EDT&Partners unveils Lecture the first open-sourced, Generative AI framework designed for education

Top 9 generative AI use cases for business

Manufacturers can offer better stuff for less money, minimize waste, and offer bespoke solutions tailored to every customer’s unique needs while keeping innovation at the low cost of goods. When it comes to pro-active risk alerting, some companies noted a 5% decrease in churn and payment issues thanks to Gen AI tools that help to analyze chat logs and identify potential issues. Similar techniques can also be used to combat fraud, which is a major concern in many industries. Around 10% of companies noted that Gen AI tools have helped them to boost their quality control.

By pairing this with the Cognigy Playbooks reporting platform, service teams can verify bot flows, validate outputs, and add assertions. The tool bombards virtual agent applications with mock customer conversations to test how well the bot stands up to various inputs. The weblinks and contact center knowledge sources that the conversational AI platform integrates with inform the response – helping to automate more customer queries. Many CCaaS providers now offer the capability to automate quality scoring, giving insight into all contact center conversations. Nevertheless, transferring that knowledge into specific, measurable, and fair quality assurance (QA) scorecard criteria is easier said than done, not to mention time-consuming.

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. 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. Enterprise security departments generally obtain GenAI capabilities as part of their security software; very few have the resources to build their own AI models. Next, develop a robust knowledge base to ensure the virtual assistant provides accurate, efficient responses.

While traditional financial analysis relies heavily on manual interpretation, generative AI brings a new level of speed and insight to the process. AI tools can quickly analyze complex financial data sets and generate detailed narratives that highlight key trends, anomalies, and opportunities. One of the most immediate benefits of generative AI in finance is its ability to automate time-consuming routine tasks. Finance teams are finding that tools like Power Query in Excel can transform hours of manual data cleaning and invoice reconciliation into automated processes that take minutes.

Company Overview & History

This shows the capabilities of the Arm CPU to process generative AI workloads entirely on the device. Earlier this year, we demonstrated a chatbot demo deployed to be a virtual teaching assistant for science and coding that runs on mobile on the CPU. The success of the demo meant we started exploring other practical generative AI mobile use cases that run on the Arm CPU and could be used every day by the average smartphone user. This led to the creation of three new demos – group chat summarization, voice note summarization and a real-time voice assistant.

By providing this data, customers expect generative AI to help with the discovery and decision-making phases of their purchasing journey, specifically by finding products aligned with their timing, context, past purchases, and preferences. For example, if a retailer knows (from other transactions) that the shopper looking at a page of car seats is a new parent, it could highlight relevant comments from other new parents about a car seat’s quality or ease of installation. One of the main reasons why governments have embraced AI is that they believe it can help them improve efficiency and provide greater services to citizens. Cutting down manual work to collect and parse data, approve requests and answer citizen questions all have a knock-on effect of improving their lives. For example, AI can make collecting information for legal documents and processing and shipping out items, such as passports, quicker.

See how different industries can collaborate to improve global health outcomes with digital health solutions. AI-powered chat bots have grown in popularity, with an Inside Higher Ed survey this fall showing 50percent of chief technology officers are building their own chat bots and assistants. At its heart, the solution contains a wealth of anonymized contact center conversation data that NICE has pulled together and used to develop sector-specific benchmarks for many metrics. The Net Promoter Score (NPS) is a common customer experience metric, typically tracked in the contact center. From there, Sprinklr customers may harness the provider’s omnichannel capabilities to distribute these surveys, converge the data, and – again, using GenAI – analyze the feedback.

Implementing generative AI to gain a competitive edge can significantly benefit business leaders. This game-changing technology generates new data from existing patterns, enhances productivity, and reduces costs. Key applications include virtual assistants for improved customer interactions, intelligent search for precise data insights, and content summarization for efficient information processing. By tailoring LLMs to their specific needs, businesses can revolutionize operations and drive strategic advancements.

