This collection brings together six research-based perspectives on the next era of engineering, manufacturing, capital projects and supply chain reinvention. Drawing on Accenture research, executive interviews and field observations, the pieces examine how AI, data and new operating models are reshaping the industrial value chain. Together, they show why performance now depends on designing technology, operations and work as one system. From factory roles and engineering productivity to capital project execution and workforce strategy, the collection offers practical guidance for leaders seeking to scale intelligence, improve resilience and turn reinvention into measurable business value.

Every time a supply chain adds complexity, leaders reach for the same lever: hire more people. More volume means more planners. More suppliers means more buyers. More compliance pressure means more managers. That instinct has carried the function for decades. Today, the math is moving faster than that playbook.

The 2026 Sustainable Procurement Barometer, developed by EcoVadis in collaboration with Accenture, shows leading organizations now use sustainable procurement to drive performance, resilience and growth, not just compliance. Based on insights from more than 1,000 global companies, the report highlights five priorities: value measurement, environmental, social and governance visibility, workflow integration, supplier engagement and innovation at scale.

Software-defined products, tighter regulation and relentless cost pressures are changing what engineering departments must deliver. By 2030, adaptability and speed will be the defining measures of performance.

Leading companies recognize that the compounding realities of this decade cannot be resolved with incremental process tweaks or another tool added to existing systems. Instead, they are reinventing processes, data, tools and how work gets done across the value chain to dramatically improve critical outcomes like cycle times, launch reliability and update velocity.

Our new research clearly confirms this. Across our interviews with 100 engineers and 36 engineering leaders, we found legacy systems nearing their limits. Products are becoming more complex as cycles compress, and engineers report spending roughly half their day on documentation, reporting and information search rather than core engineering work. According to our estimation, this represents a US$ 39 billion annual loss in productivity globally.1 The engineering system is under strain in both its operating model and tool landscape

The problem: Industrial manufacturers run on a complex web of specialized platforms— Siemens, PTC and Dassault Systèmes across engineering and PLM; SAP, Blue Yonder and Kinaxis in supply chain planning; Rockwell, Aveva and Honeywell in manufacturing operations; Bentley and Ansys in digital twinning; and hyperscalers providing the cloud substrate underneath all of it. Each generates intelligence. None coordinates action across the others.

The urgency: Every one of these partners is now deploying agentic AI. SAP alone is targeting over 1,000 agents this year. Multiply that across Siemens, PTC, Blue Yonder, Rockwell and every tier-one supplier in a manufacturer’s network, and the coordination problem doesn’t just persist, it compounds exponentially.

The answer: Engineer three layers of coordination: inside the value chain, across ecosystem boundaries and within a governance framework that keeps humans accountable.

The payoff: Manufacturers that do this operate with greater reliability at lower cost and turn fragmented digital investment into measurable operational outcomes.

Most manufacturers concentrate AI efforts on operations and maintenance, where data is richest and ROI is easiest to prove. That focus misses the bigger opportunity. Manufacturing value accrues across the full plant lifecycle, from design through construction, commissioning, ramp-up and decades of operation. An AI program targeting only the operating phase is likely to stall. Fragmented data, unclear ownership and workforces not yet designed for AI are not late-stage problems; they run through the entire lifecycle.

As capital projects grow in scale and complexity, the industry faces mounting pressures—from supply chain disruptions to regulatory demands and net-zero commitments. Yet, only 6% of companies consistently deliver their projects on time and under budget, achieving up to 30% cost savings. What sets them apart? Our research reveals four priority actions: 1) turn insight into foresight; 2) manage diverse stakeholders; 3) embrace ESG beyond compliance; and 4) build and sustain critical skills.

Sixty-six percent of companies plan to advance their supply
chain autonomy by 2035, aiming to cut order lead times by
27%, boost productivity by 25% and reduce emissions by 16%.
Yet only 25% of leaders are making autonomy a top investment
priority. To unlock this potential, companies must act
decisively by building a secure, integrated digital core,
investing in AI-enabling technologies and redesigning how
people and technology collaborate. Those who delay these
actions risk being outpaced by more agile competitors
leveraging AI to transform supply chain resilience, enhance
responsiveness and create enterprise-wide value.

Generative AI has taken the world by storm. Its massive potential has captured the attention of business leaders across industries, with 95% of executives agreeing it will be transformative for their company. And 100% anticipating workforce changes as a result.

