Many manufacturers invest in digital transformation but see limited results. Learn the common pitfalls and how to overcome them.
By Daniel Joseph Barry, vice president of product marketing, Configit
Eight years after Gartner identified the “five barriers to digital transformation,” manufacturers continue to face the same challenges: entrenched silos, cultural resistance and fragmented solutions. Many organizations celebrate superficial wins—like digitizing documents—while avoiding the harder work of process alignment and cross-functional collaboration. The reality is that for this industry, progress often stalls.
What’s needed is true digital transformation that goes beyond incremental change, and this is becoming increasingly important in the era of AI. Digital transformation is a journey and must be treated as one.
Even as manufacturing organizations are investing more in digital transformation and AI, they are struggling with some of the same old challenges. Today’s manufacturers must tackle the difficult task of managing two concurrent shifts: AI adoption and cloud migration. However, there are residual structural and cultural barriers. Progress is slowed by data-sharing reluctance across ecosystems and teams, uneven governance models and lack of clarity about the impact of AI on jobs. However, as is the case with all technology shifts, early adopters and evolvers are the ones who gained market share.
A major barrier is focusing on superficial wins vs. structural change. Examples include:
Maturity gaps persist. There is a wide disparity in customer journey management maturity; only “orchestrated” manufacturers show sustained double-digit revenue and profit gains. The industry has deployed technology, but it has not consistently redesigned how the business operates.
A key problem is that just digitizing silos isn’t transformation. Many manufacturers have allocated significant budget to enterprise systems including enterprise resource planning (ERP), customer relationship management (CRM), product lifecycle management (PLM) and eCommerce platforms. Yet they are often implemented in isolation, leading to data that doesn’t connect. The data thus remains siloed, limiting what’s possible due to lack of cross-functional alignment.
The result is fragmentation. Without a common view of product or customer data, the engineering, sales, manufacturing and service divisions remain divided, operating on differing data sets. There is no single product definition but rather multiple interpretations of what the product is, depending on which system you’re looking at. Team outcomes are measured differently, driving conflicting priorities that disable enterprise-wide optimization.
The operational consequences are many. Rework across lifecycle stages becomes necessary due to misalignment, especially when configuration errors flow through the system. Limited traceability across variants and changes happens as configuration logic doesn’t get synched across systs. These lead to slower response to customer requests and market volatility, as this data fragmentation lowers the capacity for agility. Many manufacturers have modernized tools while preserving the same structural silos that limit performance.
There is escalating pressure to adopt AI; it’s positioned as a competitive necessity. Budgets shifting toward intelligence initiatives powered by AI, but there’s no data alignment undergirding the projects.
AI amplifies a manufacturer’s existing maturity level. Reliable AI results depend on aligned, validated data; if the configuration rules aren’t aligned and validated, AI will simply scale the inconsistency. Poor data governance accelerates error as quickly as it speeds insight. Without disciplined governance, it magnifies fragmentation.
The digital thread is a prerequisite for powering traceability across all functions; it connects product data and configuration logic across a product’s lifecycle.
AI does not compensate for fragmentation; it magnifies it. AI only works with solid data/process foundations. AI is the future, and it will remain the future until manufacturers fix the present.
Manufacturing needs a lifecycle approach rather than a system approach. This requires alignment across engineering, commercial and operational functions – a holistic viewpoint. Properly aligning lifecycles requires shared definitions of product and configuration logic.
Structural alignment of data and governance happen by breaking down organizational silos through shared data models. In this way, governance is aligned to business outcomes, not system ownership.
Data integrity is foundational infrastructure when configuration rules are validated. Downstream impacts become visible and addressable when there is traceability across changes and dependencies. Visibility across functions removes the need for rework, lowers risk and speeds the ability to make informed decisions. Transformation is structural alignment across the lifecycle, not incremental automation.
Gartner’s five barriers to digital transformation have barely budged in the manufacturing sector. It’s not because the sector doesn’t want transformation but because it’s been trying to cut corners – slapping on a bandage when what’s needed is surgery. Manufacturers need to own up to the reality that, as noted earlier, digitizing silos isn’t transformation. Neither is adding a veneer of AI.
True digital transformation requires the breaking down of data silos so that every function has complete visibility across a product’s lifecycle. That will help provide the necessary framework for AI to work well. For manufacturers under pressure to adopt AI, the real opportunity lies in treating transformation not as a one-off project, but as the foundation for everything that follows.
About the Author:
Daniel Joseph Barry is vice president of product marketing at Configit the global leader in Configuration Lifecycle Management (CLM) solutions and a supplier of business-critical software for the configuration of complex products. He has over 30 years of experience in the Telecom and IT industry, working in various technical and commercial roles.
Educated as an electronic engineer, he progressed from research and system development roles to leadership roles in business development, sales, product management, marketing and strategy in global multinationals like Ericsson, as well as startup and growth companies. After several years as an independent consultant, he joined Configitin 2023 in a role that leverages all his experience in articulating the value that CLM and Configit can provide, as well as providing insight into market needs.
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As manufacturers offer more customization than ever before, managing product complexity has become a critical challenge. Tune in with Dan Joe Barry, Vice President of Product Marketing at Configit, who explores how companies are tackling the growing number of product configurations across engineering, sales, manufacturing, and service. He explains how Configuration Lifecycle Management (CLM) helps organizations maintain a single source of truth for configuration data. The result: fewer errors, faster quoting, and the ability to deliver customized products at scale.
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