6 Things Every CEO Should Know About Generative AI

The growth of e-commerce also elevates the importance of effective consumer interactions. Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information. Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation. The potential improvement in writing and visuals can increase awareness and improve sales conversion rates. In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information.

Neither this website nor our affiliates shall be liable for any errors or inaccuracies in the content, or for any actions taken by you in reliance thereon. You expressly agree that your use of the information within this article is at your sole risk. It will enable humans to work smarter and faster and may even perform certain tasks so people don’t have to. Following are four examples of how generative AI could produce operational benefits in a handful of use cases across the business functions that could deliver a majority of the potential value we identified in our analysis of 63 generative AI use cases. In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator.

CEOs need to understand the distinction between supervised and unsupervised learning when it comes to generative AI. Generative models typically employ unsupervised learning to capture underlying patterns in the data, allowing them to generate new samples based on learned patterns. This acquired knowledge can help CEOs choose the right approach for their specific business needs, allowing their teams to capture the right data, for the right moment and the right time. Businesses have been pursuing AI ambitions for years, and many have realized new revenue streams, product improvements, and operational efficiencies. Much of the successes in these areas have stemmed from AI technologies that remain the best tool for a particular job, and businesses should continue scaling such efforts.

With such a wide range of tasks now possible with generative AI, it unleashes a lot of potential for businesses to use Gen AI to speed up and scale up. An assessment of the new frontiers opened by generative AI will rightly make management teams eager to begin innovating what every ceo should know about generative ai and capturing its value. But that eagerness will need to be accompanied by caution, as generative AI, if not well managed, has the potential to destroy value and reputations. The rapid evolution of AI technology brings with it a host of legal and ethical challenges.

Applying an ecosystem approach to partnerships

The handful of solutions leaders can concentrate on shaping or making themselves should enable them to differentiate their offerings or address a strategic business priority, such as delivering the best service or network coverage, and drive sustained economic impact. Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat.

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• As we embrace this change, it becomes clear that the future of digital innovation is not solely in the hands of the tech-savvy but is a collective journey where anyone can lead, navigating toward a more inclusive and empowered digital landscape.
• Hands-on experience in the boardroom can build familiarity with the technology and appreciation of its value and risks.
• Businesses must ensure their systems are capable of handling the demands of these advanced AI models, focusing on aspects like compute power and data processing capabilities.
• This is largely explained by the nature of generative AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI.

Structurally, this could involve department-focused teams with cross-functional members (for example, sales teams with sales reps and dedicated technical support) or, preferably, cross-departmental and cross-functional teams aligned to the business and technical platforms. While generative AI can certainly present exciting opportunities, it is critical for CEOs and their teams to focus on solving the right business problems at the right time. Identifying opportunities that generative AI can address and aligning them with organizational goals requires executive leadership to inspire a vision for success with generative AI across the organization. Artificial intelligence (AI), machine learning (ML) and data science have become increasingly vital in the business landscape with generative AI recently entering the market. Generative AI is evolving at record speed while CEOs are still learning the technology’s business value and risks.

This requires a step up in investment from the previous example but facilitates a more customized approach to meet the company’s specific context and needs. The cost of this off-the-shelf generative AI coding tool is relatively low, and the time to market is short because the product is available and does not require significant in-house development. Cost varies by software provider, but fixed-fee subscriptions range from $10 to $30 per user per month. When choosing a tool, it’s important to discuss licensing and intellectual property issues with the provider to ensure the generated code doesn’t result in violations. A series of graphs show predicted compound annual growth rates from generative AI by 2040 in developed and emerging economies considering automation. This is based on the assumption that automated work hours are reintegrated in work at today’s productivity level.

Cisco has recognized the importance of GenAI and applied it to making meetings more productive, which improves the work environment. 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. While previous AI models were narrow – i.e., they could perform only one task, foundational models can be used for a wide range of tasks. Generative AI is accelerating at a high rate while CEOs are still learning the technology’s business value and risks. You can foun additiona information about ai customer service and artificial intelligence and NLP. With GenAI, tedious tasks can be automated, which leaves more time to focus on higher-value strategic work that leads to increased productivity.

Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy—annually. In the wake of their initial successes, business leaders we surveyed say they plan to maintain or double their budgets for gen AI in the next year and invest in more than 50 dedicated full-time employees to pursue their gen AI ambitions effectively. The content does not provide tax, legal or investment advice or opinion regarding the suitability, value or profitability of any particular security, portfolio or investment strategy.

While other deep learning models can operate on sizable amounts of unstructured data, they are usually trained on a more specific data set. For example, a model might be trained on a specific set of images to enable it to recognize certain objects in photographs. Gen AI’s precise impact will depend on a variety of factors, such as the mix and importance of different business functions, as well as the scale of an industry’s revenue.

