The 4 Digital Transformation Trends Shaping Manufacturing in 2026

The manufacturing industry is on the cusp of a profound digital transformation. By 2026, the companies that lead will not be those that simply adopt new technologies, but those that build the very infrastructure that enables the next generation of AI-driven operations. From the builders of AI infrastructure, to the beneficiaries of the technology, manufacturers are embedded across this booming industry. 

The convergence of real-time data, agentic operations, and cloud-based intelligence is redefining how decisions are made, how revenue is captured, and how service and customer experience are delivered. This article will explore the trends shaping 2026 manufacturing, the strategic role of data, and how platforms like Salesforce (when applied thoughtfully) can activate commercial and service operations to operate at an unprecedented speed and scale.

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1. Manufacturers will create the infrastructure of the future

Before we can talk about how manufacturing can benefit from AI, we need to first discuss the infrastructure demand that will immediately benefit the industry as a whole. As we look toward 2026, the industry’s most underappreciated role is not as an end user of AI, but as the primary architect of the physical, digital, and industrial infrastructure that powers the AI economy.

Every meaningful AI advance depends on assets manufacturers design, build, power, and maintain. Hyperscale data centers do not exist without industrial power distribution systems, transformers, switchgear, and backup energy infrastructure. AI workloads are unusable without advanced cooling technologies, HVAC systems, and thermal management solutions engineered for continuous, high-density compute. Semiconductor fabrication (the backbone of AI acceleration) is itself one of the most expensive, manufacturing-driven industries in the world.

In other words, AI runs on manufacturing long before manufacturing runs on AI.

This reality is already visible in global investment patterns. Data center expansion to support AI workloads is driving unprecedented demand for electrical equipment, energy infrastructure, and industrial construction. Analysts project data center power demand to grow at multiples of historical rates through 2026, largely due to AI compute requirements. That demand cascades directly into manufacturing value chains–from power systems and cooling equipment to construction, engineering, and field services.

At the same time, the semiconductor industry is undergoing one of the largest capacity expansions in its history, driven by AI-specific chips, edge processors, and accelerators. Capital expenditures for advanced logic and memory manufacturing are expected to remain elevated through at least 2026, reinforcing the fact that AI scalability is constrained less by algorithms and more by industrial production capacity.

This positions manufacturers at the center of three converging forces:

• Compute at scale: AI requires physical infrastructure capable of sustained, energy-intensive workloads. Manufacturers produce the systems that make that possible.
• Energy and thermal resilience: Power generation, distribution, and cooling are mission-critical components of the AI stack.
• Industrial execution: Data centers, fabs, and AI-ready facilities require construction, engineering, automation, and lifecycle services that only mature industrial organizations can deliver.

As AI adoption accelerates across industries, manufacturers will increasingly shape where AI can exist, how fast it can scale, and how resilient it is under real-world conditions. This shifts manufacturing from a passive participant in digital transformation to an active economic enabler that influences AI capacity, cost structures, and geographic distribution.

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Related Article: How Agentforce Powers Smarter Manufacturing

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By 2026, the manufacturers that recognize this role early will gain an advantage. They will not only supply the infrastructure behind AI, but also capture the data, operational intelligence, and ecosystem positioning that come with being embedded at the foundation of the AI economy. That foundation will ultimately determine who leads as AI moves from promise to production.

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2. Shifting to agentic operations for manufacturing in 2026

As manufacturers advance beyond foundational investments, the next frontier is agentic operations. This is a form of AI characterized by autonomous, decision-oriented agents that act on real-time data to optimize outcomes without explicit human commands. By 2026, agentic architectures will reshape every layer of manufacturing operations, pushing organizations from reactive digital tools to proactive, adaptive systems.

Smart manufacturing (shop floor intelligence)

AI is already improving isolated functions like predictive maintenance or quality control, but agentic operations take smart manufacturing further by enabling closed-loop decision cycles. These systems ingest sensor data, contextualize anomalies, recommend corrective actions and, increasingly, execute them autonomously. 

International Data Corporation (IDC) projects that by the mid-2020s a significant portion of production scheduling and operational orchestration will transition to AI-driven execution frameworks, effectively transforming traditional factories into software-defined production environments where AI optimizes throughput, yield and uptime in real time. 

This evolution affects workforce dynamics and organizational design. Rather than replacing operators, agentic systems amplify human capacity. This will enable personnel to focus on exception management, process innovation and strategic oversight while autonomous agents handle routine adjustments.

