Feeling pressure to keep pace with AI innovations, many businesses have constructed the software version of a house of cards, hastily adding large language models (LLMs) and AI onto anything and everything they can. This haphazard approach is not sustainable. If businesses want to integrate AI across applications and workflows at scale, they must first adopt a cohesive AI strategy. Perhaps counterintuitively, while leveraging AI may be the finish line for that strategy, it can also be the starting point, with AI-powered composability.
What is Composability?
AI-powered composability is the evolution of composable architecture.
Composable architecture is an approach to building software that uses individual components, much like LEGO bricks. Each component performs a specific task or group of tasks, and like LEGOs, components can be swapped, combined and reconfigured to create new functions. Using this modular strategy, developers can weave functions together, all connected by application program interfaces (APIs), to create highly complex, flexible and scalable systems. No house-of-cards here.
The next phase of composability, known as modern composability, simplified integrating new technology across organizations by breaking integrations down into smaller, composable elements and providing a framework to organize them.
This is essential for businesses. Powerful technology is emerging faster than ever but businesses that lack a solid AI foundation are struggling to keep up with the pace of innovation. Like a game of IT whack-a-mole, by the time one integration is complete, it’s time for the next.
Modern composability gives organizations the agility to adapt to technology as fast as it emerges. It allows them to design, modify and optimize their integration workflows across business departments and functions.
What AI-Powered Composability Unlocks for Enterprises
Today, businesses are swiftly adapting to AI, and soon, every application will likely have predictive or generative AI capabilities. To ensure they can take full advantage of the power of AI, businesses need the next generation of composability — AI-powered composability. This innovative approach helps integrate AI seamlessly into existing business systems and architectures, enhancing their functionality and efficiency. AI-powered composability is pivotal as it lowers the entry barriers, making sophisticated, contextualized AI solutions more accessible to organizations. This enables them to be at the forefront of AI adoption in the workforce.
The core advantages of AI-powered composability include enabling virtually anyone in the organization to develop and integrate applications using straightforward natural language commands. First, by incorporating tools like the Einstein 1 Platform into an organization’s core integration and automation tooling, composing becomes as easy as typing — users simply provide a prompt detailing their desired use case and Einstein will generate the required code or integration. Second, it enables the use of AI agents — systems designed to help people with everyday tasks.
Furthermore, AI-powered composability facilitates the deployment of AI agents that can perform a variety of tasks autonomously. These agents can handle operations such as processing orders in back-office systems, issuing store credits to customers, or coordinating with suppliers to manage inventory before a significant order. This capability allows AI assistants to act as an extension of the team, automating end-to-end business processes and significantly enhancing operational efficiency.
AI-Composability Provides a Stable Foundation for an AI Enterprise
Businesses today simply can’t afford to take a piecemeal, reactive approach to implementing AI. Without a strong foundation and cohesive AI strategy in place, they will not be able to keep up with the pace of innovation.
Fortunately, composability is an architecture and strategy all in one. It helps businesses not only build AI apps and integrations but also infuse AI across the organization and into their flows of work to maximize the value of their AI investments.
That’s architecture for a stable AI future.
