The Building Blocks of an AI-Enriched Business

Production-level solutions for AI-enhanced workflows, processes, and tasks are just starting to be deployed across the enterprise. Gartner predicts that by 2026, 80% of enterprises will have used generative AI APIs, models, and/or deployed GenAI-enabled applications in production environments. Ultimately, these will not be single or standalone solutions. Instead, enterprises will look to add AI enhancements in hundreds of different areas of their business.

Seamlessly ‘plugging AI’ into these functions requires an AI orchestration platform, like Contextual, created with the following critical building blocks. These components make the design and implementation of AI-enriched business applications - from simple task automations to completely new solutions - fast to build and ready for production scale.

Data

Enterprises will discover that the breadth of data they have available to use in AI solutions will directly correlate to the value of the solution itself. Data will be pulled both from internal systems, including CRM and ERP platforms, along with additional data from third party sources. AI solutions will have iterative steps that manipulate, transform and enhance data, requiring a platform that makes it simple to create and extend data sources in a standardized manner.

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Contextual leverages JSON Schema for defining your data objects. Coupled with our SolutionAI platform, designing your AI data sets is seamless and easy.

Integrations

Successful AI solutions will often span multiple AI models or tools, including custom RAG-enhanced Assistants, general LLMs and purpose-built generative AI tools for functions like image analysis, data classification and web crawling and summarization. Streamlining the connections and reuse of these tools will be a critical value for AI orchestration platforms, which is why Contextual includes a full Services Catalog with pre-built integrations to jumpstart AI solution design.

Workflow Logic

While AIs like LLMs can respond to a range of requests in very ‘human’ focused language, a production-level system will require more traditional code to manage the actual workflow, system integration and processing. Low-code, visual solutions like Contextual provides with its flow editor can provide a perfect balance for less-technical team members to administer and manage while also providing the power of full code creation. 

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Scale 

Ultimately, AI solutions will have dramatically different scaling requirements based on use case. For a business using AI to prioritize thousands of work orders per day, the system needs to easily scale both in allocated CPU and memory as well as horizontally across multiple instances. For a business that is leveraging AI on a more ad-hoc basis or to streamline an individual task, the system needs to minimize cost in order to achieve measurable ROI. Unlike other business process management solutions, Contextual’s usage-based model is designed with fairness in mind.

With those four elements in hand - data, integrations, workflow logic and scaled compute - businesses can rapidly design, develop and deploy production AI systems across a range of business functions with positive ROI at the center. Interested in a free brainstorming session on how AI could positively impact your business? Get in touch today.