Supply chains will become a dynamic, self-adapting network

Agentic AI’s impact on supply chains cannot be overstated. Traditional supply networks are largely linear and schedule-driven, but agentic models ingest dynamic data (from supplier lead times to freight disruptions) and autonomously adjust planning and execution. By 2030, half of supply chain solutions are expected to embed agentic capabilities that execute decisions across sourcing, inventory and logistics functions without direct human triggers. 

For manufacturers, a self-adapting supply chain means:

• Responsive demand forecasting that continuously recalibrates plans based on live signals.
• Autonomous inventory orchestration that balances service levels and capital efficiency.
• Resilient sourcing engines capable of reconfiguring supplier portfolios mid-cycle.
• Collectively, these capabilities shift supply chains from static cost centers to live strategic assets that contribute to competitive differentiation.

Aftermarket and service will unlock recurring growth

Agentic operations redefine the aftermarket by tightening the feedback loop between products, performance data and service interventions. Instead of reactive maintenance or periodic support, autonomous agents enable outcome-based service contracts where equipment performance is continuously monitored, and service actions are triggered autonomously based on predictive signals.

This has two major implications:

1. Manufacturers can move from one-time sales toward recurring revenue models anchored in uptime guarantees and performance SLAs.
2. Field service efficiency increases dramatically as AI directs technicians, parts logistics and scheduling with precision previously possible only in operations planning.

Commercial operations are the new frontier of value creation

Finally, agentic AI will extend into commercial functions — from pricing and quoting to channel optimization and customer segmentation. AI agents that understand customer demand patterns, product life cycles and service outcomes can autonomously adjust proposals, forecast revenue impact and coordinate cross-functional execution. 

In a 2025 study, manufacturers signaled strong intent to expand AI investments specifically to automate workflows and improve accuracy, with most respondents reporting positive outcomes from early generative AI deployments and significant expected increases in agentic capabilities.

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3. Data accuracy and maintenance must become a priority for 2026

For decades, manufacturers have understood that data is valuable, while just as often failing to activate it at scale. The constraint was never a lack of information, but rather the structural impossibility of harmonizing it across fragmented systems, high-cost infrastructure, and rigid data models.

Historically, manufacturers were boxed in by four realities:

1. Multiple ERPs and deeply embedded legacy systems created inconsistent data models across plants, regions, and business units.
2. Operational, customer, and commercial data lived in silos, preventing any unified view of performance or demand.
3. Compute economics were prohibitive, forcing trade-offs between speed, scale, and cost.
4. Unstructured and streaming data (sensor feeds, logs, images, documents) was effectively unusable at enterprise scale.

As a result, data strategies stalled at dashboards and historical reporting. Intelligence was retrospective. Decision-making lagged reality. The AI boom has fundamentally altered this equation.

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Related Article: Digital Transformation in Manufacturing Industries

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By 2026, manufacturers are operating in a vastly different data environment that is defined less by constraint and more by architectural choice. Modern data stacks now support:

• Real-time data streaming, allowing operational signals to move continuously from machines, suppliers, and customers into analytics and decision layers.
• Unstructured data ingestion, enabling AI to reason over text, images, telemetry, and documents, not just rows and columns.
• Vector embeddings and retrieval-augmented generation (RAG), which allow AI systems to contextualize enterprise knowledge dynamically rather than relying on static rules.
• Lower-cost, high-performance computing, driven by cloud scale and AI-optimized silicon, collapsing the economics that once limited experimentation.
• Lakehouse and data virtualization models, reducing duplication while preserving governance and performance.

Industry analysts consistently point to this convergence as a tipping point. Gartner and IDC both note that the barrier to AI-driven decisioning is no longer raw data availability, but the ability to operationalize trusted data across business contexts. In other words, the bottleneck has moved up the stack.

This is the inflection moment for manufacturing. Data is no longer something to be centralized “eventually.” It is something to be activated continuously across planning, service, sales, and partner ecosystems. The organizations that recognize this shift will stop treating data platforms as passive repositories and start treating them as engines for real-time business execution.

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4. Agentforce and Data 360 will become the foundation for rapid scale

As manufacturers confront this data turning point, the question is all about where intelligence should live. This is where Salesforce’s role becomes strategically relevant, provided expectations are set correctly.

Salesforce is not for MES, ERP, or WMS decisioning

Salesforce is not (and should not be positioned as) a replacement for manufacturing execution systems, ERP cores, or warehouse control platforms. Those systems will continue to handle deterministic, transaction-heavy decisioning at the operational edge. Attempting to force Salesforce into that role creates unnecessary friction and architectural risk.

Systems like MES, SCADA, WMS, and ERP remain the authoritative platforms for:

• Production sequencing
• Quality control
• Inventory and cost accounting
• Real-time material movement
• Compliance and planning

Salesforce does not (and should not) enter these domains.

