Why Should Every CEO Know About Generative AI?

The quality and diversity of this data directly impact the AI’s ability to generate accurate and meaningful outputs. The projected need for half a billion apps underscores the importance of involving business developers and professional developers in the development process. Their expertise, coupled with no-code tools, can lead to solutions that are both technically sound and contextually relevant.

Moreover, the expertise of skilled individuals in machine learning and data science is vital to manage and fine tune the model for optimal performance. Generative AI and no-code development have become prominent and influential trends. This union is not just reshaping the way we approach software development but is also leveling the playing field, allowing everyone from tech novices to experts to harness the power of technological advancement. This collaboration shows how innovation soars when accessibility meets advanced features. While once perceived as tools for lightweight, front-end applications, today’s no-code platforms are being leveraged for mission-critical enterprise solutions. Progressive enterprises recognize the agility and flexibility of no-code platforms.

Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on a variety of factors, such as the mix and importance of different functions, as well as the scale of an industry’s revenue (Exhibit 4). Our analysis suggests that implementing generative AI could increase sales productivity by approximately 3 to 5 percent of current global sales expenditures. We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs.

The telco also used gen AI to create upskilling programs and provide agents with personalized recommendations for improvement once the solution was rolled out. In order to achieve the above-mentioned impact, organizations will need to move away from the labyrinth of proofs-of-concept and scale the technology. As with any digital or AI initiative, we find there are no shortcuts in doing this. These are fundamental pillars in effectively scaling use cases and capturing sustainable impact from gen AI in the journey toward an AI-native telco. The McKinsey Global Institute began analyzing the impact of technological automation of work activities and modeling scenarios of adoption in 2017. At that time, we estimated that workers spent half of their time on activities that had the potential to be automated by adapting technology that existed at that time, or what we call technical automation potential.

Based on these assessments of the technical automation potential of each detailed work activity at each point in time, we modeled potential scenarios for the adoption of work automation around the world. First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development. Second, we estimated a range of potential costs for this technology when it is first introduced, and then declining over time, based on historical precedents.

Project Helix’s on-premises approach also provides better infrastructure and operations control and management, yielding a higher ROI. Initial foundation models demanded substantial investment due to intensive computational resources and human effort for training and refinement. Primarily developed by tech giants, well-funded startups, and open-source research groups like BigScience, recent efforts aim to create smaller, efficient models, potentially broadening market access. Successful startups like Cohere, Anthropic, and AI21 Labs have independently developed and trained their large language models.

The road to human-level performance just got shorter

It offers firms a competitive advantage by driving innovation, optimizing operations and enhancing customer experiences. CEOs need to be aware of its potential, recognize how it may improve their operation and embrace its potential benefits to position their companies as future industry leaders. But it also forces CEOs to grapple with towering unknowns, and to do so in a space that may feel unfamiliar or uncomfortable. Crafting an effective strategic approach to generative AI can help distinguish the signal from the noise. The release of ChatGPT in late 2022 created a groundswell of interest in generative AI. Within hours, users experimenting with this new technology had discovered and shared myriad productivity hacks.

The model combines search and content creation so wealth managers can find and tailor information for any client at any moment. For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data. Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs. After the release of ChatGPT, major players like Google, Microsoft, IBM, Cisco, Dell, HPE and Lenovo quickly incorporated foundation models and generative AI into their product stacks, causing a shift in market dynamics.

If workers are supported in learning new skills and, in some cases, changing occupations, stronger global GDP growth could translate to a more sustainable, inclusive world. For one thing, gen AI has been known to produce content that’s biased, factually wrong, or illegally scraped from a copyrighted source. Before adopting gen AI tools wholesale, organizations should reckon with the reputational and legal risks to which they may become exposed. Keep a human in the loop; that is, make sure a real human checks any gen AI output before it’s published or used. Every role, including everyone from network technicians to HR professionals, will be impacted by gen AI, making vital the need for leaders to begin preparing their employees now to capture the full value of this transformative technology.

Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases. Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities.

Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. Capturing the full potential will also require significant upskilling of existing staff—everyone from data scientists to business leaders—on gen AI, including the risks of uploading proprietary data into third-party language models. Some telcos are setting up internal certification and university-led training programs to ensure their teams have the right skills and capabilities to innovate and execute with the technology. For instance, the large telco created a badging system to identify gen-AI-ready employees who have completed the company’s sessions on use, risk, and effective prompting techniques given by its AI, legal, and risk experts. Following certification, users participate in weekly discussion groups to stay abreast of changes and discuss their successes and challenges.

Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023. We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time. Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts. Generative AI could have a significant impact on the banking industry, generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion. On top of that impact, the use of generative AI tools could also enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk. In the banking industry, generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data.

Artificial intelligence (AI) is the ability of software to perform tasks that traditionally require human intelligence. Both CalypsoAI and Deloitte Middle East will be attending Leap 2024, taking place at the Riyadh Exhibition and Convention Center in Malham, Saudi Arabia from March 4-7. Those interested in learning more about how their organization can benefit from this partnership should visit CalypsoAI and Deloitte Middle East in Hall 2, Booth K20.

Balancing risk and value creation

The article serves as a guide, offering a primer on generative AI, exploring example cases, and underscoring the pivotal role of CEOs in steering their organizations toward success in the generative AI landscape. Immerse yourself in the insightful journey of AI with “The AI and I.” Witness the metamorphosis of intricate AI jargon into understandable and actionable insights. Realize firsthand how this newfound understanding can trigger unprecedented growth, efficiency, and innovation for your venture. Generative AI’s ability to analyze complex data is particularly beneficial in drug discovery.

This includes addressing concerns related to data privacy, security, and ethical AI principles​​. As generative AI becomes more integrated into business processes, it will impact tasks rather than entire occupations. Some tasks will be automated, some transformed through AI assistance, and others will remain unaffected. This shift underscores the importance of training employees to work effectively alongside AI systems​​. AI-driven chatbots and virtual assistants, powered by generative AI, are redefining customer support. These systems autonomously handle inquiries and offer support, thereby improving customer service and automating routine tasks.

Integrating generative AI into current systems requires a collaborative approach that fosters a symbiotic relationship between the new and the old. It’s about unifying the strengths of AI technology with the established processes to achieve a synergy that optimizes performance. The integration enables a smooth shift, sharing knowledge, evolving systems, leading to a more efficient operational setup. Responsible implementation of Generative AI involves establishing clear guidelines and oversight to prevent misuse. Implementing ethical frameworks and continuously monitoring AI generated content helps to mitigate risks and ensures that this innovative technology is used in a responsible and beneficial manner for the betterment of society. The alliance of generative AI with no-code platforms promises to redefine the software development landscape.

Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data. Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications. The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups. In the lead identification stage of drug development, scientists can use foundation models to automate the preliminary screening of chemicals in the search for those that will produce specific effects on drug targets.

You can foun additiona information about ai customer service and artificial intelligence and NLP. In general, training a model from scratch costs ten to 20 times more than building software around a model API. Larger teams (including, for example, PhD-level machine learning experts) and higher compute and storage spending account for the differences in cost. The projected cost of training a foundation model varies widely based on the desired model performance level and modeling complexity. Those factors influence the required size of the data set, team composition, and compute resources. In this use case, the engineering team and the ongoing cloud expenses accounted for the majority of costs.

Users don’t need a degree in machine learning to interact with or derive value from it; nearly anyone who can ask questions can use it. And, as with other breakthrough technologies such as the personal computer or iPhone, one generative AI platform can give rise to many applications for audiences of any age or education level and in any location with internet access. As organizations begin to set gen AI goals, they’re also developing the need for more gen AI–literate workers. As generative and other applied AI tools begin delivering value to early adopters, the gap between supply and demand for skilled workers remains wide. To stay on top of the talent market, organizations should develop excellent talent management capabilities, delivering rewarding working experiences to the gen AI–literate workers they hire and hope to retain. New sources of value may also emerge from turning internal uses cases into new products for their customers.

Ready to talk about what digital business transformation can do for your business, or just looking for some more information? The first example is a relatively low-complexity case with immediate productivity benefits because it uses an off-the-shelf generative AI solution and doesn’t require in-house customization. Prompt engineering refers to the process of designing, refining, and optimizing input prompts to guide a generative AI model toward producing desired (that is, accurate) outputs.

