The Connected Manufacturer: How Digital Leaders Are Redefining Experience, Operating Models, and AI in an Uncertain Market

Manufacturers are being pushed to balance efficiency and growth, to protect market share and contribution margins while still investing in future capabilities. Digital is right at the centre of this challenge.

At E-Connect Europe Business Platform 2026 – Power of 50, Vincent van Hellemondt and Vera Schnitzlein from Valtech discussed the findings in the latest edition of the Voice of Digital Leaders in Manufacturing 2026, emphasizing that the focus has shifted toward how experience, operating models, data, and AI must work together to create sustainable, profitable growth.

The Industrial Market Reality: Discipline Under Pressure

The market is facing industrial recession in parts of the sector, subdued demand, and intensified pressure on prices and efficiency. Budgets for digital are not disappearing, but they are far from limitless. As a result, leaders are being forced into more disciplined decision-making.

Sustainable profitable growth is the most cited overarching ambition, and also one of the most ambiguous. On one side, it reflects the need to drive efficiency, reduce cost-to-serve, and simplify internal operations. On the other, it signifies the economic imperative to grow, expand share of wallet, and open new revenue streams.

Many organisations are asking whether AI should be pursued primarily as an efficiency lever (automation, productivity, cost reduction) or as a growth engine (new services, better experiences, smarter pricing, enhanced sales support). Although it can do both, very few companies have the clarity and governance to prioritise and sequence those ambitions.

Experience Redefined: From Customer Journeys to Operational Capability

In previous years, customer experience was often discussed in conceptual terms, such as portals, touchpoints, and front-end interfaces. However, experience is now understood as something integrated, a connected experience for customers, partners, and increasingly, employees, enabled by aligned teams, technology, and data.

1. Employee Experience Moves to the Forefront

Happy employees make happy customers. While this may sound intuitive, the way digital leaders now act on it is new. When discussing customer and partner experience, many leaders quickly pivot to the internal experience:

• Are employees able to use the tools they are given?
• Are workflows streamlined enough that sales, service, and support can respond quickly and consistently?
• Do internal systems reduce friction, or do they create it?

Rather than seeing employee experience as a separate HR topic, manufacturers increasingly treat it as a prerequisite for any scalable customer or partner experience. The maturity shift has moved the discussion from designing nice interfaces for external users to systematically enabling internal teams to deliver consistent value.

2. From Touchpoints to End-to-End Workflows

The value of experience is redefined. Instead of optimising the portal or improving a single step in the funnel, leaders are now concerned with end-to-end flows that directly impact customers and the bottom line, primarily:

• Lead-to-order: How quickly and accurately can a lead be qualified, configured, quoted, and converted?
• Order-to-cash: How seamless is the journey from placing an order to delivery, invoicing, and payment?
• Issue-to-resolution: How effectively are service incidents captured, diagnosed, and resolved across channels?

By shifting attention from isolated touchpoints to complete workflows, manufacturers are effectively building customer value maps along the journey. This allows more targeted prioritisation and investment decisions.

3. Experience as an Operational Metric, Not a Design Concept

In consumer-facing industries, experience is often described in terms of emotion, brand perception, and aesthetic quality. The language in manufacturing is different and increasingly precise.

For B2B leaders, the currency of experience is operational:

• Time to quote;
• Lead times and handovers;
• First-time fix rates;
• Visibility into order status or installed base;
• Quality and consistency of information.

Experience is therefore measured in concrete operational capabilities. This again underlines the increasing maturity of the sector, since experience is not seen as a one-off project to improve interfaces anymore, but as a set of measurable capabilities that sit at the core of how the business runs.

The Experience Operating Model: From Initiative to Capability

Once experience is treated as an operational capability, it’s necessary to establish an organisational structure that can manage and improve it over time.

1. Experience as a Managed Capability, Not Just an Outcome

In earlier years, experience was often treated as something to be delivered and then measured with KPIs such as NPS or satisfaction scores. Digital leaders now view experience as a capability that must be continuously managed, implying:

• Ongoing ownership of journeys rather than time-limited projects;
• Clear roles and responsibilities for who steers, implements, and maintains digital experiences;
• Integration of experience metrics into operational management and performance reviews.

The mindset shift is from “launch and leave” to “run and evolve.”

2. From Projects to Journey Governance and Accountability

There is now greater recognition that delivering excellent customer and partner experiences requires more than strong IT delivery or individual initiatives. It requires governance.

Organisations are increasingly:

• Defining owners for end-to-end journeys (e.g., lead-to-order) rather than just systems or channels;
• Creating governance structures that align business, IT, sales, service, and operations around shared outcomes;
• Ensuring that experience is not owned by a single department but embedded as a shared responsibility, with clear accountability.

This is a notable departure from earlier models, where responsibility often sat implicitly in IT or in fragmented business units.

3. Ownership Moving Closer to the Business

Accountability for digital experience is gradually shifting closer to business functions and away from purely technical departments. More organisations are establishing cross-functional units, such as sales and service excellence, that:

• Own the digital portfolio related to commercial and service processes;
• Steer requirements for platforms and tools;
• Act as a bridge between business needs and IT enablement.

