What Will It Take for Generative AI Suppliers to Meet the Nearly US$60 Billion Market Size?

For generative Artificial Intelligence (AI) market players, capturing value requires stakeholders to focus on Business-to-Business (B2B) enterprise adoption. The Business-to-Consumer (B2C) market revenue opportunity will steadily grow, but the enterprise domain remains largely untouched. This resource evaluates the generative AI enterprise market and provides strategic guidance on how various stakeholders on the supply side can seize this not yet fully-tapped space.

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Market Overview

  • The enterprise generative AI market is only recently waking up to the Large Language Model (LLM) opportunity. ChatGPT’s release in 2022 precipitated huge commercial and technological strides.
  • Some of the key market trends right now include demand for greater transparency and explainability regarding AI models, greater accessibility to multi-modal generative AI tools, a debate between open- and closed-source models, enterprise data privacy and misuse concerns, low/no-code tooling, edge deployments of generative AI, and more.
  • The enterprise market might be ready to explore and, in a few isolated cases, start to deploy generative AI, but the supply side of the market is far from commercially ready. Generative AI is currently trying to navigate its huge operational cost problem without clearly defined and mature revenue models.
  • Most stakeholders across the supply chain remain reliant on external funding from Venture Capitalists (VCs))/investors or internal subsidization from other business units. Subsequently, the supply side must quickly assess the available revenue opportunities associated with generative AI and begin to deploy these, as this will determine the market leaders moving forward.        
  • By 2030, ABI Research expects the generative AI supply chain to be a US$56.8 billion market size, with application developers (US$21.5 billion) and enterprise services (US$17.9 billion) being the most significant segments.
  • Each node with the generative AI supply chain will be able to create huge revenue streams. ABI Research assumes that, over the next few years, revenue models will be identified, and the market will experience huge growth in the B2B market.

A highly competitive market with an undefined B2B enterprise space, lack of a clear business model, a deepening cost crisis, and an uncertain regulatory future will make it tough to traverse the generative AI market, especially in the early days .” – Reece Hayden, Senior Analyst at ABI Research


Key Decision Items

Choose a Viable Revenue Model

The cost crisis may be deepening, but plenty of novel monetization strategies are open to stakeholders as enterprises look to deploy generative AI. The more successful strategies will be those that actively support the enterprise deployment process; for example, transformation consulting, application development platforms, and enterprise LLM services (i.e., fine-tuning applications on enterprise data). While the freemium model is the most popular for now, it may not be sustainable. Other revenue models are expected to be deployed as stakeholders aim to turn profits in the face of significant costs associated with AI training and running LLMs.

Build “Fine-Tuned” Generative AI Applications

The combination of powerful open-source models and low/no-code Machine Learning (ML) service tools will unlock huge opportunities for enterprise adoption. Unlocking enterprise value is about fine-tuned applications. This will mean that the models are designed for a specific task, and enterprises are provided context for their generative AI models.  Open-source models and simple-to-use ML service tools will empower enterprises and stakeholders to build and deploy fine-tuned applications cheaply and easily using enterprise data sources. Before this though, the B2B market will remain stagnant, as trustworthiness, performance, reliability, and privacy concerns remain high. On the flip side though, open-source LLMs will also lower barriers to entry, making competition increasingly stiff for stakeholders.

Big Opportunities for Business Consultants

Business consultants have the capacity to lead the market, but only by building an ecosystem of stakeholders (foundation model, ML services, data generation). Generative AI requires a massive overhaul of technology and operations. Consultants have the operational change management skills necessary to support and optimize this process. System Integrators (SIs) will, of course, play a role, but their core offering is technical, not operational, so they may struggle with retraining, process overhaul, and regulatory frameworks. Currently, there is plenty of room in the market for all, but as we move forward, this node in the supply chain will be very interesting to watch.

The Pivotal Role of Data and ML Service Providers  

Data availability is drying up for stakeholders, which will turn heads toward data service providers. Synthetic data services, data curators, and enterprise data trainers will increasingly gain value in the marketplace as data privacy and other concerns make data for training and fine-tuning less accessible. Recent lawsuits concerning the use of copyrighted data, as described later in this report, will speed up this process.

Meanwhile, ML service tools, most prominently optimization platforms, are key to sorting out the commercial issues in the market. Optimization platforms like Deci.AI can help vendors and enterprises lower costs and reduce hardware efforts. Expect stakeholders across the supply chain to partner and embed optimization services within their platform.

Five Steps to Success for Generative AI Market Players

For stakeholders aiming to capture generative AI’s commercial opportunity, ABI Research believes the following actions are critical:

1. Identify and deploy a diverse range of monetization strategies that aim to support B2B adoption.

2. Develop strong commercial strategies with partners across the supply chain.

3. Focus on deploying platforms, applications, and services that target and accelerate the B2B market.

4. Be a leader in “responsible AI” by proactively enforcing safeguards and guardrails to promote enterprise adoption.

5. Effectively employ contextualization, optimization, and integration services to optimize generative AI costs, while tailoring the model to the business-specific needs.

Key Market Players to Watch

Dig Deeper for the Full Picture

ABI Research recently published its Generative AI: Identifying Technology Supply Side Revenue Opportunities report. The report provides clarity into the roles of various technology suppliers in the generative AI market, and how the enterprise segment can be monetized. Download the report today.

This report is part of ABI Research’s AI & Machine Learning Research Service and Generative AI Research Spotlight.