Editor’s Note: This post was originally published June 15, 2021 and has been updated with new information and resources.
Implementing AI in today’s rapidly changing financial landscape is crucial. It’s not a question of whether organizations will use AI, but rather when and how they plan to integrate it into their business operations. The key decision to make is whether to develop AI in-house, purchase off-the-shelf solutions, or acquire a custom AI solution. Here’s a breakdown of the options available, what each has to offer, and what to consider when selecting an AI solution.
Surveys conducted among major financial organizations have shown that there is widespread acknowledgment of the need to leverage AI to make use of complex data and mitigate business risks. According to a survey by MIT Sloan Management Review, over half of organizations are currently implementing AI (57 percent), have a strategic implementation plan in place (59 percent), and understand how AI can create value for their businesses (70 percent).
A significant number of companies have attempted to develop their own AI solutions, but only a small percentage have achieved substantial financial benefits from these endeavors.
Success in AI implementation hinges on various factors, with the choice of development platform playing a critical role in the overall outcome of the project.
DIY AI – Watch out for hidden costs
Many large corporations have dedicated data science departments, while smaller organizations are just beginning to explore AI. These teams often attempt to build their own AI models, with varying degrees of success.
Reports indicate that nearly 80 percent of AI projects fail to scale beyond proof of concept. Challenges often arise in standardizing the model building, training, deployment, and monitoring processes, leading to limitations in successful implementation.
While there are benefits to in-house development, such as full control over projects and platform ownership, challenges can emerge due to lack of expertise and unforeseen costs. Even successful projects may quickly become outdated as commercial vendors release newer, more efficient solutions.
Open-source software is commonly used for building AI platforms due to its affordability, but it comes with limitations and potential risks if not tailored to specific use cases or business requirements. Proprietary systems, on the other hand, may involve hosting complexities and ongoing investment for updates. Developing in-house AI solutions can be time-consuming and resource-intensive, especially for organizations with limited AI expertise.
Off-the-shelf, market-ready models
Leading companies have developed off-the-shelf AI solutions that are ready for immediate deployment on a global scale.
These solutions leverage vast amounts of data from daily transactions to enhance accuracy and results in industries such as retail, banking, and payments. Utilizing advanced data consortia, AI and ML solutions access real-time data to safeguard digital interactions and enhance operational efficiency.
Market-ready AI models offer businesses the advantage of quick deployment, improved scalability, and user-friendly interfaces. These solutions provide immediate return on investment and are designed to deliver superior results compared to existing systems.
Some innovators in the AI space are leveraging global network intelligence to develop off-the-shelf AI solutions that are easy to deploy and highly effective. These solutions integrate seamlessly with existing technology and yield significant improvements in performance.
Customized solutions – Tailored expertise and models
There are instances where custom AI solutions are necessary to address unique business challenges.
Collaborating with experienced third-party developers who possess expertise in data science, development, and specific industry knowledge is essential for successful custom AI projects. These developers can create personalized models that address the specific needs of their clients and provide insights on historical data requirements.
Organizations aiming to implement custom AI solutions should expect clear project goals, proof of value, effective integration with existing technologies, and scalability. Working with an experienced team that follows a structured AI development process can lead to significant improvements in performance and operational efficiency.
Finding the right AI implementation strategy
Choosing the right AI implementation strategy depends on the organization’s size, timeline, and specific challenges.
While there are cost-effective options such as open-source or DIY solutions, they often result in incomplete or outdated models and hidden expenses. Market-ready AI models are becoming increasingly effective, providing instant results and scalability for various use cases. Custom solutions offer tailored expertise and personalized models to address unique business requirements.
To learn more about market-ready and custom AI solutions, reach out to Brighterion for further information and guidance on selecting the best approach for your business.