GenAI Use Cases
As technology matures, Generative AI (GenAI) use cases for various industry verticals are becoming more impactful in 2024.
Today, we can see some solid real-world examples of industries using GenAI technology to enhance operational efficiency, simplify workflow, and bring the unimaginable power of analyzing and summarizing huge amounts of structured & unstructured data into insights.
For example, Gramener is leveraging GenAI in existing solutions and building GenAI-powered products. Doc Genie is our flagship Intelligent Document Processing (IDP) tool, and it leverages GenAI to improve the accuracy of data processing.
Now, that was just an example. In this blog, we’re listing the six most relevant GenAI use cases you should look for in 2024.
Table of Contents
Generative AI enhances content creation by automating articles, blogs, social media posts, etc. Tools like ChatGPT, copy.ai, and Jasper help produce coherent, relevant content efficiently.
Generative AI presents compelling use cases within enterprises, revolutionizing content generation across various sectors. In marketing, it enables the creation of tailored advertisements and product descriptions, optimizing consumer engagement and conversion rates. Enterprises can leverage AI to automate the production of reports and analysis, streamlining decision-making processes.
In customer service, AI-powered chatbots generate personalized responses, enhancing user experience and reducing response times.
Moreover, in design and prototyping, generative AI helps create innovative concepts and models, accelerating product development cycles. Despite these benefits, ensuring AI-generated content aligns with brand values and regulatory standards remains crucial for enterprises navigating this transformative technology landscape.
Tools like DALL·E generate images from text descriptions, allowing artists to quickly visualize concepts.
For instance, a marketer might use DALL·E for fast, creative ad illustrations, enhancing creativity and content accessibility. By interpreting textual prompts, Dall-E can create images that range from realistic to surreal, bridging the gap between human imagination and visual representation.
This capability offers immense potential for various industries, including marketing, where custom visuals can be generated on-demand, enhancing brand narratives without traditional photoshoots.
In education, DALL-E can provide visual aids to help explain complex concepts, making learning more engaging.
Additionally, its application in the arts opens new avenues for creative expression, allowing artists to explore visual ideas that were previously difficult or impossible to realize.
The ability to rapidly prototype visual concepts also benefits product design, where iterations can be visualized quickly, streamlining the development process. DALL-E’s ability to generate images from textual descriptions not only demonstrates the power of generative AI but also reshapes how we conceive and interact with digital content.
Generative AI-powered chatbots can instantly provide personalized answers to complex customer questions, irrespective of their language or location. It enhances automated interactions, potentially handling a larger share of inquiries and allowing human agents to focus on issues requiring human intervention.
Research shows that about half of the customer interactions in sectors like banking and telecom in North America are machine managed. Generative AI could further cut the need for human responses by up to 50%, based on a company’s automation level.
Generative AI can improve customer experience and operations by enhancing agent productivity. It achieves this through digital self-service capabilities and by augmenting agent skills.
Notably, research indicates significant improvements in issue resolution and reduced handling time when Generative AI is applied. For instance, at a company with 5,000 customer service agents, Generative AI increased issue resolution by 14% per hour and decreased handling time by 9%. It also lowered agent attrition and requests for manager assistance by 25%.
Generative AI offers operational improvements across various use cases, such as customer self-service, resolution during initial contact, reduced response time, and increased sales.
Providing immediate, personalized responses to customer inquiries enhances the quality of interactions and can automate a higher percentage of inquiries, freeing up human agents for more complex tasks. Additionally, Generative AI aids in retrieving customer data instantly, recommending next steps, and identifying tailored product suggestions based on browsing histories.
Applying Generative AI to customer care functions is estimated to increase productivity by 30 – 45% of current function costs. However, this estimation only accounts for direct impacts on productivity and does not consider potential effects on customer satisfaction and retention.
These indirect benefits include an improved understanding of customer context, enabling human agents to provide more personalized assistance and recommendations, ultimately leading to a better overall customer experience.
Generative AI customizes the marketing approach by analyzing customer data to create targeted emails and ads. For example, personalized email campaigns can increase engagement and conversions, making marketing more effective and relevant.
This technology also enables the creation of personalized messages tailored to individual customer interests, preferences, and behaviors and can perform tasks such as drafting ad copies, headlines, slogans, and social media posts.
In marketing, integrating generative AI requires careful consideration to avoid infringement on intellectual property rights and biases in representation. However, it offers significant operational benefits, including efficient content creation, enhanced use of data for informed strategies, SEO optimization, and personalized product discovery and search. Per McKinsey, generative AI could increase marketing productivity by 5-15% of total spending.
