The Extraordinary Ubiquity Of Generative AI And How Major Companies Are Using It

DME’s presence in the Middle East and Cyprus is established through its affiliated independent legal entities, which are licensed to operate and to provide services under the applicable laws and regulations of the relevant country. Such a valuation is high, but it’s far from bubble levels, indicating AMD could achieve a $1 trillion market cap organically. If AMD can meet part of this massive demand for AI chips, the semiconductor stock’s market cap could reach the $1 trillion level sooner rather than later. Currently, the data center and AI segment makes up around $6.5 billion of AMD’s revenue, about 29%. Additionally, AMD is prominent in the client, gaming, and embedded segments, and combined, they made up over $16 billion of AMD’s nearly $23 billion in revenue in 2023. Hence, these segments will contribute to the company’s growth, though probably at a slower pace than AI.

This situation may arise in specialized sectors or in working with unique data sets that are significantly different from the data used to train existing foundation models, as this pharmaceutical example demonstrates. You can foun additiona information about ai customer service and artificial intelligence and NLP. Training a foundation model from scratch presents substantial technical, engineering, and resource challenges. The additional return on investment from using a higher-performing model should outweigh the financial and human capital costs. This company’s customer support representatives handle hundreds of inbound inquiries a day.

Adapting existing open-source or paid models is cost effective—in a 2022 experiment, Snorkel AI found that it cost between $1,915 and $7,418 to fine-tune a LLM model to complete a complex legal classification. Such an application could save hours of a lawyer’s time, which can cost up to $500 per hour. Business leaders should focus on building and maintaining a balanced set of alliances. A company’s acquisitions and alliances strategy should continue to concentrate on building an ecosystem of partners tuned to different contexts and addressing what generative AI requires at all levels of the tech stack, while being careful to prevent vendor lock-in.

• For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design.
• In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development.
• The sudden rise of gen AI has brought the dream of the AI-native telco significantly closer to becoming a reality.
• Bear in mind too that a foundation model can underpin multiple use cases across an organization, so board members will want to ask the appointed generative AI leader to ensure that the organization takes a coordinated approach.
• BCG delivers solutions through leading-edge management consulting, technology and design, and corporate and digital ventures.
• Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data.

The MI300A combines the CPU and GPU in one unit, while its MI300X chip is the most advanced generative AI accelerator, according to the company. One European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert by synthesizing and extracting from long documents with unstructured information. The model answers complex questions based on a prompt, identifying the source of each answer and extracting information from pictures and tables.

Productivity improvements are often conflated with reduction in overall staff, and AI has already stoked concern among employees; many college graduates believe AI will make their job irrelevant in a few years. Generative AI can summarize documents in a matter of seconds with impressive accuracy, for example, whereas a researcher might spend hours on the task (at an estimated $30 to $50 per hour). Experimentation and trial and error are integral parts of adopting new technologies.

Apps keep proliferating to address specific use cases

While chatbots like ChatGPT gain attention, generative AI extends its capabilities to handle images, video, audio, and code. Generative AI will significantly alter job roles, leading to a need for extensive reskilling of employees. This change will involve decomposing current jobs into tasks that can be automated, assisted, or entirely reimagined for a future of human-machine collaboration​​​​. AI’s strength is taking large volumes of information, picking out important points, correlating them and helping employees glean new knowledge.

As AI evolves and becomes more powerful, it is important that thoughtful and judicious regulations are created to ensure the safety of future AI models. Generative AI is helping to democratize AI by putting it within the reach of large and small businesses. At the same time, pre-built modules and cloud services are lowering barriers to entry. Rather than using generative AI to enhance existing products, HPE GreenLake for LLM is an on-demand, multi-tenant AI cloud service that allows customers to train, tune and deploy Large Language Models (LLMs). The initiative also includes a set of solutions, a library of models, and full-stack solutions using Nvidia H100 Tensor Core GPUs integrated into Dell PowerEdge platforms. These come with high-performance Nvidia Networking, Nvidia AI Enterprise software and Nvidia Base Command Manager.

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Companies benefit by implementing the same model across diverse use cases, fostering faster application deployment. However, challenges like hallucination (providing plausible but false answers) and the lack of inherent suitability for all applications require cautious integration and ongoing research to address limitations. McKinsey has published an easy-to-read primer titled “What Every CEO Should Know About Generative AI,” which is freely accessible on the company’s website.

Hundreds of companies are now using generative AI to differentiate and add value to products. Generative AI models trained on biased data can perpetuate and amplify existing biases, resulting in discriminatory or unfair outcomes. There is a risk of inadvertently violating licensing and intellectual property rights when using off-the-shelf generative AI tools if the generated code is not compliant with regulations and agreements. However, we encourage you to read the complete article to gain a comprehensive understanding of generative AI and its implications for CEOs. Let’s dive into the highlights and discover the transformative potential of generative AI. Traditional analytics models have one major problem – they often inherit and incorporate the preconceived notions and biases of their creators.

