the pressure to reduce average handling times often leads to hastily completed summaries that lack detail or even get skipped altogether.

ChatGPT’s capabilities can streamline this process by efficiently summarizing lengthy customer interactions into concise outcomes. Customer service representatives can quickly grasp customers’ primary issues and concerns, facilitating more efficient tracking and resolution of complaints. Implementing ChatGPT in contact centers can significantly reduce average handling time (AHT), improving operational efficiency and resource utilization.

Furthermore, ChatGPT’s integration can enhance the effectiveness of automated AI transcription technologies in contact centers. The challenge lies in making the insights provided by these technologies more readable and actionable. Complex dashboards often hinder the implementation of recommendations derived from automated analysis.

With ChatGPT, contact centers can achieve a breakthrough by enabling a human-like communication interface for accessing insights. Agents and leadership teams can pose operational questions, and ChatGPT will respond in a manner that resembles human conversation, making the insights easily understandable and actionable. This user-friendly approach encourages the swift implementation of recommendations derived from AI transcription technologies, thereby maximizing their impact on contact center operations.

1. NICE

On January 26, 2023, NICE became the first contact center industry vendor to utilize generative modeling technology, integrating it into their CXone Expert. CXone Expert is a cloud-native customer service knowledge management solution designed to provide quick and accurate answers for resolving customer issues.

The main objective of this integration with OpenAI’s generative modeling, specifically ChatGPT, is to craft customer self-service responses in a human-friendly manner that is easy to understand while ensuring high accuracy and promptness. By doing so, customers can find the right answers without needing transfers or callbacks, creating a more human-like self-service experience.

The AI Insights feature combines ChatGPT-3 with real-time transcription to interpret customer conversations and cluster them into relevant categories, facilitating the identification of automation and process improvement opportunities within the contact centers. Furthermore, AI summaries enhance the agent-assist solutions by automatically summarizing interaction transcripts and publishing them in the CRM.

2. Salesforce

Salesforce has expanded its contact center platform by introducing Einstein GPT, a powerful generative AI CRM technology. This innovation allows AI-created content to be delivered at a hyper-scale across various aspects of the business, including sales, service, marketing, commerce, and IT interactions. By leveraging Einstein GPT, customers can integrate their data with OpenAI’s advanced AI models, enabling them to use natural-language prompts directly within their Salesforce CRM. This integration empowers businesses to generate content that continuously adapts to changing customer information and needs in real time, transforming every customer experience with generative AI.

Einstein GPT achieves this by infusing Salesforce’s proprietary AI models with generative AI technology from a diverse ecosystem of partners. Additionally, it leverages real-time data from the Salesforce Data Cloud, which efficiently ingests, harmonizes, and unifies a company’s customer data. Moreover, Einstein GPT seamlessly integrates with OpenAI, providing out-of-the-box generative AI capabilities for businesses to enhance their CRM functionalities further. With the combination of Einstein GPT and OpenAI, customers can connect data to OpenAI’s advanced AI models or even choose their external models, giving them more control over their AI-powered content generation process within Salesforce CRM.

3. Five9

Five9, a leading cloud contact center software provider, has recently launched two new products powered by GPT-3.5 from OpenAI. The first product, AI Insights, leverages the capabilities of ChatGPT and real-time transcription to automatically interpret and categorize customer conversations. This enables contact centers to identify opportunities for process improvement and automation by grouping contacts based on intent or other relevant traits.

The second product, “AI Summaries,” offers the convenience of auto-summarizing interaction transcripts and seamlessly publishing them in the CRM. This feature streamlines the post-contact processing for agents, saving valuable time and enhancing efficiency.

Mike Burkland, Chairman and CEO of Five9, emphasizes its commitment to continuous AI innovation, recognizing it as a critical aspect of its growth strategy. In addition to the new AI Insights and AI Summaries, Five9’s AI and automation portfolio includes speech analytics, workflow automation, and Interactive Voice Response (IVA) solutions.

