With the hype cycle of AI’s actual use progressing from innovation through inflated expectations and into some risk of the trough of disillusionment, the need for AI Orchestration platforms like Contextual will only increase. Specifically, these platforms provide the critical infrastructure—the picks and shovels—necessary to translate the excitement of AI technology innovation in its raw form into the day-to-day applications that will make existing enterprise processes, tasks, and workflows more efficient, cost-effective, and accurate.

At Contextual, we look at the drivers for AI Orchestration solutions across a few fundamental realities that can either enable or hinder an enterprise AI solution. Specifically:

Federated Data

The data leveraged in an AI solution does not exist in one place, and therefore the AI that will process that data doesn’t exist in one place. While you may use your CRM’s co-pilot to assist in drafting proposals for prospects, if you want to actually analyze the prospect’s website, scan recent and timely news that might impact their buying posture, asses broad industry trends and run a machine learning prediction model on outcomes based on different products, that’s not getting done in Salesforce APEX classes. AI solutions often leverage data from across the enterprise as well as sources from 3rd party systems in order to maximize the AIs analytical, predictive, or generative powers.

Federated AI

Likewise, production-level AI solutions are typically multi-model, meaning they leverage multiple AI tools or models and integrate multiple AI processing steps into a precise workflow that satisfies the objective. That can include using different GPTs generally based on their efficacy (perhaps Google Gemini is better than OpenAI at a given task), employing simple RAG-style LLMs like OpenAI Assistants to tune results to your specific dataset, or perhaps combining different types of AIs entirely, such as image classification from Anthropic Claude with trained classification LLMs from RapidAPI. With hundreds of AI tools (and growing), from web scraping and summarization to machine learning platforms and even AIs hyper-focused on identifying insect species, effective AI Orchestration requires the ability to connect to, maintain sessions amongst, organize data within, and stage requests to many federated AIs.

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Asynchronous Processing and Compute 

Given the complexity of the data sources and the breadth of the AI tools required to achieve a desired outcome, the workflow and compute necessary to run true AI solutions is often asynchronous and event-driven, dynamically scaled, and ultimately residing alongside existing enterprise systems—enhancing existing enterprise processes and tasks. The combination of data, event processing, low-code AI-assisted development, simplified connections to third-party data sources and AIs, and zero DevOps required to make a solution live makes AI orchestration platforms like Contextual critical to moving past the hype and into the happening.

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