This enables manufacturers to optimize operations, minimize downtime, and maximize overall equipment effectiveness. AI for manufacturing is also revolutionizing warehouse management, enhancing efficiency with advanced automation and intelligent data insights. The advent of AI-powered manufacturing solutions and machine learning in manufacturing has transformed how warehouses operate, improving efficiency, accuracy, and cost savings. Medical data analysis is a cornerstone of modern healthcare, and generative AI has the potential to revolutionize this field. By analyzing large datasets, generative AI can identify patterns and trends that may not be apparent to human analysts, providing valuable insights that can improve patient care and outcomes.

Financial Services & Investing

By noting where the differences are the largest, one can identify where the opportunities lie for marketers and vendors. By comparing what generative AI use cases marketers are using and what vendors are offering, we get a clear picture of the opportunities for both marketers and vendors. Success lies in choosing the right use cases, and there is huge potential for both marketers and software vendors.

For instance, maybe it helps you design a new component that’s much more efficient, cheaper to produce, and longer-lasting than you are using now. Or it predicts when a machine will likely fail so you can fix it before it breaks down and you have expensive production stops. In short, you are giving the AI enough experience to make it an expert in manufacturing processes.

These may include processing onboarding documents, mechanizing data entry, or developing a customer profile. Contact centers benefit significantly from these advancements, achieving faster resolution times, enhanced customer satisfaction, and reduced operational costs. GenAI can scour conversation transcripts to score each customer interaction and evaluate the agent’s performance. While this is also possible through NLP, GenAI is augmenting these systems and also helping to surface performance improvement and agent recognition opportunities. Generative AI has ignited a spark of innovation across the customer experience space, triggering customer-facing teams to deploy various use cases. Put AI to work in your business with IBM’s industry-leading AI expertise and portfolio of solutions at your side.

Generative AI playtime may be over, as organizations cut down on experimentation and pivot toward achieving business value, with a focus on fewer, more targeted use cases. Get in touch to develop innovative apps infused with Generative AI solutions that enhance engagement and elevate user experiences. Features that help explain the decision-making process behind the generated outputs are valuable, particularly for applications with high stakes or regulatory requirements. The ability to choose and train a suitable generative AI model architecture (e.g., customizing a GAN or VAE) for the specific healthcare task is crucial. As stated above, Generative AI models have demonstrated significant diagnostic errors, particularly in pediatric diseases, raising concerns about patient safety and outcomes.

The contact center virtual assistant has evolved, supporting customer service teams in various new ways. As the technology develops, we’re likely to see it become more flexible and adaptable. TruthfulQA evaluates an LLM’s ability to generate truthful answers, addressing hallucinations in language models. It measures how accurately an LLM can respond, especially when training data is insufficient or low quality. This benchmark is useful for assessing accuracy and truthfulness, with the main benefit of focusing on factually correct answers.

Spotting and reporting side effects of newly-tested drugs has always been a challenge in traditional pharmacovigilance processes. Especially, if trials are run across numerous clinics or research centers across the globe. AI now makes this easier by collecting and analyzing patient data from numerous sources. While it’s one of the newer AI use cases in life sciences, several studies have already confirmed its efficacy

in drug safety research.

For good applications, it is important to not only test the model in isolation, but in orchestration. In production, we have to create the same aggregations and metrics as before, just with live users and a potentially larger amount of data. That is why we introduce the PEEL framework for performance evaluation of enterprise LLM applications, which gives us an end-to-end view. However, the impact of those benchmarks is rather abstract and only gives an indication of their performance in an enterprise use case.

It is advisable that before starting with the implementation of generative AI in manufacturing, you must understand the precise role of generative AI in manufacturing. GenAI comes in, simulates each production process, and determines the most efficient ways of doing things. For example, you’d have that backstage pass to every possible scenario before that signal. Generative AI has high-end operational capabilities, but the key differentiator is how is Generative AI used in manufacturing.

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. IT analyst Forrester listed gen AI for language and AI agents as two of its top 10 emerging technologies for 2024.

Fraud Detection and Risk Management

By unlocking a more personalized and cost-efficient employee experience, generative AI makes HR more human—not less. Explore how the City of Helsinki and IBM Consulting co-created faster, more flexible citizen experiences. The responsible use of AI is integral to any government overcoming the challenges the technology presents. Several types of AI technologies are deployed in government to power many different AI use cases.

