While artificial intelligence (AI) is integral to modern business, many organizations struggle to maximize its potential. To truly impress stakeholders and achieve measurable results, a strategic and intelligent approach to AI deployment is essential.
That’s where AI orchestration comes into play. You need a strong plan and the right generative AI capabilities to turn this machine learning into a competitive advantage. Some common mistakes? Deploying AI piecemeal on small processes. Choosing projects that have difficult-to-quantify results. Or having employees using it ad hoc. These mistakes lose sight of the real goal: using AI to meet your wider business goals. Whether that’s to improve efficiency, reduce costs, or enhance customer experiences. You need to deploy AI thoughtfully—and know when to use a different business process automation tool instead.
AI orchestration tools help you stay on course to meet these goals.
In this blog, we’ll explore AI orchestration along with five critical elements for AI integration across the enterprise. From breaking down siloed data stores to leveraging the right AI tools, you’ll walk away with practical tips for creating a cohesive and effective AI-driven enterprise.
What is AI orchestration? (And why does it matter?)
Think of AI orchestration as the conductor of a complex orchestra. You may have a world-class violinist (a generative AI) and a brilliant percussionist (a predictive model), but without a conductor, you just have noise. Orchestration provides the sheet music, the tempo, and the cues to make them all play together in harmony to create a beautiful symphony (your business outcome).
Core benefits of AI orchestration
Increased ROI: Connects disparate AI tools to automate full, end-to-end processes, not just small tasks.
Reduced complexity: Manages all your AI models, data sources, and human workflows from a single AI orchestration platform.
Improved scalability: Easily swap, update, or add new AI models without re-architecting your entire process.
Enhanced data governance: Implements "human-in-the-loop" checkpoints and audit trails, which is critical for compliance.
5 ways make AI orchestration a success across the enterprise
1. Develop a comprehensive AI strategy
It’s human nature to follow trends. The chronic fear of falling behind pushes us to act, often with less information than we really need. Frankly, with AI, falling behind is a real concern. The ones who win in the AI economy will be those who strategically apply AI/ML capabilities to the right problems.
You need to tie it back to business goals.
As Todd Lohr, Principal for KMPG, LLP, said in the 2024 AI Outlook:
“A lot of organizations follow hype cycles, getting enamored with a new technology, and want to just go apply it to their business. But they lose sight of some important questions: What value am I creating for shareholders? What is the experience I'm creating for my employees? What strategic propositions am I trying to accomplish as a business, and how do I use this to pivot or accelerate that strategy? Top-performing organizations stay true to their business strategy and use AI as an accelerant.”
Look to your organizational goals first, then decide where to apply AI. For instance, let’s say you’re an IT leader for a company that wants to drive down business spending related to customer service costs. You brainstorm with that department to discover that shaving even a few minutes off average customer response call times saves the business money. You could very easily train an AI on your company policies and knowledge. This could enable support reps to give answers to customer questions in seconds rather than sifting through multiple business documents on each call. That’s a big win for your company, and the reduction in support time can be directly attributed to your technical intervention. Improving customer service can also make for happier customers.
2. Foster strong, easily flowing data pipelines
AI is nothing without data. Enabling a strong AI enterprise requires deft management of data. Yet, too often, businesses accrue technical debt by walling data off in silos. Financial data is kept in one system; sales numbers in another; customer support tracking in yet another. This gnarled data mess hobbles your ability to activate AI and make smarter decisions efficiently.
Data fabric weaves the data pipelines in these systems together, demolishing the walls between data silos. It provides a unified data management framework, making data more accessible and usable across the enterprise. This allows AI to analyze comprehensive datasets, leading to more accurate insights and better decision-making.
This is one reason to use a wider business process automation platform. A BPA platform offers multiple workflow tools such as a data fabric, process automation tools, and low-code capabilities to make development simple. Connecting these components in one AI orchestration platform facilitates the ability to fully design, automate, and optimize your mission-critical processes. And, critically, it offers the data fabric architecture needed to operationalize your enterprise data in your AI tools.
3. Use the full breadth of AI orchestration tools
It’s easy to lump all AI into one category. But the truth is that AI is a broad term that covers multiple types and formats of machine learning. For example, predictive AI generates a suggestion on what may occur based on given outputs or signals. Another form includes generative AI, which creates content like images, text, or video based on a given prompt. And many modern tools rely on natural language processing (NLP), a technique that uses AI to enable computers to understand, interpret, and respond to human language. Applying the right tool is critical for enterprise-wide AI orchestration.
