Four generative AI use cases for businesses

Generative AI: What is it, and how can it impact business?

It does this by extracting patterns and structures from vast amounts of customer and market data, giving banks deep insights into underlying factors such as potential risks or fraud and collecting customer information for loan origination. GenAI also enables banks to offer personalized banking and marketing experiences tailored to customer interests and needs. A GenAI-powered virtual assistant can understand customer intent and – typically across digital channels – suggest a reply by scouring relevant knowledge and data sources. Tom Nolle is founder and principal analyst at Andover Intel, a consulting and analysis firm that looks at evolving technologies and applications first from the perspective of the buyer and the buyer’s needs.

• These Leaders look beyond the AI hype and seek out ways to interject AI in the most pragmatic way possible.
• In a new video interview for FedScoop, Department of Homeland Security Deputy CTO for AI & Emerging Technology Chris Kraft shared insights into DHS’s pioneering efforts with generative AI.
• Among others, researchers can use Trial Pathfinder to evaluate drug efficacy and survival ratio, i.e., survival rates for patients who were taking the tested drug vs those who were given a placebo.
• This can lead to certain groups receiving less effective or inappropriate treatment, and their overall access to healthcare might be compromised – some groups will be favored while others will be discriminated against.

Through advanced data analytics and machine learning, Generative AI can enhance diagnostic accuracy, personalize treatment plans, and optimize resource allocation across healthcare systems. Moreover, its capacity to analyze vast amounts of medical data expedites diagnosis, facilitates drug discovery, and enables the development of predictive models for disease prevention. By using generative AI for personalization, businesses can improve conversion rates by delivering the right message at the right time. They can also build customer loyalty through tailored experiences and gain actionable insights by analyzing vast amounts of customer data. Generative AI models can help you analyze the market, brainstorm solutions to new problems, and offer something great to your customers and stakeholders. AI models like GPT-3 and GPT-4 can surface new ideas you may not have thought of otherwise, including new solutions and ideas that can give you an edge.

High Upfront Costs for Implementation

Advancing their capability of understanding and responding to complex human emotions and contextual cues. As they integrate with more external applications, these assistants provide a cohesive, personalized experience that simplifies daily tasks and enhances productivity. The future of customer experience is likely going to lean into hyper-personalized interactions, by using advanced analytics and machine learning to anticipate customer needs in real-time. The evolution of these technologies will drive businesses to create seamless, engaging experiences that enhance customer loyalty and satisfaction across all touchpoints. In retail, as in other industries, generative AI promises to change the customer experience. Retailers are deploying AI-enhanced tools such as summaries of product reviews, chatbots, and shopping assistants in an effort to facilitate purchase decisions, reduce friction, and increase conversion.

This can boost customer services and sales efficiency by creating more comprehensive and searchable records. Generative AI is expected to bring about big changes in many business functions in the years to come, though the technology is already here and already delivering tangible results. While some use cases still are three to five years away still, customer service and sales desk operations are already being boosted by Generative AI. GenAI is also a building component for agentic AI systems to support even more automated decision-making. Agentic AI systems can act autonomously to solve multistep problems in real time, determine the right actions to take and then take those actions to accomplish the desired outcome.

The governance of generative AI solutions is very different from normal application and management (ADM) practices. Understanding the context of data and ingesting it for Generative AI is crucial; therefore response monitoring, data governance and data management play a major role in life cycle management. Security and responsibility must be built-in to the solutions, without imposing unnecessary restrictions that would hinder use or development. According to Deloitte research, 92% of U.S. developers are already using these AI coding tools, with 70% of developers citing benefits such as better overall quality, faster production time and quicker resolution. For organizations to stay relevant, they need to upskill, reskill and continually improve employee performance.

