Generative Artificial Intelligence (Gen AI) is no longer a futuristic concept—it's here, and enterprises are quickly realizing its transformative potential. But implementing such a powerful tool isn't without its complexities. According to ABI Research, a leading technology research firm, the Gen AI software market is projected to grow from US$10.45 billion in 2023 to over US$176 billion by 2030, driven by its applications in diverse sectors.
Despite this surging growth, many enterprises face challenges in integrating Gen AI into their operations due to corporate red tape, security concerns, and the need for cultural shifts. Reece Hayden, Principal Analyst for AI & Machine Learning at ABI Research, highlights several Gen AI deployment options as businesses aim to overcome these barriers and maximize the value of Gen AI for businesses. As he will point out, each deployment option has its own strengths and weaknesses that must be considered before making the right choice for your organization.
Generative AI Deployment Options
ABI Research identifies four options for enterprises aiming to deploy Gen AI for business applications: Application Programming Interface (APIs), third-party managed services, in-house development, and third-party inference platforms.
Table 1: Deployment Options for Enterprise Generative AI Adoption
|
Deployment Option |
Example |
Explanation |
Positives |
Negatives |
|
Application Programming Interface (API) service |
ChatGPT |
Access managed, third-party AI model through API |
• One-click deployment • No management requirements • Simple integration through API into applications |
• Limited control over versioning and product • Lack of transparency • "Black box" without control over weights • Limited control over data • Security for confidential company information |
|
Third-party managed service |
System Integrator (SI), consultants |
Builds, deploys, and manages AI model or application |
• Requires no AI expertise • Management/monitoring is handled externally |
• Limited day-to-day control or oversight • High cost for compute resources/cloud |
|
In-house developed application |
Leverage open-source or licensed models to build AI applications |
• Complete control over AI development process • Control over data & deployment location |
• High talent requirement and cost involved to acquire talent • Very high Time-to-Value (TTV) (1+ years) |
|
|
Third-party inference platform or framework |
NVIDIA Inference Microservice, OctoStack, Intel AI Platform |
Frameworks that enable developers to build and deploy optimized open-source or pre-trained models "anywhere" |
• Complete control over deployment process • Control over data • Tools, software, and pre-optimized models available to support process • Access to pre-optimized models • Low barriers to deployment |
• Reliant on third-party framework • Limited to certain vendors/tools • Often limited to certain hardware types • High cost compute resources/cloud |
(Source: ABI Research)
API Services—Quick to Deploy, Limited Control
Pros
API Services like ChatGPT offer an easy entry point for businesses new to Gen AI. These services enable enterprises to access AI models developed by third-party providers and integrate them quickly through APIs into various business operations. Hayden notes that this strategy is being used by enterprises to support Proofs of Concept (PoCs) as it requires minimal expertise and limited Capital Expenditure (CAPEX). This makes it a great solution for companies that want to quickly deploy Gen AI.
Cons
However, Hayden emphasizes that the trade-offs of using APIs are striking. Using external AI models limits an organization’s control over data security and customization, which can pose risks for industries handling sensitive information. This also creates portability challenges with high barriers to redeployment. Moreover, long-term adoption may require more control over how the models are developed and managed. APIs, similar to cloud services, also bring high long-term costs, as even an API call is paid for. This disincentives deployment at scale.
Third-Party Managed Services—Outsourcing the Heavy Lifting
Pros
For enterprises without in-house AI expertise, third-party managed services offer a more tailored solution. This option allows companies to outsource AI model building, deployment, and management to System Integrators (SIs) or consulting firms. This approach to Gen AI deployment is ideal for organizations that need a custom AI solution, but lack the internal resources to develop it themselves.
Cons
Hayden notes that while this deployment option reduces the burden on the enterprise, it comes with a high CAPEX. Furthermore, outsourcing management to external vendors means that day-to-day oversight and control may be limited, slowing down the innovation process. Enterprises must weigh the benefits of managed services against the potential risks of high costs and limited control.
In-House Development—Full Control, Higher Costs
Pros
In-house development is the most comprehensive strategy for enterprises looking to fully control their Gen AI deployment. By leveraging open-source models or licensed frameworks, businesses can build AI applications that align precisely with their needs. As Hayden highlights, this strategy provides companies with the greatest control over data security, model customization, and the deployment environment.
Cons
However, this approach to Gen AI requires a significant investment in both talent and infrastructure. Hayden warns that the Time-to-Value (TTV) for in-house development is typically longer, often stretching beyond a year. While the long-term benefits can be substantial, including complete ownership of the AI system, the upfront costs and talent acquisition challenges may make this strategy unsuitable for businesses lacking substantial resources.
Third-Party Inference Platforms—Balanced Control and Flexibility
Pros
For companies looking for a middle ground between API services and in-house development, third-party inference platforms provide a compelling option. Providers like NVIDIA and Intel offer platforms that allow businesses to deploy pre-trained or open-source AI models, while retaining a significant degree of control over customization and data management. These platforms are designed to lower deployment barriers, while providing the flexibility to scale over time. Hayden posits that this strategy gives enterprises access to cutting-edge AI technology without the full burden of building data science and AI infrastructure and processes from scratch.
Cons
The downside of this strategy, however, is that businesses remain reliant on the platform provider’s tools and infrastructure, which can bring lock-in risks, limiting long run flexibility. Nevertheless, for enterprises looking to balance innovation with practical constraints, third-party inference platforms offer a viable solution.
Overcoming Cultural and Organizational Barriers
Beyond the technical and commercial challenges, Hayden emphasizes the cultural and organizational shifts required for successful Gen AI implementation. Many enterprises, particularly those in financial, governmental, or healthcare sectors, have legitimate data privacy concerns. Internal governance forbids them from exposing their internal data to third-parties either in the cloud or on hosted servers. This has caused hesitancy toward the widescale adoption of Gen AI solutions.
Another critical barrier to Gen AI adoption is the distribution of talent. Not every organization has the in-house expertise needed to manage and scale Gen AI applications. This implementation challenge is further underpinned by brownfield or legacy systems. Those operating in sectors like telecommunications or fleet management, may struggle to implement Gen AI without significant investment in new infrastructure or integration services.
Final Thoughts
Implementing Gen AI at an enterprise level is a journey, not a quick fix. Businesses must balance the desire for immediate results with the long-term strategic investments needed to create value through optimized, and fully integrated Gen AI solutions. Whether you choose to use API services, third-party platforms, or in-house development, the key to success lies in understanding both the technological and organizational shifts required. With a thoughtful approach, enterprises can unlock the full potential of Gen AI and drive innovation across their business functions.
For more Gen AI deployment best practices, check out the following resources ABI Research offers:
About the Author
Reece Hayden, Principal Analyst
As part of ABI Research’s strategic technologies team, Principal Analyst Reece Hayden leads the Artificial Intelligence (AI) and Machine Learning (ML) research service. His primary focus is uncovering the technical, commercial, and economic opportunities in AI software and AI markets. Reece explores AI software across the complete value chain, with a cross-vertical and global viewpoint, to provide strategic guidance for, among others, enterprises, hardware and software vendors, hyperscalers, system integrators, and communication service providers.
