What every CEO should know about generative AI

Generative AI has a diverse range of applications, from classifying data to drafting new content. At Digital Wave Technology, we embrace this versatility to deliver tailored solutions that cater to the unique needs of retailers, brands, and CPG companies. From automating product copywriting and generating rich product attributes to summarizing customer reviews for actionable insights, our GenAI solutions expand the possibilities for growth and innovation. For example, the lifeblood of generative AI is fluid access to data honed for a specific business context or problem.

But generative AI also presents other critical risks for companies, including copyright infringement; leaks of proprietary data; and unplanned functionality that is discovered after a product release, also known as capability overhang. You can foun additiona information about ai customer service and artificial intelligence and NLP. (See Exhibit 3.) For example, Riffusion used a text-to-image model, Stable Diffusion, to create new music by converting music data into spectrograms. For the CEO, the key is to identify the company’s “golden” use cases—those that bring true competitive advantage and create the largest impact relative to existing, best-in-class solutions. Each of these model approaches have advantages and disadvantages depending on the data captured and the prediction problem that businesses are solving.

As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work. With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance.

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To start, thousands of cell cultures are tested and paired with images of the corresponding experiment. Using an off-the-shelf foundation model, researchers can cluster similar images more precisely than they can with traditional models, enabling them to select the most promising chemicals for further analysis during lead optimization. Banks have started to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications.

Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies. More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually. Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug. This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process.

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For example, a customer care solution may be offered on demand to small business customers seeking ways to improve their own call center’s productivity and service. However, while nearly all of the 130 telcos we surveyed are doing something with gen AI, our survey findings suggest a palpable sense of caution and skepticism in the industry. More than 85 percent of the executives surveyed are cautious to attribute more than 20 percent revenue or cost savings impact by domain, with the greatest enthusiasm for a radical transformation in customer service (Exhibit 1).

Generative AI has taken hold rapidly in marketing and sales functions, in which text-based communications and personalization at scale are driving forces. The technology can create personalized messages tailored to individual customer interests, preferences, and behaviors, as well as do tasks such as producing first drafts of brand advertising, headlines, slogans, social media posts, and product descriptions. Some of this impact will overlap with cost reductions in the use case analysis described above, which we assume are the result of improved labor productivity. It also fosters innovation by assisting in the creation of unique products and services. Big deal with AI Also, by enabling better customer experiences through tailored interactions, it contributes to increased customer satisfaction and loyalty.

Arguably, therefore, AI expertise needs to be widespread so that the full board and all its committees can properly consider its implications. Company leaders should consider appointing a single senior executive to take responsibility for the oversight and control of all generative AI activities. A smart second step is to establish a cross-functional group of senior people representing data science, engineering, legal, cybersecurity, marketing, design, and other business functions. Such a team can collaborate to formulate and implement a strategy quickly and widely.

By identifying patterns and predicting viable therapeutic candidates, AI can significantly speed up the research process, leading to faster and more efficient development of new pharmaceuticals. Generative AI transforms how relationship managers analyze and interact with client information. By processing vast amounts of data, AI can uncover insights and trends, enabling personalized client strategies and more effective decision-making​​. The best AI systems identify unusual website interactions and not only send a note to the cybersecurity team about the potential problem but also take steps to isolate the interaction and keep it from spreading havoc in your systems. Lisa Krayer is a core member of Boston Consulting Group’s Technology, Media & Telecommunications; Technology & Digital Advantage, and Marketing, Sales & Pricing practices.

This significantly accelerates development, especially for complex codebases, by allowing developers to express desired functionalities in natural language and receive complete, functional code snippets in response​​. Generative AI is evolving at record speed (Exhibit 1) while CEOs are still learning the technology’s business value and risks. Generative AI automates research and development, first analyzing current data and gleaning patterns, and second and more importantly, generating hypotheses and even creating content. This transformative technology has the potential to dramatically accelerate innovation, but it also brings new hazards.

How we estimated the value potential of generative AI use cases

For example, they might use it to surface additional critical questions on strategic issues or to deliver an additional point of view to consider when making a decision. Board members can help their management teams move forward by asking the right questions. In this article, we provide four questions boards should consider asking company leaders, as well as a question for members to ask themselves.

As a result, they were developed primarily by a few tech giants, start-ups backed by significant investment, and some open-source research collectives (for example, BigScience). However, work is under way on both smaller models that can deliver effective results for some tasks and training that’s more efficient. Some start-ups have already succeeded in developing their own models—for example, Cohere, Anthropic, and AI21 Labs build and train their own large language models. The goal of this article is to help CEOs and their teams reflect on the value creation case for generative AI and how to start their journey. First, we offer a generative AI primer to help executives better understand the fast-evolving state of AI and the technical options available. The next section looks at how companies can participate in generative AI through four example cases targeted toward improving organizational effectiveness.

