From GenAI ideas to scalable solutions: Five inspiring cases

20 Excellent Use Cases for a Contact Center Virtual Assistant

IT operations and automation is the top AI use case for the telecommunication and manufacturing industry as any downtime for maintenance or network failures is time and resources spent away from customers. In telecommunications, AI is used for predictive maintenance and network optimization, enabling operators to identify and resolve issues before they impact service quality. This automation enhances operational efficiency and reduces downtime, allowing companies to deliver a more reliable and responsive experience to their customers. A recent IBM Institute for Business Value survey of 300 global telecom leaders found that most communication service providers are assessing and deploying generative AI use cases across multiple business areas. When two of the top telecom providers in India merged, the company knew it needed to consolidate while keeping any disruption to service to a minimum.

• By pairing this with the Cognigy Playbooks reporting platform, service teams can verify bot flows, validate outputs, and add assertions.
• However, experts say success hinges on robust policies, targeted pilot programs and modernized infrastructure.
• It then passes through a translation engine to pass a written text translation through to the agent desktop.

In the same way, your results will vary based on the hyperparameters of your chosen models (temperature, frequency penalty, etc.). And we cannot use the most powerful model for every use case, if this is an expensive model. MT-Bench evaluates an LLM’s capability in multi-turn dialogues by simulating real-life conversational scenarios.

Cleanse Your Data

Every minute your team spends on tasks you can automate – like data entry and information summarization – is money you can use elsewhere. Although much of the excitement about generative AI in real applications has happened recently, it’s been around for a while. The most recent trend of generative AI started in 2018 when Google released its Transformers paper.

This data depicts the promising future of AI in manufacturing and how it is the right time for businesses to invest in the technology to gain significant business results. Artificial intelligence in the manufacturing market is all set to unlock efficiency, innovation, and competitiveness in the modern manufacturing landscape. For instance, our client, a global manufacturer of heavy construction and mining equipment, faced challenges with a decentralized supply chain, resulting in increased transportation costs and manual data resolution. To address this, we developed a data-driven logistics and supply chain management system using AI-powered Robotic Process Automation (RPA) and analytics. The RPA bots automated manual processes, resolving errors and enhancing supply chain visibility by 60%, ultimately improving operational efficiency by 30%.

Content Summarization

Also, customers don’t like filling in surveys; they generally prefer low-effort experiences. The Conversation Booster by Nuance uses generative AI to combat this issue as users carry out self-service tasks within the bot. These may include making payments, scheduling appointments, or updating their personal information. It harnessed the LLM in such a way that if a virtual agent receives a question it hasn’t had training to handle, generative AI provides a fallback response. Another advantage of these auto-generated articles is that they’re in the same format, allowing agents to quickly comprehend and action them.

AI use cases are going to get even bigger in 2025 – Fast Company

AI use cases are going to get even bigger in 2025.

Posted: Wed, 25 Dec 2024 08:00:00 GMT [source]

In 45.5 percent of businesses, contact centers have received more GenAI investment than commerce, marketing, and sales. ML systems can convert raw IoT events into meaningful process conditions, which is key to analyzing and automating complex workflows. Whereas a sensor can signal an event, ML can determine what that event actually means. Learn how scaling gen AI in key areas drives change by helping your best minds build and deliver innovative new solutions. Learn how to choose the right approach in preparing datasets and employing foundation models.

Design principle No. 3: Rethink the customer data value exchange

Learning about the growing variety of generative AI use cases can help you understand its potential applications in different industries and fields. NVIDIA has introduced Generative AI microservices aimed at advancing drug discovery, medical technology (MedTech), and digital health. These microservices, available through the NVIDIA AI Enterprise 5.0 software platform, offer a wide range of capabilities, including advanced imaging, natural language processing, and digital biology generation. Generative AI allows enterprises to start with a standard LLM, also called a foundation model, which is trained on publicly available data.