The article delves into four industry examples of generative AI applications, showcasing varied resource needs and transformative potential. As the technology advances, integrating generative AI into workflows becomes more feasible, automating tasks and executing specific actions within enterprise settings. CEOs face the decision of whether to embrace generative AI now or proceed cautiously through experimentation.

The widespread adoption of user-friendly ChatGPT, reaching 100 million users in two months, makes AI accessible to a broad audience. Unlike traditional AI, generative AI’s versatility allows it to handle diverse tasks, albeit with some current accuracy challenges, emphasizing the need for careful risk management. Implementing generative AI in business operations necessitates robust governance frameworks. Companies must build controls to assess risks at the design stage and ensure the responsible use of AI throughout their business processes.

—Generative AI was selected as a “top priority” by 51% of respondents, and 30% said it would have the “greatest impact” on their business. Users may have concerns about AI-generated content, and building trust, transparency, and addressing resistance or scepticism is crucial for successful adoption. Welcome to our summary of the article “What Every CEO Should Know About Generative AI” written by McKinsey & Company. In this concise overview, we’ve distilled the key insights from the original 17-page article, saving you valuable time. Instead of spending approximately 45 minutes reading the full text, we’ve condensed the most important points into a five-minute read.

Software engineering, the other big value driver for many industries, could get much more efficient

Generative AI presents a transformative opportunity for businesses across all sectors. By understanding and strategically implementing these technologies, companies can revolutionize their operations, innovate in product and service offerings, and redefine their workforce for the future. Generative AI, a sophisticated branch of artificial intelligence, has emerged as a pivotal force in the realm of technological innovation. Unlike traditional AI systems, which are dependent on predefined rules and explicit data patterns, generative AI utilizes advanced neural networks to learn from extensive datasets, empowering it to autonomously generate original content such as text, images, and music​​.

In any event, the AI revolution shows no signs of slowing down, let alone stopping. And as more organizations look to AI for analysis and cost savings, Palantir stands ready to sign them up as new customers. Investors should monitor the new CEO’s performance but not let this recent development scare them away from the stock. Data could become one of the great investing trends of the future, so Snowflake is as good a bet as any to become one of the next great megacap tech companies. Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12).

Bear in mind too that a foundation model can underpin multiple use cases across an organization, so board members will want to ask the appointed generative AI leader to ensure that the organization takes a coordinated approach. This will promote the prioritization of use cases that deliver fast, high-impact results. Importantly, a coordinated approach will also help ensure a full view of any risks assumed. By working closely with our in-house data science and software engineering teams, we ensure the creation and implementation of effective AI models.

Creatio, a global vendor of one platform to automate industry workflows and CRM with no-code. Katherine is the CEO of Creatio, a global vendor of one platform to automate industry workflows and CRM with no-code. Lenovo has also expanded the availability of AI-ready smart devices and edge-to-cloud infrastructure to include new platforms what every ceo should know about generative ai purpose-built for enabling AI workloads. The new devices will incorporate Lenovo’s View application for AI-enabled computer vision technology, enhancing video image quality. As a heavy video conferencing user, I understand and appreciate what a time saver it would be to have documentation automatically created for each call.

Digital, Technology, and Data

This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. Generative AI is a subset of artificial intelligence that specifically focuses on creating new content or data based on patterns and existing information.

• But since ChatGPT came off the starting block in late 2022, new iterations of gen AI technology have been released several times a month.
• To maximize value, companies are increasingly fine-tuning pretrained generative AI models with their own data.
• Armed with this industry-leading AI security and governance platform, organizations will receive a customized deployment methodology from Deloitte that seamlessly creates the foundation needed to successfully deploy generative AI.

We also modeled a range of potential scenarios for the pace at which these technologies could be adopted and affect work activities throughout the global economy. A generative AI bot trained on proprietary knowledge such as policies, research, and customer interaction could provide always-on, deep technical support. Today, frontline spending is dedicated mostly to validating offers and interacting with clients, but giving frontline workers access to data as well could improve the customer experience. The technology could also monitor industries and clients and send alerts on semantic queries from public sources.

The Company

Generative AI, a powerful technology, finds diverse applications across various business sectors. In marketing, it creates personalized content like ads and product recommendations, enhancing customer engagement. It optimizes operational processes by automating tasks, thus reducing human error and enhancing efficiency. A software engineering company is enhancing productivity by implementing an AI-based code-completion tool.

Large language models (LLMs) make up a class of foundation models that can process massive amounts of unstructured text and learn the relationships between words or portions of words, known as tokens. This enables LLMs to generate natural language text, performing tasks such as summarization or knowledge extraction. GPT-4 (which underlies ChatGPT) and LaMDA (the model behind Bard) are examples of LLMs.

They can be quickly fine-tuned for a wide array of tasks, making them versatile tools for businesses seeking to reinvent work processes and amplify human capabilities​​. This versatility is central to generative AI’s value proposition, offering multifaceted applications while balancing the high costs of development and hardware. The company’s vision is to be the trusted partner and global leader in the AI security domain, empowering enterprises and governments to leverage the immense potential of generative AI solutions and Large Language Models (LLMs) responsibly and securely. CalypsoAI is striving to shape a future in which technology and security coalesce to transform how businesses operate and contribute to a better world. While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit their application.