Let’s consider some applications. AI can process information in critical documents or emails. AI-driven intelligent document processing (IDP) enables you to classify and extract critical information from your business documents. For instance, you could deploy this to your billing department to classify incoming emails, read attachments, pull invoice information, and enter it into an application for review by a human. Or within your customer service department where IDP and NLP can perform sentiment analysis on customer feedback to route messages appropriately.
Other common AI tools include AI copilots, also called AI agents. AI agents are AI assistants that provide support, suggestions, and automation on various tasks. These tools use natural language processing (NLP) and large language models (LLMs) to provide answers to user queries. For instance, some AI agents can enable your low-code development team to generate user interfaces from PDF forms or automatically generate functional unit tests.
4. Add an AI orchestration layer
Businesses have complex workflows and processes. Seamless integration is the key to operationalizing AI within your enterprise. This requires using an orchestration layer to route work between humans and digital workers.
A good AI process platform provides this orchestration layer. They allow you to use business process modeling diagrams and visualization tools to clearly represent an end-to-end process. Then, you can pass data and tasks from one step to another. This orchestration process enables you to mix AI and AI agents with other elements like robotic process automation (RPA) or business rules to route work to the right team.
5. Establish robust AI governance and human-in-the-loop (HITL) workflows
AI systems are coming under scrutiny. For proof, look no further than the passage of the AI Act in the European Union. People are rightly concerned about AI making decisions autonomously that could negatively affect outcomes. AI hallucinations could cause an AI agent or the employee using AI agent systems to provide the wrong information to a prospective customer. Mistakes could lead to bias and discrimination with real-world consequences.
New regulatory standards increasingly push for additional compliance checks on AI models—and critically, human oversight. AI orchestration allows you to include humans in the loop to prevent issues. By adding this level of governance to usage of AI systems, you not only minimize mistakes but set your organization up to avoid potential regulatory problems.
What 'human-in-the-loop' actually means
Having a human-in-the-loop isn't just about oversight. It's about designing processes where humans and AI collaborate. A good orchestration platform doesn't just run an AI model. It routes the AI's output, like a drafted contract, to the legal team for approval to serve as the human-in-the-loop before it's sent to a customer.
Key pillars of governance to enforce in AI orchestration
- Transparency: Provide a visual process model that shows exactly where, when, and how an AI model is being used.
- Accountability: Create an immutable audit log of every AI decision and any human modification, crucial for regulatory compliance.
- Bias and fairness: Use business rules to flag high-risk AI decisions like loan application denial so you can automatically route them for mandatory human review to check for bias.
AI orchestration in action: Real-world examples
AI orchestration has many practical applications—often it can help speed up boring internal tasks and processes.
Customer service resolution
- Problem: High call times, agents searching multiple systems for information.
- AI tools: An NLP-powered chatbot (to understand intent) and a Generative AI (to draft answers).
- The AI workflow automation: The BPA platform (1) receives the customer query, (2) uses a data fabric to pull the customer's order and conversation history from the ERP, (3) feeds the query and customer history details to the generative AI to draft a contextual answer, and (4) presents the answer to a human agent in their console to review and send.
Invoice document processing
- Problem: Manual, slow invoice processing.
- AI tools: IDP (to extract data) and a predictive machine learning model (to flag for fraud).
- The AI workflow automation: The orchestration layer (1) monitors an email inbox, (2) sends new attachments to the IDP model, (3) receives the extracted data (invoice #, amount), (4) runs the data through a fraud model, and (5) if the fraud score is low, automatically enters it into the financial system. If the score is high, it creates a task for a human to review.
Key features to look for in an AI orchestration platform
When choosing an AI orchestration platform, it's essential to consider the features that will best support your enterprise's unique needs and goals. Look for a solution that provides a robust set of capabilities to ensure seamless AI integration and optimal performance.
Visual process modeler: To design, map, and manage complex workflows that include AI, RPA bots, and human tasks.
Data fabric capabilities: To connect and unify data from all your systems before feeding it to your AI.
AI 'bring your own model' (BYOM) support: The ability to integrate with best-in-class AI services (like OpenAI, Google Vertex AI, and others) as well as your own custom-built models.
Low-code development tools: To quickly build the user interfaces that humans use to interact with the AI-driven process.
Integrated business rules engine: To declaratively manage the logic (for example "IF invoice > $10,000, THEN send to manager") that governs the process.
AI orchestration and the modern enterprise
AI can absolutely bring efficiency and effectiveness to organizations and businesses. But it’s critical to recognize that AI is only one tool among many that are involved. You’ll need additional tools, like low-code to simplify development, data fabric to connect and manage enterprise data workflows, and business rules to route tasks between employees and bots.
To extract the most value, take a platform approach to AI orchestration. The right platform enables you to design, automate, and optimize your mission-critical enterprise processes.
