The ability of artificial intelligence (AI) to generate content and ideas autonomously has opened up a world of possibilities for companies to optimize their processes. Generative AI, unlike traditional AI that focuses on analysis, can create things like images, code and text without human supervision. This makes it possible to automate manual tasks and drives improvements in core business functions.
Generative AI focuses on generating original data and content, as opposed to traditional AI approaches that rely on data interpretation and analysis.Deep learning models, such as neural networks, are used by generative AI to understand patterns and structures in data sets and then produce new data that follow those patterns.
Although generative AI is a topic of great interest, its early implementations focused on knowledge management rather than process transformation.
However, the potential is there. Let’s look at the most important functional use cases that demonstrate the capability of this technology:
Data extraction and contextual search
Generative AI has a strong ability to extract data from unstructured sources such as text documents, images, audio and video. By automating manual data capture, generative models accelerate processes such as accounts payable, customer onboarding and contract analysis.
Contextual search goes further by indexing and tagging content for quick access. Doctors can find relevant information in medical records and lawyers in legal contracts. This saves time and reduces errors.
Virtual assistants for employees and customers
Language model-driven chatbots like ChatGPT make customer service easier by automating frequently asked questions. Meanwhile, in-house assistants streamline technical support and employee training.
The natural language interaction of virtual assistants enables an enjoyable conversational experience for humans. And they have the potential to understand context and intent for more accurate responses.
Generating intelligent business metrics
From large volumes of data, generative AI can produce forecasts, projections, risk analysis and other advanced metrics. For example, in sales to estimate demand, in finance to model scenarios, and in insurance for risk assessment.
These predictive analytics outperform traditional spreadsheets and queries by finding interrelationships and patterns that are difficult to detect manually. They transform management decision making.
Personalized recommendation engines
Whether to suggest products, prices, discounts, or sales scripts, generative AI creates hyper-personalized recommendations by analyzing customer habits and preferences.
Retailers can increase conversion by optimizing recommendations on their websites. And salespeople receive real-time suggestions to cross-sell and increase average ticket.
Creative content generation
For marketing, generative AI writes blog copy, social media posts and advertising scripts quickly and consistently. It also creates attractive descriptions for product catalogs in online stores.
In graphic design, it generates original images, modifies photos and video, and designs packaging, labels and 3D renderings of products. This speeds up the creation of quality content by reducing creative bottlenecks.
Application development and maintenance
In IT, generative models streamline software development by writing code autonomously once the logic is defined. They also generate test cases and scripts, technical documentation and facilitate migration between programming languages.
This increases programmers’ productivity, allows them to focus on higher-value tasks and drives innovation by accelerating the release of new functionality.
Strategic predictive analytics
Predictive models identify patterns in historical data to make reliable projections about sales, product demand, staff turnover and other key KPIs.
For example, a retailer can better anticipate demand for seasonal products. And a bank can predict the probability of default on a loan. This makes it possible to improve business performance.
A promising future
These and other cases demonstrate the great potential of generative AI to reshape internal and external processes across industries. It’s adoption is advancing rapidly even though it is still in its infancy.
Companies need useful information and a plan of action. They need to develop a strategy to help them think about AI at the enterprise level over the next three to five years and design an implementation architecture that best serves their goals iteratively.
Companies that learn to use these skills strategically will be able to significantly improve productivity, customer satisfaction and decision making. This will result in a significant competitive advantage in the new AI-driven economy.
References
AI for Everyone. Generative AI Technology in Business | Accenture
Generative AI for Business. Generative AI for Business | NVIDIA
Building the future of business with generative AI. Building the Future of Business with Generative AI (ibm.com)
State of Applied Generative AI Market Report: State of Applied Generative AI Market Report | ISG (isg-one.com)