This technology ensures consistent product standards and minimizes waste, leading to more efficient production processes and improved overall outcomes. Using artificial intelligence in order management entails optimizing and streamlining the entire order fulfillment process. AI examines past data, consumer preferences, and market trends using machine learning algorithms to estimate demand precisely. This makes it possible to process orders automatically, optimize inventories, and make dynamic pricing changes. Additionally, AI improves fraud detection, lowering the dangers connected to fraudulent orders.

Lastly, vendor invoice processing is crucial for enterprise procurement departments dealing with invoices at scale. The ability to automatically scan these invoices for information extraction is a non-trivial task due to thousands of variations in invoice structures. “We saw that in a microcosm, people trying to build [chatbots] themselves with teams that really weren’t dedicated to support and didn’t have any kind of AI background,” he says. We turned corporations almost into VCs,” funding IT projects as if they were startups. Too often, organizations have launched AI pilots without thinking about the hidden costs, says Courtney Schuyler, CEO and co-founder of SkyPhi Studios, a digital transformation advisory firm.

In other industries, generative AI enables voice or text chat interfaces for simpler interaction with products. Pilots have already delivered promising results in a variety of uses, meeting and exceeding this goal. In our survey, the most valuable use cases for generative AI all facilitated decision making. These applications could address the familiar pain point of online shoppers feeling overwhelmed by huge product assortments lacking personalized recommendations or curatorship. Traditional personalized recommendations don’t rely on generative AI, but there are ways to use generative AI capabilities to guide and inspire customers throughout their discovery process. For many customers, these recommendations are not just about getting to specific products immediately, since they may also enjoy the discovery process.

There is also the fear that the AI becomes too powerful or quickly evolves into superintelligence where machines start making decisions that benefit themselves and not humanity. While AGI and superintelligence are not expected to happen anytime soon, and might never happen at all, it demonstrates why governments must be closely involved in AI development and regulation. Governments that use AI can have more powerful predictive analytics that help them with important tasks such as external threat detection, health crises and financial issues like inflation.

This allows analysts to focus their expertise on validating and refining these insights rather than spending hours compiling basic observations. These deployments highlight how marketing teams are utilizing GenAI to automate much of the underbelly of their operations. Yet, these did not break the top three use cases for GenAI in marketing, leveraged by more than one-third of businesses already.

• However, generative AI in the manufacturing industry invades the playing field to flip this script.
• Some of the more popular generative AI tools for customer interaction and support include HubSpot, Dialpad Ai, and RingCX.
• 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.
• By tailoring LLMs to their specific needs, businesses can revolutionize operations and drive strategic advancements.
• 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.
• 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.

We can use a generative AI workflow to make the “find and write answer” activity more efficient. Yet, this activity is often not a single call to ChatGPT or another LLM but a collection of different tasks. In our example, the telco company has built a pipeline using the entAIngine process platform that consists of the following steps.

In fact, GenAI saves researchers and lawyers time by generating abstracts and analyzing decisions and cases from the vast pool of legal texts it’s trained on. Tax attorneys told Thomson Reuters they use GenAI for accounting, bookkeeping and tax research. Manually extracting daily transaction data from financial documents, such as bank statements or investment reports, can take anywhere from a few minutes to 10 hours, depending on the number of transactions. Annual reports from a single financial institution could contain over 1,000 transactions. GenAI-powered accounting tools, such as DocuAI, also improve financial reporting by producing detailed forecasts, simulating various financial scenarios and generating insightful reports. By analyzing successful customer conversation transcripts – across a particular intent – some GenAI applications can decipher how to best answer that query.

Again, the contact center must plug the solution into various knowledge sources for this to happen – as is the case across many other use cases – and an agent stays in the loop. As such, GenAI has made capabilities such as case summarization, sentiment tracking, and customer intent modeling much more accessible and cost-effective. Even with the best scheduling and workforce management (WFM) strategy, the contact center can be unpredictable. Sometimes, spikes in contact volume happen without warning, and business managers need to make quick decisions to handle an influx of requests.

