Leveraging Generative AI for Business Growth

Generative AI is a powerful tool for enterprises seeking to create content efficiently. It can produce articles, marketing materials, and even code, saving both time and resources. For example, the most common use case for marketers (76%) and sales (82%) specialists is basic text pieces creation and copywriting. As they evolve, AI use cases promise to further impact innovation and productivity across industries. The panel considered the impact generated by the integration of generative AI into electronic design automation (EDA) tools and how it shapes the landscape of chip design.

• Generative AI stands at the cusp of transforming how we interact with and benefit from technology.
• As AI capabilities expand, there’s an anticipation that generative AI will extend its applications to various data types beyond text.
• It claims that it tests its outputs frequently with A/B testing and that its content has been optimized for search engine positioning.
• While generative AI is seeing its profile rise as a means to build solutions, companies that claim to offer their own AI products shouldn’t lose focus of their core offerings, SaaSCan’s report suggests.

Generative AI will drive future impact, distinguish how businesses compete and revolutionize the ways people do business with a focus on strategy, product, engineering, experience and data. Organizations that want to seize the moment will need to evolve with generative AI to keep pace with competitors and continue to produce measurable value. Generative AI is a rapidly growing technology with various potential use cases and applications. As this technology continues to evolve, expect to see these risks and limitations addressed with growing AI capabilities and access to real-time data.

Unearthing Promising Use Cases for Generative AI Adoption:

The models are largely restricted to large tech companies, as they require massive amounts of computing power and data. GPT-3 was trained initially on 45 terabytes and uses 175 billion coefficients or parameters to make predictions. The majority of companies do not have the budget or data center resources to create their models. Moe Tanabian is chief product officer at Cognite, an industrial software provider of scalable industrial digital solutions, including a comprehensive suite of industrial generative AI capabilities. To master the industrial data problem, we need a shift towards industrial AI at scale.

This approach focuses on delivering business-ready, trusted, actionable data to all users. It promises to improve time to value, quality, predictability and scalability in data analytics. Generative AI technology is a powerful resource that can be leveraged in businesses of all sizes and backgrounds, especially since so many models come in affordable limited versions that still have extensive capabilities.

Table of Contents: How Generative AI Can Support Business Operations

With its versatility and ability to be tailored to specific departments or companies, the real-world applications of generative AI are expected to multiply in the coming years. General Electric (GE) utilized Generative AI to design a 3D-printed jet engine bracket. By inputting constraints and requirements, the AI system generated an optimized design that reduced the weight of the bracket while maintaining its strength. This resulted in a 75% reduction in weight compared to the previous design, leading to significant cost savings and improved fuel efficiency. For instance, GPT-3.5, a foundation model trained on extensive text data, can be adapted to answer questions, summarize text, or perform sentiment analysis.

Some of our program faculty experts also serve as consultants for today’s top AI companies and will provide a unique, insider’s perspective on the transformational capabilities of real-world generative AI applications. Although generative AI is still in the early stages, the potential applications for businesses are significant and wide-ranging. It can be used to write code, design products, create marketing content and strategies, streamline operations, analyze legal documents, provide customer service via chatbots and even accelerate scientific discovery. It can be used on its own or with “humans in the loop”; the latter is recommended at present, given its current level of maturity. By analyzing vast amounts of data and generating insights, generative AI models can assist business leaders in making informed and data-driven decisions.

Computer Science > Software Engineering

The real differentiator will be the data quality a company can use to train language models and the domain expertise it can bring. Having been at the helm of innovation in customer experience (CX), IT, and sales and marketing technologies for over a decade, Freshworks brings distinct data and rich insights, including our own models. Our customers have been using our AI offerings out of the box as part of the solutions they already know and love. Gen AI’s ability to create personalized content and recommendations can significantly enhance customer experiences in GBS operations. It can be used in conjunction with other AI tools to provide more tailored solutions to customers, resulting in increased satisfaction and engagement.

“Our data engineers can use natural language to create baseline SQL queries,” Cannava said. We plan to use AI to automatically create reports, including detailed ESG or competitive analyses for private equity firms. We see it as a way to speed up our growth and establish SESAMm as a key player in the industry. For example, we use advanced AI models to automate data annotation for ESG and SDG (Sustainable Development Goals) alerts. This has saved our analysts 30% of their time.We’re also creating a client-friendly interactive tool that will be a part of our dashboard. Our aim is to start with a demo and then fully automate the extraction and summary of key ESG and SDG events.

Transformative Impact of Generative AI on the Future of Business

Heinz used an image with a label that looked similar to Heinz to argue “This is how ‘ketchup,’ looks to AI.” However, this meant that the model had been trained on a large number of Heinz bottle photos. Nestle created an AI-enhanced Vermeer painting to promote one of their yogurt brands. Stitchfix is a clothing company that already uses AI for recommending specific clothing to its customers. They are now experimenting with DALLE 2, to create visuals of clothing that reflect customer preferences in terms of color, fabric, and style. The use of AI-based models in business threatens to revolutionize the content creation industry, having a significant impact on marketing, software design, entertainment, and interpersonal communication.

The personalized customer journey, optimized operations, and efficient workflows, coupled with enhanced decision-making, sales growth, innovation, and sustainability efforts, showcase the broad spectrum of opportunities. Data scientists should be spending much more of their time building AI models for forecasting and decision-making. Improving data quality can give data scientists more time to concentrate on building AI models that are reliable and accurate. Success with generative AI inside your organization requires powerful data management, and sophistication in determining what data to include and how to process that data.

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