The launch of ChatGPT in November 2022 set off a generative AI gold rush, with companies scrambling to adopt the technology and demonstrate innovation.
Many of the AI use cases entrenched in business today use older, more established forms of AI, such as machine learning, or don’t take advantage of the “generative” capabilities of AI to generate text, pictures, and other data. Legacy chatbots, product recommendation engines, and several other useful tools may rely only on earlier forms of AI.
Some industries such as biotech are finding ways to use gen AI, but many enterprises experimenting with the technology have found a limited number of use cases so far, says Kjell Carlsson, head of AI strategy at Domino Data Lab, provider of an enterprise AI platform. For many enterprises the return on investment for gen AI is elusive, he says.
“The reality for a lot of users is that they just don’t have enough [information] to make an AI strategy leveraging generative AI use cases, and they’re not going to get to enough value quickly,” he adds. “They have a couple of use cases that they’re pushing heavily on, but they are building up this portfolio of traditional machine learning and ‘predictive’ AI use cases as well.”
Many AI experts say the current use cases for generative AI are just the tip of the iceberg. More uses cases will present themselves as gen AIs get more powerful and users get more creative with their experiments.
However, a handful of gen AI use cases are already bubbling up. Here’s a look at the most popular and promising.
Advanced chatbots
While simple chatbots using word and phrase recognition have been around for decades, newer chatbots with gen AI capabilities can make conversations sound more natural while dealing with many customer requests.
IT analyst Forrester listed gen AI for language and AI agents as two of its top 10 emerging technologies for 2024. European ridesharing and delivery service Bolt, for example, has deployed an intelligent chatbot to deal with most customer complaints, creating a huge cost savings.
Many companies experimenting with gen AI have worried about hallucinations, but for low-level customer complaints, a few misfires aren’t the end of the world, Carlsson notes. “The risk is very low if we accidentally go in and give away a meal when we should have denied somebody credit for a meal,” he says.
In another example, Deutsche Telekom has used gen AI to improve its Frag Magenta AI assistant, and the company anticipates the chat assistant will be able to handle 38 million customer interactions each year.
Digital assistants
Several large IT companies, including Microsoft and Google, have been touting gen AI digital assistants, or copilots, even though CIOs may not be entirely sold on their ROI. These assistants can search the dark corners of the organization for information, create documents and slide presentations, and summarize email chains and videoconferences. Copilot AIs can also generate supply-chain documents, such as requests for quotes from suppliers.
Some videoconferencing applications now generate transcriptions and summaries, as do standalone tools such as Otter.ai. Apps such as Grammarly correct mistakes in grammar, spelling, and punctuation.
Digital assistants can also be specialized for specific needs, says Nick Rioux, co-founder and CTO of Labviva, provider of an AI-assisted purchasing solution. For example, if a company regularly purchases sensitive chemical or biological compounds, gen AI can add special handling instructions to the purchase order.
“The most promising use cases for enterprise generative AI are those that streamline human-originating tasks with augmentation like content generation, suggestions, and manual task automation,” he says.
Coding assistants
One of the use cases for gen AI that pops up the most frequently is the coding assistant. Gen AI can write basic software code, allowing human programmers to focus on more complicated tasks.
These code copilots can also help programmers keep their focus on code when they run into a problem, instead of turning to a search engine or other resources to find answers, says Julian LaNeve, CTO at data orchestration startup Astronomer.
“They can instead write a code comment and let an LLM complete their code for them,” he says, referencing large language models. “This keeps developers in what we refer to as the ‘flow state’ and ‘in the zone’ instead of breaking focus to search for examples.”
Gen AI is particularly helpful for web development, adds Natalie Lambert, founder and managing partner at GenEdge Consulting, an AI consulting firm. Gen AI, by creating website code, can significantly reduce the time and cost needed to update websites.
“By leveraging tools like ChatGPT, even users without deep technical expertise can develop and implement code directly on their websites,” she says. “This democratizes the development process, allowing web specialists to actualize their vision with AI assistance.”
Many enterprises that have been implementing gen AI across the software development lifecycle are currently working through the technology’s limits and team impacts, as well as their own lessons learned.
