A recent report by McKinsey indicates that current generative AI technologies have the capacity to automate tasks that currently consume 60-70% of employees' time. However, generative AI isn't a cure-all; it can produce fictitious or incomplete data and may even lead to data breaches. These limitations underscore why generative AI isn't poised to replace human workers just yet.
Nonetheless, with a strategic approach, generative artificial intelligence has the potential to streamline and enhance business operations. In this piece, we delve deeper into the benefits that generative AI could offer your company.
What is Generative AI?
Generative AI refers to artificial intelligence technology that generates new content—be it code, text, images, audio, video, or other data types—that humans typically produce. It accomplishes this by analyzing and identifying patterns from existing data.
Generative AI models primarily rely on one of three deep learning architectures: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers.
GANs operate through the competitive interplay of two neural networks—the "generator" and the "discriminator." The generator produces an image, while the discriminator endeavors to discern its authenticity.
VAEs also consist of two neural networks—the "encoder" and the "decoder." The encoder analyzes the data, and the decoder generates something akin to it based on the resultant array.
Transformers excel at grasping textual context thanks to the "self-attention" mechanism.
One of the notable advantages of generative AI models is their adaptability to diverse learning approaches during training, including unsupervised or semi-supervised learning.
Prominent examples of generative AI include ChatGPT, DALL-E, Bard, Scribe, Jasper, GitHub Copilot, and AlphaCode.
Nevertheless, the capabilities of generative AI for sophisticated writing and visuals are still nascent. AI models often produce "peculiar" content riddled with bias or hallucinations. Nonetheless, with rapid advancements in various models, generative AI holds significant potential for transforming businesses.
Learn more about the mechanics of generative AI.
Why is Generative AI Crucial for Businesses?
Generative AI's impact on productivity has the potential to revolutionize our work methodologies.
Gartner forecasts that by 2025, 30% of enterprises will have AI-augmented development and testing strategies in place—a significant leap from the 5% adoption rate in 2021.
McKinsey estimates that about 75% of the value generative AI use cases could deliver lies across four key areas: customer operations, marketing and sales, software engineering, and R&D. Industries such as banking, high-tech, and life sciences stand to substantially augment revenue by fully embracing generative AI technology.
For instance, widespread adoption of generative AI technology in the banking sector could translate to an additional revenue of $200 billion to $340 billion annually. Similarly, in retail trade and consumer goods production, the potential revenue surge ranges from $400 billion to $660 billion per year.
Generative AI is pivotal as it reshapes business operations through automation, personalization, and data-driven decision-making, making it indispensable for thriving in the digital landscape.
We will delve deeper into the benefits of generative AI in the subsequent section.
How Can Businesses Leverage Generative AI?
Generative AI holds vast potential across various business domains. Here, we spotlight five primary benefits that companies can harness.
Utilize Data for Informed Decision-Making
Generative AI serves as a conduit for more informed decision-making by elucidating insights previously concealed within data.
In the retail sector, generative AI algorithms unveil valuable insights by analyzing customer reviews, social media sentiment, and market trends.
Amazon, for instance, harnesses generative AI to distill customer reviews into concise summaries on product detail pages, aiding shoppers in swift decision-making.
Enhance Customer Service
Generative AI finds multifarious applications in customer service, including:
AI chatbots: Many companies deploy AI chatbots on websites and messaging platforms to cater to customer queries round the clock.
Virtual assistants: Popular virtual assistants like Siri, Alexa, and Google Assistant offer assistance to users, furnishing product recommendations, fetching information from the internet, among other tasks.
Sentiment analysis: Generative AI can analyze social media comments and reviews, extracting insights regarding customer sentiment and satisfaction. Airbnb utilizes sentiment analysis to refine user experience based on guest feedback.
Streamline Data Exploration and Analysis (Converse with Your Data)
Converse-with-your-data generative AI applications, trained on a company's databases, documentation, and policies, simplify customer support inquiries.
This approach expedites data exploration and analysis, rendering data comprehensible and accessible to non-technical personnel. Business users can glean valuable insights through natural language interactions with the AI, sans intricate queries or coding.
For instance, users can query a chatbot about total sales figures for the previous month or the bestselling product, receiving pertinent data-driven responses.
The primary advantage of converse-with-your-data generative AI lies in its ability to facilitate interactions with AI and extract insights from data through conversational interfaces.
Optimize Software Engineering
Generative AI confers several benefits to software engineering, including task automation, code quality enhancement, and bolstered security.
Key benefits in software engineering include:
Code generation: Tools like OpenAI's Codex and GitHub Copilot aid developers in code composition.
Code review and optimization: Generative AI analyzes codebases to recommend refactoring, optimizing code quality.
Bug detection: Platforms like DeepCode and CodeWhisperer identify potential software bugs and security vulnerabilities.
Automated testing: Generative AI generates test cases and scenarios, such as Diffblue Cover, which automatically generates unit tests for Java applications.
Rapid Content Creation and Personalization
Automated content creation and personalization are quintessential generative AI benefits across media, entertainment, and content production sectors.
Applications include:
Copywriting and marketing: ChatGPT facilitates the generation of various content types, from blog posts to product descriptions.
Visual content: Platforms like Midjourney, Canva, and Adobe's Sensei enable non-designers to create high-quality visuals for marketing purposes.
Video content: Lumen5 and Runway leverage generative AI algorithms to produce video content from textual or visual inputs.
Music and digital art: Generative AI tools like MuseNet and OpenAI's DALL-E generate music compositions and unique digital artworks.
Mitigate Employee Workload
Automation emerges as a pivotal generative AI benefit, exemplified in real-world applications.
Financial institutions utilize AI models to automate financial report analysis, trend identification, and investment recommendations.
Amazon leverages generative AI to forecast inventory, optimize delivery routes, and simplify product page creation.
Generative AI tools such as Microsoft Copilot, an integrated knowledge base chatbot, markedly reduce workload through task automation and content creation.
Cost Savings
Generative AI presents a compelling avenue for cost reduction and competitive sustainability in a swiftly evolving business landscape.
Businesses can automate routine tasks and minimize expenditure on specialized software and equipment.
This automation spans functions encompassing content creation, customer support, data
analysis, and software development.
Generative AI Challenges for Business
Notwithstanding the benefits outlined earlier, the implementation of generative AI poses significant challenges.
The Exadel AI team identifies three principal challenges:
Hallucinations: Generative models may fabricate information, leading to fictional or unfounded content. Preventative measures involve training AI models on high-quality data, incorporating human feedback, and ensuring transparency and continuous quality control.
With meticulous model fine-tuning and prompt engineering interventions, the risk of hallucinations can be mitigated.