Understanding the Generative AI Value Chain A CEO Guide
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Every CEO is looking ahead to the generative AI revolution, hoping to capitalize on the technology’s enormous economic potential. Given its recent emergence, many leaders are still working out how to best use this powerful technology.

In this article, we’ll untangle the GenAI value chain, highlighting how businesses can unlock significant value by applying generative AI across various business areas. We’ll also look at the current landscape and opportunities for tech companies aiming to ride the wave of generative AI’s growing popularity. 

Let’s start with some key insights from a BCG survey that provide a snapshot of where things stand with generative AI initiatives: 

  • 71% of executives are planning to increase their tech investments in 2024. 

  • 85% say they’ll up their spending on AI and GenAI this year. 

  • 89% of leaders see AI and GenAI as top tech priorities for 2024. 

  • 54% believe AI will help cut costs in 2024. 

  • By fully adopting a variety of GenAI solutions, companies could boost productivity by 10% to 20%, or even more. 

Understanding the GenAI value chain is vital for several reasons. It helps leaders create a roadmap to tap into this tech’s full potential. It also encourages rethinking business processes and roles to get the most out of generative AI. This knowledge is crucial for CEOs, CTOs, and CIOs to make informed tech investments and decisions, gaining a competitive edge and planning for growth. 

Finally, exploring the GenAI value chain enables executives to discover and address pain areas in their operations. This approach includes identifying talent gaps within their teams, upgrading technological infrastructure, and executing strategies to sustain a competitive advantage in the constantly shifting market. 

What Is Generative AI? 

Before we dive into the generative AI value chain, let’s clarify what generative AI is and how it stands apart from the traditional AI technologies businesses often use for tasks like predicting customer churn, forecasting product demand, or suggesting the next best product. 

The standout feature of generative AI is its ability to generate new digital content. This can take many forms, from written text (like articles or question responses) to visuals such as photorealistic images or paintings, videos, and even 3-D environments suitable for video games

While most generative AI models focus on producing content in one specific format, there are emerging multimodal models. These can create, for instance, a complete webpage or presentation slide featuring both text and graphics from just a user prompt

This capability stems from training neural networks—a kind of deep learning algorithm—on vast datasets and utilizing “attention mechanisms.” These mechanisms enable the AI to detect word patterns, understand relationships, and grasp the context of a prompt, making generative AI adept at creating relevant and coherent new content. 

Though traditional AI may also employ neural networks and attention mechanisms, it’s not designed to produce new content in the way generative AI does. 

The Five Links in the Generative AI Value Chain 

The generative AI value chain represents a comprehensive framework that illustrates the progression from the foundational elements required to build generative AI models, through to the hardware, platforms, and infrastructure that support their deployment, and finally to the applications and services that utilize these models to create real-world solutions. Here’s a closer look at each link in the chain

Link 1 – Hardware: The Foundation

At the heart of the generative AI ecosystem lies the hardware, the bedrock upon which Large Language Models (LLMs) and Small Language Models (SLMs) are developed and deployed.  

This segment highlights the indispensable role of advanced chips and high-performance computing resources, crucial for handling the massive datasets that define generative AI’s lifecycle. 

NVIDIA emerges as the leader in this first link, with its GPUs celebrated for their capacity to handle parallel processing tasks efficiently. These processors are essential for conducting the complex calculations needed for AI model training, offering the rapid model iteration and scalability necessary for the expanding field of generative AI. 

This dynamic between the evolution of generative AI and advancements in computing technology signals a future where AI’s potential continuously grows.  

Link 2 – Cloud: The Scalability  

Moving further up the generative AI value chain, cloud platforms shine as key players, giving the necessary setup for spreading AI models far and wide, and letting companies use powerful computer resources whenever they need them

Is expected that tech leaders spend more on cloud services in 2024. Gartner predicted that the global cloud market would hit $678.8 billion in 2024, growing by 20.4% from $563.6 billion in 2023. The biggest growth is expected in cloud infrastructure, which is the base for running AI, software, and apps

The reason behind the projected rise is multifaceted. Cloud infrastructure serves as the backbone for generative AI, offering the computational power and scalability required to train complex AI models. These models demand extensive data processing capabilities that only cloud infrastructure can provide efficiently and cost-effectively. 

