Five real-world generative AI use cases

Copilots & Virtual Assistants: Emerging Use Cases, Trends, & Strategies

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. 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. Copilots and virtual assistants are continuing to drive efficiency across customer-facing teams. Many manufacturing organizations still use outdated legacy systems, which can create significant barriers to incorporating AI technologies. AI-driven predictive maintenance is revolutionizing how manufacturers handle equipment upkeep.

This will lead to new product designs that GenAI can produce, optimize production, and predict what might go wrong, which will happen. Manufacturing is truly getting a serious makeover for the future, and it’s not full of buzzwords and techno-speak; with the dawning of AI technologies, manufacturing is no longer about nuts and bolts and conveyor belts. “Successful players acknowledge Generative Al’s strengths in leveraging unstructured data and have actively taken efforts to utilize its potential,” noted Van Engelen. “A lot of the analytics in organizations requires users to have an understanding of the data they’re looking at,” she said.

Current Trends in AI and Manufacturing

Estimates say that, by 2032, the value of the global general AI healthcare market will reach $17.2 billion. In the race to make the most of generative AI, some companies are leading the charge and are not just adopting this technology but defining its future. Three of the top generative AI companies that push the boundaries of AI transformation include OpenAI, Microsoft, and Google. Enterprise use cases for generative AI include everything from writing marketing copy to discovering new pharmaceuticals.

When using a GenAI tool connected to the internet, you can also have it assist you in finding properties and land you could acquire or represent as an agent. As it is so good at research and large-scale analytics, acquisition teams can also use it to run financial simulations and assist in creating property development timelines. The more details you feed it with, the better the output will be – share who you’re targeting, what tone of voice to use, how long the copy should be, etc. Later, this data can be fed back into a model that will be able to predict the type of finishes and furniture that appeals the most to various customer segments. The most eye-catching of all the benefits of generative AI in manufacturing is that it can drive data-driven decision-making, which is a game-changer in determining and sustaining success.

This pilot cohort will refine deployment strategies, validate performance measures, and highlight improvement opportunities before scaling the solution. They must make decisions more quickly and cater to increasingly discerning and demanding customers. Typically, an experienced agent with deep organizational expertise, this individual will manage the data and workflows that copilots consume. They will also deliver far richer reporting by synthesizing large volumes of data – for example, summarizing thousands of transcripts to reveal trends instantly. I am working in my day-real-world applications with generative AI, especially in the enterprise. We have an additional screen that we use to evaluate real user feedback in the productive phase.

“The range of actual solutions are fundamentally amazing,” said Duncan Ng, VP of solutions engineering at cloud infrastructure company Vultr. Many contact centers will even have multiple LLMs powering numerous use cases across their chosen platform, and – so they know which to use where – some vendors, including Salesforce, will benchmark LLMs against particular use cases. That involves rearchitecting their initial solutions to ensure the best possible performance.

Cobots, or collaborative robots, are essential to AI-driven manufacturing because they increase productivity by collaborating with human operators. These cobots work in unison with human workers, navigating intricate areas and identifying objects with the help of AI systems. Also, as per a recent survey conducted by VentureBeat, it has been reported that 26% of organizations are now actively utilizing generative AI to improve their decision-making processes.

Enhancing Knowledge Bases

Healthcare stakeholders express concerns about the reliability of AI-generated recommendations, including the risk of misdiagnoses or inappropriate treatments. Healthcare regulations pose significant challenges for the adoption of generative AI technologies, particularly regarding data privacy, safety, and efficacy. Research by the Deloitte Center for Health Solutions suggests that medical organizations are increasingly recognizing the benefits of Generative AI for Healthcare.

However, there are others who would prefer calling and talking to a government agent. As governments embrace AI and build tools that use it, they also must meet the needs of citizens who do not want to use them. The Brazilian government is taking steps (link resides outside of IBM.com)15to boost AI leadership in South America. It recently announced a multi-billion-dollar campaign to invest in homegrown AI technologies to reduce its dependency on external tools. The use of artificial intelligence (AI) in government refers to the implementation of AI in governmental affairs and the rules and regulations those officials make to legislate how private companies and individuals use it. The company has already developed five successful compounds, including a potentially revolutionary Inflammatory Bowel Disease (IBD) drug which has entered Phase I clinical trials in early-2024.

Other Google Shopping tools use GenAI to intelligently display the most relevant products, summarize key reviews, track the best prices, recommend complementary items and seamlessly complete the order. Firms such as fintech marketplace InvestHub use generative AI to personalize at scale. Stakeholders can also query ChatGPT or other generative AI tools, such as Claude, Bing or Gemini, for explanations of images.

