Future of Supply chAIns - designing a target operating model for AI-driven enterprises

The legacy operating model are too rigid to deliver resilience and speed

Supply chain and logistics leaders are standing at a structural inflection point. For decades, the operating model of Supply Chains was built around standardization, cost optimization, and stability. But the landscape has shifted. Real-time volatility is the new constant—driven by geopolitical shocks, pandemics, technological waves, and rising demands for speed, resilience, and sustainability. In this environment, the traditional supply chain will become inefficient and fundamentally unfit for purpose.

At the heart of this disruption is the growing mismatch between legacy processes and rapidly evolving market conditions. Most supply chain systems still operate on predefined rules, lagging data, and batch decision-making. Companies lose revenue to stockouts they didn’t predict, build up excess inventory they didn’t need, and fail to react fast enough to shifts in demand or supply. The more global the footprint, the more fragile the system.

Executives now face a decisive question: should they continue refining a linear pipeline, or re-architect for intelligence at scale? For those ready to lead, the answer is clear. The future target operating model is not an evolution of the past. It is a reset—a shift to an adaptive, Demand Driven, AI-native architecture capable of sensing change, making decisions, and executing in real time. The true transformation is not about a one more technology project, it’s an enterprise design choice.

AI is powering a new era of personalized, predictive and resilient logistics

The adoption of AI is already happening, but its impact today is still uneven. According to our research, demand forecasting, powered by machine learning, is already in use by 30% of organizations, with measurable gains in accuracy and forecast responsiveness. In parallel, production scheduling tools are evolving beyond rigid calendars into responsive, scenario-based systems that adjust in near real time to demand changes.

Most organizations still struggle to move beyond pilots or isolated processes. AI runs in a silo, disconnected from broader processes, teams, and governance. The real unlock will only come when AI is embedded into the operating core.

Looking ahead to 2028, the roadmap for AI in Supply Chain is expanding rapidly. AI-driven scenario planning is becoming critical for enterprise agility, allowing organizations to test hundreds of possible futures and align resources accordingly. According to our research 32% of organizations plan to implement scenario planning by 2028.

Sustainability is moving from afterthought to operational priority. AI is helping companies measure, monitor, and act on their environmental footprint, including their supplier ecosystems. Already, 28% of supply chain leaders say they plan to integrate sustainability modelling into their core planning stack within three years.

These trends signal a broader shift: AI is moving from operational efficiency to enterprise intelligence. The next phase of adoption will be about creating adaptive systems that can reconfigure themselves in response to disruption.

By 2028, leaders’ supply chain operating model will be adaptive by design

Firms that have embedded AI across their logistics operating model are already seeing significant improvements in delivery performance, with reductions in time-to-ship and fulfilment cost. The real advantage is not just cost, it’s adaptability and resilience.

AI-enhanced logistics allows for dynamic, personalized delivery at scale. Rather than offering one-size-fits-all shipping options, companies can now tailor delivery promises based on a customer’s location, preferences, and past behaviour continuously optimized through AI learning loops. This personalization also increases loyalty and margin by aligning service level with value.

At a deeper level, AI is fundamentally altering how decisions are made. Predictive analytics are replacing backward-looking reports. Forecasting isn’t a monthly cycle but a real time process. With extended risk visibility, organizations are no longer caught off guard by disruptions. AI engines can now track indicators across every node and flag vulnerabilities before they cascade.

To unlock this potential, organizations must do more than implement algorithms. They must design for intelligence and build a talent model that prioritizes cross-functional fluency in AI, data, and operations.

The companies that win in the next decade will be the ones that predict what’s coming and reconfigure before others even realize they need to. That’s the true promise of the AI-enabled supply chain.

_{¹ BearingPoint Survey on AI adoption and Future Operating Models of 300 executives from UK, US, France and Germany, April 2025}