How Businesses Can Ensure They Acquire All the Benefits of Generative AI

This resource identifies and evaluates generative Artificial Intelligence’s (AI) commercial opportunity for enterprises and assesses some of the key questions that Chief Technology Officers (CTOs) and Chief Information Officers (CIOs) will be focusing on over the next decade as they aim to capture all the benefits of the technology.

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Market Overview

  • By 2030, ABI Research expects generative AI to add around US$434.4 billion to 12 industry verticals. But before acquiring all the benefits of generative AI, enterprises must mitigate certain ethical, societal, and business challenges.
  • The three largest industry verticals for generative AI are retail & e-commerce (US$142.1 billion in value-added in 2030), marketing, advertising & creative (US$108.3 billion), and financial services (US$79.4 billion).
  • Enterprises are interested in deploying generative AI, but insufficient corporate governance, solution trustworthiness, internal skills gap, legal confusion, data privacy fears, and many other factors are constraining enterprise adoption.
  • Expect initial enterprise adoption to focus on closed-source models, but as the skills gap declines, additional value will be slow to reach open-source vendors. The Business-to-Business (B2B) market remains immature with few deployments, but these are mostly constrained to closed-source models.
  • At present, generative AI is subject to hallucinations, which impacts performance and constrains use cases, while most models are “black boxes,” meaning that they cannot explain why they produce certain responses. These massive structural issues are likely to have a significant impact on early enterprise adoption. In addition, enterprise unreadiness will slow adoption of generative AI, as most lack strong corporate strategies with employee guidelines, internal governance, and expected business benefits/outcomes.

“Generative AI’s potential for enterprises is huge with use cases across business processes and verticals expecting to see large productivity jumps, quicker time to market for new products/services, and improved customer service. However, the B2B market remains in a very early stage, as enterprises remain in a heavily exploratory/Proof of Concept (PoC) phase.” – Reece Hayden, Senior Analyst at ABI Research

 

 

Key Decision Items

Aim to Combine Open- and Closed-Source Models

An industry-wide hybrid model that meshes open- and closed-source models may be challenging to build, but that should be the goal with generative AI, as it will create significant enterprise value. Enterprises will benefit from closed-source functions to protect Intellectual Property (IP) and contextualize models to use cases, while an open-source community approach will speed innovation and lower barriers to adoption.

Develop a Comprehensive Corporate Strategy before Adopting Generative AI

Undefined generative AI corporate strategies will slow adoption at best, but contribute to internal fragmentation at worst. Enterprises are, in most cases, not ready for generative AI given the transformative impact it will have across business units. Before starting to deploy, enterprises need to create a strong corporate strategy that includes governance, legal approach, employee guidelines, and beneficial business outcomes.

Smaller Firms Aren’t Ready for Generative AI Yet

Small and Medium Enterprises (SMEs) will struggle to deploy generative AI safely now. Security concerns make Application Programming Interface (API) access to closed-source models largely unworkable. On top of this, the cost of owning and deploying a model on-premises or in the cloud remains prohibitively high and most do not have the skillset to deal with the huge operational complexities.

Generative AI Will Come in Three Waves

Enterprise use cases of generative AI are numerous and cross over into each vertical, but deployment will come in three waves. The first wave of generative AI  benefits will be limited to employee augmentation through which content generation, data analysis tools, etc., will help productivity gains. The second wave of benefits will allow businesses to deploy new services and start to build new internal processes. The third wave will be the biggest value creator, enabling automation and optimization of business processes.

Focus on “Low-Hanging” Use Cases for Generative AI

Generative AI remains in an early stage of enterprise B2B maturity, and issues persist around trustworthiness, performance, and deployment costs. This means that enterprises should look to deploy generative AI now to access “low-hanging” valuable use cases around employee productivity and content generation. Although some verticals like manufacturing will benefit most in the “third wave” of generative AI use cases, deploying now in low-risk use cases will help enterprises adapt to new technologies, frameworks, governance, and regulation.

For example, Adobe has deployed generative AI to augment Creative Cloud, while Allen & Overy has introduced Harvey AI to support employee research and contract drafting. These use cases have a high degree of human oversight, but it will take time for enterprises to trust generative AI for more mission-critical use cases without human oversight.

“Responsible AI” Is a Key Piece Missing from the Generative AI Puzzle

Regulatory frameworks that focus on “responsible AI” will likely drive enterprise adoption. Trustworthiness, data privacy, IP, and workforce fears are currently making it harder for enterprises to deploy generative AI. Regulatory frameworks enforcing guardrails, watermarks, etc., will support enterprise adoption.

However, regulation alone will not be sufficient. Therefore, vendors must implement “responsible AI” practices. Some are already doing so; for example, NVIDIA has partnered with Getty Images to access its IP for training purposes. This move highlights its responsible approach to gathering IP for training. ABI Research expects that enterprises will respond well to “responsible AI” strategies.

Key Market Players to Watch

Dig Deeper for the Full Picture

Get a better understanding of the enterprise use cases for generative AI, optimal implementation strategies, and other decisive considerations by downloading ABI Research’s  Generative AI Business Outcomes: Identifying Enterprise Commercial Opportunities report.

Not ready for the report yet? Check out the following Research Highlight What Will It Take for Generative AI Suppliers to Meet the Nearly US$60 Billion Market Size?

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