Marketing support
Several AI experts and users point to marketing support as one of gen AI’s sweet spots. Gen AI can create personalized marketing materials, analyze customer data, and aid with content creation, says Stefan Chekanov, co-founder and CEO of Brosix, provider of a secure instant messenger tool.
“In my experience, content creation and social media management are much more efficient with the help of gen AI,” he says. “Less time spent on menial scheduling, optimization, and editing means experts get to focus on high-value tasks, which equals cost savings down the line.”
Gen AI can conduct market analysis based on product reviews, and it can predict customer problems even before they recognize the issues, others say.
“For product companies, understanding customer feedback is crucial,” says Aswini Thota, director of AI and data science at banking and insurance provider USAA. “They need to know what customers like or dislike, emerging trends, regional preferences, and how customers will value new products.”
Gen AI can extract customer insights from product reviews instead of companies needing to commission surveys, he says. Before gen AI, data scientists built custom natural language processing (NLP) models for sentiment analysis and intent extraction, but gen AI has added to those earlier efforts.
“Gen AI allows us to craft multiple prompts on the same data set, and with a push of a button, organizations can extract sentiment, topics of discussion, and intended usage,” Thota adds.
Drug discovery
Gen AI is being used in drug discovery by modeling complex molecules and predicting their interactions “at speeds that would make traditional methods look like they’re stuck in dial-up internet days,” says Lars Nyman, CMO of CUDO Compute, an AI infrastructure platform. Gen AI can significantly cut down the time it takes to bring new drugs to market, he says.
Gen AI can help pharmaceutical companies predict drug interactions, repurpose existing drugs, and create personalized therapies based on a patient’s genetic makeup, according to MSRcosmos, a global IT services provider.
In early 2024, NVIDIA announced its AI-driven Clara computing platform targeting the healthcare industry and its BioNeMo, a gen AI platform for drug discovery.
Some biotech and pharma companies, including Johnson & Johnson, are promoting gen AI as the next big thing in drug discovery.
Cybersecurity and fraud detection
Several cybersecurity firms are using gen AI to enhance tools that look for suspicious or unusual behavior on a customer’s network and computing infrastructure. AI systems can also be used for advanced fraud detection that predict fraudulent activities with great accuracy by analyzing transaction patterns and user behaviors, says Jim Kaskade, CEO of Conversica, a provider of conversational automation solutions.
For example, Palo Alto Networks offers the Cortex XSIAM security operations platform, which combines the company’s expertise in ML models and its data store along with Google’s BigQuery enterprise data warehouse and its Gemini AI model. The goal is to alert security analysts to threats in real time, while the cybersecurity platform continually learns about new threats.
Business process augmentation
Generative AI is finding a sweet spot in enterprise business process augmentation. Here, companies are exploring the use of gen AI to provide efficiencies for business-critical workflows, often unique to their verticals.
For example, some firms in the finance and insurance industries are using gen AI to assist underwriters evaluating prospective clients. Credibly, a lending platform for small businesses, uses gen AI, paired with machine learning, to evaluate loan risk and to speed up the lending process, says Ryan Rosett, co-CEO and founder of the company.
“Gen AI at Credibly is being used to give our underwriters superpowers,” he says. “As a fintech lending company, our success depends on fast and accurate risk assessment of business owners seeking financing.”
Nearly all insurance carriers had adopted gen AI or were interested in it as of late 2023, according to a survey from EY. About 42% of insurers had already invested in gen AI, and about two-thirds expected a revenue boost of more than 10% through the use of gen AI.
In the legal arena, legal information services giant LexisNexis is embracing generative AI to keep in front of what EVP and CTO Jeff Reihl sees as a disruptive threat in the company’s industry.
“We were all-hands-on-deck,” Reihl told CIO.com. “We did a major pivot because this was a game changer in terms of its interactive abilities, as well as the comprehensiveness of its answers and its data generation capabilities. It was just staggering in terms of its capabilities.”
LexisNexis since release its own generative AI solution, Lexis+ AI, to provide linked legal citations to ensure lawyers have access to accurate, up-to-date legal precedents.
