Customer service and sales are top current use cases for Generative AI

Five real-world generative AI use cases

By keeping track of the text segments that are retrieved, we can very precisely show the source of the retrieved text chunk and use it as context in a call to a large language model. The illustration shows the start of a simple business that a telecommunications company’s customer support agent must go through. Every time a new customer support request comes in, the customer support agent has to give it a priority-level. When the work items on their list come to the point that the request has priority, the customer support agents must find the correct answer and write an answer email.

From predictive maintenance to seamless supply chain management, our bespoke services not only cut downtime but also ensure a level of quality control that empowers you to stay ahead in the manufacturing game. Contact center virtual assistants can identify when conversations are beginning to go downhill, identifying negative customer sentiment or specific keywords in real time. Everything in a company can be a business process, such as customer support, software development, and operations processes. Generative AI can improve our business processes by making them faster and more efficient, reducing wait time and improving the outcome quality of our processes.

Enhanced Medical Imaging Analysis

The principle for collecting feedback in production is analogous to the scenario approach. If the user has larger degrees of freedom of interaction, we might need to create new scenarios that we did not anticipate during the building phase. WinoGrande tests an LLM’s commonsense reasoning through pronoun resolution problems based on the Winograd Schema Challenge. This benchmark is beneficial for resolving ambiguities in pronoun references, featuring a large dataset and reduced bias. At Appinventiv, we successfully assisted Edamama, an eCommerce platform, in implementing tailored AI-driven recommendations. By offering personalized suggestions to mothers based on their child’s gender and age, Edamama secured an impressive $20 million in funding.

Back-office operations are particularly well suited for generative AI improvements, given the vast number of routine processes that are comparatively easy to automate. In the finance function, for example, generative AI can improve the efficiency of drafting internal audit reports, preparing documentation for tax audits, and running custom financial analyses. The market for AI products and services could reach between $780 billion and $990 billion by 2027.

Generative AI is being embedded into security tools at a furious pace as CISOs adopt the technology internally to automate manual processes and improve productivity. Automation and improved preventive maintenance eliminate labor-intensive tasks and enable more competitive pricing for outsourcing services. Approximately 28 percent of enterprises expect to train large language models (LLMs) in private clouds or on-promise. Additionally, AI assistants support code generation and bug fixing, reducing manual efforts and improving overall code quality. The GenAI use case with the most financial investment is customer service chatbots with 53 percent of enterprises saying it’s their top GenAI priority, while the most common GenAI use case is automated IT testing.

Customer support

Yet, sometimes, there is no knowledge article for the solution to leverage as the basis of its response. Elsewhere, a Japanese telecoms provider is trialing a similar software that modifies the tone of irate customers. Unfortunately, there are seemingly no purpose-built solutions for contact centers quite yet. Still, Google has pledged to make such a feature available on its Google Contact Center AI Platform soon.

Because they leverage speech-to-text to create a transcript from the customer’s audio. It then passes through a translation engine to pass a written text translation through to the agent desktop. Generative AI unlocks several chances to turn insight into action – including insights that conversational intelligence tools uncover. Alongside this, the solution provides a rationale for the automated answer in case quality analysts, supervisors, or coaches wish to delve deeper or an agent wants to challenge it.

AIOps enables advanced services like real-time data analysis and predictive analytics, enhancing the provider’s service quality. Coding assistance was one of the first use cases for generative AI, and coding assistants have been widely adopted by developers across enterprises. Task-specific variants of Llama (Code Llama) and Gemma (CodeGemma) are excellent alternatives to large language models like GPT-4 for this use case. As more companies adopt language models to automate customer service interactions, it is extremely important to ensure that no toxic content finds its way into the models’ responses. One of the key concerns for organizations is the exposure of personally identifiable information (PII) from their data when used for training or asking questions to an LLM.

Or they can switch up scheduling strategies in real-time to minimize the risks of understaffing or overstaffing. Each of the applications is a set of processes that define workflows in a no-code interface. Processes consist of input templates (variables), RAG components, calls to LLMs, TTS, Image and Audio modules, integration to documents and OCR. This functionality is, at the moment, already completely implemented in the entAIngine platform and can be used as SaaS or is 100% deployed on-premise. A. Using AI for manufacturing operations significantly enhances product quality and reduces defects by utilizing advanced data analysis, anomaly detection, and predictive maintenance techniques.

How Artificial Intelligence is Revolutionizing the Manufacturing Space – Use Cases and Examples

A great example of AI in cybersecurity investment is United Family Healthcare and IBM’s security solution IBMQRadar® SIEM. The company, which operates more than 10 hospitals in seven cities across China and beyond, needed a security operations center (SOC) platform to store and manage security incidents and generate reports of noncompliance. An IBM Institute for Business Value research report found 47% of executives worry that implementing generative AI in operations might result in new types of attacks aimed at their AI models, data or services. The majority are fearful that generative AI will make a security breach likely in their organization within the next three years. Generative AI tools are fortifying business defense through automated security processes and analyzing vast amounts of data faster than ever before.

