Short-term gains, long-term pains: The costs of delaying reinvention in operations and supply chains

In today’s world of tariff hikes, supply chain disruptions and technological upheaval, operations leaders face an onslaught of challenges including rising costs, geopolitical shifts and the urgent need to modernize legacy systems. Given all that, it’s little surprise that PwC’s 2025 Digital Trends in Operations Survey found that many operations and supply chain leaders struggle to balance short- and long-term priorities. Cost efficiency, process improvement and customer retention consume most of their attention, while only about a third prioritize future-proofing their operations.

To help you understand the current landscape and what’s ahead, Matt Comte, PwC’s Operations Transformation Leader, recently shared key survey insights on how companies can deliver immediate results in their operations while still preparing for an uncertain future. He also cautioned against trying to resolve the issues of the day without building resiliency for tomorrow.

I think it stems from several converging factors — cost pressures and inflation, geopolitical shocks, and the underlying need to modernize legacy business models and systems. The survey shows that process efficiency, customer retention and cost management are top priorities, but long-term transformation efforts like operating model redesign and building resilience aren’t getting as much attention. This imbalance suggests many leaders remain stuck in a reactive posture.

The reality is that firefighting eats the resources needed for reinvention, and companies waiting to prioritize transformation may fall further behind competitors making bold moves now to secure their future. Leaders should be asking: Are our digital investments aligned with our long-term business model and strategy, or are we just solving today’s problems? Do we have a clear roadmap for operational reinvention? Are we allocating sufficient leadership attention to future-proofing our business?

AI’s biggest opportunity is moving companies from reactive to predictive operations — but most leaders aren’t taking full advantage yet. According to the survey, only 18% of respondents currently use AI in deeply integrated ways to actively drive decision-making or automation throughout their organization. But in another question, 98% of those who say their companies are currently using AI say it’s effective in creating value. More than half say it’s very effective. That tells me that bigger commitments are more likely to bring better outcomes.

AI and advanced analytics can optimize inventory, predict trade risk and automate decision-making across complex value chains. More companies should thoroughly integrate AI tools into planning, logistics and demand sensing — not just embed them in enterprise systems or run pilots with them. Now it’s about transitioning from testing and reacting to predicting and proactively managing operations.

Not at all. We’ve asked this question for the past four years, and responses are typically in a similar range. That tells me the velocity and scale of technology-based transformations are real and escalating, but that the fatigue of doing large transformations is very real.

It’s interesting that integration complexity and data issues were the top reasons cited. But the surprise for me was that only 31% cited business cases as a top reason. These are things like unclear objectives, weak rationale or difficulty understanding costs, and it suggests that many organizations still aren’t rigorously connecting tech investments to value levers and outcomes in a disciplined way. I think the work we’re doing with our clients to change that is doubling down on ROI visibility and using AI tools in what we call operations intelligence to create models that can tie investments to measurable outcomes.

While understandable — these are tangible, immediate concerns — I think this approach is incomplete. What’s missing is resilience and future planning. Only about a third of leaders said they use technology for scenario planning or disruption mitigation analysis, which represents a gap in operational maturity. It’s important to remember that short-term gains will evaporate if companies can’t adapt quickly when the next disruption hits. Efficiency matters, but adaptive execution is becoming the true differentiator. Leaders should prioritize investments that help strengthen adaptability – not just efficiency – establish KPIs that can assess readiness for future disruptions and avoid overreliance on cost and margin at the expense of strategic flexibility.

I don’t think it should be an either/or question. Speed without scale leads to pilots that never land, while scale without speed misses windows of opportunity. I’ve seen both extremes: organizations stuck in perpetual pilots because they’re innovative but never move forward, and others that “boil the ocean” and never deliver. Success lies in “testing fast, failing fast,” and then scaling with purpose. Governance is also critical as operations become more integrated because scaling operational capabilities without proper commercial, pricing and customer support can lead to problems.

Beyond governance and purpose, I’d add three things. First, a clear link to strategy — using technology to amplify priorities, not distract from them. Second, strong data foundations — integration, interoperability and management across the enterprise cannot be afterthoughts. Third, people-centric execution. Effective organizations pair digital capabilities with future roles, optimized processes and the right skills and incentives. They focus on how people will actually use these tools in their daily work.

Procurement implementations surged 14 months ago, planning came into focus in the last six to 12 months and now manufacturing capabilities have joined the party. We’re certainly seeing AI move from testing into production deployment. Conversations have shifted from “What use cases exist?” to “How can we do this at scale?” While data quality remains important, one advantage of AI is its ability to work around some data imperfections — though enterprise data still needs to meet baseline quality standards. ERP modernization seems to be one of the biggest focus areas for many clients, and the challenge is turning these investments into repeatable, scalable capabilities that help drive results.

AI excels at automating decisions, improving planning, and transforming maintenance and quality management. However, real risks exist in system integration, data readiness and master data management. Cost is also a growing concern — AI can consume substantial computing resources, creating financial variability. As leaders reshape operating models, they should also enhance data quality, upskill talent and balance costs against benefits to help AI investments deliver lasting value.

We need a mindset shift from implementation to integration. Too many companies layer technology onto broken processes. The winners will be those who rethink their workforce and operating model end-to-end, aligning new cross-functional AI capabilities into the fabric of how work gets done. This means building integrated planning capabilities, embedding scenario-based planning into operations and upskilling teams to move from execution to orchestration.

I believe that operations will move from triage to transformation this year. The data confirms leaders are ready, but execution gaps remain. We work to help companies close these gaps by linking strategy, technology and people into one cohesive agenda that helps drive the capabilities critical to modern operations. The key question for leaders isn’t whether to transform. It’s how fast and how well.