What every CEO should know about generative AI.

Introduction

Generative AI, highlighted by innovations like ChatGPT, is capturing CEOs' attention as a potential game-changer. The widespread adoption of user-friendly ChatGPT, reaching 100 million users in two months, makes AI accessible to a broad audience. Unlike traditional AI, generative AI's versatility allows it to handle diverse tasks, albeit with some current accuracy challenges, emphasizing the need for careful risk management.

Generative AI, driven by foundation models, offers transformative potential, as seen in scenarios like real-time sales call support. The focus is on enhancing existing processes rather than complete automation. The immediate value lies in integrating generative AI into everyday tools used by knowledge workers, promising substantial productivity gains.

CEOs face the decision of whether to embrace generative AI now or proceed cautiously through experimentation. 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.

A generative AI primer

Generative AI technology is advancing quickly (Exhibit 1). 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.

Exhibit 1

Generative AI has been evolving at a rapid pace.

Timeline of some of the major large language model (LLM) developments in the months following ChatGPT’s launch

Making AI Accessible: More than the Chabots

Generative AI goes beyond mere chatbots, offering diverse applications in automating, enhancing, and speeding up various work tasks. While chatbots like ChatGPT gain attention, generative AI extends its capabilities to handle images, video, audio, and code.

It performs tasks like classifying, editing, summarizing, answering questions, and drafting content, transforming how business functions operate. Examples include fraud detection, customer call categorization, grammar correction, image editing, video summarization, answering technical queries, and code generation.

As the technology advances, integrating generative AI into workflows becomes more feasible, automating tasks and executing specific actions within enterprise settings.

The Power of Foundation Models

Generative AI, distinct from prior AI forms, excels at efficiently producing new content, especially in unstructured formats like text and images. 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.

For instance, large language models can learn from extensive internet text on various topics. This versatility enables a single foundation model to perform multiple tasks, from answering questions to content generation.

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.

The emerging generative AI ecosystem

The generative AI ecosystem is evolving to support the technology's training and application. Specialized hardware provides essential computing power, and cloud platforms facilitate access to this hardware. MLOps and model hub providers offer tools and technologies for adapting and deploying foundation models in end-user applications. Numerous companies are entering the market, providing applications built on foundation models for specific tasks, like assisting customers with service issues.

Exhibit 2

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.

Versatility in Action

CEOs are urged to explore generative AI, viewing it as essential rather than optional. It holds value across various use cases, with manageable economics and technical requirements. CEOs should collaborate with their teams to strategize its implementation, whether as a transformative force or through gradual scaling. Generative AI's organizational impact often stems from existing software features, enhancing productivity for knowledge workers.

The article delves into four industry examples of generative AI applications, showcasing varied resource needs and transformative potential.

Exhibit 3

1: Changing the work of Software Engineering

A software engineering company is enhancing productivity by implementing an AI-based code-completion tool. The off-the-shelf solution integrates with existing coding software, allowing engineers to write code descriptions in natural language. The AI suggests code block variants, accelerating code generation by up to 50%. While more experienced engineers benefit most, the tool cannot replace human expertise, and risks include potential vulnerabilities in AI-generated code. Costs are relatively low, with fixed-fee subscriptions ranging from $10 to $30 per user per month. Implementation involves minimal workflow and policy changes, overseen by a small cross-functional team.

2: Helping relationship managers keep up with the pace of public information and data

A corporate bank invests in a custom generative AI solution to enhance relationship managers' (RMs) productivity. The solution, utilizing a foundation model accessed through an API, scans large documents and provides synthesized answers to RMs' questions. Additional layers ensure a streamlined user experience, integration with company systems, and application of risk controls. The generative AI accelerates the RM's analysis process, potentially capturing overlooked insights and improving job satisfaction. Development costs involve building the user interface and integrations, requiring expertise from a data scientist, machine learning engineer, designer, and front-end developer. Ongoing expenses include software maintenance and API usage costs, varying based on model choice, vendor fees, team size, and time to minimum viable product.

3: Freeing up customer support representatives for higher-value activities

A company optimizes a foundation model for customer service conversations, fine-tuning it on high-quality customer chats and sector-specific Q&A. Operating in a sector with specialized terminology, the company introduces a generative AI customer-service bot to handle most inquiries, aiming for swift, brand-aligned responses. The phased implementation involves internal piloting, learning from employee feedback, and gradually shifting toward customer-facing use cases with human oversight. Generative AI frees up service representatives for higher-value inquiries, enhancing efficiency, job satisfaction, service standards, and customer satisfaction. Significant investments in software, cloud infrastructure, tech talent, and internal coordination are required for this transformative use case. Fine-tuning foundation models costs 2-3 times more than building software layers on top of an API, encompassing talent and third-party cloud computing or API costs.

Lessons CEOs can take away from these examples

The use cases outlined here offer powerful takeaways for CEOs as they embark on the generative AI journey:

• Transformative use cases that offer practical benefits for jobs and the workplace already exist. Companies across sectors, from pharmaceuticals to banking to retail, are standing up a range of use cases to capture value creation potential. Organizations can start small or large, depending on their aspiration.
• Costs of pursuing generative AI vary widely, depending on the use case and the data required for software, cloud infrastructure, technical expertise, and risk mitigation. Companies must take into account risk issues, regardless of use case, and some will require more resources than others.
• While there is merit to getting started fast, building a basic business case first will help companies better navigate their generative AI journeys.

Considerations for getting started

The CEO has a crucial role to play in catalyzing a company’s focus on generative AI. In this closing section, we discuss strategies that CEOs will want to keep in mind as they begin their journey. Many of them echo the responses of senior executives to previous waves of new technology. However, generative AI presents its own challenges, including managing a technology moving at a speed not seen in previous technology transitions.

• Organizing for Generative AI:Convene a cross-functional leadership team representing various business functions.Identify and prioritize high-value use cases with coordinated implementation.
• Reimagining End-to-End Domains:Focus on transformative potential across business domains rather than sporadic use cases.Envision a target state enabled by generative AI working in tandem with traditional AI applications.
• Enabling a Fully Loaded Technology Stack:Assess technical capabilities including computing resources, data systems, and model access. Design a scalable data architecture with governance and security procedures.
• Building a 'Lighthouse':Adopt a proactive approach to showcase internal impacts through a "lighthouse" project. Implement proofs of concept deliberately and coordinate efforts across functions.
• Balancing Risk and Value Creation:Establish ethical principles and guidelines for generative AI use.Understand risks associated with each use case, aligning with overall risk tolerance.
• Applying an Ecosystem Approach to Partnerships:Build alliances with partners addressing generative AI needs at various tech stack levels.Leverage partnerships to accelerate execution, avoiding vendor lock-in.
• Focusing on Required Talent and Skills:Identify required capabilities based on prioritized use cases. Build technical capabilities and upskill the workforce with a mix of talent across functions.
• Navigating the Regulatory Landscape:Stay current with generative AI regulation, particularly related to data protection and intellectual property rights. Adapt working approaches to handle evolving regulatory challenges.
• Concluding:Tailor the generative AI journey based on the company's ambition and risk appetite. Learn by doing and embark on experiments to discover the best approach for the organization.

Conclusion: Taking the First Step

While generative AI is still new, it's worth exploring. Companies should start with small experiments and learn as they go. CEOs need to lead the way, adapting their approach based on what works best for their company.

In the end, generative AI opens up exciting possibilities. By taking the first step and learning from experience, businesses can stay ahead in the ever-changing world of artificial intelligence.

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