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Many industries today are leaping towards generative AI, with information buzzing around about its potential to boost business productivity and efficiency. The economic impact of AI on business productivity is projected to be trillions, prompting many businesses to consider it a definitive solution to their challenges. The current article is about throwing the limelight on whether the ongoing perception about generative AI model is justified or is merely hype. Exploring the current applications of generative AI in different companies and pinpointing the existing deficiencies may help.
Common Use Cases of Generative AI
Enterprises are harnessing the potential of generative AI across different critical areas.
Sales
The sales department can enjoy great benefits by implementing generative AI in the segment. It helps in automating repetitive tasks like creating sales call transcripts and follow-ups, saving much time for the team to focus on qualifying leads and improving business conversions. The generative AI solutions also aid in pitching tailored content to target new segments.
Customer support
Generative AI solutions have revolutionized the customer support services offered by businesses. The customer service platform empowered by AI solutions can serve as versatile support assistance, handling multiple queries from different customers in parallel with the utmost efficiency. The potential of generative AI in customer support is vast, from indulging in engaging interactions with clients to gathering feedback to provide data that helps enterprises personalize the client experience. They aid in a significant way in improving response times as well as customer satisfaction by handling queries around the clock.
Marketing
Marketing teams in business deploy AI tools to maintain a uniform voice and style, enhancing brand recognition and style. The tools can be used to brand consistent content like e-mails, newsletters, and blog posts to maintain a uniform voice and style. AI technology can also help in analyzing market trends and customer behavior and optimizing campaign strategies.
The HR department, too, has witnessed a significant efficiency boost in their tasks by integrating generative AI solutions. The technology can automate a wide range of activities, from scheduling holidays to collecting employee feedback. Many organizational activities of the HR department can be automated, reducing administrative burden and allowing the HR team to focus on strategic initiatives.
Research and documentation
Generative AI solutions can be a crucial asset in handling organizational documents and addressing legal questions. The technology is capable of performing compliance checks. Integrating generative AI solutions in a controlled environment reduces human error, ensuring regulatory compliance.
AI is used to automate repetitive tasks, saving 60%–70% of employee work time, boosting enterprise processes like accounting and knowledge management, and enhancing customer engagement through improved sales interactions.
Generative AI for enterprises: deployment models
Generative AI is available in three different deployment models for enterprises: foundational models, custom models, and prompt engineering models.
Foundational models
This popular deployment model involves using pre-built solutions like ChatGPT and Brad to handle various business challenges and objectives. It offers a wide range of opportunities for experimenting and skill development. The foundational AI models are quick to install, facilitating easy use. There is a huge scope for experimentation, and employees can gain extensive experience in using AI tools through their continuous interaction. The foundational application, however, lacks customization and flexibility for high-end applications. There is a wide range of tools available to securitize AI-generated content, but the need for exclusivity and flexibility in the foundational model still persists. Also, the lack of customization fails the models to cater to business needs in full. Many platforms scrutinize AI-generated content and take no action for publishing due to changes in search engine updates.
Prompt engineering
The prompt engineering model of generative AI is a step ahead of the foundational model, enabling customization of business inputs and outputs and delivering more valuable outcomes that align business plans and visions. Small businesses can easily integrate the affordable, prompt engineering model. Prompt engineering models can offer precise results with customization and enhance alignment with business goals. But, it calls for employing AI engineers in-house as it demands substantial expertise in handling AI models.
Custom model
Custom models are the peak of generative AI models offering highly tailored solutions leveraging large language models adapted to enterprise needs and goals. The organization deploying custom models can enjoy significant benefits, but huge investment, time, and human resources are always needed at the juncture to make the most of it. They can effectively address internal data management issues, streamline interactions across different document types, and offer customization to different departments of the business like marketing, sales, HR, and data analytics. Customized generative AI models can offer tailored solutions to specific business needs and offer customized approaches for various business functions. However, it takes a substantial investment for implementation and longer timelines, along with complexity in development and deployment.
Conclusion
Generative AI is highly influential in digitizing business operations, but its value lies in the thoughtful implementation of the technology for business-specific needs. By strategically applying the right tools aligned with business needs, they can unlock the true potential of the generative AI model and gain a competitive advantage. Don’t just get excited about empowering your business operations with AI models; instead, focus strategically on implementing scalable applications that can drive practical business results.