The implications for supply chain leaders? There are countless opportunities to apply generative AI across end-to-end supply chain networks. Performance gains are waiting to be tapped in everything from sourcing and planning, through manufacturing and fulfilment, to aftersales and service.

Companies can reduce operational expenditure by up to 24% and cut total supply chain costs by 20% by applying AI and autonomous technologies where they matter most. In today’s volatile environment, supply chains must be both efficient and resilient—yet most remain fragmented and manual.

This report introduces a pragmatic, self-funding path to autonomy: start with high cost, high-impact levers; use early savings to fund reinvestment; and scale intelligent capabilities across planning, procurement, manufacturing and fulfillment. Four companion domain blogs explore each function in detail, showing how targeted AI initiatives deliver measurable savings, productivity gains and sustainable growth.

By scaling intelligent technologies across the product lifecycle—from robotic high-throughput process design to AI-augmented real-time analytics and decision making—biopharma leaders can reduce costs, accelerate launches and build more reliable, adaptive manufacturing systems.

Global shocks, regulatory volatility and inflation continue to disrupt global supply chains, making procurement a critical driver of agility and competitive advantage. Many organizations, however, remain stuck with outdated, reactive processes. Manual workflows limit organizational focus, supplier insights are locked in fragmented systems and talent gaps are widening as new digital skills become critical. Existing systems struggle to adapt to rapid market and economic changes, leaving businesses exposed and unable to respond with speed or confidence.

Capital project leaders are investing in digital tools, planning systems and governance, yet a persistent gap between leadership strategy and site execution continues to undermine performance. Accenture’s research among 1,050 leaders and frontline workers reveals a stark disconnect: 73–77% of senior leaders believe they positively influence cost, planning and schedule performance, but only 13% of workers say leadership decisions consistently shape their daily work. The report recommends strengthening the flow of real-time field intelligence, aligning leaders and site teams around a shared definition of value and designing technology around execution realities to enable faster decisions and improve project outcomes.

Engineering is reaching an inflection point as software-defined products, tighter regulation and cost pressure expose the limits of legacy systems. This report finds engineers spend about half of their day on documentation, reporting, information searches, and meetings rather than core engineering. To turn engineering from a cost center into a growth engine, companies need a cloud-based digital core and governed access to lifecycle data. The report recommends five moves: continuous evidence across the V-model, simulation-first development, automated verification and compliance, AI-augmented talent with humans retaining final judgment, and structured partner collaboration. Together, these shifts can improve speed, economics, and lifecycle performance.

A sharp rise in defense acquisition spending is putting new pressure on the industrial base, with global spending projected to grow at a 12% CAGR between 2025 and 2030. Defense firms are still struggling to ramp up production fast enough to meet government requirements. As part of Accenture’s annual International Defense Insight report series, we surveyed global defense industry leaders and found that seven in ten consider meeting growing demand as a top challenge over the next three years, both for scaling existing production and for developing new products. Our research points to a widening gap between demand and delivery and to close the execution gap, defense firms simply cannot rely on building more production lines. They’ll be better positioned to compete and grow if they can deliver, sustain and innovate at the speed governments now require.

Across industries, organizations are at an inflection point. For more than a century, they have been built on two primary assets: labor and capital. Now, a third has emerged—data intelligence. Real-time data flows, AI agents and the embedded knowledge they generate are rapidly becoming the foundations of autonomous activities that create value. This new asset class is forcing leaders to rethink not just how their companies operate, but how they are organized, led and governed.

Autonomy is not automation. While automation executes predefined tasks, autonomy (enabled by AI) perceives context, analyzes situations, makes decisions and acts—without human intervention. Autonomy reorganizes decision-making itself, shifting away from human bottlenecks and toward intelligent systems that operate at speed and scale.

AI is reshaping data center development, but power has become the industry's biggest constraint. Accenture’s latest research on the subject argues that success now depends less on securing the largest power allocation and more on becoming the easiest project to energize. The report outlines four priorities for developers: selecting sites based on power readiness, earning community support early, building flexibility into facility design and co-planning capacity with utilities. Organizations that integrate power strategy, community engagement and infrastructure planning from the outset can accelerate deployment, reduce project risk and maximize the value of constrained energy resources.