Organizations are increasingly focusing on translating artificial intelligence investments into real operational value, shifting their attention from experimentation to practical execution. At the AWS re:Invent 2025, key industry leaders discussed how agentic AI is reshaping enterprise supply chains by enhancing planning, reasoning, and action across interconnected business processes.
During a panel featuring Rima Olinger, director of Amazon Quick Suite at AWS; Chris Jangareddy, managing director and partner at Deloitte; and Jason Ballard, vice president of digital innovations at Toyota Motor North America (TMNA), the conversation centered on the transformative potential of agentic AI. This new paradigm enables systems to operate across multiple data sources, supporting seamless decision-making rather than isolated predictions.
Challenges and Opportunities in Adoption
Despite the enthusiasm around agentic AI, many enterprises find it difficult to clearly define the concept and differentiate it from traditional automation or generative AI. AWS describes agentic AI as systems capable of interpreting business objectives, reasoning through constraints, and executing actions autonomously across various data environments. While this capability offers substantial value, it also adds layers of complexity that organizations must navigate.
At TMNA, the challenges of supply and demand planning were evident. Their existing processes relied on over seventy interconnected spreadsheets that required manual assembly by dozens of planners each month. This fragmented approach is a common barrier across enterprises, which can hinder the effectiveness of agentic systems without broader modernization efforts.
Organizational readiness is another significant challenge. Agentic AI can influence decision-making, customer engagement, and employee workflows, necessitating trust, transparency, and clear governance. AWS emphasized that technology alone cannot drive transformation; organizations must also prepare their workforce by clarifying how AI decisions are made and establishing accountability. Without this alignment, the adoption of agentic AI often stalls due to uncertainty and resistance.
From Pilots to Production: A Structured Approach
The gap between proof-of-concept pilots and full production remains a persistent issue. Many organizations experience initial success with agentic AI prototypes but struggle to scale them across the enterprise. Deloitte noted that a lack of standardized architectures, risk controls, and repeatable operating models often prevents promising use cases from advancing.
To address these challenges, AWS and Deloitte have developed a joint approach to aid enterprises in transitioning agentic AI from concept to production. AWS provides the secure infrastructure and foundational AI services necessary for multi-step reasoning and orchestration. In contrast, Deloitte contributes domain expertise, transformation frameworks, and operational execution that align AI initiatives with business objectives.
Deloitte outlined a structured, three-phase methodology to guide organizations in implementing agentic AI. The first phase focuses on identifying high-impact use cases that can deliver measurable returns within six to twelve weeks, ensuring that efforts are grounded in business value. The second phase introduces a multi-agent system accelerator, co-developed with AWS, which integrates governance and compliance from the outset. This phase includes establishing one hundred controls using AWS Audit Manager to enable safe testing and operationalization of agentic AI workloads. The third phase centers on value realization, measuring use cases against defined performance indicators, such as efficiency gains and forecast improvements.
Together, these elements provide enterprises with a repeatable foundation for the secure and scalable deployment of agentic AI. AWS highlighted that this partnership also involves joint investment and specialized AI resources, allowing customers to progress from concept to impact in weeks rather than years.
TMNA aimed to innovate its supply chain operations, focusing on goals such as responsiveness, planning accuracy, and enhanced employee and customer experiences. Deloitte’s methodology aligned closely with these objectives by integrating agentic AI directly into end-to-end workflows instead of layering it onto existing processes.
The engagement began with a comprehensive assessment of TMNA’s challenges, including lengthy planning cycles, manual data aggregation, demand volatility, and limited flexibility in responding to disruptions. AWS and Deloitte collaborated with TMNA to establish an agentic architecture that consists of a standardized platform layer, an intelligence layer for AI models, an agentic foundation for managing agents, and an experience layer tailored for planners and operations teams. This structure enabled TMNA’s business units to operate within a secure framework rather than developing isolated solutions.
Agentic workflows introduced an AI-enabled “companion” for planners, capable of generating recommendations, simulating scenarios, and continuously learning from outcomes. TMNA emphasized that this approach was designed to augment rather than replace human expertise. By moving away from spreadsheet-driven coordination, planners gained broader operational visibility, allowing them to focus on complex decision-making and strategic analysis.
Both Deloitte and AWS stressed the importance of maintaining human oversight, especially in situations requiring contextual judgment. Workforce enablement was central to this initiative, with AWS highlighting its enterprise AI training programs. The partnership with Deloitte ensured that TMNA’s teams developed the necessary skills and confidence to adopt agentic workflows responsibly.
The adoption of agentic AI at TMNA has led to measurable operational improvements. The transition from a spreadsheet-heavy process involving forty to fifty planners to an agent-assisted model has simplified operations significantly. Over time, TMNA expects that planning activities will be managed by a smaller group of planners, overseeing broader responsibilities and reflecting role elevation rather than workforce reduction. Forecast accuracy improved by approximately 20%, and planner productivity saw an 18% increase. Additionally, agent-driven simulations have introduced proactive, self-healing capabilities, allowing TMNA to anticipate issues and receive automated recommendations to maintain operational continuity.
Customer-facing processes have also benefited from these advancements. Vehicle delivery workflows evolved from legacy systems to a modern agent-driven experience that identifies delays, resolves routine issues independently, and escalates exceptions when human judgment is needed, enhancing both employee satisfaction and customer experience.
TMNA’s progress would likely have been slower without the combined strengths of AWS’s AI platform and Deloitte’s transformation expertise. The collaboration underscores the principles that enterprises should consider when pursuing agentic AI. Successful initiatives begin with clearly defined business challenges rather than technology-first experimentation. Value is maximized when agentic AI is applied to operational bottlenecks and repetitive coordination tasks.
Early and continuous involvement of the workforce is equally important. Positioning AI as a trusted collaborator rather than a replacement fosters confidence and promotes adoption. Establishing standardized architectures with embedded governance and security is crucial for ensuring scalability and trust. By starting with narrowly scoped use cases, organizations can validate value, refine controls, and build momentum before expanding to more complex systems.
Ultimately, continuous measurement and iteration are vital, as is collaboration across technology, operations, and leadership. When these elements align, agentic AI can evolve beyond a mere innovation initiative to become a durable engine for operational resilience, efficiency, and long-term enterprise modernization.