This is the second in a blog series of four that reviews discussion that occurred during ARC Advisory Group’s 2026 Industry Leadership Forum. Specifically, it details a keynote conversation held with senior executives from Rolls-Royce, BTX Precision, and MxD. Read the full four-part series here: Connected Manufacturing Networks and the New Supply Chain – Logistics Viewpoints

Pillar 1: The Market Signal

During my opening keynote at the 30th annual ARC Industry Leadership Forum in Orlando, I led the audience through a simple exercise. I handed a production order to a group representing a traditional, linear supply chain and watched as the information slowly, painfully made its way to the manufacturers. Intentionally, I jokingly noted the awkward silence that filled the room as the lag time compounded was palpable. Then, I demonstrated the alternative: broadcasting a transparent market signal directly to the entire ecosystem, instantly aligning everyone to the same objective.

That stark contrast in approach represents the growing competitive gap for those companies unable to align with the first pillar of the new industrial reality: The Market Signal. Too often, industrial enterprises today continue to mistake their own internal projections for market signals. They look at historical data, pass a forecast over the wall from sales to production, and call it a strategy. In today’s hyperconnected reality, market signals can change quickly and dramatically. In that environment, speed and accuracy in responsiveness are the metrics of value.

A Catalyst is Not an Order

Let’s be clear, the fundamentals of supply and demand are still in place. People want products, and manufacturers still need to build them. What has changed is the hyperconnectivity of the world and the radically compressed time to both value and volatility.

In the past, industrial enterprises had some operational elasticity to absorb market shifts, allowing information to work its way through siloed departments. In the past, a demand signal was, effectively, a purchase or a commitment to purchase. Today, it’s a complex reflection of inputs across an entire value chain. The market signal defines the “what” and the “when,” and it does so continuously over time, shaping what success and risk look like in real-time.

Today, the speed of responsiveness is absolutely crucial to value. As I discussed with the panel, this compression no longer stops at the procurement. Instead, it ripples all the way down into production, demanding agility directly on the shop floor.

Defining Value, Risk, and Success

During our panel discussion, Berardino Baratta, CEO of MxD, perfectly illustrated how this reshaping of market signals is playing out in the defense sector. Traditionally, the Department of Defense would launch a platform with a 30-to-40-year lifespan, guaranteeing massive, predictable quantities. Now, the acquisition process is modernizing, shifting toward buying in smaller, dynamic slices. A manufacturer might receive an initial order of just 100 parts but must invest in the capacity to build 10,000, all without the safety net of massive, guaranteed long-term orders. Add to this the recent rollout of stringent, audited cybersecurity requirements with relatively fast compliance timelines, and the market signal fundamentally changes what risk and success look like.

True digital transformation leaders deeply understand this reality. They always begin by looking at external market signals rather than internal technology desires. If an enterprise focuses solely on internal efficiency and margins, it will inevitably be surpassed by a competitor who is laser-focused on reading and reacting to competitive market signals.

Bringing the Market Signal to the Shop Floor

Greg Davidson of Rolls-Royce shared that his organization is moving away from traditional, static purchase orders. Instead, they provide their supply chain with access to a digital marketplace based on an Indefinite Delivery, Indefinite Quantity (IDIQ) model. Rolls-Royce now trades secure 3D design models rather than traditional drawings, allowing manufacturing partners in its ecosystem to instantly see how demand is shifting at any point in time or over time.

Chief Revenue Officer, Jamie Goettler, of BTX Precision highlighted how this manufacturing agility looks from the supplier side. BTX’s top aerospace customers are now using AI-driven “should cost” systems. Customers upload a design model, run it against BTX’s pre-shared operational parameters (like overhead and cycle times), and instantly generate a highly accurate price and capacity check. This radically shortens the supply chain and allows BTX to respond to the market signal in real-time without the traditional friction. It’s a stripped-to-the-essence view of value. If BTX can continuously and proactively align its business with the market signal, it wins.

