Any artificial intelligence (AI) system that can generate new text, photos, videos, audio, code, or synthetic data is referred to as generative AI. Generative AI has impressed the world in the past few months by releasing a large language model like ChatGPT. So what is the hype around generative AI?

Although ChatGPT and deep fakes are frequently associated with generative AI, the technology’s roots are in the automation of repetitive tasks for digital picture and audio reconstruction. Machine learning and deep learning can also be seen as forms of generative AI due to their emphasis on generative processes. The new wave form like Chatgpt has the potential to transform businesses. If someone is looking to be an industry leader in today’s times then they need a compelling and clear AI generative strategy in place…

So, what exactly is generative AI?

An artificial intelligence technique and model class known as “generative AI” aims to create new information that is comparable to—or occasionally identical to—content produced by humans. This covers written, graphic, audio, and even visual information. With the use of probabilistic methodologies and a lot of training data, generative AI models create fresh content that closely resembles the patterns and styles of the training data. Language models like GPT-3 and image creators like StyleGAN are some of the most well-known instances of generative AI models.

In the area of artificial intelligence, a generational change is currently taking place. Machines have never before been able to accurately emulate human behavior. New generative AI models, however, have made it so that automated machines can now produce things that appear to be entirely original in addition to having intricate dialogues with users.

ChatGPT is a generative AI model that has learned patterns from a diverse range of data types, including audio, text, video, images, 3D models, code, and more. With input from about 100 million users as of January 2023, and around 13 million unique visitors per day during the same period, ChatGPT continues to learn and expand its knowledge.

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What Can Generative AI Do?

When enterprises lack strong AI or data science skills, the new generative AI models aid in expediting AI deployment.The adoption of a generative model for a particular activity can be accomplished with only a limited amount of data or examples using APIs or quick engineering, even though major customization still requires skill.

By increasing the effectiveness of personalizing experiences and creating content ideas to a degree that was previously impossible, technology has the potential to disrupt a number of industries. Three categories can be used to roughly classify the capabilities enabled by generative AI:

1. Generating Content and Ideas:

As generative AI has emerged as a revolutionary technology it allows you to create unique content across a range of modalities from text to images, audio, and also video. This involves creating new and unique outputs in various formats, including videos, advertisements, and even new antimicrobial proteins.

The language model GPT-3 is among the most well-known instances of generative AI models. This model can produce text that closely resembles the patterns and writing styles of the training data, producing outputs that are amazingly integrated and engaging. Similar to this, picture makers like StyleGAN are capable of producing images that are not only incredibly realistic but also distinctive and varied.

2. Improving Efficiency:

Generative AI can accelerate manual or repetitive tasks such as writing emails, coding, or summarizing large documents. Generative AI can help in automating tasks that are monotonous and it helps to save time for humans to use it productively somewhere else benefiting businesses more. Hence, humans are able to focus on tasks that are very complex or creative.

3. Personalizing Experiences:

This involves tailoring content and information to specific audiences, such as chatbots for personalized customer experiences or targeted advertisements based on a particular customer’s behavior patterns. So, generative AI is useful for engaging the audience and increasing the conversion rate which leads to a high return on investment and extreme customer satisfaction.

However, it is essential to note that some generative AI models have been trained on vast amounts of data sourced from the internet, which may include copyrighted materials. Hence, it is crucial for organizations to follow responsible AI practices. With proper implementation and responsible use, generative AI can lead to significant advancements and improvements in numerous fields.

How Generative AI is governed?

The rules and regulations of generative AI, commonly referred to as artificial intelligence that can generate new content, is a complicated and developing subject. There are several moral, legal, and social concerns raised by the creation and application of generative AI that must be addressed. So, there are some laws around it.

The application of AI in areas like healthcare, banking, and transportation is governed by laws and regulations in some nations. The regulatory environment for generative AI is still developing, and there are still a lot of unsolved issues.

Some experts contend that ethical standards and best practices, such as openness, accountability, and justice, should serve as a guide for the development of generative AI. Others need a more comprehensive legal structure that takes into consideration the advantages and disadvantages of this technology.

The OECD Principles on AI, the EU’s Ethical Guidelines for Trustworthy AI, and the Global Partnership on AI are among the many worldwide projects that support the ethical development and application of AI.