Generative AI systems allow workers to get more done by automating processes that require workers to create. This comprehensive guide takes you on a deep dive into the multifaceted impact of generative AI on business, highlighting the potential benefits and pitfalls. We’ll shed light on the pros and cons of AI, unraveling the complexities and challenges of its application within the professional sphere. This is part of JLL’s broader AI-powered Dynamic Occupancy Management platform, which helps building tenants optimize their office space usage and adjust to hybrid work models.

Automated Medical Coding

After years of call and contact monitoring and CSAT/sentiment analysis, experienced team leaders and quality analysts understand what an excellent customer conversation looks like. These aim to enhance many facets of customer service, from workforce engagement management (WEM) to conversational AI. Global businesses are pumping funds into generative AI (GenAI) use cases for customer service.

Atomwise

is an preclinical pharma research company which uses AI to speed up new drug discovery. According to their website, AtomNet®, a neural network-based technology they developed has helped identify more “undruggable targets » than any other drug discovery platform powered by AI. Pharmaceutical and biotechnology companies can use sensors in their manufacturing facilities to gather data about the manufacturing process, including the state of the facility, machine performance, process progress, etc. The speed with which AI in life sciences can examine large data volumes can cut down the drug discovery and development time

to months, rather than years, and I think that is truly revolutionary.

To realize the full impact of AI in manufacturing, you will need the support of expert artificial intelligence development services. Appinventiv’s expertise in developing cutting-edge AI and ML products specifically tailored for manufacturing businesses has positioned the company as a leader in the industry. Demand prediction is one of the major AI manufacturing use cases that is transforming the industry. The use of artificial intelligence in manufacturing for demand prediction brings several benefits, including allowing businesses to make data-driven decisions by analyzing historical sales data, market trends, and external factors. This helps them anticipate fluctuations in demand and adjust their production accordingly, reducing the risk of stockouts or excess inventory. Walmart, the globally renowned retail giant, heavily uses artificial intelligence in supply chain management to improve productivity and customer satisfaction.

This portfoio of AI products was used to create a Catch Me Up feature that was available on the Wimbledon app. The feature created summaries for every single player that detailed what happened during their matches and upcoming matchups. A prime example is the partnership between Novo Nordisk Pharmaceuticals, a global leader in diabetes treatment and a South-Korean startup, Kakao Healthcare

. They aim to offer advanced digital health services that will help chronically ill people better manage their conditions.

Artificial General Intelligence: EY on the Short-Term Future – AI Business

Artificial General Intelligence: EY on the Short-Term Future.

Posted: Wed, 22 Jan 2025 16:25:39 GMT [source]

Generative AI use cases in the customer support industry includes AI-enhanced customer interactions, sentiment analysis, and AI-driven information access. GenAI technologies enable more intelligent, personalized, and faster services, resulting in remarkable refinements in how businesses engage and assist their customers. Some of the more popular generative AI tools for customer interaction and support include HubSpot, Dialpad Ai, and RingCX. Generative AI use cases are expanding rapidly as business across industries embrace the dynamic technology for creating new content, data, or solutions based on input prompts. GenAI allows organizations to automate tasks, uncover insights, and improve operations, ultimately boosting efficiency and sparking innovation.

The survey results highlight the need for organizations to both embrace AI’s potential and recognize the practical challenges for implementation. Once these challenges are overcome, however, the next phase of AI adoption will likely focus on more sophisticated applications that continue to transform IT operations. Finally, contrary to reported concerns that AI will replace workers, 63% of the responding service and operations stakeholders believe that genAI will improve jobs, with only 1% expressing concerns about job displacement. This suggests that IT professionals see AI as an enhancing force rather than as a replacement for human expertise. Yet, optimism remains high, with 98% of the respondents expecting to reap future benefits from genAI, such as increased productivity, reduced maintenance needs, and lower costs.

Furthermore, the business optimizes logistics with AI-powered routing algorithms, enabling faster and more economical delivery. In the fiercely competitive retail sector, Walmart’s utilization of AI into supply chain operations exemplifies how cutting-edge technologies enhance decision-making, responsiveness, and overall supply chain resilience. As per a study by PwC, Reinforcement Learning (a subset of AI) is capable of optimizing electronic device production by dynamically adjusting machine parameters in smart manufacturing.