Share:

Hire, Onboard, Manage: Treating AI Agents Like New Team Members

Special Guest Blog Post written by Deborah Dull 

 

Imagine it’s launch season in your supply chain, and a team of AI agents is hard at work: coordinating shipments with suppliers, allocating inventory, and handling a missed delivery.

It’s hard to go a day without hearing about the latest AI headlines: new LLMs, new applications, new use cases. These discussions are part of my every day. Business leaders and IT teams are eager to explore – and they have a lot of questions… and apprehension.

In these discussions, we have found one framework that lands consistently: consider AI agents like new hires, not new software.

 

Step 1: Hire Like You Mean It

Just like hiring humans, this step is about defining what you need. The first decision is the purpose of the role: what problems are you trying to solve? Where are your people overwhelmed? Where is your business growing in coming months?

From here, the next decision is to build the business case just like you would for a new headcount. For example, consider the ROI of having a digital employee who can process supplier performance data 24/7.

Now, decide on the type of agent that will join you team. You might hire an embedded AI from your existing software providers such as ServiceNow or Kinaxis, specialized agents that perform a single task such as from Zip for procurement or Daybreak for forecasting, or full agentic workforces such as from Qurrent or Auxo.

The last decision is to “interview” your candidates and select the one that will be the best fit technically and culturally.

 

Step 2: Onboard for Success

Just like that nervous new hire walking through your office doors on their first Monday, your AI agent needs to go through an onboarding step. The difference between digital and human workers is that your digital employee learns faster, remembers everything, and never needs coffee breaks.

The first onboarding phase lasts about two weeks. Your AI agent needs system access and basic training. For example, an overview of the process to manage supplier performance. The result feels like an overeager intern, fresh to the workplace and unaware of the way work “really” gets done.

The magic really happens in the next phase. In this phase, your human workers give feedback to your digital workers, just like they would give feedback to a human. “This supplier has struggled with on time performance but has recently committed to improve. Be strict on follow-ups but friendly on tone” has immediate influence on how the agent interacts with that supplier. But unlike humans who might forget the details of a supplier issue from six months ago (or go on vacation!), AI agents remember everything. The overeager intern becomes an expert in just a few weeks.

 

Step 3: Manage Your Hybrid Workforce

This step is all about performance management activities. The first activity is setting expectations. For example, when a distribution company deployed an AI agent to handle their returns process, they established clear performance standards: process 95% of return requests within four hours, maintain customer satisfaction scores above 4.5, and escalate complex issues to human staff within defined parameters.

The next activity is supervision. Since AI agents work around the clock, their “first line supervisors” do also. This activity is about having agents watching agents, with clear instructions on the type of performance to manage. Humans provide final oversight and handle exceptions.

Another activity is managing human workers. Managing the performance of a hybrid team feels a little different for the human workers. As “run business” and on-the-fly issue resolution moves to AI agents, humans can focus their time on structured problem solving and relationship building. This shifts the performance management time horizon into longer term goals and results. This means a reduction in the daily fire-fighting dopamine hits, replaced by the delayed satisfaction of longer-term wins. For some, this is a welcome break. For others, they’ll need help through the transition.

Ready to Start Hiring?

The stepwise change of Agentic AI in the workplace is happening. Following these steps to lead your supply chain into the new age of hybrid teams.

Ready to dive deeper? “Self-Driving Supply Chain: 5 Steps to Building Your Agentic AI Workforce” is launching later this year and provides the framework for welcoming digital workers into your supply chain

 

Deborah Dull is Managing Partner of Trillium Digital Services leading Agentic Services. With experience at GE, Microsoft, and Zero100, she specializes in AI-driven supply chain innovation. Her upcoming book “Self-Driving Supply Chain” explores agentic AI workforce implementation in supply chain operations.

More Blogs

MODEX 2026
Blogs
May 13, 2026

Think Small to Win Big: Rethinking Supply Chain Design in the Age of E-Commerce

At MODEX 2026 in Atlanta, Scott Luton sat down with RD Deshmukh, Chief R&D Officer at ID Logistics US, to explore how supply chains must evolve to keep pace with a rapidly shifting retail and e-commerce landscape. From AI deployment to warehouse design and customer expectations, RD offers a clear message: the old playbook no longer applies; and those who fail to adapt risk falling behind quickly.   A “Day-One” Mindset for Continuous Change Drawing from his experience at Amazon, RD emphasizes the importance of maintaining a “DayOne” mindset, which is one rooted in curiosity, adaptability, and constant reinvention. “Be curious, be innovative… the day you’re not curious, it will kill you,” he explains. In today’s environment, where disruption is constant, this mindset isn’t optional. It’s essential. Leaders must prepare their teams not just to respond to change, but to expect it. As RD puts it, the only certainty is that “tomorrow is going to change.” This philosophy underpins how ID Logistics approaches innovation, engineering, and customer solutions; always with an eye toward flexibility and resilience.   AI: Start with the Problem, Not the Technology While artificial intelligence continues to dominate industry conversations, RD cautions against rushing into implementation without…
automotive
Blogs
January 6, 2026

How a Supply Chain Management Data Gateway Benefits Almost Every Industry

Special Guest Blog Post written by Mark Holmes with InterSystems   A data gateway provides unified access to supply chain data from various sources, including enterprise systems, supply chain applications, data feeds, data warehouses, data lakes, data marts, and business entities. It helps companies meet their supply chain challenges head on and be ready for supply chain disruptions. Quick and easy access to live and historical data is critical for supply chain managers and practitioners, data analysts, stewards, engineers, and supply chain professionals in any industry. Here are just a few examples of industries that can benefit from a supply chain data gateway:   Fast Moving Consumer Goods and Consumer Packaged Goods In FMCG and CPG, the ability to make rapid, data-driven decisions is crucial for staying competitive in a fast-paced market. Companies can optimize their supply chain operations by using a data gateway that provides a unified and harmonized view of data. For instance, a logistics manager can monitor real-time data on inventory levels, customer orders, and transportation schedules to make better informed decisions and reduce lead times and costs while improving customer satisfaction. Healthcare In healthcare, a data gateway can improve visibility and inventory optimization by providing a…