DataWorksAI

Artificial intelligence (AI) is rapidly evolving. It is expected to radically transform all kinds of jobs over the next few years, from content creation to product development, bringing great gains in employee productivity. At the center of it all is generative AI (GenAI). GenAI, a fascinating and unique advancement in AI technology, has garnered widespread public interest and ignited global enthusiasm.

Machine Learning (ML) and Deep Learning (DL) subdomains in AI have long been considered black boxes and have typically been regarded as the exclusive purview of technologists. Business executives who previously struggled to grasp the value of investing in ML technology, however (still unconvinced of its value despite the extensive business justifications provided by their engineers, CIOs and CTOs), are now under tremendous pressure to understand GenAI and are beginning to pay attention.¹ This is a positive development, since the technology is powerful and vital and enables many new business possibilities. Many executives, swept up in the frenzy around GenAI, are beginning to jump in head first and embrace the technology, despite the fact that it remains difficult for businesses to reliably define and measure their return on GenAI investments. ²

The fall 2022 release of ChaptGPT, a web application using GenAI behind the scenes, set the business world on fire by providing an aperture for business leaders to interact with AI. According to a report published in September 2024 by FactSet, an analyst group, 40% of S&P 500 companies cited “AI” on earnings calls for Q2 2024—a significant increase from previous years—signaling their commitment to embracing innovation and efficiency and staying ahead of market trends.³ According to a 2024 report from Harvard Business Review, most business functions and more than 40% of all U.S. work activity can be augmented, automated, or reinvented with GenAI.⁴ Partnerships between major industry players and innovative startups are now a significant market feature. VC investment in GenAI now accounts for more than 43% of overall AI funding; the total deal value in 2024 is expected to exceed $45 billion, based on actuals through the end of November and projections for December.⁵

According to a 2024 MarketBridge report based on interviews with C-suite executives:⁶

57% of C-suite executives believe that AI will disrupt the way they structure their organizations to capitalize on new opportunities, while 53% state it will change how they innovate their product portfolio mix to meet customer needs, and 58% report that it will change how they explore new business model opportunities;
52% will use AI to deploy marketing and sales resources more cost-effectively to align with buyer needs and capture new market opportunities, leveraging AI to position solutions in a personalized, relevant way at scale; and
46% expect to see increased value creation from analyzing vast amounts of data to optimize performance and return on GTM investment.

Many businesses remain optimistic about generative AI’s overall net positive impact and view this technology as the key driver for the next wave of digital transformation and automation, not only due to the constantly evolving technological landscape and the billions of dollars invested but also due to the ingenuity with which people apply it.

Why now?

The present success of generative AI is due to several factors, including:

Significant progress in algorithmic development in the areas of natural language processing (NLP) and DL;
Improvements in computer hardware — e.g., GPU throughput and memory have increased 10-fold over the last four years;
Development of the transformer model architecture [Vaswani et al. 2017], which leverages the parallelism of the hardware to train much more expressive models than before;
The availability of large, labeled, annotated datasets;
The low cost of storage and the adoption of LakeHouse Data Architectural patterns to store and harness data; and
An active and collaborative research community.

With the list of use cases continuing to rapidly evolve, let us examine popular applications of GenAI across functions, from customer service to marketing and R&D, where early corporate adopters have found GenAI helps reduce workload and increase productivity.

One potential use of GenAI is the automation of tasks and processes, especially those that require reasoning capabilities. Here are a few applications for foundation models:

Self-service Applications: These include intelligent interrogation applications with reasoning capability that can provide data to meet a wide range of needs. For example, financial numbers a CFO might require before a quarterly earnings call; drug dosage recommendations required by a medical clinic; product description and pricing information required by a company’s marketing and sales division.
Software Engineering: Significant productivity-boosting capabilities include creating source code from natural language code comments; designing and prototyping new features; fast-tracking testing; improving bug detection; generating and autocompleting code in an Integrated Development Environment (IDE); generating natural language summarization or documentation for a given source code; searching code based on a natural language query of code snippets; and boosting overall code quality.
Machine Learning and Data Science: GenAI can accelerate Analytics Product Life Cycle (APLC^TM) by allowing data scientists and ML engineers to focus on high-value tasks, reducing time spent on classification, performing descriptive analytics, generating synthetic data, and creating new features from existing data.

Here are a few general areas where GenAI is used most, according to a report published by Harvard Business Review in 2024.⁷

Technical assistance and troubleshooting (23%)
Content creation and editing (21%)
Personal and professional support (17%)
Learning and education (16%)
Creativity and recreation (13%)
Research, analysis and decision making (10%)

Businesses across the globe are beginning to integrate GenAI into their operations to boost their operational efficiency, and the technology is disrupting every industry. Future blog posts in this series will provide an overview of GenAI and outline its core principles. They will also discuss the various generative foundational models, large language models, embeddings, related algorithms, and architecture and highlight typical use cases demonstrating GenAI’s transformative potential. We will introduce additional terms and concepts in the context of GenAI and learn how these concepts are interconnected to AI, ML, and DL.⁸

Embracing GenAI in business means being open to radical change, questioning existing business processes without fear of disrupting the status quo, and being dauntless in throwing out the rulebook and starting anew to achieve better business outcomes. Trailblazers, innovators, and those who are curious and on the lookout for technological developments that lie around the corner will reap the greatest benefit from GenAI. AI will not replace the role of humans in critical functions, but those incapable of embracing AI technologies will find themselves at a disadvantage, unable to partner and collaborate with AI practitioners within their organizations and beyond.

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