Similar use cases are likely to become more common as businesses begin to adopt these AI solutions into IT operations. The tech sector of a business is only going to grow in importance as time goes on and more advanced technology continues to push IT teams and the work they do on an every day basis. Deploying AI is a transformative journey that aims for significant productivity growth, but involves addressing challenges that span technological integration, human adaptation in ways of working, and reimagined business processes. Companies are deploying generative AI in product and feature development to create simpler and more user-friendly products and interfaces, and to deliver greater customization and personalization. For example, in healthcare, AI can quickly analyze patient data and offer personalized care plans.

Generative AI for legal professionals: Its growing potential and top use cases – Thomson Reuters

Generative AI for legal professionals: Its growing potential and top use cases.

Posted: Mon, 20 May 2024 07:00:00 GMT [source]

Generative AI models are trained on massive datasets, often containing millions of works. While individual pieces may contribute minimally, the sheer scale of usage complicates the argument for fair use. Fair use traditionally applies to specific, limited uses—not wholesale ingestion of copyrighted content on a global scale. Over the past two years, use cases for generative artificial intelligence in higher education have grown, offering opportunities for experiential learning, building course materials, academic advising and research to support students.

Conversational Knowledge Curation

That is when Vi turned to IBM to execute the massive digital transformation, which included consolidating and integrating projects seamlessly. The teams worked together to consolidate IT systems and to determine which enterprise applications might best support the newly merged organization. In addition, IBM worked with Vi on its network domains and delivered its first major production milestone for core network functions on its open universal hybrid cloud, which is powered by IBM and Red Hat. With the platform, the organization’s IT and network applications can run on a common cloud architecture. 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. According to Deloitte

, more than 90% of biopharma and medtech leaders realize the impact of genAI on the industry.

Use separate datasets not used in training to assess accuracy, reliability, and generalizability. Ensure the data is anonymized and adheres to healthcare data privacy regulations and compliances. The application must prioritize robust security measures to safeguard sensitive patient information throughout its lifecycle, including storage, processing, and generation of outputs. The application should effortlessly pull data from various healthcare sources, such as EHRs and imaging databases, for model training and generation tasks. Generative AI struggles with medical administrative tasks, such as summarizing patient health records, leading to suboptimal performance in healthcare workflows.

Generative AI — a subset of deep learning, and itself a branch of machine learning — offers capabilities that expand the potential of IoT systems. Consequently, utilities involved in the distribution or handling of power, gas, water and wastewater are a fast-growing application for ML and IoT. These applications combine real-time missions, such as early detection of problems, with historical analysis to inform capacity planning, resource allocation and environmental impact management. In the context of IoT, the objective of training is to transform raw sensor data into meaningful process conditions.

CSO Executive Sessions: Standard Chartered’s Alvaro Garrido on cybersecurity in the financial services industry

As organizations scale their use of copilots, this role becomes a linchpin, ensuring virtual assistants remain relevant, compliant, and consistently aligned with business objectives. Beyond real-time supervision and reporting, supervisor copilots enhance knowledge bases, facilitate AI training and feedback loops, and support compliance monitoring. In the test editor, we work on an evaluation scenario definition (“evaluate how good our customer support answering RAG is”) and we define in this scenario different test cases. We can try 50 or 100 different instances of test cases and evaluate and aggregate them. For example, if we evaluate our customer support answering, we can define 100 different customer support requests, define our expected outcome and then execute them and analyze how good the answers were.

Generative AI in finance drives increasingly automated and intelligent capabilities, but the transition doesn’t have to be overwhelming. By focusing on clear objectives and building AI capabilities gradually, you can optimize your workflows while maintaining control and accuracy. These include utilizing the tech to update sales materials, recommend up/cross-sell opportunities, and make in-call coaching suggestions. Some GenAI applications can assess a conversation, summarize it, and then send it to the CRM. Also, they may help to tag the intent and automate a post-contact follow-up to shave more seconds off every customer interaction.

Generative AI is already improving federal agency operations by streamlining processes, enhancing decision-making and improving service delivery. However, experts say success hinges on robust policies, targeted pilot programs and modernized infrastructure. Ng said that he is seeing the use of generative AI models in the area of healthcare documentation that is the basis for negotiation and back-and-forth among doctors, hospitals and insurance companies.

