In light of frequent supply chain disruptions, companies are now examining supply chain autonomy. A new survey from Accenture, Making Autonomous Supply Chains Real, found that 66% of companies now plan to increase supply chain autonomy in the next decade.
Furthermore, about 40% aim to reach higher autonomy levels where systems handle most operational decisions.
The report notes that leaders in the autonomous supply chain journey build solid data foundations through a secure digital core, which standardizes platforms and governance frameworks.
They invest strategically in AI-enabling technologies, starting with targeted pilots before scaling proven solutions. And they restructure how people and technology collaborate, shifting human roles from routine execution to strategic guidance and oversight.
“Today, it's simply not enough to be cost-efficient,” the report authors wrote. ”Supply chains must also be fast, agile and sustainable; they must reach a new value frontier.
Key insights from the research:
- Before the most recent tariff developments, only 25% of companies saw autonomous supply chains as a strategic priority.
- Just 4% aimed for full autonomy.
- Today, the average autonomy level remains low—only 21% on a 0–100% scale.
- Most companies still rely heavily on manual interventions, with few using AI to adjust sourcing, reroute logistics, or rebalance inventory in real time.
Yet the business case for change is compelling. AI-led autonomous supply chains are enabling organizations to:
- Predict and respond to disruptions faster
- Balance supply and demand dynamically
- Free up human talent for innovation instead of firefighting
The expected impact is significant:
- 62% faster response to shocks
- 60% quicker recovery from disruptions
- 27% shorter order lead times
- 25% increase in productivity
- 5% rise in EBITDA
- 7% improvement in return on capital employed
The report offers the following recommendations (excerpted below):
Build a solid and secure data foundation
Building an autonomous supply chain starts with standardized data platforms, processes and governance frameworks. A data ontology or structured model helps ensure that everyone (and every system) understands data elements in the same way. Without this, insights become fragmented, slowing decision-making. A unified approach ensures accurate, actionable intelligence that supports business goals. AI can play a key role in automatically cleaning and structuring data from sources like inventory levels, shipment tracking and supplier information.
A decentralized data operating model further enhances agility. Empowering business domains to manage their own data as a product ensures relevance and quality, while enabling faster, more informed decisions across the supply chain.
Collecting data from across the organization—not just a few areas—should be a priority. For example, internet of things (IoT) sensors provide real-time data, while digital twins simulate scenarios to optimize workflows, reduce risks and minimize downtime. This shift moves supply chains from reactive to predictive. In the future, AI could even generate synthetic data for companies to use, for example, to train models like building a cost benchmark database for target costing. Without data integration made possible by the digital core, companies will struggle to capture value from implementing autonomous supply chains.
Invest in critical AI-enabling technologies
Organizations must upgrade legacy systems and build an adaptive stack of AI capabilities supported by agentic architecture. This will enable organizations to orchestrate workflows across complex processes by integrating AI into their operations. AI agents can perform routine, high frequency tasks, combine multiple functions and synthesize data, and even oversee end-to-end processes. These agents enhance efficiency, drive strategic workflows and break down silos. They create new levels of operational intelligence and scalability.
Companies should initiate targeted pilot programs addressing specific pain points in critical areas such as logistics, manufacturing, demand forecasting and inventory optimization. By starting small, organizations can quickly demonstrate value, refine approaches and address challenges effectively.
Once pilots prove successful, businesses should scale them incrementally. This approach manages costs, demonstrates ROI and secures stakeholder confidence in the transition toward an autonomous supply chain.
Furthermore, protecting the supply chain requires robust cybersecurity measures. Supply chains are attractive targets for cyber threats. Implementing comprehensive security protocols—such as supplier security audits and advanced multi-factor authentication—ensures data and systems remain secure against increasingly sophisticated threats.
Re-structure how people and technology work together
A successful transition to autonomous supply chains hinges on how companies prepare their talent for a workforce transformation that reimagines the work experience, learning and reskilling. Involve supply chain experts early to build trust among frontline employees who will use and refine these systems. Technology alone can’t ensure success—people’s input, through a continuous feedback loop, is essential to designing solutions that truly meet operational needs.
As they introduce new technologies, leadership must build trust and agility within their workforce at a realistic, cross-organizational pace. One way to do this is by offering personalized upskilling programs, such as targeted training that meets people where they currently are in their digital journey. Leaders also can build networks of “influencers:” approachable team members who become change evangelists. They are empowered with tools and formal leadership support to increase engagement and trust throughout the transformation.
Transparency and explainability of how the systems work and make decisions will also help build trust within the workforce. Meaningful human oversight coupled with training people to be good data stewards through rigorous validation will help prevent trust-eroding biases and inaccuracies. Overcoming trust issues in AI and autonomous systems will help talent upskill faster and achieve the full potential of these technologies.
Organizations should also embrace the shift from traditional business unit structures to platform based operating models. This allows multifunctional teams that include internal and external stakeholders to collaborate and problem-solve faster across the supply chain—not just in parts of it.
Finally, businesses must revisit their governance and leadership styles, transitioning from reactive practices like crisis management to proactive approaches focused on risk assessment and anticipation. Leaders who think ahead about future risks and how to improve their teams’ effectiveness will guide more resilient, adaptable supply chains.
Ultimately, the rise of autonomous systems will fundamentally change how organizations are structured and how people work with each other and technology. It also is an opportunity to reimagine work and reshape the workforce. Businesses that adopt autonomous supply chains will see real value—such as increased production speed and cost reduction—only if they rewire their organizations to support radically different processes and ways of working.
