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AI Orchestration: Setting the Stage for Enterprise Modernization

Dan O'Keefe, Appian
June 20, 2024

Integrating artificial intelligence (AI) into business operations is no longer optional—it’s necessary. Yet, too often, businesses fail to reap the full rewards. AI can’t produce the results that impress stakeholders and drive tangible results unless you take a strategic approach to its deployment. 

That’s where AI orchestration comes into play. You need a strong plan and the right capabilities to turn AI to a competitive advantage. Some common mistakes? Deploying AI piecemeal on small processes. 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 five critical elements for orchestrating AI across the enterprise. From breaking down data silos to leveraging the right AI tools, you’ll walk away with practical tips for creating a cohesive and effective AI-driven enterprise.

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5 ways to orchestrate AI capabilities 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 solutions to the right problems. 

You need to tie it back to business goals.

As Todd Lohr, Principal for KMPG, LLP, said in a 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 and the business wants to drive down customer support 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.

2. Foster strong, easily flowing data connections

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 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 tools such as a data fabric, process automation tools, and low-code capabilities to make development simple. Connecting these components in one 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.

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3. Use the full breadth of AI 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. 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 orchestrating change across the enterprise. 

Let’s consider some applications. AI can process information in critical documents or emails. AI-driven intelligent document processing 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. 

Other common AI tools include AI copilots. Copilots 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 questions. For instance, some copilots can enable your low-code development team to generate user interfaces from PDF forms or automatically generate functional unit tests. 

4. Add an orchestration layer

Businesses have complex workflows and processes. The key to operationalizing AI is to work it seamlessly into 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 to visually 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 with other elements like robotic process automation (RPA) or business rules to route work to the right team.

5. Establish governance

AI is coming under scrutiny. For proof, look no further than the recent 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 someone to provide the wrong information to a prospective customer. Mistakes could lead to bias and discrimination with real-world consequences. 

Regulations increasingly push for additional checks—and critically, human oversight. AI orchestration allows you to include humans in the loop to prevent issues. By adding this level of governance, you not only minimize AI mistakes but set your organization up to avoid potential regulatory problems.

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. You’ll need other tools, like low-code to simplify development, data fabric to connect and manage enterprise data, and business rules to route tasks between employees.

But 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.

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