Engineers can select one of the AI’s proposals, make needed refinements, and click on it to insert the code. They are artificial neural networks that use special mechanisms called “attention heads” to understand context in sequential data, such as how a word is used in a sentence. Deep learning is a subset of machine learning that uses deep neural networks, which are layers of connected “neurons” whose connections have parameters or weights that can be trained. It is especially effective at learning from unstructured data such as images, text, and audio. It democratized AI in a manner not previously seen while becoming by far the fastest-growing app ever. Its out-of-the-box accessibility makes generative AI different from all AI that came before it.

We recently provided a view of how CEOs might start preparing for what lies ahead.1“What every CEO should know about generative AI,” McKinsey, May 12, 2023. Many board members tell us they aren’t sure how to support their CEOs as they grapple with the changes that generative AI has unleashed, not least because the technology seems to be developing and getting adopted at lightning speed. Generative AI goes beyond mere chatbots, offering diverse applications in automating, enhancing, and speeding up various work tasks.

Within Consumer Products Industry

Making the right call when it comes to incorporating generative AI tools and solutions could deliver a strategic advantage that pays off massively (while keeping in mind data privacy, legal and ethical concerns). The introduction of generative AI, like any change, also requires a reassessment of the organization’s talent. Companies are aware they need to reskill the workforce to compete in a world where data and AI play such a big role, though many are struggling to attract and retain the people they need. Some roles will disappear, others will be radically different, and some will be new. Such changes will likely affect more people in more domains and faster than has been the case with AI to date. To keep pace with generative AI, companies may need to review their organizational capabilities on three fronts.

As the technology evolves and matures, these kinds of generative AI can be increasingly integrated into enterprise workflows to automate tasks and directly perform specific actions (for example, automatically sending summary notes at the end of meetings). Excitement around generative AI is palpable, and C-suite executives rightfully want to move ahead with thoughtful and intentional speed. A new McKinsey survey shows that the vast majority of workers—in a variety of industries and geographic locations—have tried generative AI tools at least once, whether in or outside work. One surprising result is that baby boomers report using gen AI tools for work more than millennials. Deloitte refers to one or more of DTTL, its global network of member firms, and their related entities.

The downside to such versatility is that, for now, generative AI can sometimes provide less accurate results, placing renewed attention on AI risk management. While foundation models serve as the “brain” of generative AI, an

entire value chain is emerging to support the training and use of this technology (Exhibit 2).1For more, see “Exploring opportunities in the generative AI value chain,” McKinsey, April 26, 2023. Specialized hardware provides the extensive compute power needed to train the models.

This isn’t just a small change; it’s a big leap forward, which makes the digital world open and accessible to everyone. Lenovo’s AI infrastructure generates over $2 billion a year in revenue as a result of Lenovo’s early recognition of AI’s importance. Over the past six years, the company has invested $1.2 billion in AI, which has allowed it to build AI innovation centers around the world and perform early research and development on AI used in today’s products. Lenovo plans to invest another $1 billion over the next three years to develop more AI-ready solutions. Project Helix is a powerful initiative that provides a simplified method to build on-premises generative AI models.

However, generative AI represents another promising leap forward and a world of new possibilities. While the technology’s operational and risk scaffolding is still being built, business leaders know they should embark on the generative AI journey. CEOs and their teams will also want to stay current with the latest developments in generative AI regulation, including rules related to consumer data protection and intellectual property rights, to protect the company from liability issues. Countries may take varying approaches to regulation, as they often already do with AI and data.

And it should lay out how to scale a pilot, for example to extend a pilot that serves 100 call agents to serve more than 10,000 agents with the same latency and cost profile. The blueprint should also have a framework for determining which gen AI capabilities can be turned into ready-to-use modules to be plugged into different use cases. Generative AI models—deep learning models trained on extremely large sets of unstructured data—have the potential to increase efficiency and productivity, reduce costs, and generate new growth. The power of these “foundation” models lies in the fact that, unlike previous deep learning models, they can perform not just one function but several, such as classifying, editing, summarizing, answering questions, and drafting new content. This enables companies to use them to launch multiple applications with relative ease, even if users lack deep AI and data science know-how. Combined, these gen AI capabilities will enable telcos to redefine industry standards and set themselves apart in the market.

The Company

By fostering a culture that encourages experimentation, CEOs can create an environment where “failures” are seen as stepping stones toward success. An effective enterprise data strategy is key to being able to acquire, govern and retain key information needed at an organizational level. Opt in to receive our email newsletter with AI thought leadership, and be the first to hear about upcoming webinars, new products, and more. With Digital Wave Technology, create and share the best product stories wherever customers shop. Unstructured data lack a consistent format or structure (for example, text, images, and audio files) and typically require more advanced techniques to extract insights.

One European telco recently increased conversion rates for marketing campaigns by 40 percent while reducing costs by using gen AI to personalize content. A Latin American telco increased call center agent productivity by 25 percent and improved the quality of its customer experience by enhancing agent skills and knowledge with gen-AI-driven recommendations. With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated.