Investment that Increases Clarity and Reduces Latency

While the strategic vision is clear, a significant portion of industrial companies in the US are resource challenged in executing to it. Baratta noted that 75 percent of US manufacturers have fewer than 20 employees. Many of them are critical lower-tier suppliers, but they lack IT departments, CISOs, or even basic ERP systems, still relying on paper and spreadsheets. Realigning to this vision requires step-change thinking.

Regardless of size, an enterprise cannot simply mandate digital agility by assuming it can rely on technology as an outcome to pass a hyper-fast market signal down a supply chain that lacks the infrastructure to receive it. To capitalize on modern market signals, verticality and context are important. Industry leaders must actively invest in the technological uplift and cybersecurity readiness of their smaller partners. Without addressing this reality, the entire ecosystem remains blind and unresponsive.

The market signal is the ultimate arbiter of value and risk. To truly harness it, participants of all sizes must strive to define VOI (Value on Investment), not just ROI. The goal of deploying technology here is to create clarity so that latency is removed from your decision, thus removing artificial barriers to value. If an enterprise cannot sense and respond to the signal proactively through a digitally integrated supply chain, no amount of internal efficiency will save it from obsolescence.

Next up in Part 3, I’ll outline the most visible and active change agent, the Demand Architect, closing the gap on production and supply chain. The blog will outline how companies like Rolls-Royce have shown leadership by structurally reorganizing their ecosystem to continuously align with these new market signals.

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Recent moves by Meta and Standard Chartered show that AI is no longer just a productivity tool. It is becoming a structural force reshaping roles, workflows, and enterprise operating models.

Standard Chartered’s plan to reduce more than 7,000 corporate-function roles by 2030 is not just another white-collar layoff story. Meta’s decision to reassign roughly 7,000 employees into AI-related initiatives is not just another technology-sector restructuring. Together, they point to something larger: AI is moving from a tool-level productivity story to an operating-model redesign story.

That distinction matters for supply chain leaders.

Standard Chartered is targeting a reduction of more than 15 percent of corporate-function roles by 2030, supported by automation and AI. Reuters reported that the bank is also aiming for return on tangible equity above 15 percent by 2028 and around 18 percent by 2030. The bank’s restructuring is tied directly to productivity improvement, automation, and the substitution of technology capital for some categories of labor.

Meta provides the second signal. Reuters reported that Meta is preparing a major restructuring while reassigning approximately 7,000 employees into AI-related initiatives. The reorganization includes new AI-focused groups, fewer managerial layers, and smaller teams designed around AI-native workflows.

One case emphasizes productivity and labor substitution. The other emphasizes organizational redesign. Both point in the same direction.

AI is becoming an operating-model decision.

AI Is Moving Beyond the Copilot Phase

The first phase of enterprise generative AI was largely additive. Companies gave employees new tools and asked them to become more productive. A planner could summarize a forecast variance faster. A procurement analyst could draft an RFQ more quickly. A logistics coordinator could generate a carrier email in seconds instead of minutes.

That was useful. It was also incremental.

The next phase is different. Companies are beginning to ask whether the work itself should be reorganized. If AI can retrieve data, summarize context, recommend actions, route exceptions, draft communications, and document decisions, then the surrounding workflow changes. The staffing model changes. The number of handoffs changes. The role of managers changes.

That is why the Meta restructuring is a useful signal. AI is not being treated only as software. It is being treated as an organizing principle.

Supply chain organizations should pay close attention.

Supply Chain Is Built on Coordination Work

Supply chains are full of coordination labor. A shipment is late. A planner checks inventory exposure. A buyer looks for alternate supply. A transportation team evaluates expedited capacity. A customer-service representative communicates the delay. Finance may later reconcile the cost.

Some of this work requires judgment. Much of it is structured checking, updating, routing, documenting, and escalating.

Those are exactly the activities AI is beginning to absorb.

The most exposed areas include planning support, procurement operations, transportation execution, trade compliance, freight audit, and customer or order support. These functions depend heavily on data retrieval, rules interpretation, workflow routing, document handling, and exception management.