Finally, cooperation and communication amongst a variety of stakeholders, such as legislators, business executives, academics, and civil society organizations, will be essential for the governance of generative AI.

Text Models:

1. GPT-3:

The Generative Pretrained Transformer 3 (GPT-3) is an autoregressive model that generates high-quality natural language text using a large corpus of text. It is a versatile paradigm that may be adjusted for a variety of language activities like summarizing, responding to questions, and translating between languages.

2. LaMDA:

The Language Model for Dialogue Applications (LaMDA) is a transformer language model that has already been trained to produce excellent natural language content. LaMDA is trained on dialogue as opposed to GPT-3 with the aim of picking up on nuances of conversation that is open-ended.

3. LLaMA:

Compared to GPT-4 and LaMDA, the Lightweight Language Model for All (LLaMA) is a less complex but equally effective natural language processing model. It is a transformer-based autoregressive language model that is trained on more tokens to increase performance with lower training data.

Multimodal Models:

• The most recent addition to the GPT class of models is the Generative Pretrained Transformer 4 (GPT-4). It is a significant, multimodal model that generates text outputs from both picture and text inputs. A transformer-based model called GPT-4 has been pre-trained to anticipate the next token in a document. The process of post-training alignment leads to better performance on tests of factual accuracy and adherence to intended behavior.
• DALL-E is an example of a multimodal algorithm that can work with multiple information modalities and generate original images or artwork from inputted natural language text.
• Similar to DALL-E, Stable Diffusion is a text-to-image model that gradually reduces picture noise until the image matches the text description.
• Progen is a multimodal model that generates proteins depending on desired features given by plain language text input after being trained on 280 million protein samples.

Tools That Are Serving Up Generative AI Features to Enable Marketing and Sales

Different tools offer generative AI characteristics to support marketing and sales. To produce content, enhance personalization, improve advertising, and do other things, these solutions leverage generative AI. Typical AI is used by automated content production technologies like Articoolo and Copy.ai to produce articles, blogs, and other written content.

Acquia Lift and Dynamic Yield are two personalization platforms that leverage generative AI to provide clients with tailored product recommendations. Then there are chatbots that are powered by AI, such as Drift and Intercom, which employ generative AI to provide responses in natural language to consumer enquiries. A few optimization tools, such as Adext and Albert, use generative AI to boost ROI and optimize advertising campaigns.

Voice assistants and virtual agents that respond to user requests in natural language using generative AI, such as Amazon Alexa and Google Assistant. These are but a few of the several technologies that use generative AI to improve marketing and sales initiatives. Let’s see what more tools are available that can offer Generative AI features and enable marketing and sales campaigns more effectively:

1. Phrasee

Phrasee is a marketing platform that uses artificial intelligence (AI) and natural language generation (NLG) to generate engaging and effective email subject lines, social media posts, and other marketing copy. With its unique blend of language expertise and machine learning algorithms, Phrasee helps companies increase their email open rates, click-through rates, and other important metrics, ultimately driving sales and revenue growth.

One of the key benefits of Phrasee is its ability to generate subject lines and marketing copy that are tailored to each individual recipient. By analyzing a wide range of data points, such as past email performance, customer demographics, and even weather patterns, Phrasee’s algorithms can create personalized messages that resonate with each recipient and increase the chances of engagement.

In addition, Phrasee’s AI-powered copywriting capabilities help marketers save time and resources, while still producing high-quality content that drives results. With Phrasee, marketers can quickly create engaging email campaigns and social media posts, without having to spend hours brainstorming or A/B testing different variations.

2. Persado

Persado is an AI-powered marketing platform that uses natural language generation (NLG) algorithms to generate highly personalized marketing messages. The platform is designed to help marketers deliver more effective campaigns by automating the process of creating and testing marketing messages.

The Persado platform analyzes a company’s existing marketing materials, including emails, social media posts, and landing pages, and uses machine learning algorithms to identify the language and messaging that is most likely to resonate with the target audience. The platform then generates new marketing messages that are optimized for engagement, conversion, and other key performance indicators.

One of the key benefits of the Persado platform is its ability to create highly personalized messages at scale. The platform can generate thousands of unique messages in a matter of seconds, allowing marketers to test and optimize their campaigns more quickly and efficiently.