IT remains critical in running platforms and ensuring technical robustness, but it is less often the sole owner of digital experience. This aligns with the broader recognition that experience is an operational, commercial, and organisational question, not just a technology one.

Technology, Data, and AI: From Complaint to Realism

Previous reports frequently surfaced frustrations about siloed data, legacy systems, and the inability to “move forward.” Those issues have not disappeared, but the tone has shifted towards pragmatism and constructive realism.

1. Shared Integration Patterns Across Complex Organisations

Manufacturers typically operate across multiple business areas, divisions, and countries. This complexity has historically led to fragmented technology landscapes.

Leaders now increasingly report:

• The establishment of shared integration patterns and standards;
• More systematic approaches to connecting systems across entities and regions;
• A recognition that integration is not a one-time activity but an architectural discipline.

This is particularly important for global organisations where consistency and reuse of integrations can significantly reduce time-to-market and operational risk.

2. Evolving Data Models and “Data as a Product”

Data quality remains a challenge, but approaches to data are maturing. A growing number of organisations treat data less as a by-product of systems and more as a product in its own right, by:

• Designing data models to be reusable across use cases rather than tied to individual applications;
• Moving away from point-to-point data fixes and towards more holistic architectures;
• Building iterative improvements in data quality into ongoing operations rather than treating them as standalone clean-up projects.

This evolution is important for any AI ambition, because without structured, reliable, and accessible data, AI will only scale noise.

3. Clearer Role Split Between Technology and Business Operations

There is a new distinction between ownership of technology platforms and ownership of the business processes that run on them.

Common patterns include:

• IT focusing on platform stability, integration, security, and scalability;
• Business-led teams taking responsibility for how platforms are used in sales, service, and operations;
• Joint steering to balance innovation needs with technical feasibility and risk.

This reflects the broader shift towards experience as an operational capability rather than a purely technical outcome.

The AI Moment: Adoption Without Clarity

Artificial intelligence stands out as both the most significant opportunity and the most pressing source of risk. The speed of adoption has been remarkably high.

When digital leaders were asked in 2025 whether advanced decision-making was being done by AI or large language models in their organisations, only about 30% agreed. Today, almost all leaders report that their organisations are at least testing or using AI broadly – and most individuals use AI tools in their own work on a daily basis.

1. AI Is Only as Good as the Data, and Data Improvement Is Never Done

Leaders emphasise that AI brings value only if the underlying data is reliable and well-structured. Improving data quality and models is an iterative process. As data improves, the value and reliability of AI applications compound over time.

There is a growing recognition that any AI roadmap must be tightly integrated with a data roadmap. Investing in AI without a plan for data is increasingly seen as high risk and low yield.

2. Strategic Ambiguity: Efficiency vs. Growth

Despite rapid adoption, there is still significant ambiguity around AI priorities. Organisations struggle to answer fundamental questions:

• Should AI be used first to automate internal workflows and drive cost savings?
• Should it be applied to enhance customer-facing experiences, such as configuration, quoting, or service?
• How should AI investments be sequenced to deliver both short-term wins and long-term strategic advantage?

Many leaders acknowledge they lack clear criteria to judge where AI will create the most value, especially in the short term. This uncertainty risks spreading investments too thinly across pilots that never scale.

3. AI Archetypes: Builders, Accelerators, and Optimisers

There are three emerging archetypes in how organisations approach AI:

• Builders: Aim to establish a robust AI foundation across the organisation. They focus on getting the architecture, governance, and data structures right from the start, even if progress appears slower initially.
• Accelerators: Prioritise speed and quick wins. They focus on targeted use cases, rapid experimentation, and proving value fast, often with less emphasis on building a comprehensive AI backbone at the outset.
• Optimisers: Focus primarily on enhancing existing processes and capabilities using AI, typically in a more incremental fashion.

Most leaders fall into the builder or accelerator categories. It is too early to conclude which approach will ultimately generate the most value. However, there is an emerging consensus that as organisations scale AI, even accelerators will need builder capabilities to ensure resilience, compliance, and consistency.

A critical question for every manufacturer is therefore: which archetype currently dominates your approach, and do you have the complementary capabilities required to scale safely and effectively?

From Hype to Embedded Capability

Across market, experience, organisation, and technology/AI, digital is no longer treated as a set of disconnected initiatives. It is gradually becoming an embedded capability that shapes how manufacturers operate, compete, and grow.

For industrial leaders, this implies:

• Market pressure is not an excuse to slow digital investment, but a reason to be more selective and disciplined about where digital and AI can create real value.
• Experience must be anchored in operational metrics and workflow improvements that employees can realistically deliver every day.
• Operating models need to reflect the fact that experience and AI are ongoing capabilities with clear ownership, governance, and cross-functional collaboration.
• AI strategies must be built on data strategies. Adoption is already widespread, so the differentiator will be clarity of priorities, quality of data, and ability to scale safely.

In just five years, manufacturers have moved from debating whether digital channels matter to designing operating models around connected experiences and AI-enabled workflows. In some respects, B2B manufacturing is now ahead of B2C, precisely because complexity and risk leave less room for experimentation without structure.

The next phase will determine which organisations move from experimentation to systemic advantage, and which remain stuck in perpetual pilot mode.