Moreover, cloud platforms facilitate the rapid deployment and iteration of generative AI applications, allowing organizations to innovate and adapt to market demands swiftly. The flexibility of cloud services enables companies to scale their AI operations up or down based on current needs, without the hefty investments and limitations associated with on-premise hardware. 

Why companies are spending more in cloud.

Link 3 – LLMs & SLMs: Core Intelligence 

At the heart of the generative AI ecosystem are the foundation models, encompassing both Large Language Models (LLMs) and Small Language Models (SLMs). These sophisticated deep learning models serve as the backbone of generative AI, pre-trained on vast datasets to generate specific types of content.  

LLMs, such as GPT-3 and GPT-4, are renowned for their ability to produce content that rivals human quality, underpinning a wide range of applications requiring sophisticated language processing. They excel at generating diverse and high-quality content across various tasks, making them suitable for applications requiring nuanced output

In contrast, SLMs offer a streamlined alternative optimized for scenarios where computational resources are limited or speed and efficiency are paramount. Despite having fewer parameters and a simplified computational framework, SLMs retain effectiveness and prove invaluable in environments like mobile customer service applications, where they deliver rapid responses within computational constraints. 

The creation of these foundation models is a complex endeavor, demanding a blend of specialized knowledge in data preparation, model architecture, and an iterative process of training and fine-tuning.  

Notably, the development landscape is evolving, with ongoing efforts aimed at devising smaller, more efficient models that retain effectiveness while mitigating costs and computational demands.  

Link 4 – Apps: The Value Creators  

Applications are where generative AI translates its theoretical potential into tangible benefits for end-users, marking the segment with the highest growth and value-creation potential. 

 The agility and innovation in application development are crucial for capturing market share, with a direct impact on enhancing customer experiences, resolving service issues, and crafting targeted marketing communications. 

This burgeoning market segment promises significant opportunities for both established technology firms and newcomers. Generative AI’s transformative power is reshaping entire industries, from finance to public sector applications, showcasing its broad appeal and potential.  

Rapid adoption rates, such as ChatGPT’s milestone of a million users within days of its release, underscore the technology’s disruptive impact. These applications fall into two main categories: those that utilize foundation models as-is with minor customizations and those that fine-tune these models with specific data to meet unique use case requirements.  

Consider the example of Harvey, a generative AI application designed to answer legal queries. By feeding GPT-3 legal datasets and fine-tuning prompts, Harvey’s creators were able to generate superior legal documents, showcasing the potential for highly specialized applications. Similarly, a bank might enhance its customer service chatbots by integrating conversational data, and continuously refining the AI to improve user interactions. 

In summary, the application layer of generative AI is where the conceptual meets the practical, delivering solutions that not only drive efficiency and innovation but also open new avenues for growth and competitive differentiation. 

Link 5 – Talent Acquisition: The Human Element  

Talent acquisition is not only crucial but also indispensable within the generative AI value chain, as it permeates through all other links. Skilled professionals are essential for driving innovation and ensuring that progress continues at a steady pace. They play a vital role in maintaining the integrity and security of systems, guarding against biases and toxicity inherent in AI algorithms. 

Without a talented workforce, advancements in hardware, cloud platforms, foundation models, and applications would be stymied. Talent is the driving force behind the development of robust AI systems that meet ethical standards and deliver reliable performance.  

Organizations that invest in talent acquisition gain access to diverse skill sets and fresh perspectives, enabling them to stay ahead of the curve and capitalize on emerging opportunities. 

When a business lacks expertise in-house, outsourcing becomes an efficient option for filling skill shortages and boosting creativity. Organizations can enhance their internal capabilities by leveraging specialized expertise from external talent pools. Outsourcing also provides scalability and flexibility, enabling businesses to respond to changing project requirements and technology demands. 

In conclusion, talent acquisition is not merely a component of the generative AI value chain; it is the lifeblood that sustains innovation and drives real value.  

Value Chain of Generative AI

Who Are the Winners in the Generative AI Value Chain 

Navigating the generative AI value chain is a complex challenge. With 77% of business leaders worried their company is missing out on generative AI and nearly half lacking clear guidelines on AI use in the workplace, the path forward can seem overwhelming. 