The integration of ChatGPT into Five9’s offerings is expected to significantly fuel further AI innovation. The large language models, such as ChatGPT-3, present opportunities for quick wins in tasks like custom data charting, trending analysis, routing optimizations, and potentially transformative breakthroughs.

By combining ChatGPT-3 with real-time transcription, AI Insights can intelligently interpret customer conversations and cluster them into relevant categories. This clustering approach enables contact centers to swiftly identify automation possibilities and opportunities for process improvement.

Moreover, AI Summaries enhance the agent-assist solutions by automatically summarizing interaction transcripts and seamlessly integrating them into the CRM for easy access and reference.

4. Genesys

Genesys is taking a significant step forward by beta-testing generative AI on its Cloud CX platform, exploring various use cases for this technology. One notable application is the introduction of new agent-assist capabilities, specifically summarization. This feature lets agents receive automatically generated summaries after voice calls or digital interactions, streamlining their work during wrap-up time.

While GPT-4’s knowledge base and language processing capabilities surpass other technologies, companies are proceeding with caution due to its limitations. Although the responses generated by GPT-4 are impressively accurate and convincingly human-like, they are still reliant on the dataset it was trained on and may not always reflect the absolute truth or the most up-to-date information.

Despite the cautious approach, Genesys is forging ahead with its beta-testing of generative AI on the Cloud CX platform. In addition to the summarization feature, the company plans to introduce more capabilities soon. These planned additions include smart replies, auto-tagging of charts and articles, and machine translation.

Genesys aims to enhance agent productivity and deliver more efficient and effective customer service experiences by integrating generative AI into its platform. However, they are mindful of the technology’s limitations and actively work to balance innovation and accuracy in their solutions.

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ChatGPT leverages machine learning algorithms to generate human-like responses based on user inputs. It operates on a neural network architecture called a Transformer. By training the model on large datasets containing text from websites, books, and articles, it learns the patterns and structure of language. This allows it to predict the next word in a sentence based on the preceding one.

When generating text, ChatGPT predicts subsequent words in a sentence or prompt to construct a complete response. An attention mechanism is also employed, enabling the model to selectively focus on specific input parts, resulting in more accurate and coherent responses. For conversational AI purposes, the model is often fine-tuned using a smaller dataset of conversational text to enhance its ability to generate human-like dialogue.

Common use cases for ChatGPT

ChatGPT finds applications for various end-user services in the information technology and digital workplace domain. Some common use cases include:

• Virtual Assistants: ChatGPT can be utilized to develop virtual assistants capable of handling scheduling, email management, and customer service tasks.
• Email Responders: ChatGPT can generate automated responses to common customer inquiries, streamlining communication and providing timely assistance.
• Knowledge Base: ChatGPT can contribute to creating a knowledge base that answers frequently asked questions and provides information to users.
• IT Service Desk: Automated assistance can be provided to users encountering IT-related issues, like password resets and account lockouts, using ChatGPT.
• HR Assistance: ChatGPT can offer automated support to employees seeking assistance with HR-related matters, such as benefits and time-off requests.
• Document Automation: ChatGPT can automate the generation of documents, such as contracts and reports, by utilizing predefined templates and prompts.
• Language Translation: It can translate messages, emails, and documents, facilitating multilingual communication.
• Meeting Summary: ChatGPT can generate summaries of key points discussed in meetings, aiding in progress tracking and ensuring everyone is on the same page.