CEOs know about generative AI must grasp its capabilities and limitations to leverage it effectively. With a deep understanding of Generative AI, leaders can propel innovation, enhance creativity, and drive efficiency within their organizations. Generative AI, a technology that creates new content, requires vast amounts of data to function effectively.

In this section, we highlight the value potential of generative AI across business functions. Our estimates are based on the structure of the global economy in 2022 and do not consider the value generative AI could create if it produced entirely new product or service categories. Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies. Empowering minds through AI knowledge, Aifeeld.com envisions a future where artificial intelligence enriches lives and fuels innovation. Generative AI’s success hinges on the volume and quality of the data it’s fed, coupled with the computational power and skilled professionals needed to train and refine the model. The convergence of ample data, robust training, and proficient resources is pivotal in enabling Generative AI to produce sophisticated, realistic, and contextually relevant outputs.

This means companies should be careful of integrating generative AI without human oversight in applications where errors can cause harm or where explainability is needed. Generative AI is also currently unsuited for directly analyzing large amounts of tabular data or solving advanced numerical-optimization problems. The underlying model that enables generative AI to work is called a foundation model. Transformers are key components of foundation models—GPT actually stands for generative pre-trained transformer. A transformer is a type of artificial neural network that is trained using deep learning, a term that alludes to the many (deep) layers within neural networks.

Project Helix includes validated generative AI designs, AI-optimized servers, resilient and scalable unstructured data storage and cloud-based monitoring with CloudIQ, Dell ProSupport and Dell ProDeploy services. Cisco recently announced using generative AI in its collaboration and security products. As its name suggests, the Catch Me Up feature lets users rapidly catch up on missed meetings, calls and chats.

A relentless focus on solving the right problem with the right technology stack is key. Being able to prioritize speed of implementation versus go-to-market strategy are all considerations that should align with the organization’s policies, vision and goals. Supervised learning involves training the model using labeled data, whereas unsupervised learning extracts patterns and structures from unlabeled data. Generative AI has the power to overcome prediction problems by generating synthetic data that helps fill in the gaps where limited or incomplete data exists.

This capability marks a significant shift from earlier AI models, positioning generative AI as a catalyst for unprecedented innovation and creativity across various industries. Corporations have complex business processes designed to create products, serve customers and comply with industry regulations. Applications of AI have the power to automate repetitive tasks, reduce manual input and boost productivity. Now generative AI, in its capacity as a first-draft content generator, will augment many roles by increasing productivity, performance, and creativity. Employees in more clerical roles, such as paralegals and marketers, can use generative AI to create first drafts, allowing them to spend more of their time refining content and identifying new solutions. Coders will be able to focus on activities such as improving code quality on tight timelines and ensuring compliance with security requirements.

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. It is using a partner to provide on-demand, multi-tenant AI cloud service so customers can train, tune and deploy Large Language Models. Generative AI, distinct from prior AI forms, excels at efficiently producing new content, especially in unstructured formats like text and images.

A user can also navigate important parts of videos and efficiently consume long-form text from digital chats. Automatic meeting summaries with key points and action items is another feature that Cisco enabled with generative AI. The use of generative AI by Microsoft and Google quickly became a template for creating or defending a competitive advantage using GenAI.

In fact, while generative AI may eventually be used to automate some tasks, much of its value could derive from how software vendors embed the technology into everyday tools (for example, email or word-processing software) used by knowledge workers. A waterfall graph shows the potential additional value that could be added to the global economy by new generative AI uses cases. An initial $11.0 trillion–$17.7 trillion could come from advanced analytics, what every ceo should know about generative ai traditional machine learning, and deep learning. And additional $2.6 trillion–$4.4 trillion of incremental economic impact could be added from new generative AI use cases, resulting in a total use-case-driven potential of $13.6 trillion–$22.1 trillion. Gen AI tools can already create most types of written, image, video, audio, and coded content. And businesses are developing applications to address use cases across all these areas.

Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, have much broader areas of application than models optimized for a single task. They can therefore accelerate time to market and broaden the types of products to which generative design can be applied. For now, however, foundation models lack the capabilities to help design products across all industries. To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making.

