FourKites Announce Agentic AI Innovations—Is Targeted Applicability Crucial?

In February, Intelligent Control Tower™ solution provider FourKites unveiled the first two Artificial Intelligence (AI) agents in its Digital Workforce, Tracy and Sam. These agents bolster FourKites’ orchestration and execution capabilities, and aim to automate up to 80% of routine tasks. The FourKites Digital Workers will not only aim to fulfill myriad execution-based use cases, but also address and fill data gaps in order to bridge silos within supply chains. Unlike conventional Robotic Process Automation (RPA)-based chatbots, which are rules-based and “do as told,” these AI agents have an enhanced degree of autonomy, enabling them to learn from available real-time data and react accordingly without explicit instructions from humans.

Of the first two Digital Workers that have been released, Tracy is curated toward track & trace functions. The AI agent can provide expertise and context to complex workflows. Key use cases include automatic shipment updates, generating actionable reports, contacting carriers for verification, etc. These use cases can save hours of manual effort for processes like dispatch management within enterprises. Meanwhile, Sam focuses on stakeholder collaboration by processing purchase orders and invoices across a firm’s supplier network. This AI agent also enables instant supplier onboarding, reducing integration costs significantly.

Ultimately, the goal for FourKites with its new Digital Workforce is to enhance supply chain resilience by preventing disruptions, coordinating responses across stakeholders, and eliminating manual processes. As more data get harnessed and as more tasks are executed, these AI agents should improve their capabilities over time, making them even more valuable and integrated into their customers’ businesses.

FourKites isn’t the only solution provider rolling out AI agents across different aspects of Supply Chain Management (SCM). project44 announced distinct new capabilities within its Movement platform. project44 introduced AI Data Quality Agents that aim to reduce visibility gaps and data verification issues across the supply chain. AI agents can autonomously conduct phone calls with carriers, suppliers, and other stakeholders to resolve data quality issues or obtain missing information—this eliminates the need for manual follow-ups. Similarly, its AI Disruption Navigator solution aims to detect external supply chain disruptions and enables businesses to react accordingly. It also launched an AI-based digital worker called MO, which can reduce query response time by up to 90%. MO retrieves answers about shipments, carriers, etc. using natural language. Working alongside human workers, MO can not only identify anomalies or potential disruptions, but will also eventually execute tasks.

project44 also released new AI-based optimization capabilities. Its enhanced “freight procurement analytics” capability aims to identify cost-saving opportunities and facilitates direct carrier competition. Similarly, its task management capability seeks to boost operational efficiency by anomaly identification and instant resolution to prevent oversights. In addition, its new AI capabilities also aim to enhance returns visibility with end-to-end transparency to reduce returns processing. The fact that project44 claims to have the largest logistics and transportation database globally shows that an abundance of supporting data will back these AI-enabled capabilities.

Software giant SAP also expressed intentions to venture further into the Agentic AI space. SAP says it will launch two agents—a sales AI agent and a supply chain AI agent that will work together. SAP says that sales AI agents will recommend the best price and how to bundle products to "hit the consumer at the right point in time" while communicating with a supply chain AI agent to determine if the product is in stock and if it will be delivered on time. With this, the value SAP aims to provide is linking the two siloed business units for enterprises for operational efficiency.

Rather than jumping into the Agentic AI fad without a concrete plan, there is a need for solution providers to prove value to prospective customers. This can depend on the different markets in which target enterprises operate. Demand variability, seasonality, and product optimization might be areas of importance for manufacturers and retailers, while driver behavior analysis and compliance automation might be the biggest pain points for logistics service providers. Each market segment has distinct challenges, and vendors need to recognize this when pushing out and packaging AI agents. In addition, showcasing quantifiable use cases with time savings, cost reductions, or efficiency gains is key—something both FourKites and project44 seem to be doing on the surface. Demonstrating the ability to gain immediate wins through crucial Key Performance Indicators (KPIs), such as reduced downtime or faster replenishment cycles, will also be important.

More importantly, vendors should look to highlight how AI agents can augment human decision-making and create a culture of human-AI collaboration, enabling employees to focus on high-value tasks. Positioning AI agents as modular plug-and-play enhancements, rather than complete overhauls that could highlight corporate inertia could be key. In addition, displaying a clear decision rationale behind the AI models and showcasing how AI can enhance decision-making with the use of confidence scores can be an ideal way to build interest and traction among prospects as well, something Descartes looks to be doing. It is obvious that Agentic AI has promising applications in the SCM space, and it will be interesting to see what solutions providers do offer as the year progresses.