In addition to its NLG capabilities, the Persado platform also includes advanced analytics and reporting tools that allow marketers to track the performance of their campaigns in real-time. This data can then be used to fine-tune marketing messages and strategies, leading to better engagement, higher conversion rates, and increased revenue.

3. Amplero

Amplero is an AI-powered marketing platform that uses machine learning algorithms to analyze customer data and generate personalized marketing messages that are optimized for each individual customer. The platform was founded in 2016 by a team of data scientists and marketing experts with the goal of helping companies deliver more effective and personalized marketing campaigns.

Amplero’s platform uses a proprietary machine learning algorithm called the “Artificial Intelligence Marketing (AIM) Platform” to analyze customer data and identify the most effective marketing messages for each individual customer. The AIM Platform takes into account a wide range of data points, including customer behavior, purchase history, demographic data, and more, to create a personalized profile for each customer. This profile is then used to generate customized marketing messages that are tailored to each customer’s unique preferences and behaviors.

One of the key benefits of Amplero’s platform is its ability to automate the marketing process, allowing companies to deliver personalized messages at scale. The platform can be integrated with a wide range of marketing channels, including email, social media, and mobile, allowing companies to reach customers wherever they are most active.

4. Adext

Adext is a machine learning-based advertising optimization platform that uses AI algorithms to optimize Google and Facebook ads in real-time. The platform is designed to increase the efficiency and effectiveness of digital advertising campaigns by automatically adjusting ad bids, targeting, and creative elements based on user behavior and conversion data.

Adext uses a proprietary AI algorithm called the “Audience Expansion Algorithm,” which is designed to identify high-performing audiences and optimize ad targeting accordingly. The algorithm analyzes data from various sources, including website visits, click-through rates, and conversion data, to identify patterns and insights that can be used to improve ad performance. One of the key features of Adext is its ability to optimize ad campaigns in real-time. The platform continuously monitors campaign performance and makes adjustments to ad bids, targeting, and creative elements based on user behavior and conversion data. This real-time optimization ensures that ad campaigns are always performing at their best, maximizing ROI and minimizing wasted ad spend.

Another feature of Adext is its simplicity and ease of use. The platform is designed to be user-friendly and accessible to marketers of all skill levels, with an intuitive dashboard that provides real-time insights and performance data.

5. Albert

Albert is an AI-powered marketing platform that has revolutionized the way digital marketing campaigns are run. The platform uses machine learning algorithms to automate and optimize digital marketing campaigns across multiple channels, including search, social media, and email. The AI technology behind Albert allows it to learn from past campaigns, making it more effective over time.

One of the key features of Albert is its ability to identify the most effective strategies for a particular campaign. The platform uses real-time data to analyze customer behavior and identify the most effective targeting strategies, ad creative, and messaging. This not only saves time but also helps to improve the overall performance of campaigns.

Another advantage of Albert is its ability to manage multiple campaigns simultaneously. The platform can handle complex campaigns across multiple channels, freeing up marketing teams to focus on other tasks. This allows teams to be more productive and efficient, ultimately leading to better campaign results.

6. Emarsys

Emarsys is a marketing automation platform that utilizes artificial intelligence (AI) to personalize content and predict customer behavior, allowing marketers to target customers more effectively and deliver more personalized experiences. Founded in 2000, Emarsys is headquartered in Vienna, Austria, and has offices around the world.

The platform uses AI algorithms to analyze customer data from a variety of sources, including email, social media, and e-commerce platforms, and then provides marketers with insights on how to optimize their campaigns. For example, Emarsys can help identify which customers are most likely to make a purchase, what types of products they are interested in, and what messaging will resonate with them.

One of the key features of Emarsys is its ability to provide personalized recommendations to customers based on their behavior and preferences. For example, if a customer has previously purchased a certain type of product, Emarsys can recommend similar products that they may be interested in. This helps to improve customer engagement and ultimately drive sales.

Emarsys also offers a range of other features, including email marketing, social media management, and predictive analytics. The platform is used by a variety of businesses across industries, including retail, e-commerce, and travel.

Overall, Emarsys is a powerful marketing automation tool that helps businesses of all sizes to improve their marketing campaigns and deliver more personalized experiences to their customers.

7. Acquia

Acquia is a cloud-based digital experience platform that provides tools and services to help organizations create and manage engaging digital experiences across multiple channels. The platform combines content management, digital asset management, personalization, and commerce capabilities to deliver a comprehensive solution for creating and managing digital experiences.