Common shortfalls of using ChatGPT in a professional environment

1. Quality of Generated Text: While ChatGPT is trained on extensive data, the quality of generated text may vary. It can sometimes produce responses that lack coherence or accuracy, leading to potential misunderstandings or misinformation.
2. Bias in the Training Data: GPT-based models like ChatGPT learn from vast amounts of internet text, which can contain inherent biases. If not carefully addressed, these biases can be perpetuated in the model’s responses, potentially leading to biased or unfair outcomes.
3. Lack of Contextual Understanding: ChatGPT may struggle to fully comprehend a conversation’s specific context or nuances. It may generate tangential responses or fail to address the user’s intended meaning, resulting in less useful or relevant information.
4. Legal and Ethical Implications: The deployment of ChatGPT in enterprise services can raise legal and ethical concerns. Privacy and data protection laws, consent, and transparency must be carefully addressed to ensure compliance and prevent potential legal or reputational risks.
5. High Computational Cost: GPT-based models like ChatGPT are computationally intensive, requiring powerful hardware and infrastructure to operate efficiently. Fine-tuning or running the model at scale may demand substantial computational resources, impacting operational costs.
6. Limited Handling of Structured Data: While ChatGPT excels in natural language tasks, it may struggle with structured data handling. Tasks that require precise extraction or manipulation of structured information may be challenging for the model, requiring additional solutions or integration with specialized tools.
7. Vulnerability to Adversarial Examples: GPT-based models, including ChatGPT, can be vulnerable to adversarial attacks. Adversaries can craft inputs designed to mislead or manipulate the model into generating incorrect or undesired responses, potentially posing security risks or enabling malicious activities.
8. Lack of Explainability: GPT-based models operate on complex deep learning techniques, making it difficult to understand the model’s inner workings and decision-making process. Lack of explainability can hinder trust, transparency, and accountability, especially in sensitive professional applications.
9. Absence of Service Level Agreements (SLAs): Currently, there are no predefined SLAs for enterprise usage of ChatGPT. This absence of formal agreements regarding performance, availability, and support can make it challenging to set realistic expectations or obtain necessary assurances for enterprise deployments.
10. Security and Legal Approval: Since ChatGPT is an open-source model, ensuring security and obtaining legal approvals or agreements for customer data usage can present challenges. Organizations must carefully assess and address these considerations to maintain data protection and regulatory compliance.
11. Development Effort and Maintenance: Implementing ChatGPT professionally requires dedicated teams and effort. Setting up, integrating via APIs, and customizing the model to align with specific customer environments can involve significant development and maintenance effort.
12. Training and Learning: ChatGPT is trained on internet data until 2021. For specific enterprise (DWP) training, a large amount of data for supervised training and reinforced learning is needed, requiring continuous development effort. Additionally, updating the model with current information may necessitate regular retraining.
13. Lack of Dedicated/Agreed Support: Obtaining specific support tailored to enterprise customer environments may be challenging in an open-source model. Organizations should be prepared to address potential support limitations or invest in dedicated resources to ensure smooth operations.

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The ChatGPT can perform previously unheard-of tasks that are incredibly complex, such as writing an article, story, poem, or essay; expanding or summarizing a text; changing texts to reflect a different viewpoint; and even creating and debugging original computer code.

Even though this development in AI appears to revolutionize education, educators have mixed feelings about ChatGPT’s extraordinary capacity to carry out difficult tasks.

It has turned into a divisive issue among educators. While some see ChatGPT as the future of education, others are more pessimistic and see it as a threat to most forms of learning and as a way to make teachers and students lazy with weak or nonexistent analytical skills. In this post, we will discuss the pros and cons of ChatGPT in education and training.

Benefits of ChatGPT in Education

1. Personalized Tutoring

Students can receive individualized tutoring and feedback from ChatGPT based on their learning requirements and development. Students can receive individualized math tutoring from a conversational agent built on a generative model (ChatGPT), which enhances learning outcomes. The conversational agent can adjust explanations to students’ comprehension levels and misconceptions.

2. Automated Essay Grading

ChatGPT can be programmed to grade student essays, freeing up teachers’ time to focus on other aspects of the classroom. With a correlation of 0.86 with human grades, a generative model (ChatGPT) trained on a dataset of human-graded essays can accurately grade essays written by high school students. The model can recognize key features of well-written essays and provide feedback comparable to human graders.

