AI Vendor Management for Manufacturers

With AI rising on manufacturing radar, C-Suite decision-makers are evaluating tech and tools from third-party vendors that can be onboarded wholesale or tailored by consultants and in-house teams to their production processes and enterprise systems.

This makes vendors both a source of the solutions that boost the pace of AI uptake and ROI. And, of unforeseen challenges that stall projects and increase costs.

Vendor management is a key consideration for manufacturers pursuing  buy and buy-and-build strategies for their AI implementations. Awareness among decision-makers and line mangers of the many issues surrounding vendor systems will mitigate problems in onboarding and integration, increasing employee buy-in and benefits for the enterprise.

Fortunately for manufacturers (and those that serve them), the vendors and consultants operating in sectors and specialisms industrywide offer advice on getting the most from third-party systems and with the least amount of undue effort.

Adapted for manufacturers with AI in mind from a blog post1 by e-payments software specialist Bridgeforce, what follows highlights challenges common across sectors that can arise when evaluating, implementing and integrating vendor tools and tech. And stresses early action on the guidance offered to mitigate them.


Beware of ‘Out of the Box’ product descriptions and customer journeys in vendor marketing materials that omit mention of the customization that adds time and cost to AI implementations. Involving compliance, customer experience and marketing teams in vendor demos will better determine fit and the resources needed to achieve it. Business-partner assessments of resource needs  improves forecast accuracy of timelines and budgets for AI implementations.

Standard deployment times quoted by vendors assume manufacturers have addressed key factors in strategy and systems. Thus, it is imperative decision-makers work with direct reports to determine whether schedules are realistic. To ensure readiness, consider roles and responsibilities of the employees and departments affected by AI implementation, as well as reporting, compliance requirements, and end-user experience.

Hiring affiliated consultants, whose familiarity with vendor systems can smooth AI onboarding and customization. Discussing integration during vendor demos shines a light on the trade-offs from using partners, independent consultants, and in-house teams. Along with timeline and budget, doing so creates contingencies for AI implementation strategy and tactics.


Manufacturers are responsible for a number of deliverables when implementing third-party tech and tools – a requirement often overlooked in pitches and demos. Making employees from IT, Operations, Analytics, Compliance and Risk Management aware of vendor deadlines mitigates delays that compromise the integrity of AI implementations and increase costs.

Know that responsibility for operationalization of AI tools and tech lies with the buyer. Because most vendors define a completed installation as “deployment of the software,” manufacturers who fail to incorporate design, coding, and analytics in their AI implementations risk slowing progress and increasing costs. Vendors may provide call-center support in configuring applications and little else. Planning for the end-to-end particulars of processes and workflows in production and in the enterprise will make for faster times to launch.

Vendors often fail to provide scripts for User Acceptance Testing (UAT), such as test cases, conditioning test data, running test cycles in a test bed, and ensuring that controls do not adversely impact production processes. That is because a vendor’s obligation starts and ends with the technical install.

Being unprepared for UAT can throw off AI implementation timelines and cause reputational or regulatory damage. Identifying operations across the enterprise affected by AI implementation well before testing ensures that knowledge of vendor tools and tech are reflected in test plans and scripts.


Once implemented, AI requires technical expertise in-house. Providing users of new tools and tech with an onboarding roadmap speeds the knowledge transfer that underpins AI implementation success. Leveraging this capability enables manufacturers to increase reponsiveness enterprise-wide.

Because optimizing ROI from AI implementations hinges most on quality data, conducting a data review helps decision-makers and their reports understand ‘what’ data is available and ‘how’ to extract and package the information for machine learning. Review results will key decisions on the sourcing of third-party tools and tech. While confidence in data quality boosts buy-in of AI throughout the organization.