Automating Social Media Management Processes (39.9 percent)

A virtual assistant available to managers in their voice of the customer (VoC) platform may be able to ingest feedback from conversation transcripts and surveys to generate a “trend overview”. Even the most highly trained and professional contact center agents can struggle with difficult conversations. Sometimes, a supervisor stepping in and offering support is the best way to stop an issue from escalating or reduce the risks of customer churning. Yet, during certain conversations, mid-discussion tasks can take up a lot of time, like entering details into a form, copying and pasting information, or initiating processes like refunding customers. While this use case is usually reserved for digital channels, some contact center virtual assistant providers are taking steps to translate voice calls, thanks to the GenAI advancements. This ensures agents can deliver the same quality of service to customers from different locations.

• By unlocking a more personalized and cost-efficient employee experience, generative AI makes HR more human—not less.
• 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.
• For example, the GenAI-powered tool BlueDot alerts public bodies to outbreaks or potential threats from new or known pathogens, such as influenza and dengue.
• In a study published in Nature Medicine, a group of over 35 scholars revealed that they’ve developed a new pancreatic cancer detection technology called PANDA

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• One of the most powerful capabilities of generative AI in healthcare is offering tailored recommendations and individual support.

In this blog, we will delve into various manufacturing AI use cases and examples showing how the merger of artificial intelligence and manufacturing improves efficiency and ushers in an era of smart manufacturing. We will also study the impact of AI in the manufacturing industry and understand how it empowers businesses to scale. As businesses and policymakers navigate the moving target of regulating a technology with capabilities are still taking shape, the need for disciplined action has grown. Concerns around regulatory compliance have emerged as the top barrier holding organisations back from developing and deploying GenAI tools and applications – increasing by 10 percentage points from the Wave 1 survey (28%) to Wave 4 (38%). Sixty-nine percent of respondents say fully implementing a governance strategy will take more than a year to resolve, underscoring the need for perseverance and a strategic approach to setting the correct governance foundation. To act decisively in the face of uncertainty, organisations should focus on market sensing and scenario planning, with an eye on uncovering potential blind spots in their strategies and make more informed decisions today.

2 Evaluation of Generative AI Applications in the Development Lifecycle

They might use GenAI to identify such opportunities, or they might use GenAI as the basis for their innovations, products and services. For instance, it might find that customers regularly ask about return policies and that a return policy document isn’t available for team members. A virtual assistant may then create this necessary content and even translate it into different versions for agents and consumers.

The biotech company leverages genAI to find molecules that could be used in new drugs and to forecast their clinical performance. If any potential adverse effects are detected, then an alert can be triggered to the drug development team. They can then act immediately, i.e., investigate the issue and prevent any health- or life-threatening incidents. Not only does this improve drug trial safety, but can also lead to financial savings. The life sciences industry operates under stringent controls and regulations, so another AI use case in life sciences, which I think is worth mentioning is making the manufacturing process more efficient.

Finally, security teams must guard against unintended biases and hallucinations when using AI of any kind and be cognizant of the unknowns that come with vendor-supplied AI. «Vendors work in their own black box environment and we don’t always have transparency into how the model was trained,» Frantz said. Moreover, those teams must ensure they don’t violate any data privacy regulations or data security laws during that training, she added. For example, cybersecurity professionals can use GenAI to review code more quickly and precisely than manual efforts or other tools can, boosting workers’ efficiency and the organization’s security posture.

Once we designed a set of test cases, we can execute their scenarios with the right variables using the existing orchestration engine and evaluate them. On the tests screen, the user can create new evaluation scenarios or edit existing ones. When creating a new evaluation scenario, the orchestration (an entAIngine process template) and a set of metrics must be defined. We assume we have a customer support scenario where we need to retrieve data with RAG to answer a question in the first step and then formulate an answer email in the second step. Then, we use the new module to name the test, define / select a process template and pick and evaluator that will create a score for every individual test case.