In sales, generative AI presents opportunities to identify and prioritize leads, improve client engagement, and automate lead nurturing processes. Implementation of generative AI could potentially increase sales productivity by 3-5% of current global sales expenditures.
While these estimations focus on direct productivity impacts, the broader implications of generative AI, such as improved lead generation and enhanced customer interactions, may lead to additional revenue streams not fully captured in the analysis.
GenAI is changing several aspects of the pharmaceutical industry, including document processing, privacy protection, and regulatory adaptation.
In document processing and automation, GenAI streamlines the creation, review, and submission of complex regulatory documents in pharmaceutical regulatory affairs.
Utilizing advanced algorithms and natural language processing, it drafts and revises documents to meet regulatory standards, significantly reducing manual effort and allowing teams to focus on strategic tasks.
From automating labeling for consistency across regions to generating safety reports with high accuracy, GenAI marks a shift towards more efficient, accurate, and agile operations.
Regarding privacy and data protection, GenAI offers an intelligent approach to data anonymization, addressing the limitations of traditional methods.
By generating synthetic data that replicates statistical characteristics of original datasets without identifying individuals, it enhances privacy in sensitive areas like clinical trials and patient records.
Moreover, GenAI ensures regulatory compliance by adapting data handling to evolving privacy laws, proactively identifying risks and simplifying compliance processes for pharmaceutical companies.
GenAI can accelerate regulatory affairs for Pharma & Lifescience companies. It can swiftly detect changes in regulatory requirements and suggest updates for compliance. Its real-time adaptability is crucial for maintaining market access and patient safety in a fast-evolving regulatory environment.
Additionally, GenAI supports proactive compliance monitoring, reducing the risk of penalties, and enables companies to anticipate future regulatory trends for strategic alignment.
Watch our insightful webinar to understand the role of generative AI in pharma regulatory affairs.
As logistics leaders explore GenAI applications to enhance their operations, assessing the organization’s maturity, capabilities, data, and talent availability is crucial. Well-equipped organizations can develop and tailor solutions, whereas those less advanced may benefit from integrated tech or service offerings.
Prioritizing quick wins by leveraging existing technologies can yield immediate benefits, especially in common logistics functions. Moreover, the human aspect of supply chain and logistics shouldn’t be underestimated.
Despite GenAI’s potential to boost efficiency, integrating human expertise into the strategy is essential for realizing its total value. AI optimizes logistics by forecasting demand and planning routes. This saves costs and improves customer satisfaction with timely deliveries.
At Gramener, we use GenAI and advanced analytics to offer data solutions for intelligent warehouses, predictive analytics, and resilient supply chains. Find out how GenAI in supply chain is solving use cases that were the most challenging for leaders.
The adoption of AI, including GenAI, has increased, with around 40% of supply chain organizations investing in GenAI, particularly in knowledge management applications. GenAI creates new content based on trained data, presenting opportunities for innovation in supply chain operations.
As companies invest in GenAI, executives are tasked with understanding its implications for operations and business models.
When implemented strategically and with an awareness of limitations, GenAI can serve as an agile co-advisor, strengthening supply chains. However, rushed or poorly integrated implementations pose risks, including reliance on input data quality and availability, as well as ethical and legal considerations.
Effective governance and collaboration with experienced partners are crucial for managing these risks and maximizing the benefits of GenAI in supply chains.
GenAI offers significant advantages in the manufacturing industry, particularly in reducing time to market, enhancing innovation, enabling predictive maintenance, and improving decision-making processes.
In reducing time to market, GenAI streamlines product development stages like design, prototyping, and testing, leading to faster iterations and improvements.
Our Digital twin technology for simulations and predictive analysis, optimizing operations and providing valuable insights without disrupting production. Additionally, predictive maintenance powered by GenAI detects equipment failures before they occur, minimizing downtime and reducing repair costs.
Enhanced decision-making is another benefit, as GenAI analyzes historical data to provide valuable insights and recommendations for optimizing production planning and identifying cost-effective opportunities. Case studies show significant productivity boosts and ROI within one to three years across various applications, such as reducing scrap rates and equipment breakdowns while improving inspection accuracy.
Generative AI significantly boosts manufacturing production and operations. Utilizing sophisticated algorithms and analyzing data in real time aids in overseeing daily activities and promoting a smooth and efficient workflow in production.
Gramener – A Straive Company is a design-led data science firm that builds custom data and AI applications and solves business and sustainability challenges with insightful data stories. Check out our top GenAI projects include building a Conversational bot for a public policy think tank, a document summarization tool for an American multinational pharmaceutical company, sentiment analysis for a global soft beverage company, and more.
If you don’t know where to start, book a free discovery call with us.
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