Planning support includes forecast variance review, replenishment recommendations, inventory exception analysis, and scenario preparation. Procurement operations include supplier data gathering, spend classification, RFQ preparation, contract lookups, and risk monitoring. Transportation execution includes appointment scheduling, shipment status updates, delay detection, carrier communication, and freight audit support.

Trade compliance is also highly exposed. Classification support, restricted-party screening, tariff lookup, document review, and exception documentation are information-heavy workflows. Customer and order support will face similar pressure as AI becomes better at order-status responses, delivery ETA updates, claims intake, and service-level exception routing.

These are not peripheral activities. They are the connective tissue of supply chain operations. But they are also susceptible to automation when data is structured, workflows are repeatable, and decision rules are well understood.

The Impact Will Be Uneven

AI will not affect every supply chain role in the same way.

Roles built primarily around data retrieval, reporting, transaction processing, and routine coordination will face the greatest pressure. Roles built around judgment, negotiation, escalation, governance, and cross-functional tradeoff management will become more important.

A transportation analyst who spends much of the day checking shipment status across portals is exposed. A transportation leader who can redesign carrier strategy, evaluate service-cost tradeoffs, and manage disruption response is not exposed in the same way.

A procurement coordinator who manually gathers supplier data is exposed. A category manager who understands supplier markets, negotiation leverage, resilience risk, and geopolitical exposure remains central.

A planner who only reconciles spreadsheet exceptions is exposed. A planner who can interpret demand uncertainty, align commercial and operational priorities, and guide executive decisions becomes more valuable.

This distinction matters. The future supply chain organization will not simply be smaller. It will be differently shaped.

From Systems of Record to Systems of Decision

The deeper issue is architectural. Traditional enterprise systems were built as systems of record. ERP, TMS, WMS, procurement, and order-management platforms hold transactions, rules, workflows, and master data. They were not originally designed to reason continuously across changing conditions.

AI introduces a new layer: a system of decision.

That layer can monitor events, retrieve relevant context, evaluate options, recommend actions, and in some cases initiate workflows. In supply chain operations, this means AI can help move work from manual intervention to machine-assisted orchestration.

This is the same basic argument developed in the AI in the Supply Chain white paper: AI should be understood not as a bolt-on feature, but as a new operational layer that extends existing enterprise systems with real-time awareness, adaptive decision-making, and automation at scale.

That shift has workforce implications. If AI can detect an exception, retrieve the relevant policy, evaluate alternative actions, communicate with other systems, and document the decision, then the human role changes. The person is no longer the default processor of the transaction. The person becomes the supervisor of the system, the handler of edge cases, and the owner of judgment when tradeoffs become material.

The Risk Is Poorly Designed Automation

The danger is not simply job loss. The danger is poorly designed automation.

Supply chain decisions are rarely isolated. A late shipment can affect production, inventory, customer commitments, transportation cost, and revenue recognition. A sourcing decision can affect resilience, compliance, working capital, and supplier concentration risk. A warehouse labor decision can affect service levels, safety, and downstream transportation flow.

If AI is implemented only as a cost-reduction tool, companies may automate tasks without understanding the dependencies behind them.

That is where supply chain leadership matters. The right question is not, “How many people can AI replace?” The right question is, “Which decisions can be automated safely, which should be machine-recommended but human-approved, and which must remain under human judgment?”

That requires domain expertise. It also requires governance.

What Supply Chain Leaders Should Do Now

The practical response is not to resist AI. It is to get ahead of the redesign.

Supply chain leaders should begin by mapping work at the task level, not the job-title level. Which tasks are repetitive? Which require judgment? Which depend on poor data? Which create the most latency? Which are high risk if automated incorrectly?

They should also identify the workflows where AI can improve speed without creating unacceptable operational risk. Freight audit, document retrieval, shipment-status communication, exception triage, and supplier-risk monitoring are often good starting points. Fully autonomous sourcing, production allocation, or customer-priority decisions require more caution.