Acquia was founded in 2007 by Dries Buytaert, the creator of the Drupal open-source content management system (CMS). The company’s platform is built on Drupal, and it provides a range of services and tools to help organizations build, deploy, and manage Drupal-based websites and applications.

One of the key features of the Acquia platform is its AI-powered personalization capabilities. The platform uses machine learning algorithms to analyze user behavior and preferences, and it provides personalized content recommendations to website visitors based on their interests and behavior.

Acquia also offers a range of services and tools to help organizations improve their digital marketing efforts. These include email marketing, social media management, search engine optimization (SEO), and analytics.

Overall, Acquia is a powerful and flexible platform that provides organizations with the tools and services they need to create engaging and personalized digital experiences. With its focus on open-source technology and AI-powered personalization capabilities, Acquia is well-positioned to help organizations stay ahead of the curve in the rapidly-evolving world of digital experience management.

8. Saleswhale

Saleswhale is an AI-powered sales assistant that uses natural language processing (NLP) to engage with leads and prospects via email. The platform was designed to help sales teams automate lead follow-up and lead nurturing, freeing up sales reps to focus on closing deals.

Saleswhale works by analyzing email conversations and using NLP to understand the context of the conversation. It can then respond to the lead in a personalized manner, answering any questions they may have and scheduling meetings with the sales rep as needed. The platform also integrates with popular sales tools like Salesforce and HubSpot, allowing sales reps to manage their conversations and schedule meetings directly from their CRM.

One of the key benefits of Saleswhale is its ability to scale personalized conversations with leads. It can engage with hundreds or even thousands of leads at a time, all while delivering personalized responses that feel like they were written by a human. This helps sales teams to nurture their leads more effectively, resulting in higher conversion rates and shorter sales cycles.

Overall, Saleswhale is a powerful tool for any sales team looking to automate lead follow-up and lead nurturing. With its ability to engage in personalized conversations with leads at scale, it can help sales reps to focus on closing deals while still delivering a high level of customer engagement.

9. Conversica

Conversica is an AI-powered sales assistant that automates the process of engaging with leads and prospects via email and SMS. The platform uses natural language processing (NLP) to simulate human conversation and provide personalized responses to inquiries, follow-up messages, and appointment scheduling.

Conversica’s AI assistant can handle high volumes of leads, follow up with them promptly, and qualify them based on predefined criteria, such as budget, timing, and interest. The system can also integrate with customer relationship management (CRM) platforms and other sales and marketing tools to streamline the sales process and provide a seamless customer experience.

Conversica’s conversational AI technology can also help companies nurture leads and increase sales conversion rates by providing timely and relevant information to prospects. The system can send personalized messages, such as product demos, testimonials, and pricing information, based on the prospect’s stage in the buying cycle and their interests and preferences.

Overall, Conversica is a powerful tool that can help businesses automate and optimize their sales process, improve customer engagement, and increase sales revenue.

10. Blueshift

Blueshift is a leading customer data activation platform that leverages artificial intelligence and machine learning to provide personalized customer experiences. The platform helps businesses to automate and optimize customer engagement across multiple channels, including email, SMS, push notifications, and more.

Blueshift uses a combination of AI-powered algorithms and real-time customer data to personalize messages for each individual customer, enabling marketers to deliver relevant, timely, and personalized experiences. The platform’s advanced predictive capabilities enable marketers to anticipate customer needs, identify buying patterns, and make data-driven decisions that increase customer engagement, loyalty, and revenue.

The platform is designed to be easy to use, with a user-friendly interface that allows marketers to quickly and easily create and launch campaigns. The platform also provides real-time reporting and analytics, allowing marketers to track campaign performance and make data-driven optimizations on the fly. Blueshift is used by leading brands across multiple industries, including LendingTree, Udacity, and Discovery. The platform has received numerous industry accolades, including recognition as a leader in the Gartner Magic Quadrant for Multichannel Marketing Hubs. With its advanced AI-powered capabilities and user-friendly interface, Blueshift is a must-have tool for any marketer looking to drive customer engagement and revenue growth.

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Is Generative AI changing the landscape for marketing and sales?

As we know Generative AI has a remarkable potential of generating ideas, content, and more, and it can change the landscape of marketing and sales. Technology is emerging at this point and it is helping businesses to improve the efficacy of their campaigns and generate personalized content to engage customers.