3. Language Translation

ChatGPT can translate educational materials into multiple languages, making them more accessible to a wider audience. A generative model (ChatGPT) trained on a dataset of bilingual sentence pairs can translate between languages with high accuracy, achieving state-of-the-art results on several translation benchmarks. The model can comprehend the meaning of sentences in one language and produce accurate translations in another.

4. Interactive Learning

ChatGPT can create interactive learning experiences where students can converse with a virtual tutor. A conversational agent based on a generative model can effectively support students learning English as a second language, leading to improved language proficiency. The agent can understand students’ questions and respond appropriately and appropriately.

5. Adaptive Learning

ChatGPT can design adaptive learning systems that adapt their teaching methods based on the progress and performance of their students. An adaptive learning system based on a generative model (ChatGPT) can help students learn to program more effectively, resulting in better performance on programming assessments. The model can comprehend students’ knowledge and, as a result, adjust the difficulty of the problems it generates. ChatGPT has the potential to be a powerful tool for improving teaching and learning by offering personalized tutoring, automated essay grading, language translation, interactive learning, and adaptive learning.

Possible drawbacks of ChatGPT in education

While there are many potential benefits of using ChatGPT and other generative AI models in education, there are also some drawbacks. Research studies support these drawbacks:

1. Lack of Human Interaction

ChatGPT and other generative models cannot match the level of human interaction a real teacher or tutor provides. This lack of human interaction can harm students who benefit more from a personal connection with their teacher. Students who interact with a virtual tutor who mimics human-like affective behavior may learn more effectively than those who interact with a virtual tutor who lacks this behavior.

2. Limited Understanding

Generative models are based on statistical patterns in the data they are trained on, and they do not truly comprehend the concepts that assist students in learning. This can be an issue when providing explanations or feedback tailored to a student’s specific needs and misconceptions. A tutoring system based on generative models could not provide explanations tailored to students’ misconceptions.

3. Bias in Training Data

Generative models are only as good as the data on which they are trained, and if the training data contains biases, so will the model. Assume a model is trained on a dataset of essays written primarily by students from a specific demographic. In that case, it may be unable to accurately grade essays written by students from other demographics. Gender bias in language generation can be observed in a generative model trained on a large corpus of text from the internet.

4. Lack of Creativity

Generative models can only generate responses based on the patterns in the data they discovered during training, limiting their creativity and originality. A generative model-based music composition system may be limited in generating unique and varied melodies.

5. Dependency on Data

Generative models are trained on large amounts of data, and the model’s quality is highly dependent on the data’s quality and quantity. The model will not perform well if the data is insufficient or irrelevant. A generative model-based question-answering system can perform poorly when the training data is irrelevant to the task.

6. Lack of Contextual Understanding

Generative models cannot comprehend context and situation, resulting in inappropriate or irrelevant responses. In a conversation, a generative model-based dialogue system may be limited in understanding and generating contextually appropriate responses.

7. Limited ability to personalize instruction

ChatGPT and other generative AI models can provide general information and assistance, but they may not be able to personalize instruction to meet the specific needs of a particular student.

8. Privacy

There are concerns about privacy and data security when using ChatGPT and other generative AI models in education. It’s important to remember that while ChatGPT and other generative AI models are useful, they’re not a replacement for human teachers and tutors. Using these tools responsibly and in conjunction with human instruction and support is critical.

While generative AI models such as ChatGPT can be effective tools for improving teaching and learning, it is critical to understand their limitations and use them in conjunction with other teaching methods emphasizing human interaction and understanding.

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ChatGPT’s context comprehension and relevant response generation have made it a popular choice for businesses looking to improve customer experience and operations.