The next step is data readiness. AI cannot reliably automate supply chain decisions if master data is inconsistent, shipment data is delayed, supplier records are incomplete, or policy documents are scattered across disconnected repositories. Many organizations will discover that the bottleneck is not the model. It is the operating architecture around the model.

Finally, leaders need to redesign roles deliberately. AI should reduce routine coordination work, but it should also elevate the work of experienced supply chain professionals. The objective should be fewer manual handoffs, faster exception resolution, better visibility, and more time spent on decisions that require judgment.

The Bottom Line

Meta and Standard Chartered are useful signals because they show that AI is becoming part of enterprise restructuring logic. One case emphasizes productivity and labor substitution. The other emphasizes role reassignment, flatter structures, and AI-native organizational design.

For supply chain leaders, the implication is clear. AI will not remain confined to dashboards, copilots, and pilots. It will increasingly reshape how work is allocated across people, systems, and software agents.

The companies that manage this transition well will not simply cut labor. They will build more responsive operating models. They will use AI to reduce routine coordination work, improve decision speed, and focus human expertise where it matters most.

The companies that manage it poorly will automate fragments of work without understanding the system they are changing.

That is the real lesson. AI is not just a technology investment. It is an operating-model decision.

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The 2026 National Freight Strategic Plan frames freight not simply as infrastructure, but as a national operating system for supply chain resilience, energy security, industrial competitiveness, and logistics modernization.

The U.S. Department of Transportation has released the 2026 National Freight Strategic Plan, a multi-year framework for modernizing the nation’s freight network. The plan covers the nearly seven-million-mile multimodal system that moves goods by truck, rail, water, air, pipeline, port, terminal, and intermodal hub. According to USDOT, that network moves more than 54 million tons of goods valued at more than $68 billion every day.

For supply chain executives, the important point is that freight has again been placed at the center of national economic strategy.

The 2026 plan identifies six strategic priorities: safety, efficiency, security, resilience, innovation, and workforce capability. These are familiar words in transportation policy. But taken together, they point to a more important shift. Freight is no longer being treated only as a physical infrastructure problem. It is being framed as a national operating system that supports industrial production, energy flows, retail availability, defense mobility, and private-sector supply chain performance.

That framing matters.

For decades, freight policy has often been fragmented across modes, jurisdictions, and funding programs. Highways were treated separately from ports. Ports were treated separately from rail. Rail was treated separately from warehouse and distribution networks. Pipelines and energy corridors sat in a different policy conversation altogether. But real supply chains do not operate that way. They are multimodal, interdependent, data-intensive networks.

The new NFSP reflects that reality more directly.

The Freight Network Is Now a Strategic Asset

The plan’s efficiency goal focuses on reducing delay and unreliability at nationally significant freight bottlenecks, improving the use of existing infrastructure, streamlining federal processes, and promoting integrated freight planning. That is important because many of the most damaging supply chain failures are not caused by the absence of infrastructure. They are caused by weak coordination across existing infrastructure.

A port delay can affect rail dwell time. Rail congestion can affect inland distribution. A highway bottleneck can affect replenishment reliability. A warehouse labor constraint can neutralize gains from faster transportation. The freight system behaves as a network, not as a collection of isolated assets.

This is why the plan’s emphasis on multimodal connectivity is significant. The most valuable improvements will not always come from building entirely new capacity. They will often come from better orchestration of existing capacity across corridors, terminals, carriers, agencies, and private operators.

This also explains the plan’s emphasis on data-driven planning. Public agencies cannot manage freight performance effectively if they lack visibility into bottlenecks, route alternatives, utilization patterns, and critical dependencies. Private companies face the same problem inside their own supply chains.

In that sense, the NFSP is aligned with a broader shift already underway in logistics technology: moving from static planning to network-aware decision-making.

