Task-Specific Point Solutions. Initially, GenAI can be deployed through chatbots to support routine, daily operational tasks. These point solutions offer basic automation and efficiency improvements by leveraging standard GenAI capabilities. Such solutions have matured to become plug-and-play tools that companies can easily adopt.
Process Enhancements. At the second level, GenAI acts in combination with current planning and execution systems to improve the effectiveness of existing processes, such as monitoring supply chains for disruptions, generating alerts, and simulating responses. The first GenAI products to provide such enhancements have been released, and more are in development.
Deep Process Transformation. At this stage, GenAI agents continuously verify and update the master data set and drive a rethinking of entire workflows, profoundly transforming core processes. GenAI plays a pivotal role in automating and enhancing the quality of decision making, with humans remaining in the loop. (See “Supercharging Supply Chain Simulation with GenAI.”)
A leading Europe-based industrial goods company, managing thousands of supply chain decisions daily, sought to supercharge its simulation capabilities. By identifying bottlenecks, testing strategic options, and running complex scenarios, it aimed to improve decision making and operations across its supply chain.
To achieve these objectives, the company partnered with BCG to implement an advanced solution leveraging two BCG X assets: AgentKit, a GenAI agent toolkit (developed in partnership with LangChain and now an open-source solution) and End-to-End Plan by BCG X, an AI-powered planning capability. BCG integrated these tools through a natural language interface, making them accessible to users without technical barriers. BCG also used autonomous GenAI agents to connect the tools and run the technical workflows in the background.
This seamless setup streamlines the company’s sales and operations planning processes, giving planners more autonomy and agility in managing daily operations. Planning professionals can create and run simulation scenarios, analyze the root causes of issues that emerge, summarize KPIs, conduct sensitivity analyses, and share critical insights—all without the need for extensive technical knowledge.
The introduction of this GenAI-driven solution delivered significant business impact. The underlying AI capabilities contributed to a more than 2-percentage-point increase in EBITDA by the second year. Additionally, the GenAI agent facilitated the upskilling of more than 20 planning professionals, enabling them to fully use the tool’s capabilities and achieve a threefold reduction in process cycle times.
Some GenAI agents are already available and are expected to be key drivers of GenAI-enabled value over the next five years. However, to fully unlock their potential, significant process re-engineering will likely be required. In many cases, this re-engineering will necessitate custom-built solutions or at least tailored add-ons to ensure that GenAI is seamlessly integrated.
Cross-Functional Process Automation. The most advanced level involves the automation of cross-functional processes, such as the sales and operations execution process. A consortia of self-organizing GenAI agents orchestrate supply chain operations across different functions, creating an automated, intelligent, and collaborative system. (See “A Vision of Deep Automation.”) This visionary use of GenAI is in the research and development stage.
We expect that the sales and operations execution (S&OE) process—one of the most critical functions in supply chain management—will be transformed by GenAI. This process, which balances demand, inventory, and supply over a short time horizon to maintain performance, will evolve from a time-consuming, manually driven task into an automated, intelligent system capable of running continuously without human intervention.
Currently, dedicated teams follow the S&OE process several times a week, aligning demand and supply to meet customer orders and maintain KPIs over a 1-to-12-week planning horizon. Although effective, this process is labor-intensive, limited by data availability, and heavily reliant on manual oversight to ensure smooth execution.
We expect the S&OE process of the future, in contrast, to be powered by an always-on network of autonomously collaborating and learning agents that dynamically balance the supply chain in real time, ensuring optimal performance continually. (See the exhibit.) These agents will be specialized software programs powered by foundational large language models (LLMs), such as ChatGPT, that continuously interact with data inputs and adapt to changing conditions. To minimize potential LLM-generated errors, or hallucinations, the agents will be constrained to operate under business rules and decision-making frameworks. By processing vast data sets, the agents will coordinate tasks without human intervention.
