Agentic AI: Building a Transplantable Skeleton for Business

Here's the thing about corporate restructuring: it's often necessary, sometimes painful, and almost always disruptive. As a services delivery leader, you know this better than most. But have you considered how it impacts your shiny new agentic AI initiatives? Specifically, what happens to all that AI infrastructure you've been building when the org chart gets redrawn? Let's dive into why ensuring the "skeleton" of your agentic AI can survive these changes is crucial for seeing a return on your investment.

Agentic AI, for those still catching up, isn't just about automating simple tasks. It's about creating AI systems that can reason, plan, and act independently to achieve specific goals. Think of it as giving your AI a degree of autonomy to handle complex projects - like managing scope creep, for example. This potential is powerful, but it hinges on a stable foundation.

Here are three key takeaways to ensure your agentic AI infrastructure doesn't become another casualty of corporate restructuring:

1 - Modularity is Your Best Friend:

Imagine building a house and bolting every single piece together. If you need to move a wall, the whole structure is compromised. That's what happens when your AI infrastructure is tightly coupled. Instead, embrace modularity.

• Break down complex AI agents into smaller, independent components. Each module should handle a specific function, such as data ingestion, task planning, or execution monitoring. This means you can swap out or reconfigure modules without affecting the entire system.

• Use APIs and standardized interfaces for communication between modules. This allows different teams to work on different components simultaneously and ensures that the system can adapt to changes in the underlying technology.

• Document everything meticulously. This might seem obvious, but it's often overlooked. Clear documentation of each module's function, inputs, outputs, and dependencies is essential for future maintenance and adaptation.

Think of it like building with Lego bricks. You can rearrange them, add new ones, or remove existing ones without destroying the entire structure. This approach not only makes your AI infrastructure more resilient to organizational changes but also makes it easier to scale and maintain. For example, consider the use case of project scope management. By modularizing the scope management agentic AI into smaller components, like scope definition, change request management, and impact analysis, these independent modules can be readily adjusted or replaced without disrupting the whole project delivery workflow.

2 - Decouple AI Logic from Organizational Structure:

This is where things get tricky. It's tempting to design your AI agents around your current organizational structure. For example, you might have an agent that's specifically designed to work with the marketing department. However, what happens when the marketing department gets reorganized or merged with another team? Your AI agent becomes obsolete, or worse, starts producing inaccurate results.

• Focus on business processes, not organizational silos. Instead of designing AI agents around specific departments, design them around the core business processes they support. For example, an agent could be responsible for managing the entire customer onboarding process, regardless of which department is responsible for each step.

• Use data and metadata to drive decision-making. Instead of hardcoding organizational structures into your AI agents, use data and metadata to dynamically determine the appropriate actions to take. For example, an agent could use customer data to determine which support team to route a request to, regardless of the customer's location or the time of day.

• Abstract away organizational dependencies. Use abstraction layers to hide the details of the organizational structure from the AI agents. This allows you to change the organizational structure without affecting the agents' behavior.

Consider a situation where your agentic AI is handling project resource allocation. Instead of directly assigning resources based on the department they belong to, the AI should focus on skills, availability, and project requirements. That way, even if departments merge or resources shift, the AI can still effectively allocate resources based on the underlying needs of the project.

3 - Prioritize Knowledge Transfer and Training:

Even the most robust AI infrastructure is useless if no one knows how to use it. Corporate restructuring often leads to employee turnover, which can result in a loss of critical knowledge and expertise. To mitigate this risk, prioritize knowledge transfer and training.

• Create comprehensive training programs for all users of the AI system. These programs should cover not only how to use the system but also how it works and how to troubleshoot common problems.

• Establish a center of excellence for AI within your organization. This center should be responsible for developing and maintaining the AI infrastructure, as well as providing support and training to users.

• Document everything thoroughly. (Yes, again!). Create detailed documentation of the AI system's architecture, functionality, and usage. This documentation should be accessible to all users and kept up-to-date.

• Cross-train team members. Ensure that multiple people understand each component of the AI system. This reduces the risk of knowledge loss if someone leaves the company.

Imagine a scenario where your agentic AI is managing project scope. If the project manager who understands how to interact with the AI leaves the company, the entire scope management process could grind to a halt. By training multiple team members on how to use the AI and documenting the scope management workflows, you can ensure that the process continues to run smoothly even in the face of employee turnover. Furthermore, implementing a robust knowledge base ensures that any services delivery leader can seamlessly interact with the agentic AI to manage change requests and ensure projects stay within defined boundaries.

These three principles - modularity, decoupling, and knowledge transfer - are essential for building agentic AI infrastructure that can withstand the inevitable disruptions of corporate restructuring. By following these guidelines, you can ensure that your AI investments continue to deliver value, regardless of what the org chart looks like next quarter. It is about building a business that can survive and thrive. The goal is to build an AI system that is not only intelligent but also resilient and adaptable.

Are you ready to build AI that is ready for anything?

About Continuum

Continuum, developed by CrossConcept, is a Professional Services Automation (PSA) solution that helps service delivery organizations optimize project delivery, improve resource utilization, and increase profitability. One of the key challenges faced by services organizations is scope creep - uncontrolled changes or continuous growth in a project's scope. Continuum PSA helps address this challenge by providing robust scope management features that allow you to define clear project boundaries, track changes, and manage the impact of those changes on project timelines and budgets. With Continuum, you can maintain control over project scope, minimize costly overruns, and ensure that your projects are delivered on time and within budget.