The Future of Generative AI: What Can We Expect in the Next 5 Years?

What is Generative AI?

For starters, Generative AI is a subset of Deep learning, which is a subset of Machine learning. It uses neural networks (artificial but similar to those found inside the human brain) to process labeled and unlabeled data using supervised and unsupervised methods.

A generative AI model creates new data cases based on existing information. Essentially, generative AI models register patterns and standard methodologies to create new content primarily based on the data sets they were trained on.

The worldwide adoption of generative AI is changing the way things were done earlier

Generative AI tools are a huge hit amongst present generation techies and service-oriented professionals. Masses across the globe are interested in knowing the what’s and how’s of generative AI. This is because people are finally getting a chance to focus solely on the core aspects of their jobs and sideline the non-core yet essential ones. The feeling of allowing a generative AI tool to cater to uninteresting or repetitive tasks is like owning a second brain specifically dedicated to jobs with no creative, strategic, or tactical angle to them.

For instance, contact center agencies aren’t shying away from using generative AI tools to enhance their customer experience journeys. Agencies like HGS are using Natural Language Processing (NLP), image and audio analyzing capabilities of AI to improve front and back office CX operations.

Healthcare is another sector that is trying to make the most of generative AI tools. Generative AI algorithms process and analyze vast amounts of medical data and case histories to identify patterns and suggest the best approach to treat a patient. In addition to this, AI along with predictive analytics can help detect and diagnose many ailments at an early age, thereby assuring a scope of speedy recovery for the patient.

What do the next five years look like?

Generative AI is on the cusp of transforming the way business decisions are made entirely.  At present, more and more service-based enterprises are incorporating generative AI tools to achieve excellence for a wide range of operations. ChatGPT was a trailblazer in generative AI technology, but the capacity of generative AI goes beyond ChatGPT. Its ability to learn and process vast amounts of data makes it adept to accomplish a good number of enterprise-level jobs, but there is more to come, including:

• New and unique content ideas: Generative AI tools like ChatGPT and Copy.ai generate short and long form written content for a wide range of topics. Professional writers have begun referring them to add more substance to their articles, and it seems inevitable that there will come a time when authoring a book using any of these tools will not come across as obscure. In another world, songwriters and music composers will monumentally start utilizing the permutation and computation capabilities of generative AI to create new songs and poetic compositions. Once a thought or a theme is fed to the tool, it can come up with many ideas to fuel an artist’s creative mindset.

• Faster chip design: Generative AI is capable of achieving precision in semiconductor chip design. By perfecting component placement in one go, it can shorten the product life cycle, and eliminate recurring costs. This will allow the semiconductor industry to grow and work on the hardware aspects that have been on the backburner for a long time. Additionally, AI will be detecting defects at an early stage to make sure quality standards are met with ease.

• Accelerated and hassle-free customer support: Despite the internet offering a sea of information, it is quite a task to scour out data that is pragmatic and effective at the same time. Conversational AI bots are fed with past case studies and correct answers for a broad match of questions. In a few years, it’s expected that contact centers across the globe will make use of these bots extensively to answer repetitive and easy queries so that contact center agents can focus on the creative and strategic aspects of their work.

• Fast and advanced product design: In no time, traditional methods of product design where a singular person or team was responsible to come up with design prototypes will be disregarded. With generative AI, one will be able to define a set of design goals through machine language programming and generate a range of design options. The tenure of a design process will be shortened without compromising on practicality and aesthetics.

• Early fraud detection in banking: Lately, bank account holders all around the world are falling prey to a diverse set of banking scams. Since Generative AI learns patterns from data sets inclusive of customer behavior and transaction history, it will be adept at detecting unusual behaviors and anomalies at an earlier stage. Real-time frauds will be detected immediately, and notifications will be sent out to alert the concerned person in advance. Generative AI platforms will educate bank account holders about necessary actions and procedures to guard their finances.

The recent advancement in Artificial Intelligence is coaxing humans to ditch traditional methods of doing tasks and switch to tools that can not only make things easier but effective and profitable as well. It is high time professionals in every field start acknowledging and preparing themselves to work alongside generative AI tools for a more optimum and healthy work environment.