In this envisioned system, the demand agent will continuously monitor demand forecast changes, learning and adjusting actions accordingly. It will leverage specialized machine learning algorithms to generate the forecasts. The meta-agent, acting as the orchestrator, will oversee data flows, extract new demand information, and cleanse data to remove anomalies.
When demand changes occur, the demand agent will collaborate with the inventory agent to assess the impact. The meta-agent will equip the inventory agent with precise, real-time data from various enterprise resource planning systems, allowing it to evaluate supply and inventory adjustments. If KPIs for service, inventory, or cost are at risk, the inventory agent will trigger corrective actions, such as stock transfers or production reorders, to restore balance.
The supply agent will then assess the feasibility of these changes, coordinating with suppliers or adjusting production schedules. If the proposed adjustments incur high costs, such as changeovers, the supply agent will explore alternative scenarios. For example, with support from the meta-agent, the supply agent may collaborate with external optimization tools such as mixed-integer linear programming solvers to determine the best production sequencing.
Through this ongoing collaboration, the agents will continuously learn and refine their ability to rebalance the supply chain autonomously. During the initial stages, human oversight will be necessary, with agents presenting decisions for approval. However, as the system matures, automation will increase—especially in handling transactional tasks—and the agents will eventually make decisions independently within a widely automated network.
Ultimately, the decisions from this autonomous system will feed directly into execution systems, seamlessly orchestrated by the meta-agent. This will help the entire supply chain remain consistently optimized and fully aligned with the company’s strategic goals.
Making It Happen
GenAI enables a comprehensive transformation that improves core aspects of supply chain management, including ways of working and user engagement, process automation, and analytical development.
Successfully implementing GenAI in supply chains requires a structured approach that aligns technical capabilities with business objectives. Here are five essential steps.
Set the ambition. Align the company’s ambition for GenAI adoption with its broader strategic goals. GenAI should not be adopted simply for its novelty. Instead, focus on how it can enhance business outcomes, whether improving operational efficiency, reducing costs, or addressing specific market challenges. Take stock of potential talent shortages and the future of how work will get done, and assess how GenAI can fill gaps by performing tasks or alleviating burdens in operational areas where skilled supply chain professionals are scarce. Defining a clear ambition helps ensure that all efforts are focused on achieving measurable business impact.
Map key decisions across the supply chain. Identify where GenAI can make the biggest impact toward achieving the ambition. This involves mapping out where the most important decisions are made throughout the supply chain and analyzing how GenAI can improve the quality and speed of those decisions. Rather than trying to integrate GenAI into every aspect of the company’s operations, focus on high-value decisions that are critical to success. Such decisions often relate to integrated business planning, inventory management, or production scheduling. Zeroing in on the most valuable areas lets companies target the highest-return investments.
Prioritize where to start. Give priority to the areas in which GenAI can have the greatest initial impact, considering the business value and ease of implementation. Initiatives could involve, for example, automating a particular process or integrating GenAI into a decision-making system. By choosing areas with immediate benefits, companies can build momentum and fund the journey.
Rethink the end-to-end workflow relating to key decisions. Integrate GenAI to streamline processes and improve decision-making quality, pivoting from human-operated to human-designed workflows. For example, automating repetitive tasks, such as inventory replenishment or demand planning, can free up workers to focus on more complex, strategic decisions. Ensuring that GenAI is fully embedded into the workflow—rather than bolted on—can lead to higher adoption and better outcomes.
Start building with the right ecosystem. Successful GenAI implementations rely on a strong ecosystem of partners, including technology developers, user-interface designers, and AI and supply chain experts. Leverage those external partnerships to fill in-house capability and resource gaps, gain access to the latest technologies, and accelerate deployment. By building this ecosystem early, organizations can have the support they need to scale GenAI solutions effectively.
GenAI promotes higher adoption of AI tools through user-friendly interfaces and agent-based automation. It also excels at cross-functional orchestration, connecting different systems and teams to enable faster, more coordinated actions. The results—evident in increased productivity and agility—demonstrate that embracing GenAI bolsters competitive advantage and is an essential strategy for future-proofing supply chains.