Manufacturers are already betting on AI
AI adoption is already substantial among the UK manufacturers surveyed by SAP Engagement Cloud. Some 86% of manufacturing decision-makers believe AI will be essential to winning and retaining customers over the next year. More than four in five reportedly say AI is already embedded in business workflows.
SAP Engagement Cloud surveyed 750 UK decision-makers across IT, technology, marketing, service and revenue roles, including manufacturing respondents. The findings reflect respondents’ views rather than measured outcomes from AI deployment.
Once AI enters daily workflows across customer engagement, supply chains, inventory management and fulfilment, executives face a practical question: what information can the system use when it acts?
Customer-facing AI depends on operational data
A customer interaction in manufacturing can depend on information far beyond the customer record. Production schedules, stock availability, supplier activity, fulfilment status and service interactions can all affect what a company can promise and deliver.
An AI system may generate a personalized response or help shape an offer. Its commercial value depends on having sufficiently current, trusted information about operational conditions.
Sara Richter, Chief Marketing Officer at SAP Engagement Cloud, describes manufacturers as depending on “thousands of micro-signals every day,” ranging from inventory updates to fulfilment and service interactions. She argues that connecting those signals gives AI enough context to make customer engagement more relevant and grounded in what the business can deliver.
Richter represents a vendor with a commercial interest in companies investing in customer-engagement technology. Her claim is therefore a vendor view of the value of connecting customer and operational information.
Customer commitments may depend on factories, suppliers, inventory and delivery operations. An AI workflow making those commitments needs access to the relevant information.
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DAW shows how fragmented data affects AI plans
DAW, a European manufacturer of paints for the construction industry and thermal insulation materials, provides an SAP-supplied example. SAP presented DAW as working to bring fragmented data sources together, build a fuller customer picture and support future AI-led products, services and decision-making.
Alexander Kuhl, Head of Data at DAW, said the company wanted to tailor its offering to different professional target groups and design painters’ digital customer experience around their everyday needs.
“In order to further develop our product and service offering in a targeted manner, we had to make better use of our data sets,” Kuhl said.
DAW identified better use of existing datasets as a prerequisite for developing customer experiences, products and services. Here, work on fragmented data preceded future AI use and formed part of a broader business change.
Wider AI use expands the governance problem
Two in five manufacturing respondents in the SAP Engagement Cloud survey identify cybersecurity and privacy as the biggest barrier to scaling AI.
The risk grows when AI workflows combine customer and operational information. Leaders need to know where data originated, whether it contains personally identifiable information (PII), who may use it and where human approval is required. PII is information that can identify a specific person.
More than four in five decision-makers in the SAP Engagement Cloud survey reportedly say their organizations have established AI guardrails covering data lineage, PII and human approval points. Data lineage means tracking where information comes from and how it changes as it moves between systems.
These reported guardrails indicate that governance is part of AI deployment for many respondents. They do not by themselves show that the controls are effective. For manufacturers bringing previously fragmented information into AI workflows, data-access decisions determine who can use that information and under what conditions. Executives should define those access rules and approval points before expanding AI workflows across customer and operational data.
Main highlights
- AI is moving into core workflows: Manufacturers increasingly see AI as important to customer retention and are embedding it in daily operations. Leaders should assess whether their data foundations can support wider deployment.
- Customer-facing AI needs operational context: Production, inventory, supplier, fulfilment and service data can determine what AI can credibly promise customers. Leaders should connect relevant operational and customer data while maintaining data quality.
- Fragmented data can constrain AI plans: DAW’s experience shows that bringing dispersed datasets together can precede more tailored customer experiences and future AI use. Manufacturers should address fragmentation before expecting AI to support broader business change.
- AI adoption raises governance demands: Combining customer and operational data increases privacy, cybersecurity and access risks. Leaders should establish data lineage, PII controls, access rules and human approval points before scaling AI workflows.
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