Salesforce’s domain is everything that surrounds and amplifies them:

• Commercial lifecycle (quotes → orders → agreements → revenue)
• Aftermarket service
• Channels and distribution partners
• Installed base intelligence
• Customer operations
• Connected frontline workflows

Salesforce is the agentic layer for a more connected workforce

Where Salesforce does matter is above the operational core, at the intersection of people, processes, and decisions that span functions. Agentforce represents a shift from workflow automation to agentic assistance, enabling AI agents that reason across customer, product, service, and commercial data to support human judgment.

For manufacturers, this is critical. Most high-value decisions do not happen on the shop floor. They happen in planning meetings, service escalations, pricing negotiations, and partner coordination. That is where agentic systems deliver leverage, not by replacing humans, but by compressing decision cycles and increasing confidence.

Data 360 is not a data lake replacement 

Salesforce Data 360 is not competing with lakehouses or enterprise data platforms. It does not aim to store everything. Its value lies in activating what already exists.

By virtualizing and harmonizing data from ERP, PLM, service, and external sources, Data 360 enables consistent business context without forcing massive data migration projects. Analysts increasingly describe this layer as essential for AI adoption, not because it centralizes data, but because it makes trusted data usable in real time by business users and agents.

Salesforce as the commercial and service operating system

Looking toward 2026, Salesforce’s strategic role in manufacturing consolidates around one outcome: becoming the system of action for commercial and service operations.

As products become smarter and service models shift toward outcomes and recurring revenue, manufacturers need a platform that connects:

• Installed base intelligence
• Customer engagement
• Field service execution
• Revenue and contract performance

Salesforce sits at this convergence point. Not necessarily intended to be the brain of the factory,  but rather the operating system for how manufacturers monetize, service, and grow in an AI-driven economy.

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Related Article: Data 360: Everything You Need To Know

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The manufacturers that get this right will avoid a common trap: overloading core systems with responsibilities they were never designed to handle, while underutilizing the platforms built for cross-functional intelligence. In 2026, clarity of role (not platform sprawl) will separate scalable AI strategies from expensive experiments.

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The leadership playbook for 2026

As manufacturing enters the agentic era, competitive advantage will no longer come from isolated system upgrades or incremental automation. It will come from how effectively leadership enables the organization to act on intelligence, consistently, safely, and at scale.

To compete in this environment, manufacturers must establish six non-negotiables:

1. A resilient commercial core capable of adapting pricing, contracting, and demand strategies as market conditions shift.
2. A connected service engine that links installed base intelligence, field execution, and customer outcomes into a single operating motion.
3. Data harmonized enough for AI to act, not perfectly centralized, but trusted, contextualized, and accessible in real time.
4. A unified operating platform that empowers teams, rather than forcing coordination across disconnected tools and interfaces.
5. Governance and guardrails that ensure AI operates within defined boundaries — secure, auditable, and aligned with business intent.
6. Insights that convert into actions, not dashboards that explain yesterday’s performance.

This is where many transformation programs stall. ERP modernizations are necessary, but they are expensive, multi-year undertakings that primarily optimize transactional integrity. MES upgrades are essential to operational excellence, but they are deeply technical and focused on deterministic execution at the plant level. Data lakes and lakehouses unlock analytical potential, yet they remain largely the domain of engineering and data science teams.

None of these investments, on their own, solve the central problem executives face in 2026: How to turn intelligence into coordinated business action across revenue, service, and customer experience.

Salesforce addresses this gap, not by replacing core systems, but by activating the front and mid office. It transforms sales, service, and commercial operations into an agentic operating engine where AI supports decisions, orchestrates workflows, and accelerates execution across the business-facing workforce. This is the kind of value ERP and MES were never designed to deliver alone.

In the agentic era, manufacturing leadership is not defined by how many systems are modernized, but by how effectively the organization can sense, decide, and act across the enterprise. The manufacturers that win will be those that treat the commercial and service layers not as downstream functions, but as strategic control points — powered by AI, governed with intent, and built to scale.

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Partner with TELUS Digital for your 2026 digital transformation

The path to thriving in the agentic manufacturing era starts with clarity: understanding where AI adds value, how data should flow, and which operating layers drive measurable business outcomes. Modernizing ERP, MES, or data lakes is critical, but these investments alone will not unlock the full potential of an intelligent, connected enterprise.

TELUS Digital works with manufacturers to activate the business-facing workforce, harmonize data for action, and deploy agentic operations across commercial and service functions. Speak with our experts today to understand how your organization can turn intelligence into action, scale confidently, and position itself as a leader in the AI-driven manufacturing economy of 2026.