In the near future, we expect applications that target specific industries and functions will provide more value than those that are more general. While this new technology democratizes AI by requiring fewer highly specialized data scientists to build the models, it requires new skills, such as gen AI prompt engineering, which may sometimes be a separate skill embedded within traditional roles. These use cases can both enhance existing AI capabilities (through the inclusion of new unstructured data sources) and provide new sources of value (through gen AI and in combination with traditional AI solutions) to deliver significant impact across all key domains. Customer service and marketing and sales currently make up the largest share of total impact (Exhibit 3). Pretrained models that can be fine-tuned in days for use cases are readily available, enabling organizations to bring proofs-of-concept to life with minimal up-front investment, achieve impact out of the gate, and scale their efforts.

However, the quality of IT architecture still largely depends on software architects, rather than on initial drafts that generative AI’s current capabilities allow it to produce. Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity. This analysis may not fully account for additional revenue that generative AI could bring to sales functions. For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover new leads and facilitate more effective outreach that would bring in additional revenue.

This application not only enhances customer experience but also frees up human resources for more complex tasks​​. Corporations have recognized that keeping an existing customer is much cheaper and more effective than finding a new one. AI can help companies tighten the bond with their clients by analyzing vast amounts of data and generating personalized responses in real time. Businesses can also run special promotions that target subsets interested in specific parts of their product lines. Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities.

Talent and third-party costs for cloud computing (if fine-tuning a self-hosted model) or for the API (if fine-tuning via a third-party API) account for the increased costs. To implement the solution, the company needed help from DataOps and MLOps experts as well as input from other functions such as product management, design, legal, and customer service specialists. As a result, we’re beginning to see telcos adopt more centralized decision making around gen AI development.

Organizations undergoing digital and AI transformations would do well to keep an eye on gen AI, but not to the exclusion of other AI tools. Just because they’re not making headlines doesn’t mean they can’t be put to work to deliver increased productivity—and, ultimately, value. Previous waves of automation technology mostly affected physical work activities, but gen AI is likely to have the biggest impact on knowledge work—especially activities involving decision making and collaboration. Professionals in fields such as education, law, technology, and the arts are likely to see parts of their jobs automated sooner than previously expected. This is because of generative AI’s ability to predict patterns in natural language and use it dynamically. The speed of innovation that is now possible with gen AI puts new pressure on telcos accustomed to outsourcing tech talent to build in-house AI expertise.

Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others.

This technology is still nascent, but of those who have used it, few doubt its power to disrupt operating models in all industries. In the following sections, we will explore operational and strategic considerations for integrating generative AI, governance and risk management practices, and the future outlook for this technology in business settings. Generative AI, exemplified by tools like GitHub Copilot, revolutionizes software development by enabling more efficient code generation and reducing bugs.

Although the company first found a market for its services with government intelligence agencies and the defense industry — partnerships it still maintains today — Palantir’s future depends on its ability to win over commercial clients. Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do. Our previously modeled adoption scenarios suggested that 50 percent of time spent on 2016 work activities would be automated sometime between 2035 and 2070, with a midpoint scenario around 2053. As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent.

The release cycle, number of start-ups, and rapid integration into existing software applications are remarkable. In this section, we will discuss the breadth of generative AI applications and provide a brief explanation of the technology, including how it differs from traditional AI. Our research found that equipping developers with the tools they need to be their most productive also significantly improved their experience, which in turn could help companies retain their best talent. Developers using generative AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow. They attributed this to the tools’ ability to automate grunt work that kept them from more satisfying tasks and to put information at their fingertips faster than a search for solutions across different online platforms. When we had 40 of McKinsey’s own developers test generative AI–based tools, we found impressive speed gains for many common developer tasks.

In the weeks and months since, organizations have scrambled to keep pace—and to defend against unforeseen complications. Some organizations have already adopted a more formal approach, creating dedicated teams to explore how generative AI can unlock hidden value and improve efficiency. At Digital Wave Technology, we recognize that the true value of Generative AI lies in its integration into everyday tools used by knowledge workers. Our solutions, whether standalone or integrated as one cohesive platform, are designed to seamlessly embed Generative AI into business processes, enhancing productivity, and driving innovative outcomes.

Most impressive is that these telcos deployed the models in just weeks—the first went live in two weeks, and the second in five. For an industry with a mixed track record for capitalizing on new technologies and legacy systems that slow innovation, these early results and deployment times illustrate the potentially transformative power of gen AI. Within this new partnership, Deloitte Middle East will offer access to Moderator so enterprises can overcome common regional security and governance hurdles.

These models typically calculate a confidence level in how accurate they think the prediction will be. Leveraging the capabilities of Generative AI within our platform opens new horizons of innovation! With our modular and integrated platform, businesses can choose to adopt our solutions individually or embrace a holistic approach that unites the power of Generative AI with our comprehensive suite of enterprise tools. In the rapidly evolving world of technology, Generative AI has emerged as a groundbreaking force that holds the power to revolutionize work processes for retailers, brands, and CPG companies.