Summary: What Every CEO Should Know About Generative AI by McKinsey & Co

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.

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.

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.

At the same time, generative AI could offer a first draft of a sales pitch for the salesperson to adapt and personalize. With new gen AI research and capabilities being announced weekly and sometimes daily, technology teams will also need a dedicated gen AI innovation lab to keep abreast of industry changes and test emerging solutions. For example, one large telco’s chief data and analytics officer recruited PhD graduates from universities to staff a gen AI innovation lab and build bespoke solutions ahead of the market to gain a competitive edge. Instead, leaders should strongly consider partnering with gen AI solution providers and enterprise software vendors for solutions that aren’t very complex or telco specific. This is particularly critical in instances where any delays in implementation will put them at a disadvantage against competitors already leveraging these services.

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.

Executives are prioritizing generative AI, but most feel ill-equipped to lead their companies through the AI revolution

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. 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.

But with highly specialized data—as might be the case for drug development—the company may need to build a generative AI model from scratch. Many companies took an experimental approach to implementing previous generations of AI technology, with those keenest to explore its possibilities launching pilots in pockets of the organization. But given the speed of developments within generative AI and the risks it raises, companies will need a more coordinated approach. Indeed, the CEO of one multinational went as far as to ask each of his 50 business leaders to fully implement two use cases without delay, such was his conviction that generative AI would rapidly lend competitive advantage.

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).

Operating model: Orchestrate efforts enterprise-wide

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.

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.

Others may want to exercise caution, experimenting with a few use cases and learning more before making any large investments. Companies will also have to assess whether they have the necessary technical expertise, technology and data architecture, operating model, and risk management processes that some of the more transformative implementations of generative AI will require. According to MIT Sloan Management Review’s The State of Generative AI in the Middle East report, 72% of executives from companies in the region say they use generative AI tools within their organization, with an additional 21% seriously considering adopting the technology.

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”).

These cases reflect what we are seeing among early adopters and shed light on the array of options across the technology, cost, and operating model requirements. Finally, we address the CEO’s vital role in positioning an organization for success with generative AI. The preceding example demonstrates the implications of the technology on one job role. But nearly every knowledge worker can likely benefit from teaming up with generative AI.

Palantir’s market cap could eventually crack the $1 trillion mark

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.

To streamline processes, generative AI could automate key functions such as customer service, marketing and sales, and inventory and supply chain management. Technology has played an essential role in the retail and CPG industries for decades. Traditional AI and advanced analytics solutions have helped companies manage vast pools of data across large numbers of SKUs, expansive supply chain and warehousing networks, and complex product categories such as consumables. In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence. For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools.

This article gives an insight into why every CEO should familiarize themselves with generative AI today. You can foun additiona information about ai customer service and artificial intelligence and NLP. 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.

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 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.

Some companies will be able to drive growth through improved offerings; Intercom, a provider of customer-service solutions, is running pilots that integrate generative AI into its customer-engagement tool in a move toward automation-first service. Growth can also be found in reduced time-to-market and cost savings—as well as in the ability to stimulate the imagination and create new ideas. In biopharma, for example, much of today’s 20-year patent time is consumed by R&D; accelerating this process can significantly increase a patent’s value. Generative AI derives its strength from foundation models—expansive neural networks trained on vast amounts of diverse, unstructured, and unlabeled data. At Digital Wave Technology, our platform harnesses the potential of foundation models, unlocking the full capabilities of Generative AI across our solutions.

Yet, despite this enthusiasm, 64.7% of respondents shared that lack of governance is a major hurdle to adopting generative AI. CalypsoAI’s Moderator solution addresses security and governance concerns by giving enterprises visibility into how models are being used within the organization and the ability to set specific controls and parameters to mitigate risk. The model-agnostic platform blocks prompt-driven techniques like role-playing and reverse psychology that would otherwise breach boundaries, keeping sensitive data secure within an organization. With Moderator, threat actors attempting to exploit LLM responses are effectively barred from infiltrating a company’s digital ecosystem.

Microsoft also used generative AI to create a Microsoft 365 tool called Copilot that provides context-aware, real-time help and suggestions for documents, presentations and spreadsheets. IBM Institute for Business Value interviewed C-suite executives and found out that investment in generative AI is expected to grow nearly 4 times in the next three years. In the analytics industry, therefore, CEOs ought to consider implementing Generative AI as a must, not a maybe. With the emergence of GenAI solutions, even the data analytics and research landscape has experienced a transformation.