• In certain zero-sum scenarios, governments that excel at AI might put other countries at a disadvantage.
• Sentiment analysis is becoming sophisticated, aiding companies as they look for ways to learn more about customers and what drives loyalty and retention rates.
• Or, in the case of cross-organizational initiatives, even from multiple collaborating entities.
• Its main benefit is comprehensive reasoning assessment, but it’s limited to scientific questions.
• While AI can assist with healthcare tasks, ultimate responsibility for patient care and decision-making lies with healthcare professionals, necessitating physician oversight.

By leveraging AI-based analytics manufacturing software can, speed up time to market,  optimize semiconductor layouts, cut down expenses, and increase yields. This application demonstrates how AI supports data-driven decision-making and innovation in product development processes in the semiconductor manufacturing industry. A digital twin is a virtual replica of a physical asset that captures real-time data and simulates its behavior in a virtual environment. By connecting the digital twin with sensor data from the equipment, AI for the manufacturing industry can analyze patterns, identify anomalies, and predict potential failures.

One challenge with smaller models is that they tend to be less accurate than their larger counterparts. To harness the strengths of smaller models while mitigating their weaknesses, enterprises are looking at domain-specific small models, which must be accurate only in the specialization and use cases they support. This domain specialization can be enabled by taking a pre-trained small language model and fine-tuning it with domain-specific data or using prompt engineering for additional performance gains.

While productivity is one of the most recognized benefits of AI across many industries, for the life science sector, it’s just the tip of the iceberg. But IDC also stresses that enterprises still need to proceed with caution when deploying gen AI-powered cybersecurity tools. On average, AIOps improvements lead to an estimated 28 percent to 50 percent increase in efficiency, translating to substantial cost savings and more reliable infrastructure performance, according to ISG data.

They can also consider using GenAI in leasing, ESG reporting, capital planning, and risk identification. Some people need more persuading to decide whether they see themselves living in an apartment/home or not. A real estate agent can use GenAI tools to create property visualizations – they might select modern furniture, cherrywood floors, etc. Seeing how a cold, empty apartment turns into a cozy home can speed up the decision-making process. Smaller companies will use it to lower waste, streamline operations, and improve production efficiency, thus making it less of a discriminator in an intensely competitive manufacturing environment. A. The future for the Generative AI market in manufacturing across geographies is bright since it is expanding further with a projected CAGR of 41% from 2023 to 2032.

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A September 2024 report from Enterprise Strategy Group found that 35% of respondents cited content creation as a GenAI benefit. These GenAI-enabled improvements save money and increase productivity; they can also boost environmental sustainability, particularly if the GenAI tool is prompted to consider sustainability as part of its analysis. Medical experts can now use generative AI to streamline their review of patient notes to understand patient needs faster and enhance the quality of care. At NYU Langone Health, researchers are developing an LLM trained on a decade of patient records. This isn’t limited to summarizing; it’s about predicting a patient’s risk of readmission within 30 days and other health outcomes. The fair use doctrine was designed for specific, limited scenarios—not for the large-scale, automated consumption of copyrighted material by generative AI.

Integration with Healthcare Systems

People rely on intelligent search every single day, thanks to LLMs trained on internet datasets. Enterprises have tons of proprietary data in private documents and platforms like Snowflake Data Cloud or Oracle Cloud ERP, crucial for business operations. Internally, these assistants complement and even empower employees by automating tasks and providing insights, which frees up time for more strategic work. Externally, they improve customer interactions by quickly understanding and responding to queries through simple conversational prompts.

This is largely thanks to the computing capabilities of today’s AI-enabled flagship smartphones and the large language models (LLMs) behind generative AI becoming smaller and more efficient. And these trends will continue to evolve, with generative AI set to be a part of every single mobile application in the near future. Early adopting organizations have already incorporated gen AI to automate and accelerate security workflows and improve incident response. Security vendors are also increasingly introducing gen AI-powered toolsthat boost productivity for security analysts and offer a multitude of practical applications.

AI in the manufacturing industry plays a key role in improving productivity, efficiency, and decision-making processes. AI-driven predictive maintenance is used in production to optimize maintenance schedules and minimize downtime by analyzing equipment data to anticipate possible faults. Google is a key player in GenAI, driven by its research through DeepMind and Google Brain.

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Just like in the real world, think of different agents that take different roles and collaborate with each other. The real power lies in a composite AI setup – a hybrid model where both approaches work together. Mapping use cases carefully ensures the AI can flex where needed while keeping high-efficiency automation where it fits best.

Government agencies must establish their own safety protocols to protect their citizens’ human rights and minimize any harm from AI to its citizens. AI can improve government operations by providing policymakers with more information and the ability to query generative AI to understand potential strategies. Governments can use AI models to decide budgets quicker and allocate the funds to the agencies and nonprofits that depend on them. Many federal governments are the largest purchasers of goods and services within their borders, and many have specific rules in place that dictate where and from whom they can purchase those items. AI procurement can collect information about potential suppliers and help governments select the right organization based on their stated criteria.

However, its rise has sparked significant debates around copyright law, particularly regarding the concept of fair use. “As you look at your long-term budget cycle, start investing in accelerated infrastructure so that you’ll be able to support the AI workloads that your customers and your employees are going to expect,” he says. Since 1982, RCR Wireless News has been providing wireless and mobile industry news, insights, and analysis to mobile and wireless industry professionals, decision makers, policy makers, analysts and investors.