For example, network operations could be enhanced and quality standards radically recast with AI copilots that evaluate images from technicians, provide accurate recommendations for remedies, and automatically initiate interventions or work orders. In sales, cognitive copilots could conduct sentiment analysis on customer calls in real time and guide sales representatives on how best to respond, profoundly altering sales strategies, customer engagement, and overall sales outcomes. Customer service channels using cognitive chatbots could seamlessly answer complex queries in real time while taking into account privacy and fairness concerns, thereby revolutionizing efficiency while offering customers a human-like experience. Across the enterprise, greater efficiency and productivity could emerge as domain-specific solutions endowed with an organization’s institutional knowledge power an unprecedented wave of automation and AI-driven decision making. Our analysis finds that generative AI could have a significant impact on the pharmaceutical and medical-product industries—from 2.6 to 4.5 percent of annual revenues across the pharmaceutical and medical-product industries, or $60 billion to $110 billion annually.

In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year. Alos, adaptability remains a cornerstone for CEOs navigating unpredictable environments. Adapting to changes swiftly, whether in consumer behaviors, what every ceo should know about generative ai economic shifts, or technological advancements, allows CEOs to make informed decisions and lead their teams effectively. CEOs’ ability to adapt ensures steering through tough times, nurturing resilience, and fostering innovation in their organizations effectively.

In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development. A Guide for CEOs is an essential PDF that provides insights into the transformative power of Generative AI for business leaders. This document explains how Generative AI generates new, original content, such as images, text, and even music, offering CEOs a comprehensive understanding of its capabilities. 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.

Because the tool is purely off-the-shelf software as a service (SaaS), additional computing and storage costs are minimal or nonexistent. MLOps refers to the engineering patterns and practices to scale and sustain AI and ML. It encompasses a set of practices that span the full ML life cycle (data management, development, deployment, and live operations).

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Anybody can derive value from such a tool, and that’s what distinguishes generative AI platforms. CEOs are used to solving the toughest problems and how those problems are solved often charts the course for an organization’s future. How CEOs choose to adapt and adopt generative AI into their organization’s framework may be one of those defining moments. Board members can equip their C-suite to harness this potential power thoughtfully but decisively by asking the following four broad questions. So, if your company need any type of software AI solution we can collabrate with you.

GovCDOiq.org is a collaborative, public-service resource that seeks to accelerate success for federal agency Chief Data Officers and their teams – including data architects, data strategists, data analysts, data stewards, and AI/ML leaders. Abhishek Gupta is the senior responsible AI leader & expert with Boston Consulting Group (BCG) where he works with BCG’s chief AI ethics officer to advise clients and build end-to-end responsible AI programs. He is also the Founder & Principal Researcher at the Montreal AI Ethics Institute, an international non-profit research institute with a mission to democratize AI ethics literacy. Through his work as the Chair of the Standards Working Group at the Green Software Foundation, he leads the development of a software carbon intensity standard toward the comparable and interoperable measurement of the environmental impacts of AI systems. Finally, leaders should caution employees against using public chatbots for sensitive information.

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Companies must be vigilant about intellectual property rights, discrimination issues, product liability, and maintaining trust and security in AI applications​​. In the next sections of the report, we will delve into specific use cases of generative AI in business, strategic implementation considerations, the impact on workforce and job roles, and the future direction of this transformative technology. First, they must recognize how much generative AI knowledge the company has internally. In that case, they’ll likely need help from third-party specialists for planning and initial implementation.

The foundation model, such as GPT (Generative Pre-trained Transformer), is pivotal. Unlike earlier deep learning models, foundation models, with their transformers, can be trained on vast, diverse, and unstructured datasets. Because of the increased importance of data science and engineering, many companies will benefit from having a senior executive role (for example, a chief AI officer) oversee the business and technical requirements for AI initiatives. This executive should place small data-science or engineering teams within each business unit to adapt models for specific tasks or applications. Technical teams will thus have the domain expertise and direct contact to support individual contributors, ideally limiting the distance between the platform or tech leaders and individual contributors to one layer. The most complex and customized generative AI use cases emerge when no suitable foundation models are available and the company needs to build one from scratch.

All information typed into generative AI tools will be stored and used to continue training the model; even Microsoft, which has made significant investments in generative AI, has warned its employees not to share sensitive data with ChatGPT. Leaders can adapt existing recommendations regarding responsible publication to guide releases of generative AI content and code. They should mandate robust documentation and set up an institutional review board to review a priori considerations of impact, akin to the processes for publishing scientific research. Licensing for downstream uses, such as the Responsible AI License (RAIL), presents another mechanism for managing the generative AI’s lack of a truth function.