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The massive retail chain uses machine learning algorithms to forecast customer demand, evaluate previous sales data, and manage inventory levels. Using AI-driven demand forecasting, Walmart guarantees product availability, minimizes stockouts, and saves money on surplus inventory. When used in knowledge bases, generative AI can retrieve accurate and relevant data rapidly, giving human agents the information they need, when they need it. This functionality is also useful in self-service portals, providing customers immediate access to guides, troubleshooting steps, and FAQs. Through natural language processing (NLP), generative AI understands the context of customer queries and delivers precise solutions.

The overall increased adoption of artificial intelligence and generative AI — a subset of AI that focuses on creative content generation — has also led to an increase in proven use cases for ESG and climate purposes. While sustainability reporting remains the most commonly discussed use case, companies are utilizing the technology for a variety of decarbonization, waste management and energy management purposes, among others. Large tech companies invested heavily in new data center infrastructure in 2024 to keep up with the expected demand. Cisco Chief Sustainability Officer Mary de Wysocki told ESG Dive utilizing AI’s full capabilities requires an “understanding of the energy capability,” as well as issues like privacy, transparency and accountability.

Since artificial intelligence software can describe images so well, it’s hardly surprising that it can also suggest fitting keywords to boost discoverability. You can then use these terms not only in the description, but also in the image “alt text”. This will help potential buyers find your property while running a visual search on Google. Generative AI tools come with computer vision capabilities, i.e., they can analyze photos of the propertyand create a description based on what it ‘sees’.

That capability sits at the core of many new customer service use cases for the technology – such as auto-generating customer replies. However, the ability of a large language model (LLM) – like ChatGPT – to extract context and entities from customer conversations on the fly has removed the requirement to spend hundreds of hours engineering those NLP solutions. Well, many tangible use cases were already in the space before the advent of the tech. Just like supervisors, contact center managers can benefit greatly from access to an intuitive contact center virtual assistant. Some solutions, like the AWS Manager Assist tool, can even generate suggestions, like telling supervisors when an agent could have offered a customer a discount or expressed more empathy.

Generative AI uses machine learning models like natural language processing (NLP) tools to understand, interpret, and manipulate human language just like we do. Using AI-powered processing tools, businesses can easily access and deploy data by translating, proofreading, automating content creation, extracting and analyzing data, and personalizing documents to individual or audience preferences. Contact center virtual assistants can leverage large language model (LLM) technology can process huge volumes of information, converting countless reviews, testimonials, and other feedback forms into concise takeaways. This is extremely useful for contact center managers who need to identify “trends” in customer feedback to help them make better decisions to improve service. One of the best examples of AI-powered predictive maintenance in manufacturing is applying digital twin technology in the Ford factory. Every twin deals with a distinct production area, from concept to build to operation.

By participating in AI-powered training and treatment simulations, healthcare professionals can practice new skills and gain access to knowledge in an interactive, engaging setting. These technologies are often used with VR/AR headsets to further mimic real-life experiences. But imagine if we could use AI in healthcare to represent every single cell in our bodies, i.e., a virtual cell that mimics human cells. Scientists could use such a simulator to verify how our cells react to various factors such as infections, diseases, or different drugs. This would make patient diagnosis, treatment, and new drug discovery much faster, safer, and more efficient. That’s exactly what Priscilla Chan and Mark Zuckerberg are working on – a virtual cell modeling system

, powered by AI.

Derivatives of GenAI include chatbots, high-quality content, automated summarization, intelligent recommendation engines, virtual tutors and AI-powered creativity tools. Healthcare is among the fastest-growing verticals for ML-enabled IoT, including both real-time and non-real-time applications — although the latter has yielded more use cases so far. For example, ML analysis of broad medical records, combined with patient-specific data and real-time patient vital signs obtained via IoT, can alert the patient care team to trends that require intervention.