In fact, the summit of winners in the generative AI adoption landscape is sparsely populated. It could be said that the victors are that 10% of companies already capturing real value from GenAI because: 

  • They’ve mapped out a clear roadmap: Identifying the strategic partnerships they need, the goals they aim to achieve, the technology providers they’ll work with, and a plan to integrate people into the equation. 

  • They possess the right technological infrastructure to scale GenAI across the organization. 

  • They’ve pinpointed specific areas to start. For instance, they might focus on enhancing customer service or optimizing internal HR processes. 

  • They understand the cost of implementing and operating both models (LLMs & SLMs) in relation to the value they’ll bring to the company. 

  • They’re already measuring the impact this technology has on their teams. 

  • They’ve assessed the flexibility of their chosen solution to adapt to the changing needs of the business and the evolving AI market. 

  • They’ve fully or partially solved one of the biggest hurdles in deriving value from generative AI: the lack of necessary tech talent

Then there are other players who still have the opportunity to start leveraging the value that generative AI can bring to businesses, thus avoiding falling too far behind the early adopters. According to BCG, there’s 50% of organizations in the pilot explorer phase that are beginning their journey on a small scale to better understand the technology and how to extract value from it. 

Indeed, two-thirds of execs anticipate it will take at least two years for AI and GenAI to fully transcend the hype surrounding them. This means there’s still time to organize, pivot, test, and adapt to market and consumer demands, who will increasingly expect more interactive, personalized experiences. 

The situation for the remaining 40% is a bit more challenging. These are the observers because they haven’t yet taken any tangible action regarding GenAI. They are organizations that probably haven’t started a digital transformation process, or if they have, are in a very early stage. 

The issue these businesses face is that, aside from not having the proper technological infrastructure, they have a concerning skill gap that hinders progress in their projects. Their executives still don’t fully understand the technology and, consequently, can’t see the value it could provide to their company. 

However, it’s not a lost cause. It’s important to highlight that the gap between the 10% of generative AI champions and the rest isn’t too wide yet. The reason is simple: While we’ll start to see more tangible value from generative AI in 2024, this technology is still in an early phase and hasn’t yet demonstrated its full potential. 


In essence, generative AI, like all cutting-edge technologies, doesn’t operate in isolation. It flourishes within a mature digital ecosystem, unlocking unparalleled potential. For organizations looking to embrace generative AI, here’s a streamlined guide to getting started: 

  1. Deep Digital Transformation: Generative AI requires a robust data processing infrastructure. It’s not just about adopting a new tool; it’s about transforming your organization to harness the power of advanced data analytics. 

  1. Identify Value Areas: Determine where generative AI can make the biggest impact. Look for tasks that generative AI can perform more efficiently, enhancing outcomes beyond traditional methods. 

  1. Strategic Partnerships: Journeying into generative AI isn’t a solo venture. You’ll need technology providers, consulting services for seamless implementation, staffing agencies, and training partners to equip your team with the necessary skills. 

  1. Tech Talent is Key: A skilled team is essential. This includes: 

  • Engineers and data scientists who build the foundation. 

  • AI and ML specialists who bring the technology to life. 

  • Architects and AI ethics experts who ensure scalability and ethical application. Collaboration among these experts is crucial for developing generative AI solutions that are not only advanced but also secure, ethical, and impactful. 

  1. Measure Outcomes: It’s vital to track how well generative AI meets its goals, like improving productivity or automating processes. Measuring outcomes ensures you can see the value generative AI brings to your organization. 

  1. Maximize ROI: Embracing generative AI is a considerable commitment of time, resources, and capital. However, documenting results can substantiate this investment by showcasing tangible benefits, such as cost savings, increased revenue, and enhanced customer satisfaction. 

  1. Embrace Cultural Change: The success of generative AI goes beyond technology; it requires a cultural shift. Visible improvements and benefits from generative AI encourage teams to welcome and integrate new technologies more willingly. 

Standing at the threshold of digital transformation with generative AI? Let’s talk. At InclusionCloud, we’re at the forefront of crafting generative AI solutions that do more than just streamline operations—they redefine how you compete in your industry

Don’t miss out on the wealth of insights and breakthroughs we share on LinkedIn. InclusionCloud is your go-to source for everything from deep dives into generative AI to the latest in tech trends that can elevate your business strategy. 

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