Consider the experiences of two telcos—one that continued offshoring and outsourcing tech talent and one that created a dedicated AI team of ten data scientists and engineers. In the time the first telco took to draft requirements for outsourcing gen AI use-case development, the second built and deployed four gen AI solutions. Next, we outline key differences and provide recommendations on how telcos can best tackle them. Moreover, survey findings indicate that the technology also had a knock-on effect across all AI initiatives. Compared to responses from McKinsey’s 2022 digital twin survey, we see a 30-percentage-point increase in business leaders who want to invest in and focus more on data and analytics.

Such models are completely dependent on the functionality and domain knowledge of the core model’s training data; they are also restricted to available modalities, which today are comprised mostly of language models. And they offer limited options for protecting proprietary data—for example, fine-tuning LLMs that are stored fully on premises. Here are six things CEOs should consider taking action on as generative AI takes hold in a competitive marketplace. Embracing Generative AI through Digital Wave Technology empowers CEOs and their teams to unlock unprecedented efficiency, productivity, and innovation in the ever-advancing world of technology.

Organizations may need to adapt their working approach to calibrate process management, culture, and talent management in a way that ensures they can handle the rapidly evolving regulatory environment and risks of generative AI at scale. By focusing on early wins that deliver meaningful results, companies can build momentum and then scale out and up, leveraging the multipurpose nature of generative AI. This approach could enable companies to promote broader AI adoption and create the culture of innovation that is essential to maintaining a competitive edge. As outlined above, the cross-functional leadership team will want to make sure such proofs of concept are deliberate and coordinated. Our research has shown that such tools can speed up a developer’s code generation by as much as 50 percent. It can also help in debugging, which may improve the quality of the developed product.

Within Digital, Technology, and Data

Machine learning (ML) is a subset of AI in which a model gains capabilities after it is trained on, or shown, many example data points. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt and can become more effective in response to new data and experiences. Application programming interface (API) is a way to programmatically access (usually external) models, data sets, or other pieces of software. Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value.

Seizing the gen AI opportunity to differentiate services and achieve sustainable growth will require the hidebound industry to embrace innovation, exploration, and agility at an unprecedented level and move from decoupled AI efforts to a holistic, AI-native telco. Founded in 1993, The Motley Fool is a financial services company dedicated to making the world smarter, happier, and richer. The Motley Fool reaches millions of people every month through our premium investing solutions, free guidance and market analysis on Fool.com, top-rated podcasts, and non-profit The Motley Fool Foundation. Treating computer languages as just another language opens new possibilities for software engineering. Software engineers can use generative AI in pair programming and to do augmented coding and train LLMs to develop applications that generate code when given a natural-language prompt describing what that code should do.

• Treating computer languages as just another language opens new possibilities for software engineering.
• 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.
• This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies.
• Our research has shown that such tools can speed up a developer’s code generation by as much as 50 percent.
• 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.

These tools have the potential to create enormous value for the global economy at a time when it is pondering the huge costs of adapting and mitigating climate change. At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence. Generative AI tools can draw on existing documents and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing. In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts.

As experienced AI Consultants, our main goal is to empower companies with customized AI solutions. We are a team consists of AI Developers and Engineers, and our primary focus is on integrating AI into businesses. CEOs need to lead the way, adapting their approach based on what works best for their company. In sensitive sectors like healthcare and finance, generative AI’s ability to generate synthetic data while maintaining the statistical properties of the original dataset is crucial. This approach not only facilitates data sharing and collaboration but also ensures individual privacy​​. Response rates for such ads are demonstrably higher than conventional, non-personalized equivalents.

Keeping data and AI operations on premises is inherently less risky than transferring valuable company IP to the cloud—even the most secure cloud. Dell Technologies and Nvidia joined to create a generative AI initiative called Project Helix that provides customers with a simplified method to build on-prem generative AI models. Project Helix’s primary objective is to simplify and accelerate GenAI deployment for large and small businesses and scale models with safe and valid outcomes. Yet, our conversations with board members revealed that many of them admit they lack this understanding.

Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications. Acknowledging the importance of human oversight, ethical considerations, and bias management is crucial. Embracing Generative AI responsibly ensures its fruitful integration, paving the way for groundbreaking advancements in various industry sectors. CEOs need to understand that AI can enhance efficiency, improve decision making, and drive innovation across various business operations. For any CEO, understanding Generative AI is like unlocking a treasure chest of smart robots.