For the purposes of this report, we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by what every ceo should know about generative ai the billions of neurons connected in the human brain. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks.

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.

Building and training custom generative AI models require high-quality and diverse data, necessitating privacy, security, and compliance with data protection regulations. CEOs need not fully understand the intricacies of how generative AI tech works, but knowing how the tech will impact their organizations and industries is vital. By leveraging generative AI to make strategic choices and manage challenges, CEOs can open up a ton of opportunities for their business.

Seeking expert guidance and investing in compatible infrastructure can mitigate integration obstacles. Even with the surge of business-developer-friendly tools, the role of the professional developer remains invaluable. In addition to these Webex developments, Cisco is adding new AI features to its Security Cloud to make managing security policies easier and improve threat response.

Optimizing you web with AI Chatbots, and Virtual Assistants in your startup businesses

A modern data and tech stack is key to nearly any successful approach to generative AI. CEOs should look to their chief technology officers to determine whether the company has the required technical capabilities in terms of computing resources, data systems, tools, and access to models (open source via model hubs or commercial via APIs). In this example, a company uses a foundation model optimized for conversations and fine-tunes it on its own high-quality customer chats and sector-specific questions and answers. The company operates in a sector with specialized terminology (for example, law, medicine, real estate, and finance). Companies may decide to build their own generative AI applications, leveraging foundation models (via APIs or open models), instead of using an off-the-shelf tool.

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.

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.

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.

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.

Generative models can generate more accurate forecasts by including multiple variables and evaluating a wider range of different scenarios for faster and more precise analysis. This can be used to assess the feasibility and consequences of actions much more efficiently. From there, boards need to be satisfied that the company has established legal and regulatory frameworks for the knowable generative AI risks assumed across the company and that AI activities within the company are continually reviewed, measured, and audited. They will also want to ensure mechanisms are in place to continually explore and assess risks and ethical concerns that are not yet well understood or even apparent. How, for example, will companies stand up processes to spot hallucination and mitigate the risk of wrong information eliciting incorrect or even harmful action? Generative AI, highlighted by innovations like ChatGPT, is capturing CEOs’ attention as a potential game-changer.

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.

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.

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.

Experimentation should be encouraged; however, it is important to track all experiments across the organization and avoid “shadow experiments” that risk exposing sensitive information. These policies should also guarantee clear data ownership, establish review processes to prevent incorrect or harmful content from being published, and protect the proprietary data of the company and its clients. Leaders will need to carefully assess the timing of such an investment, weighing the potential costs of moving too soon on a complex project for which the talent and technology aren’t yet ready against the risks of falling behind. Today’s generative AI is still limited by its propensity for error and should primarily be implemented for use cases with a high tolerance for variability. CEOs will also need to consider new funding mechanisms for data and infrastructure—whether, for example, the budget should come from IT, R&D, or another source—if they determine that custom development is a critical and time-sensitive need.

We work in a uniquely collaborative model across the firm and throughout all levels of the client organization, fueled by the goal of helping our clients thrive and enabling them to make the world a better place. Generative AI is reshaping the landscape of automation by automating, augmenting, and accelerating work processes like never before. Our unique platform with enterprise generative AI built in relieves teams of tasks by employing auto-copywriting to increase conversions and auto-product attribution for improved SEO. We automate repetitive responsibilities and free up valuable resources for higher-value activities, driving transformative change across the board.

• Generative AI platforms are powered by foundational models that involve large neural networks that are trained on expansive quantities of unstructured data across multiple formats.
• Once the decision is made, there are technical pathways that AI experts can follow to execute the strategy, depending on the use case.
• Our experience working with clients indicates the potential for telcos to achieve significant EBITDA impact with gen AI.
• But under the right conditions, generative AI has the power to eliminate the compromise between agility and scale.
• Customer service and marketing and sales currently make up the largest share of total impact (Exhibit 3).

To effectively apply generative AI for business value, companies need to build their technical capabilities and upskill their current workforce. Organizations do not have to build out all applications or foundation models themselves. Instead, they can partner with generative AI vendors and experts to move more quickly. For instance, they can team up with model providers to customize models for a specific sector, or partner with infrastructure providers that offer support capabilities such as scalable cloud computing.