• A simple algorithm that looks at historical data isn’t enough to provide an accurate delivery date.
• Generative AI models are trained on vast datasets, often containing copyrighted materials scraped from the internet, including books, articles, music and art.
• GenAI also enables banks to offer personalized banking and marketing experiences tailored to customer interests and needs.
• A contact center virtual assistant can help supervisors by alerting them to positive recognition and coaching opportunities.
• If a contact center can continuously feed such a solution with knowledge sources, contact centers can continually monitor customer complaints and act fast to foil emerging issues.

As generative AI monitors customer intent, many vendors have built dashboards that track the primary reasons customers contact the business and categorize them. However, even that can impede an agent’s ability to engage in active listening as they multi-task, resulting in increased resolution times. That final part is crucial, keeping a human in the loop to lower the risk of responding with incorrect information and protecting service teams from GenAI hallucinations. In trawling these, GenAI automates a relevant customer response, which the agent can evaluate, edit, and forward to customers. Indeed, GenAI applications – like Service GPT by Salesforce – can do this by first understanding the customer query and sieving through various knowledge sources looking for the answer.

Learn how to confidently incorporate generative AI and machine learning into your business. The IBM AI in Action survey not only revealed the value of AI, but also the importance of the human touch and human experts that provide crucial input and bring it to life. But without the nuanced understanding and decisive expertise of human employees organizations might never become Leaders and miss what the AI landscape has to offer. Investment in IT operations and automation requires a significant mindset shift for organizations. Technical debt is a leading impediment for companies looking to accelerate transformation with generative AI. Recent findings from the IBM Institute for Business Value research found a typical organization spends just 23% of its technology budget to drive revenue.

When it comes to sustainable farming practices, GenAI uses its massive database to simulate historic and current farming practices, predicting long-term environmental impacts. According to McKinsey, generative AI could add $200 billion to $340 billion in annual value to banking, largely through increased productivity. While traditional AI helps banks analyze data and forecast trends, GenAI goes beyond by providing coherent, contextually relevant outputs based on immeasurably larger inputs.

Integrating ML with IoT leads to numerous benefits, such as efficiency in real-time analytics and predictive maintenance. Because ML and IoT complement each other’s strengths, they’ve successfully been applied across many industries, including healthcare, industrials, manufacturing, utilities and business management. IBM consultants from around the globe will now be equipped with role and domain-specific AI assistants to modernize consultancy approaches with deeper insights and improved efficiency. IBM Consulting Advantage can help organizations in many different domains, including finance and procurement, marketing, IT, cybersecurity, data transformation and hybrid cloud.

Learning and analyzing the data gives manufacturers insights into making lightning-fast, well-informed decisions. Whether shifting gears in the face of jarring market changes or shifting production to fulfil customer demand, manufacturers now have an upper edge by keeping agility and responsiveness on their side. Now, generative AI is beyond number-crunching or data sifting, as with traditional AI; it goes beyond all that. Unlike most other impostors, this one does a little more than recite history for you.

Transformers were a new form of neural networks and deep learning that formed the basis of many AI technologies today. Meanwhile, a local real estate agency that focuses on residential property sales might only require a chatbot and a description generator to support their support and marketing teams. People look for properties online, this includes both Google search and social media platforms.

• It’s hard to find a person that hasn’t tested or at least heard of generative artificial intelligence – it’s taking the business world by storm, with life sciences and healthcare being no exception.
• When a contact escalates, the customer must often repeat their problem and the information they shared with the first agent – which is a common source of customer frustration.
• The German government worked with IBM to use AI to improve its ability to work through its backlog of cases.
• But GenAI, while evolving rapidly, isn’t perfect and can make up results — known as AI hallucinations — that could end up in production if a skilled human isn’t part of the process, Nwankpa explained.

On a bolder scale, a radio station in Poland replaced all its journalists with AI presenters but quickly abandoned the so-called experiment weeks later in the face of listener backlash. The Washington Post uses its GenAI-powered Heliograf tool to automate simple news stories on sports or election results. India Today employs AI news anchors, and Reuters built its own AI-assisted LLM to support clients with legal research. Upgrading to a paid membership gives you access to our extensive collection of plug-and-play Templates designed to power your performance—as well as CFI’s full course catalog and accredited Certification Programs. CFI’s AI for Finance Specialization equips you with practical AI skills to drive smarter, data-informed decisions in finance roles.

Together we can make evaluating the impact and feasibility of a use case efficient, fun and easy. For automakers, generative AI aids in research and development, vehicle design, quality control, testing, validation and predictive maintenance. As panelists at Germany’s renowned IAA Mobility International Motor Show pointed out, generative AI can simulate various scenarios for safer, innovative designs and more energy-efficient systems. Even more remarkable are GenAI-powered engines like OpenAI’s Harvey, whose arguments are as sophisticated as those of veteran lawyers.

Generative AI in Customer Experience: The 11 Most Implemented Use Cases – CX Today

Generative AI in Customer Experience: The 11 Most Implemented Use Cases.

