The structure of an AI integration into business is something we’re starting to hear more about as the term “AI” creeps into every corner of the workforce and workplace. One of the elements (aka “platforms”) that is a major controlling factor, as in any software implementation, is known as “orchestration”—and aids in managing the AI solutions, no matter the size or scale of the system.
Fundamentally, an AI orchestration platform goes beyond just simple software integration.
Every major software and cloud company is adding AI agents (generative AI, or “GenAI”) to their platforms—claiming, marketing-wise, that their solution will transform productivity and accelerate growth. But not controlling or managing coordination (as in “orchestration”) may result in having multiple disconnected agents from different vendors that can lead to confusion, security risks and inefficiency, delivering little real value or scalability.
Standards
An orchestration, when framed to include AI, is the connected end-to-end (“E2E”) application of GenAI tools. “AI agents” and automation should rationally extend across workflows, teams and systems. As more AI integration is implemented across all sectors of industry, it is important to apply some level of enterprise standards for consistency and uniformity. The problem is that there are precious few standards in place for AI, whether for integration, validation, authenticity or orchestration.
For example, platforms that use tools such as Model Context Protocol (MCP), external AI tools and agents (e.g., Microsoft Copilot, Salesforce Agentforce or even standalone ChatGPT/Claude instances) can enable and leverage a vast library of searches, actions and authentications. That means relevant capabilities can be used outside of a single MCP, integrating other AI systems. This is a significant architectural shift from Integration Platform-as-a-Service (iPaaS) tools, which are typically about connecting systems exclusively within their own framework.
What Is an AI Agent?
An AI agent (see Fig. 1 for examples) is an autonomous system that perceives its environment through sensors and then reasons, makes decisions and takes actions to achieve specific goals using actuators (functions with “calls to action”). AI agents can operate with a high degree of independence, breaking down complex tasks into smaller subtasks and adapting their strategies over time through large language model learning (LLM).
This workflow structuring is one of the core functions of artificial intelligence as we know it at this point in time. The ability to take a large or small set of complex tasks and break them down into smaller “chunks,” then reassemble them to achieve answers (i.e., “conclusions” or “results”) and produce a single-thread solution applicable to the needs of the inquiry is one of the more prominent capabilities and practices of AI.
What Is Overlooked
Of key importance to the constructs of AI, often missed or overlooked by the media or AI naysayers, is this principle of slicing any task into small enough pieces that it can be “worked on” by compute platforms that leverage a large knowledge base of data that relates to solving the issues (equations, ideas, concepts and such) that pertain to the specifics of the inquiry.
The growth and dimensions of where and how AI will meet future needs is beyond comprehension, which infers that we don’t really know just how successful a system will be and how, if or when it will make sustainable impacts on the workforce. But we do know that without the principles of orchestration, the success of GenAI and associated analytics becomes constrained and less satisfying.
According to Databricks, a leading data-intelligence platform, 99% of global enterprises will be using GenAI by 2027. Many may struggle to scale their projects or find gaps in infrastructure and data integration that can lead to inaccurate or irrelevant results, potentially limiting AI’s impact.
Selecting the right AI agents will ensure your choices in GenAI applications are accurate, scalable and tailored to your business needs. Structurally, the enterprise must still learn and appreciate that standalone models aren’t enough for AI success. The overall leverage part is the need to properly orchestrate systems that will ultimately allow AI agents to free up time and resources for important and new strategic work principles and concepts.
Without outside integration resources, the enterprise will find that AI platforms deliver less accuracy, are more domain-specific and, depending upon the application, are less functional when employing multiple autonomous GenAI outputs. (See Fig. 2 for a graphic representation of AI agent functionality.)
Getting More Work Done
AI agents can integrate with your existing data and applications, to get more work done while by reducing the number of errors in a project; improving efficiency (yielding less time to market); automating repetitive tasks undertaken by employees, such as customer-
service representatives, project managers and accountants; and reducing the time it takes to onboard new customers—while simultaneously generating better and more usable, trusted data.
Agentic AI apps (aka “GenAI”) can seamlessly integrate user experience, autonomous process execution and AI-powered data products to drive real business outcomes.
Business and team categories where data analysis would typically leverage GenAI include business and industry (“BI”), finance, IT, marketing, sales and operations (“ops”). Prescribed specific templates, driven continuously by your data, can be displayed across multiple interfaces or devices in graphical or tabular/columnar forms that simultaneously conform to business practices.
However, accessing, integrating and leveraging that data isn’t always straightforward. Results can help to drive better outcomes (“decisions”) by unlocking the hidden but real power of the data you collect automatically.
For example, in news media segments, producers and newsroom managers likely need to know what local competitors are showing live on the air, as well as how successful their own stories are in terms of viewer demographics, the length of time they stayed connected to a story and any ancillary information (e.g., comments or if additional searches of specifically related topics were conducted).
By continually scrubbing competitors’ live TV screens, capturing the textual lower-third info and tracking when and where a story runs in real time, teams can build a more complete picture of performance. And when a story has a “link” or QR code “to see more information,” data can be collected and corroborated as to when the link was utilized, by whom, how much information was accessed and within what time period the viewer engaged—as well as verifying the authenticity of advertising links for sales purposes.
This is all complicated information gathered from a huge volume of data sources that cannot be collected, calculated or categorized by humans in real time, as was attempted less than a decade ago. Today, this information is timely and very important when making additional content decisions for an upcoming show or a web page with follow-up information. Today’s Nielsen ratings or overnights are seldom useful in a live or breaking-news situation.
The Power of AI Augmentation
The previous examples are not about where AI is replacing a job or task—these are things even a dozen or more staff members could not reliably or continually do, especially at all hours of the broadcast day or overnight. Nonetheless, humans will ultimately make the appropriate decisions, with or without bias or compromise—and those tasks are likely to remain this way for years (maybe decades) to come.
Many up-and-coming software solutions providers are unleashing data “transparency” tools that can be customized for the needs of business and industry. This ensures specialists inside the enterprise can continually improve a company’s performance across various business segments. This is why AI, in general, is becoming so important to senior leadership and division workforces regardless of the industry they support. Faster results, more efficient practices, more trusted communications and better performance—this is what AI is coming to be and is really about.
