Top 5 Applications of Generative AI in Tomorrow’s Enterprise

  • Posted by: Recode

From analyzing market and performance data to personalizing messaging, the recent Salesforce survey outlines just how much marketers are putting generative AI to work. About 71% of marketers in the survey affirm that generative AI is allowing them to focus on more strategic portions of their work. But marketing is merely one of the areas being disrupted by the power of generative AI.

With generative AI, businesses can boost worker productivity to levels never seen before — and do so at a fraction of the cost and time taken by traditional approaches. 

Unlike conventional AI, which operates based on structured data often governed by predefined rules and patterns, generative AI can create entirely new and contextually relevant information, making it a game-changer for businesses seeking innovative methods to source new opportunities, bring efficiency to their operations, and gain competitive advantage. It, well and truly, represents the next phase in AI evolution.

Here are the top five applications of generative AI for tomorrow’s enterprise:

Software Engineering

Within the purview of software engineering, generative AI solutions can work to reduce the hassle around generating the first drafts of code, performing root cause analysis, and more. There’s a massive productivity boost that is often associated with the inclusion of generative AI in the software development process. 

For instance, a recent study outlined how developers using Microsoft’s GitHub CoPilot were able to complete their tasks 56% faster. Another report claimed that 60-75% of CoPilot users were more satisfied and fulfilled with their job. 73% affirmed that CoPilot helped them stay in the flow, while 87% reported that it helped them preserve mental energy that would have been otherwise spent on performing redundant tasks. This, in particular, is a key benefit because the SDLC often constitutes redundant operations. Enterprises can do away with them and accelerate a product’s time-to-market by leveraging generative AI across key areas.

Computer Vision

Computer vision is the ability of machines to interpret what they see. This is accomplished through a number of machine learning algorithms, including a generative approach called Generative Adversarial Networks (GANs). GANs enable machines to interpret and process images and videos with unprecedented accuracy. Generative AI solutions can also help reduce the noise in the visual data to help with improved visual analytics.

Within an enterprise setting, such computer vision capabilities can be utilized for quality control, real-time object detection, and enhancing autonomous systems. From manufacturing to healthcare, the ability to analyze visual data with precision holds immense potential for an enterprise.

Enterprise Data Analytics & Reporting

With Generative AI, synthetic data can be generated to fill in missing values and optimize the existing datasets, enabling businesses to utilize available data more effectively. In addition, a generative model can also predict an outcome based on the current dataset using probabilistic algorithms.

What stands out is that generative AI solutions can very well communicate insights to business stakeholders by summarizing the pertinent points in a well-formatted report. We’ve already seen how linguistically adept a tool like ChatGPT can be. Such capabilities can be integrated into the analytics function to democratize access to important insights.

Also Read: Making the Leap from AI to Applied AI

Anomaly Detection

This will be a key application of generative AI going forward. Usually, machine learning algorithms are employed to learn from past samples and classify the anomalies from normal data. A generative approach can be used for creating new data analogous to existing classes, analyzing patterns within existing data, and unearthing deviations that were previously unknown.

This way, generative AI excels in anomaly detection, making it a crucial tool for identifying irregular patterns that may signify fraudulent activities or security breaches. By continuously learning and adapting, generative AI systems can stay one step ahead of cyber threats, safeguarding enterprise assets and customer trust.

Customer Support

Generative AI is reshaping customer support and experience with the deployment of advanced chatbots and virtual assistants. These intelligent systems can engage in natural, human-like discussions, provide real-time assistance, and personalize interactions. This not only enhances customer satisfaction but also streamlines the overall customer support operations, reducing response times and improving efficiency.

In essence, there are two ways that generative AI solutions usher in a significant change in the way customer service is delivered. First, they can be leveraged for training human-like agents. Second, they can create unique and contextually relevant content in real-time to advance discussions, route queries to human agents, and more. 

In fact, generative AI-powered chatbots can evolve their responses depending on user feedback, learn and adapt over time, and even have localized knowledge of the user to provide a personalized experience.

Wrapping Up

Generative AI promises to revolutionize the future workings of enterprises. It will energize new creations, amplify productivity, and enhance competitiveness through its applications across key business areas.

While still in the nascent stage, we’re progressing towards hyper-customization of generative AI solutions to suit specific business needs. By doing so, enterprises would be able to decipher the true potential of this transformative technology and drive innovation and excellence. 

Interested in learning more about the potential of this technology for your enterprise needs? Book a strategy call today!

Author: Recode

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