Posted: Mon, 20 Jan 2025 14:18:24 GMT [source]

Then, the platform spits out a bot, which the business can adapt and deploy in its contact center. To automate customer queries, GenAI-based solutions drink from various knowledge sources. Already, 12 of the top 20 customer service BPOs have leveraged the solution, reportedly cutting agent attrition by up to 50 percent. Background noise cancellation specialists – such as Sanas and Krisp – generate much of their business in customer service and have long sought ways to bolster their tech stack to increase their presence in contact centers. Technically, this works, and agents and customers can engage in phone conversations while speaking different languages.

How scalable solutions are helping federal agencies unlock AI’s potential

Collaborate with healthcare organizations to identify and prioritize tasks that can benefit from AI automation. Continuously improve AI models through rigorous testing and validation processes, focusing on specific healthcare domains and populations. Businesses can invest in extensive training datasets and collaborate with healthcare professionals to identify and address potential biases or limitations in AI algorithms. Implement ensemble or hybrid approaches combining AI with expert knowledge to enhance diagnostic accuracy.

Thankfully, with this process automated, agents have one less task to complete across every contact. Moreover, the contact center can more accurately track customer intent and implement better customer contact strategies. In the past, companies that wanted to scale into new geographies would have to spend a fortune hiring multilingual customer service team members. Sentiment analysis is becoming sophisticated, aiding companies as they look for ways to learn more about customers and what drives loyalty and retention rates.

For example, some organizations are using gen AI to extract data from video surveillance systems, says Sriram Nagaswamy, executive vice president at FourKites, vendor of a supply chain visibility platform. Here, companies are exploring the use of gen AI to provide efficiencies for business-critical workflows, often unique to their verticals. Some biotech and pharma companies, including Johnson & Johnson, are promoting gen AI as the next big thing in drug discovery.

Navigating the Generative AI Challenges and Potential Solutions in the Healthcare Ecosystem

Generative AI is still subject to hallucinations, and these kinds of mistakes erode trust. More than half the customers we surveyed said the biggest negative impacts to user experience are obvious errors (57%) and inaccurate product information (56% say it’s very or extremely negative). This is yet another reason to be transparent with customers about experimental uses of generative AI and to lean into passive applications, which can be more closely controlled (see Figure 4).

Microsoft is a major company that uses its vast resources and cloud infrastructure for the comprehensive integration of generative AI technologies in its product ecosystem. Through its partnership with OpenAI, this company has embedded cutting-edge AI capabilities into platforms like Azure, Microsoft 365, and GitHub. Microsoft Copilot, its AI assistant, helps users with coding and content creation by bringing smart, context-aware suggestions. Microsoft’s widespread implementation and continuous expansion of generative AI functionalities position it at the forefront of AI innovation.

Powerful ML platforms can help federal agencies concerned with law enforcement to better track threats in and outside of the country and solve crimes. Using pattern recognition, AI can analyze surveillance cameras, such as at airports, to identify suspects. However, governments must use this technology fairly and avoid discriminatory behaviors to protect their citizens’ civil liberties. The German government worked with IBM to use AI to improve its ability to work through its backlog of cases. IBM customized an AI bot named OLGA that extracted metadata and provided case categorization.

Clinics can also upload their own videos to the app from external drives and via integrations with laparoscopic or surgical robot systems. AI also automatically blurs patients’ identities to ensure the highest security and privacy standards. NY state plans to invest another $700,000 in 2024 to offer setups, maintenance, and device security

to patients under the care of the Office for the Aging. Among others, DoT can detect contradictory thoughts to help professionals notice cognitive distortion in patients.

The solution is not in production but has functioned as a demo to illustrate how convenient the “ask from a friend” type of user interface can be. Let’s look at five recent examples where we have experienced the value of GenAI jointly with our customers. Learn about the scalable and unique approach of Tietoevry Tech Services’ SmartGen Suite. The technology is so convincing that schools in Arizona and London plan to replace their human teachers with AI-driven instruction. Access and download collection of free Templates to help power your productivity and performance. For example, an FP&A team might use ChatGPT to draft initial variance analysis narratives, explaining why operating expenses exceeded the budget in specific regions.

Through continuous learning and adaptation, the system maximizes output, minimizes defects, and enhances resource utilization, leading to heightened profitability and a competitive edge. According to a Deloitte survey, manufacturing stands out as the foremost industry in terms of data generation. This indicates a significant volume of data being generated within the manufacturing sector, showcasing the industry’s substantial impact on the data landscape. Manufacturers must adopt AI to analyze this humongous amount of data generated in the sector. PANDA provided a proper CT scan analysis of over 92.9% in cancer-positive cases and 99.9% in non-cancer cases. The AI-powered tech is now evaluated as a method for analyzing large groups of asymptomatic patients, at a very modest cost.