Major technology companies are investing heavily in artificial intelligence (AI). Microsoft, for example, has announced a $10 billion investment in OpenAI and plans to integrate ChatGPT into its Azure OpenAI suite. This will enable businesses to incorporate AI assets into their technology infrastructure, such as DALL-E, a program that generates images, and Codex, which converts natural language into code.

While ChatGPT has several advantages for financial institutions, such as improved customer service and task automation, it also has some risks that must be addressed. Major banks and other institutions in the United States have prohibited their employees from using ChatGPT. Concerns have been raised about sensitive information being entered into the chatbot.

Risks associated with ChatGPT

Let’s look at the potential risks that are currently being discussed regarding the use of ChatGPT:

1. Data exposure

Accidentally exposing sensitive data is one potential risk of using ChatGPT in the workplace. Employees who use ChatGPT to generate data insights and analyze large amounts of financial data, for example, may unintentionally reveal confidential information while conversing with the AI model, resulting in privacy or security breaches. Another known data exposure case is when employees inadvertently include confidential information in training data, potentially exposing private code. This could happen if an employee includes code snippets containing sensitive or proprietary data, such as API keys or login credentials.

2. Misinformation

ChatGPT may generate inaccurate or biased responses based on its programming and training data. Financial professionals should use it cautiously to avoid spreading misinformation or relying on untrustworthy advice. ChatGPT’s current version was trained only on data sets available until 2021. Furthermore, the tool uses online data that is not always accurate.

3. Technology dependency

While ChatGPT provides valuable insights for financial decision-making, relying solely on technology risks ignoring human judgment and intuition. Financial professionals may misinterpret or become overly reliant on ChatGPT recommendations. As a result, striking a balance between technology and human expertise is critical.

4. Privacy concerns

ChatGPT collects a lot of personal information that users unwittingly provide. Most AI models require a large amount of data to be trained and improved; similarly, organizations may need to process a large amount of data to train ChatGPT. If the information is exposed or used maliciously, it can pose a significant risk to individuals and organizations.

5. Social engineering

Cybercriminals can use ChatGPT to impersonate individuals or organizations and create highly personalized and convincing phishing emails, making it difficult for victims to detect the attack. This can result in successful phishing attacks and an increase in the number of people falling for the scam.

6. Creating malicious scripts and malware

Cybercriminals can use ChatGPT to train them on massive amounts of code to create undetectable malware strains that bypass traditional security defenses. This malware can dynamically change its code and behavior by employing polymorphic techniques such as encryption and obfuscation, making it difficult to analyze and identify.

Conclusion

First, financial institutions should develop clear policies and guidelines for using ChatGPT in the workplace to protect confidential information and reduce data exposure risks. Second, anonymized data should be used to train an AI model to protect the privacy of individuals and organizations whose data is being used. Employees’ use of ChatGPT information during their work should be subject to strict controls. Employees accessing ChatGPT should receive training on the technology’s potential risks, such as data exposure, privacy violations, and ethical concerns. Limiting access to ChatGPT reduces the risk of data exposure and misuse of the technology.

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However, no one believed that AI would replace physicians until the launch of ChatGPT, which scored 66% and 72% on Basic Life Support and Advanced Cardiovascular Life Support tests, respectively, and performed at or near the passing threshold on the US Medical Licensing Exam.

Although AI is notoriously bad at context and nuance, particularly in safe and effective patient care, which requires implementing medical knowledge, concepts, and principles in real-world settings, the likelihood of administrative healthcare job automation is relatively high (e.g., 91% for health information technicians). However, Frey and Osborne estimate that the likelihood of physicians’ and surgeons’ jobs being automated is only 0.42%.

Why? Although evidence suggests that fully autonomous robotic systems may be just around the corner, experts argue that a surgeon’s job extends far beyond surgical procedures. The physician’s job is complicated by the ability to provide fully integrated care by providing treatment and compassion, a clinical skill that computer algorithms have yet to comprehend. As a result, the enormous potential of AI in healthcare lies not in the ability to replace physicians but in the ability to increase physicians’ efficacy by redistributing workload and optimizing performance.