• A deeper dive into each one can start to answer this question and help organizations better understand the great value that AI can bring to their business.
• It’s easy to imagine how this accelerated ability for drug development will optimize the work of researchers and, ultimately, how it could benefit patients in the years to come.
• Learn how Hewlett Packard Enterprise helps public sector entities transform how they deliver services to staff and citizens.
• AI can help governments look at historical weather and current environmental data to better predict potential issues such as floods, hurricanes or wildfires.
• The development of new products in the manufacturing industry has witnessed a significant transformation with the advent of AI.

Perhaps it should link your production management system, provide supply chain logistics, or track your inventory. That often goes hand-in-hand with Gen AI-boosted internal knowledge searches, which help customer service agents find the information they need, when they need it, reducing call times and increasing resolution rates. Organizations are using GenAI to innovate — whether that’s to create new products and services or to find new ways to differentiate themselves in the market, Wong said.

Let’s see how companies like Honeywell leverage generative AI to facilitate production efficiency. Contact center virtual assistants can evaluate requests for schedule changes by reviewing workforce management data and demand trends, helping supervisors make better approval decisions. Contact center virtual assistants can be valuable coaches and guides for team members, gathering extensive live data and using it to provide real-time training to every employee. 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.

Industrial settings are a significant area for ML and IoT use cases and a fast-growing domain for real-time applications. ML-driven process control is more flexible than traditional fixed programming of automated systems. Because ML algorithms can adapt over time, they help improve process control systems over time by learning from and adjusting to new conditions. ML-enabled IoT applications mix real-time and historical data to inform their recommendations and analyses. How that integration is done, and which specific use cases it supports, varies by vertical market segment.

Zebra Medical Vision employs Generative AI to analyze medical imaging data, such as X-rays, CT scans, and MRIs, to assist radiologists in detecting and diagnosing various diseases. Their algorithms can detect abnormalities in imaging studies and prioritize cases requiring urgent attention, enhancing the efficiency of radiology workflows. Utilizing patient data, Generative AI forecasts disease progression, facilitating early intervention and personalized treatment strategies. One of the prominent Generative AI use cases in healthcare is medical image construction. Generative AI reconstructs medical images to enhance resolution and clarity, aiding in accurate diagnosis and treatment planning. Generative AI expedites drug discovery by simulating molecular structures and predicting their efficacy, facilitating the development of innovative therapeutics.

In manufacturing, IoT sensor data collected over time can be combined with real-time data from production processes and logistics, such as manufacturing steps, transportation, and storage of parts and finished goods. This can improve efficiency in areas like parts delivery and traditional just-in-time manufacturing practices, reducing costs, vehicle miles traveled and carbon footprint. As we head into a new year shortly it’s likely virtual assistants become more intuitive, thanks to getting better trained and tuned over time.

Moreover, AI trends in the manufacturing sector are enhancing predictive quality assurance. By analyzing historical data and real-time sensor data, ML algorithms detect patterns and trends that may indicate potential quality issues. This enables manufacturers to proactively address potential defects and take corrective actions before they impact the final product quality. GE is one practical example of how artificial intelligence changes factory performance optimization. GE has integrated AI algorithms into its manufacturing processes to analyze massive volumes of data from sensors and historical records.

Shipping schedules can be unpredictable, with several factors affecting the time to get to the final destination, he says. A simple algorithm that looks at historical data isn’t enough to provide an accurate delivery date. “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. Some videoconferencing applications now generate transcriptions and summaries, as do standalone tools such as Otter.ai. The launch of ChatGPT in November 2022 set off a generative AI gold rush, with companies scrambling to adopt the technology and demonstrate innovation.

It’s easy to imagine how this accelerated ability for drug development will optimize the work of researchers and, ultimately, how it could benefit patients in the years to come. I think we’re witnessing a positive change, where great minds come together to use their knowledge and expertise to make healthcare more interconnected. To help you navigate this dynamic field, I discuss the top AI in life sciences trends and mention a few examples of successful implementations by researchers and clinicians.

25 Use Cases for Generative AI In Customer Service – CX Today

25 Use Cases for Generative AI In Customer Service.

Posted: Wed, 28 Aug 2024 07:00:00 GMT [source]