Generative AI
With the rapid development of AI technologies, Generative AI is transforming the way employees work in businesses and the way customers interact with businesses.
We’ve written this article to help you understand what Generative AI is and how you can use it to unlock the power of your data and accelerate your business.
Like the Internet, this field is developing very fast. Businesses that do not rapidly adopt productive AI will be left behind.
Although artificial intelligence research and development has been going on for a long time, it would not be an exaggeration to say that advances in the field of Generative AI have ushered in an era of industrialization for artificial intelligence.
So much so that ChatGPT’s speed of reaching 100 million monthly active users within two months of its launch is similar to the internet in the history of technology and reveals that we are at the beginning of a technology revolution.
When artificial intelligence moves out of general use and focuses on specific issues, its use and output quality increases. As such, optimizing processes with the use of artificial intelligence in corporate processes increases the efficiency of each step. The increase in speed and quality come with the increases the interaction of internal/external customers in direct proportion.
Commercial off-the-shelf models are powerful, but initial experiments require customization and tuning to meet performance and reliability standards at the enterprise level and processes specific to many companies. For these, you need to develop AI in-house or deploy AI focused on organisational processes. However, it is costly and often difficult to manage at enterprise scale.
What Can Generative AI do for Enterprises?
Productive Artificial Intelligence, as the new generation of company employees, quickly becomes involved in their processes, plays a decisive role in creating new products or services, contributes to the customer experience with personalised interactions, makes improvements, and increases internal/external customer efficiency.
Let’s look at a few examples of how businesses are using Productive AI with the introduction of Productive AI into the industry.
Financial Services
Financial services companies are developing investment research assistants that analyze and summarize financial statements, historical market data, and other proprietary data sources, provide interactive charts, and act as plug-ins. These tools improve investors’ efficiency and effectiveness by uncovering the most relevant trends and providing actionable insights to help boost returns.
Retail and e-commerce
Companies use customer chatbots as personal assistants to create striking product images, social media ads and product-related images at scale.
Insurance
Insurance companies are using Generative AI to improve their operational efficiency. It saves time in providing support in correctly routing, summarizing and classifying tasks and reviewing large amounts of documents.
Regardless of the sector, productive artificial intelligence can be easily used to find solutions to companies’ similar needs.
How can I become a member of the Generative Artificial Intelligence Team?
1. Evaluate your use cases
- What are the cost factors? Can these costs be reduced by making sense of your data, visualisation speed, and summary production?
- Do you process documents in your processes?
- How do we organise our internal knowledge bases, and how quickly do we access information?
- How effective is your customer interaction?
- What is the impact on your company of the cost of locating resources such as software engineers or data scientists whose work can be accelerated?
- How much time does the impact of the time it takes for units to receive a response from the data scientist cost your company?
2. Scope your requirements
How do you define "good" in terms of performance?
Cost of hosting and maintaining the application?
Will we explore and compare multiple generative models?
Even asking and answering these and many similar questions requires turning into a project. Instead of employing a small team of ML and software engineers in your business, it will be more effective to bring in expertise and develop internal talent. Depending on your use case and desired timelines, the low application learning curve and acquisition of external applications will allow you to ignore the high costs that must be incurred. Will provide.