With generative AI marketing and sales team can create unique content and effective sales pitches that are free of grammatical errors as well. They can automate the process to create and distribute content which is helpful in saving time and resources while delivering more accurate and effective results. This is also bringing some changes in the industry and the way people do business.

1. An example to understand how generative AI is helping sales and marketing team?

So, if we see then a few months back Microsoft took an important step when it unveiled Viva Sales, a program that incorporates generative AI technology. Its goal is to help salespeople and sales managers create customized customer emails, gather information about clients and prospects, and produce reminders and suggestions.

Similarly, just after this, Salesforce also introduced Einstein GPT shortly after. When it comes to applying digital technology, the field of sales has lagged behind others like finance, logistics, and marketing due to its highly unpredictable and unstructured nature and need for human activity. The tides are changing, though, and sales is ready to become a pioneer in the adoption of generative AI, the type of AI used by OpenAI (the firm behind ChatGPT) and its competitors. AI-powered solutions are quickly taking over as a necessity for digital assistants.

AI has the potential to counteract administrative overload, such as aiding sales representatives in composing emails, addressing proposal inquiries, arranging notes, and automatically refreshing CRM information.

2. How Generative AI can handle salespeople’s communication with clients?

Businesses can use generative AI to build smart chatbots and virtual assistants that can converse with clients in natural language. These virtual assistants and chatbots may assist with customer service, provide information, and even conduct transactions.

The adoption of AI is gaining momentum in every sphere and sales and marketing teams are using their intelligence to deploy this technology more usefully. In order to engage with customers, it is helpful to suggest individualized content and product offerings, as well as the best sales channel. Recommendations are made based on a customer’s preferences, behavior, and previous contacts with the company. To help the algorithms, salespeople can accept or reject these recommendations and grade their quality.

These models can offer even better recommendations by using generative AI. For instance, by examining linguistic quirks and covert cues of customer interest or mistrust in emails, discussions, and social media posts. Salespeople can work in real time with the system to enhance the recommendations. It is simple to use thanks to an interactive, conversational design. Even the customer is welcome to speak in a collaborative setting.

3. How reporting systems are improved with Generative AI?

With the help of generative AI, reporting systems can be improved. Managers will be able to ask questions and gather the information that will help salespeople develop and will allow them to receive more targeted and stimulating coaching input. Generative AI may be used to find opportunities, create key account strategies, and decide how much effort should be put into different markets, customers, products, and activities. Sales planning tasks that used to take weeks can now be finished in an hour.

4. How do Generative AI models handle the data which helps a sales and marketing team?

The sales process is an excellent fit for generative AI models due to their features. Large volumes of data are produced during sales operations, including unstructured text from email chains, audio recordings of phone calls, and video of in-person contacts. Generative AI models are specifically made to handle such kinds of data. Additionally, the organic and dynamic nature of sales offers generative AI a variety of options for learning, interpreting, linking, and customizing.

With this data, it is simple to develop a broad variety of subject matter, including posts, articles, blogs, etc. Because the tools use natural processing algorithms to produce high-quality content, it is more distinctive, engaging, and caters to the target audience. As a result of analyzing consumer data and personalizing their marketing and sales efforts, the sales and marketing teams are more capable to develop things that is both relevant to the audience’s needs and highly recommended for each individual customer.

By analyzing data in real-time, forecasting patterns, and making recommendations for modifications to enhance campaign performance, generative AI may also assist companies in optimizing their marketing campaigns.

5. What challenges are there that need to be addressed for a smooth sales and marketing process?

To make the most of generative AI’s potential in sales, there are challenges that must be handled appropriately. One significant challenge is integrating the technology seamlessly into current sales operations and processes so that sales teams can quickly adopt its capabilities into their workflow. Additionally, it is crucial to resolve these problems prior to deployment because generative AI models may reach incorrect, prejudiced, or inconsistent results.

Millions of consumers have found publicly available models like ChatGPT to be useful, but the true potential for sales teams resides in modifying and fine-tuning models on business-specific data and circumstances. But doing so can be expensive and necessitates specialists in both sales and AI, as well as other fields.