The deployment of generative AI and other technologies could help accelerate productivity growth, partially compensating for declining employment growth and enabling overall economic growth. In some cases, workers will stay in the same occupations, but their mix of activities will shift; in others, workers will need to shift occupations. Banking, a knowledge and technology-enabled industry, has already benefited significantly from previously existing applications of artificial intelligence in areas such as marketing and customer operations.1“Building the AI bank of the future,” McKinsey, May 2021. In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems.

It uses advanced machine learning models to generate original and realistic outputs. AI, on the other hand, is a broader field that encompasses various techniques and approaches to simulate human intelligence in machines, including generative AI. This has the potential to increase productivity, create enthusiasm, and enable an organization to test generative AI internally before scaling to customer-facing applications. Many organizations began exploring the possibilities for traditional AI through siloed experiments. Generative AI requires a more deliberate and coordinated approach given its unique risk considerations and the ability of foundation models to underpin multiple use cases across an organization. The company found that major updates to its tech infrastructure and processes would be needed, including access to many GPU instances to train the model, tools to distribute the training across many systems, and best-practice MLOps to limit cost and project duration.

With guiding resources like the No-code Playbook, organizations are empowered to evaluate the difficulty of their projects and select strategies that yield maximum efficiency. This has led to the deployment of a range of solutions using no-code platforms, from basic tools like feedback systems to complex platforms streamlining intricate banking operations or infrastructure coordination. Besides the impressive power and flexibility of GPT-3, OpenAI’s introduction of ChatGPT should not have been a surprise for the major tech companies. Microsoft, Google, Lenovo, IBM, Dell, HPE and others have been experimenting with foundation models and generative AI for years. CEOs ought to start acting now to fully harness the transformative powers of generative AI solutions for their companies. Gen AI offers an opportunity to radically change how data analytics, forecasting, predictive analytics and decision-making take place within an organization.

It enables much easier collaboration among geographically dispersed teams, facilitating remote access to additional computing capabilities. Microsoft was the first to react to ChatGPT by adding a GPT-powered chatbot to Bing search, allowing Bing to respond to search queries with complete, conversational answers. It happened quickly because Microsoft has been one of OpenAI’s investors for years.

She has conducted in-depth research into the impact of generative AI on individuals and businesses for the BCG Henderson Institute. Clearly, generative AI is a rapidly evolving space, and each of the pillars above involves short- and long-term considerations—and many other unanswered questions. But CEOs need to prepare for the moment when their current business models become obsolete. When exploring generative AI, CEOs should plan for contingencies and embrace challenges as learning opportunities that can accelerate change and open new avenues of growth potential.

This article gives an insight into why every CEO should familiarize themselves with generative AI today. In addition, this article covers use cases where generative AI can make a significant impact in the analytics industry and the role GenAI plays in ensuring strategies are future-facing. Companies will therefore need to understand the value and the risks of each use case and determine how these align with the company’s risk tolerance and other objectives. For example, with regard to sustainability objectives, they might consider generative AI’s implications for the environment because it requires substantial computing capacity. Generative AI also has a propensity to hallucinate—that is, generate inaccurate information, expressing it in a manner that appears so natural and authoritative that the inaccuracies are difficult to detect. By taking the first step and learning from experience, businesses can stay ahead in the ever-changing world of artificial intelligence.

This technology is developing rapidly and has the potential to add text-to-video generation. Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs. Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks. The speed at which generative AI technology is developing isn’t making this task any easier.

Today, we work closely with clients to embrace a transformational approach aimed at benefiting all stakeholders—empowering organizations to grow, build sustainable competitive advantage, and drive positive societal impact. Another near-term imperative is to train employees how to use generative AI within the scope of their expertise. About 40% of code generated by AI is insecure, according to NYU’s Center for Cybersecurity—and because most employees are not qualified to assess code vulnerabilities, this creates a significant security risk. AI assistance in writing code also creates a quality risk, according to a Stanford University study, because coders can become overconfident in AI’s ability to avoid vulnerabilities. Companies need policies that help employees use generative AI safely and that limit its use to cases for which its performance is within well-established guardrails.

Since the foundation model was trained from scratch, rigorous testing of the final model was needed to ensure that output was accurate and safe to use. For example, another European telco saw firsthand the importance of change management and upskilling when it created a gen-AI-driven knowledge “expert” that helped agents get answers to customer questions more quickly. The initial pilot, which didn’t include any process changes or employee education, realized just a 5 percent improvement in productivity. As the organization prepared to scale the solution, leaders dedicated 90 percent of the budget to agent training and change management processes, which facilitated the adoption of the solution and resulted in more than 30 percent productivity improvement.

These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8). The analyses in this paper incorporate the potential impact of generative AI on today’s work activities. They could also have an impact on knowledge workers whose activities were not expected to shift as a result of these technologies until later in the future (see sidebar “About the research”).