There are also some ethical concerns about using conversational AI in medical practice. Training a model necessitates massive amounts of (high-quality) data, and current algorithms are frequently trained on biased data sets. The models are vulnerable to availability, selection, and confirmation biases, which they cannot amplify.

ChatGPT, for example, can produce biased results and perpetuate sexist stereotypes, which must be addressed before similar AI can be successfully and safely implemented in clinical practice. Other ethical concerns are associated with the legal framework. For example, it is unclear who is to blame when an AI physician makes an unavoidable error.

ChatGPT, a chatbot-scientist

The launch of ChatGPT by San Francisco-based company OpenAI, which gained more than 1 million users in the first few days and 100 million in the first two months, positioning itself as the fastest-growing consumer application in history, has prompted many to consider the exciting ways artificial intelligence (AI) may change our lives very soon.

The hype surrounding ChatGPT is understandable: the model is (still) free, simple to use, and capable of authentically conversing on a wide range of topics nearly indistinguishable from human communication. ChatGPT created essays, scholarly manuscripts, and computer code, summarized scientific literature, and ran statistical analyses.

Furthermore, AI may soon be capable of performing more complex tasks, such as designing experiments or conducting peer reviews. ChatGPT performed admirably in some of the tasks mentioned.

In a recent experiment, researchers used existing publications to generate 50 research abstracts that could pass a plagiarism checker, an AI output detector, and human reviewers’ scrutiny. On the one hand, ChatGPT’s remarkable ability to write specialized texts suggests that similar tools may soon be capable of writing complete research manuscripts, allowing scientists to focus on designing and performing experiments rather than writing manuscripts.

Conversational AIs, on the other hand, are simply language models that have been trained to sound convincing but cannot interpret and understand the content. As a result, ChatGPT-generated manuscripts may be misleading because they are based on unverified or fabricated sources. Worse, ChatGPT’s ability to write text of surprising quality may deceive reviewers and readers, accumulating dangerous misinformation. A popular forum for computer programming-related discussions, StackOverflow has prohibited using ChatGPT-generated text since the average rate of getting correct answers is too low, and posting of answers created by ChatGPT is significantly harmful the site and users who are asking and looking for correct answers.

Blanco Gonzalez concluded that ChatGPT is ineffective for producing reliable scientific texts without significant human intervention. It lacks the knowledge and expertise to convey complex scientific concepts and information accurately and adequately. Furthermore, the chatbot appears to have an alarming tendency to invent references to sound convincing.

The ChatGPT creators have openly admitted that ChatGPT occasionally writes plausible-sounding answers but that correcting incorrect or nonsensical answers will be difficult. Without acknowledging the limitations of conversational AI, the publishing system may become overburdened with meaningless data and low-quality manuscripts.

Aside from the issue of unreliability, there are several other ethical concerns. There is no legal framework to decide who owns the rights to an AI-generated work: the author of the manuscript, the author of the AI, or the authors who contributed training data. Furthermore, because ChatGPT frequently fails to disclose the source of information, who is responsible for plagiarism if the chatbot decides to plagiarize? Most publishers agree that using any AI should be acknowledged and that chatbots should not be listed as authors until the ethical difficulties are resolved.

Conclusion

Conversational AIs are here to stay as a powerfully disruptive technology. We can only anticipate them to improve with additional optimization and training. It makes no sense to prohibit or actively discourage their use when they can significantly improve various aspects of our lives by alleviating the burden of daunting and repetitive tasks. In medicine, AI could significantly improve efficacy by removing some of the suffocating paperwork, and optimized chatbots could significantly speed up and improve literature searches. Nonetheless, we should not be swayed by AI’s enormous potential. To realize AI’s full potential in medicine and science, we should not hastily implement it but rather advocate for its gradual introduction and open discussion of the risks and benefits.

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