6. Can Generative AI be a substitute for humans working in sales and marketing teams?

Generative AI is a technology to supplement sales and marketing professionals’ abilities and improve their efficacy rather than a full replacement for them. Data input and lead generation are two examples of repetitive, boring work that AI-powered systems may automate, freeing up sales and marketing teams to focus on higher-value duties like creating relationships with customers, coming up with innovative campaigns, and completing deals.

Additionally, generative AI can give sales and marketing professionals insightful advice based on enormous quantities of data, enabling them to improve their tactics and make more educated decisions. Additionally, based on their interests and habits, AI may customize offers and content for customers, enhancing the user experience and boosting conversion rates.

A human touch and instinct are still necessary for several areas of sales and marketing, such as developing rapport with clients, comprehending their emotional requirements, and evaluating complex data in the context of business strategy. Additionally, AI algorithms are not perfect and can produce biased or erroneous recommendations, necessitating human scrutiny and involvement.

In short, generative AI is a potent tool that may help sales and marketing teams work more productively and successfully, but it cannot take the place of human skill and creativity, which are crucial for establishing trusting customer relationships and fostering company growth.

7. Is Generative AI a dependable solution for sales and marketing teams in the long run?

As we have seen the unlimited potential of AI generative tools and solutions, but still there are challenges that need to be addressed wisely. At this point, we can say that Generative can work as a companion for sales and marketing teams. With its huge potential to improve sales and marketing processes, generative AI is useful. However, there is a lack of skill in the areas of defining its purpose, training and perfecting models, and creating and putting into practice applications. Businesses must overcome the difficulties of inaccuracy and consistency, realize value rapidly, and provide outcomes while keeping expenses under control in order to successfully employ generative AI.

There are inaccuracies and inconsistencies as well. Realistic knowledge of the strengths and weaknesses of generative AI is necessary to deal with inaccuracy and inconsistent results. Although the use of technology can result in considerable increases in productivity, users must learn how to ask the proper questions and offer useful prompts in order to increase the accuracy of the responses.

AI-generated responses in risky circumstances must be vetted by a human in order to reduce hazards. By implementing generative AI into current sales systems and taking into account customer sentiment analytics, the value may be realized immediately. Purchasing pre-built applications is frequently more effective than developing bespoke AI-powered systems. Finally, outsourcing capabilities, creating a small core of internal AI professionals, and adopting an agile, iterative implementation process are all necessary for producing outcomes while keeping expenses under control.

Definitely, it is not a replacement for salespeople in difficult sales circumstances where it is crucial to assess perceived and latent needs, tailor solutions, and maneuver complex buying groups. In order to seize the potential presented by the advancements in AI technologies, the companies that sell them will build sizable sales staff.

Market Growth and future prospects:

In the upcoming years, generative AI is anticipated to experience significant market expansion. The generative AI market is anticipated to increase from USD 0.5 billion in 2020 to USD 2.3 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 28.4% during the course of the forecast period, according to a report by MarketsandMarkets.

The desire for customized and personalized products, the rising requirement for generative AI, and the expanding use of generative AI in the healthcare and automotive industries are some of the reasons that are fueling the expansion of the generative AI market.

The outlook for the future appears positive, but only time will tell how generative AI will be used and whether it will produce satisfying outcomes. For the time being, it serves as a very useful replacement for many departments and teams operating in a business, and its use is becoming essential.

Conclusion:

Generative AI is a type of artificial intelligence that can generate new data, such as images, music, or text, that resembles and often expands upon the patterns present in the training data it was trained on. Generative AI is an emerging technology that has the potential to revolutionize marketing and sales. With its ability to create highly personalized content, optimize campaigns, and engage customers in natural language conversations, businesses can expect to see significant improvements in their marketing and sales efforts.

Future application of generative AI should be dependent on the unique requirements and goals of each enterprise. Businesses stand to gain a lot from generative AI, including improved productivity, accuracy, and efficiency across a range of activities. But there are dangers and obstacles as well, such as the requirement for large amounts of data, technical know-how, and ethical considerations.

Businesses should carefully weigh the advantages and disadvantages of implementing generative AI into their operations, taking into account aspects like cost, technological viability, and potential effects on staff and clients. Additionally, it’s critical to check that ethical issues like bias and data privacy are taken into account as well as that the use of generative AI is consistent with the organization’s beliefs and aims.

**The primary author of this article is our contractual staff writer – Sakshi John.