• Specialized hardware provides the extensive compute power needed to train the models.
• Microsoft, Google and Cisco have enhanced existing products with generative AI, and companies like Lenovo are offering AI-driven products through a network of best-in-class ISVs.
• CEOs need to lead the way, adapting their approach based on what works best for their company.
• The generative AI accelerates the RM’s analysis process, potentially capturing overlooked insights and improving job satisfaction.
• Investors should monitor the new CEO’s performance but not let this recent development scare them away from the stock.
• The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language.

Organizations must navigate evolving regulations surrounding generative AI, including data protection and consumer rights, to avoid legal consequences and reputational damage. To find out more on how to unlock the full benefits of your business with generative AI, tailored to your specific needs, explore here. ChatGPT reached 100 million users within 2 months and showcased how democratized AI can be. The uber accessibility of it made generative AI different from the AI tech that came before it.

Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task. To capture the benefits, this use case required material investments in software, cloud infrastructure, and tech talent, as well as higher degrees of internal coordination in risk and operations. In general, fine-tuning foundation models costs two to three times as much as building one or more software layers on top of an API.

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. Cost and sustainable energy consumption are also central to these considerations, especially given the energy-intensive nature of generative AI operations​​​​. The rapid evolution of AI technology necessitates a focus on legal, ethical, and reputational risks, including intellectual property, data privacy, discrimination, and product liability concerns​​.

In particular, model outputs must be verified, much as an organization would check the outputs of a junior analyst, because some large language models have been known to hallucinate. RMs are also trained to ask questions in a way that will provide the most accurate answers from the solution (called prompt engineering), and processes are put in place to streamline validation of the tool’s outputs and information sources. Much of the use (although not necessarily all of the value) from generative AI in an organization will come from workers employing features embedded in the software they already have. Productivity applications will create the first draft of a presentation based on a description. Financial software will generate a prose description of the notable features in a financial report.

This level of personalization and customization fosters customer loyalty, which benefits the client and the company. They experience less churn and do a better job of generating more revenue from each client. François is studying the impact of digital on wealth distribution, national competitiveness, and social stability. CEOs need to be aware of the effect that AI has on employees’ emotional well-being and professional identity.

Making sure your platform of choice offers a balance of advanced features, including GenAI capabilities, and an intuitive interface to increase internal adoption rate, will help you gain a competitive edge. Trusted methods can refine model results by incorporating guardrails and proper tuning procedures. Building models using proprietary company data provides better results with a higher competitive value for a customer with an AI-trained workforce. It is important to note that these outcomes should be free of bias and other problems caused by using public datasets. Therefore, if the dataset is safe, it is also safe to use repeatable outcomes created by this data to scale model growth.

This big potential reflects the resource-intensive process of discovering new drug compounds. Pharma companies typically spend approximately 20 percent of revenues on R&D,1Research and development in the pharmaceutical industry, Congressional Budget Office, April 2021. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value. For example, lead identification—a step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drug—can take several months even with “traditional” deep learning techniques. Foundation models and generative AI can enable organizations to complete this step in a matter of weeks. A corporate bank invests in a custom generative AI solution to enhance relationship managers’ (RMs) productivity.

Get stock recommendations, portfolio guidance, and more from The Motley Fool’s premium services. Moreover, Allied Market Research forecasts the AI chip market will reach $384 billion by 2032, a compound annual growth rate of 38%! As a leader in CPUs, GPUs, and data centers, the company is well suited for this role, and it recently leveraged this expertise by releasing its AMD Instinct MI300 Series Accelerators to meet this demand.

For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade. And its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040. How can telco leaders use the technology to drive AI transformations and unlock new value? This article offers insights into these critical questions, drawing extensively from our research, industry survey, and first-hand experience implementing these technologies.

HPE GreenLake for LLM offers customers the performance characteristics of a supercomputer and a cloud service. According to HPE, LLM is only the first of many future HPE GreenLake domain-specific AI applications supporting climate modeling, healthcare, life sciences, financial services, manufacturing and transportation. Major tech companies are now producing and selling ready-to-use foundation models and GenAI to other businesses, which use these tools to improve or develop products for tech-savvy customers. One reason is that generative AI (GenAI) has impressive capabilities useful for various applications such as natural language chatbots, text-to-image generators and text-to-video generators capable of producing incredibly realistic outputs based on text inputs. GenAI can also create human-like recommendations, robust content, and valuable new features for digital products that can improve user experiences.

The company created a product road map consisting of several waves to minimize potential model errors. Employees were able to give “thumbs up” or “thumbs down” answers to the model’s suggestions, and the model was able to learn from these inputs. As a next step, the model “listened” to customer support conversations and offered suggestions. Once the technology was tested sufficiently, the second wave began, and the model was shifted toward customer-facing use cases with a human in the loop. Eventually, when leaders are completely confident in the technology, it can be largely automated.