Security and Resilience Are No Longer Secondary Issues

The plan gives notable weight to freight security. It calls out national defense mobility, cargo theft, fraud, cybersecurity, operational security, and secure freight corridors for strategic energy, industrial, and resource supply chains.

That is the right direction. The security risks around freight have broadened.

Cargo theft has become more sophisticated. Fraud increasingly uses digital channels. Cybersecurity risk now extends into transportation management systems, port systems, warehouse systems, telematics platforms, and carrier networks. Energy and industrial supply chains are exposed to both physical and digital disruption. A freight plan that ignores these realities would be incomplete.

The resilience goal is similarly important. USDOT’s language around single points of failure, redundancy, rerouting capability, risk analysis, preparedness, response, and recovery is directly relevant to modern supply chain design.

Resilience cannot be reduced to inventory buffers. It depends on understanding where the network is brittle. Which corridors lack alternatives? Which nodes carry disproportionate flow? Which facilities or ports create cascading risk if disrupted? Which routes are essential for energy, defense, food, or medical supply chains?

These are no longer side questions. They are becoming standard executive supply chain risk questions.

The 2026 plan’s challenge will be execution. Identifying critical nodes is one thing. Funding, permitting, coordinating, and modernizing them across multiple layers of government and private ownership is another.

Innovation Must Mean Interoperability, Not Just Technology

The innovation goal is one of the most consequential parts of the plan. USDOT points to advanced freight technologies, interoperable digital standards, federal research, pilots, and reducing barriers to adoption.

Policy and technology strategy are now converging around the same operational problem: how to make a complex freight network work better as a network.

The freight system is already becoming more digital. Carriers, brokers, shippers, ports, railroads, 3PLs, warehouse operators, and visibility platforms all generate operational data. But the value of that data is limited when it remains fragmented across incompatible systems.

The next stage of freight modernization will require interoperability. That means common data standards, better APIs, trusted event-sharing, cyber-secure integration, and practical mechanisms for public-private information exchange.

This is also where AI becomes relevant. The most useful AI applications in freight will not be generic chatbots. They will be systems that can sense network conditions, retrieve trusted operational context, reason across dependencies, and recommend or trigger corrective actions. ARC’s recent work on AI in the supply chain argues that future logistics performance will depend on connected intelligence across systems, not isolated automation tools.

That point applies directly to national freight policy. A modern freight network still has to be paved, dredged, signaled, and maintained. But increasingly, it also has to be measured, connected, and understood in near real time.

Workforce Is the Constraint Behind the Strategy

The plan’s workforce pillar should not be treated as an add-on. Freight modernization will fail if the workforce model does not evolve with the technology and infrastructure model.

Truck drivers, dispatchers, warehouse supervisors, maintenance technicians, railroad workers, port workers, customs specialists, safety professionals, and logistics planners are all operating in a more technology-enabled environment. Automation and AI will change tasks, but they will not eliminate the need for capable people across the freight system.

The workforce issue is also about retention and working conditions. A freight system that depends on chronic labor stress, unpredictable schedules, poor handoffs, and weak frontline technology will not be resilient. Capacity is not only physical. It is human and organizational.

The Bottom Line

The 2026 National Freight Strategic Plan does not, by itself, fix bottlenecks, eliminate cargo theft, build redundancy, or modernize digital freight infrastructure. But it establishes a useful national framework.

For shippers and logistics executives, the signal is clear: freight infrastructure, supply chain resilience, energy security, and digital logistics are converging. Public policy is beginning to reflect what operators already know. The U.S. freight network is not background infrastructure. It is a core component of economic competitiveness.

The organizations that benefit most will be those that apply the same discipline inside their own networks: better visibility, clearer resilience planning, more secure data exchange, stronger workforce capability, and technology adoption tied to operational performance rather than technology adoption for its own sake.

The future of freight will not be won by infrastructure alone. It will be won by the ability to coordinate physical assets, digital systems, public investment, and private execution into a more reliable national logistics network.

Reference: U.S. Department of Transportation, 2026 National Freight Strategic Plan, May 18, 2026.

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