How AI-driven Planning Capabilities Will Continue to Evolve in 2026

As 2025 winds down, companies and supply chain leaders are navigating a holiday season and business landscape that are impacted by shifting tariff and trade policies, as well as economic uncertainty, which will likely continue to play a role in 2026.

If anything has been learned over the past few years, it’s that managing uncertainty and volatility has become the new normal. As such, traditional methodologies for enterprise business planning, which often involve spreadsheets and information siloed among departments, are no longer sufficient.

As market uncertainty and supply chain disruptions persist, the proliferation of available data is also on the rise. A 2024 McKinsey report indicates that by 2030, the volume of data is expected to increase tenfold since 2020. While the two are not necessarily directly correlated, business leaders are becoming aware of the concept of leveraging greater volumes of data to help navigate business uncertainty more effectively by making data-informed decisions. More companies are taking a comprehensive approach to strategically leverage data by building data warehouses, developing enterprise data strategies, and implementing AI-powered platforms with digital twin technologies. Such platforms can ingest large volumes of data to model various business scenarios and detect disruptions in advance.

Leveraging AI to eliminate siloed data

One of the first steps required to implement a comprehensive data strategy from vision to reality is to focus on eliminating data silos by digitizing and unifying the data and knowledge that is dispersed across an organization. In many enterprises, multiple departments operate independently, each utilizing its own data sources. For example, finance uses its own set of data, procurement uses another set, and supply chain planning uses yet another. Layered on top of this, each department is using its own external data signals. This means that if one department is aware of a potential supplier delay, other teams may not have visibility into this information in time to make decisions that could impact product availability.

AI-powered planning platforms can ingest vast amounts of internal and external data, serving as a single source of truth. Digital twin models can provide visibility into the end-to-end supply chain and help teams develop insights across various planning scenarios, enabling them to make collaborative, data-informed planning and business decisions. For example, if there is a surge in demand, data can reveal which suppliers are most likely to be able to meet an increase in orders on time, which might struggle to meet large orders at the last minute, or whether consumers in certain markets are likely to pivot to another product flavor if the most popular flavor is in limited supply. If a planning team can detect trends, risk, and opportunities earlier, a business can pivot to an alternative supplier, use a different shipping lane, or reallocate inventory to distribution centers where demand is highest, before a potential risk or supply chain disruption impacts the business.

Such platforms can also help unlock and digitize internal tribal knowledge from across the organization, enabling teams to work together more efficiently. For example, if a planner with 20 years of experience can determine from the data that both Supplier A and Supplier B have enough supply levels to meet an uptick in demand, but Supplier A is more reliable, this planner knows to prioritize an order with that particular supplier. Conversely,  a less experienced planner may select Supplier B and may not receive the product in time to meet demand. With an advanced planning platform, a planner who has this context can manually input supplier information, order dates, shipment dates, delivery dates, and more, which are then codified within the software. These insights will then be flagged for other planners as well, so their decisions will be informed by a similar context.

For businesses that have already begun implementing AI tools to help connect data from across their organization and extended value chain, they may be ready to move beyond their traditional planning methodologies and rethink how technology platforms can play a role in unifying their data and business insights so that it essentially acts as a “North Star” that aligns integrated business planning across the business. This approach enables teams to access a shared, single source of truth via a digital platform, gain visibility into emerging risks and opportunities that could impact their business, and then make informed, coordinated decisions based on these insights.

Enhancing scenario planning

Once internal knowledge is digitized and all sources of data are unified to facilitate more effective collaboration among teams on planning and decision-making, teams can move beyond just detecting potential risks and disruptions earlier and start strengthening their proactive scenario planning capabilities. When teams utilize an AI platform that can ingest and connect data across multiple business functions, they can collaborate more deeply by translating a singular event or disruption into its direct impact on the business and customers. Once planning teams understand the situation and its potential impact early on, they can then explore several scenarios to help mitigate negative impacts to the business.

For example, if a supply chain disruption results in a shortage of a popular snack product, planning teams can use data-driven insights to determine how much product is currently in inventory that can be transferred to other distribution centers and also identify areas with strong consumer demand for other flavors of the same product. With this knowledge, planning teams can mitigate potential shortages and retailer stockouts by reallocating inventory to areas where the product is in high demand and rebalancing alternative flavors in areas where there is also strong demand for those products.

Automating tasks through AI agents

Platforms that also have agentic AI capabilities can help teams automate routine planning tasks and scenario suggestions, allowing them to focus on higher-level planning strategies and scenario management. AI agents can be configured to automate the process of analyzing situations, understanding their impacts, and proposing multiple options or scenarios for planners to review and consider. They can assist planning teams in evaluating different possibilities, considering factors such as cost and product margin, and respond to sudden changes in demand or supply. When initially implementing agentic AI capabilities, agents can propose recommendations for human validation. Once trust in the quality of their recommendations is established, agents can transition to greater autonomous modes with defined boundaries.

However, there are instances where a planner needs to step in to approve a plan or override recommendations based on specific guidelines. A scenario plan may suggest moving product from a distribution center in Canada, but if the planner knows that the sales team is about to close a deal with a large Canadian company, that inventory will be needed to serve that customer in the short term. Therefore, the planner would override this scenario option.

Again, for businesses that have started implementing agentic AI capabilities and have begun configuring their agents to automate specific tasks, the next step is to identify opportunities where agents can provide deeper insights to enhance the productivity of cross-functional teams and synchronize decision-making across teams and business planning timelines.

How to scale AI responsibly across the enterprise

For business leaders ready to leverage an AI-powered planning platform to connect their organization’s data and external signals (such as demand, inventory, and procurement) and build a data-driven planning strategy, one of the most important steps is understanding the business value gaps that AI tools can help address. For example, if planning teams struggle to analyze large volumes of data or keep up with an increased volume of order fulfillment, resulting in value leakage, these may be areas that would benefit from piloting a platform that can connect relevant data to provide stakeholders with greater visibility into where issues stem from and offer insights to improve decision-making.

The teams that are tasked with implementing AI-focused solutions will also need to gain buy-in from fellow colleagues who will be using the tools and platforms, and thoroughly test the platform’s results and recommendations (and incorporate user feedback) before allowing for autonomous capabilities to be widely used across the team. Once a pilot program has shown successful results, business leaders can consider where it makes sense to deploy additional AI technologies within the business and leverage platforms to integrate a more cohesive and seamless business planning and decision-making model across the organization.