June 17, 2025
June 17, 2025
Operating Model Upgrade 2025: Using AI with the right skills and key roles
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Procurement and supply chain leaders need a dynamic operating model: clear ownership, data-driven decisions, and technology integration along end-to-end processes. This makes uncertainty manageable and improvements measurable.
Understanding and developing the operating model
The operating model describes how processes, systems, and resources are organized in order to achieve strategic goals efficiently—from order processing and inventory control to supplier management and delivery.
A dynamic operating model responds quickly to market changes, integrates innovations, and uses technologies such as AI and automation to improve processes, optimize costs, and deploy resources in a targeted manner.
The goal is a setup that sets the pace today and measurably increases efficiency and sustainability tomorrow. This requires consistent stakeholder engagement, clear roles, responsibilities, and decision rights, lean governance, and defined KPIs.
The challenge: the dynamics of global markets
Volatile markets, geopolitical tensions, new trade rules, and the pressure for greater sustainability require an operating model that learns quickly and responds flexibly.
Leading companies use near- and friendshoring, dual sourcing, and more resilient supplier networks; they proactively adjust to tariffs and compliance requirements and reduce dependencies through alternative routes and capacities. They work with scenario planning and S&OP/IBP, manage inventories in a risk-adjusted way, and anchor CO₂ transparency in categories and supplier evaluation.
The result: shorter response times, more stable service levels, and secured competitiveness—without compromising on quality. The prerequisites are clear decision rights, clean data, technology integration (e.g., risk and network analytics), and governance that enables continuous adjustment.
The role of technology and AI in the transformation
In procurement and supply chain management, technology makes the difference between a reactive and a proactive approach. AI in particular helps make operations more efficient and get more out of your data. Imagine a company that works with hundreds of suppliers worldwide: here, AI can help not only in evaluating large amounts of data, but also in optimizing supplier relationships.
One example: deviations in delivery performance can be detected early and alternative suppliers suggested automatically. Transformation through technology is therefore comprehensive and far-reaching—and it requires new skills and roles in order to manage change and derive maximum benefit from it. This is where the necessary skill sets come in, which are discussed in more detail in the next section.
Key skills and roles for AI and technology
Technical skills:
This requires in-depth knowledge of data analysis and business intelligence, an understanding of programming languages and specialized software, and the ability to handle large amounts of data with confidence. It includes developing and implementing algorithms for data-driven decisions as well as using cloud technologies for scaling, process optimization, and real-time analytics.
Statistics and data visualization: Complex data sets are translated into actionable insights. Tools such as Tableau and Power BI enable dynamic dashboards and reports that accelerate decisions and make their impact transparent.
Soft skills:
Clear communication and convincing storytelling, strong collaboration with active stakeholder engagement, as well as negotiation skills and confident conflict resolution in complex supplier relationships are required. Agility, prioritization, and sound decision-making under uncertainty meet critical thinking and pragmatic problem-solving.
Empathy, intercultural competence, integrity, resilience, and a willingness to learn build trust—and thereby improve results.
Role profiles for effective implementation
Supply Chain Analyst:
Analyzes end-to-end processes, identifies patterns and bottlenecks, and derives actionable measures to increase efficiency. Core competencies: business intelligence, advanced data analysis, reporting. KPIs: lead time, OTIF, inventory coverage.
Digital Transformation Lead:
Plans and scales the introduction of AI, automation, and cloud solutions along the supply chain. Responsible for roadmap, implementation, and change management. KPIs: time-to-value, adoption rate, process costs.
Data Scientist/Architect:
Develops forecasting, optimization, and classification models for demand, inventory, risk, and quality. Translates models into productive use cases with measurable business impact. KPIs: forecast accuracy, service level, cost per order.
Supplier Collaboration Manager:
Builds digital collaboration models with suppliers and promotes transparency, quality, and resilience. Manages platforms, SLAs, and joint improvement programs. KPIs: on-time delivery, PPV/total cost, sustainability metrics.
Category Manager:
Negotiates strategic contracts, drives total-cost and sustainability targets, and orchestrates supplier development. KPIs: savings, risk index, CO₂ footprint per category.
S&OP/IBP Lead:
Synchronizes sales planning, production, and inventories, creating reliable plans and clear decisions on scenarios. KPIs: bias/MAE, service level, working capital.
Practice that delivers: developing the operating model and achieving impact
An established industrial company modernized its operating model end-to-end:
S&OP/IBP was introduced, source-to-pay digitized, AI-supported demand and inventory models rolled out, and a supplier platform for collaborative quality and risk management established.
At the same time, the organization was realigned: cross-functional category squads in procurement, a supplier collaboration team for quality, risk, and sustainability, an inventory optimization squad with responsibility for inventories and working capital, an S&OP/IBP core team for end-to-end planning, a demand planning team with data science support, and a logistics control tower for transparency and responsiveness.
In addition, a Data and AI Center of Excellence and clear end-to-end ownership for source-to-pay and plan-to-produce were established—steered through a lean governance model with clear decision rights and RACI.
After 12 months, service levels (OTIF) increased by 6–8 percentage points, inventory coverage was reduced by 15%, planning cycles were shortened by 20%, and CO₂ data per category became transparent. Measurable successes were also achieved in procurement: process costs in source-to-pay were reduced by 12–20%, savings of 5–8% per category were realized, maverick spend was reduced by 30–50%, and contract coverage increased by 15–25 percentage points—ultimately even exceeding the industry average.
The way to get there: clear ownership, focused use cases, consistent stakeholder engagement, team training, and KPIs that make the benefits visible. In this way, technology integration becomes a lever for greater efficiency, resilience, and sustainable growth—and change becomes scalable.
PROCUREMENT reWIRED provides pragmatic, partnership-based support—from diagnosis, organizational design, and roadmap to implementation and scaling.
Listen to this article
Procurement and supply chain leaders need a dynamic operating model: clear ownership, data-driven decisions, and technology integration along end-to-end processes. This makes uncertainty manageable and improvements measurable.
Understanding and developing the operating model
The operating model describes how processes, systems, and resources are organized in order to achieve strategic goals efficiently—from order processing and inventory control to supplier management and delivery.
A dynamic operating model responds quickly to market changes, integrates innovations, and uses technologies such as AI and automation to improve processes, optimize costs, and deploy resources in a targeted manner.
The goal is a setup that sets the pace today and measurably increases efficiency and sustainability tomorrow. This requires consistent stakeholder engagement, clear roles, responsibilities, and decision rights, lean governance, and defined KPIs.
The challenge: the dynamics of global markets
Volatile markets, geopolitical tensions, new trade rules, and the pressure for greater sustainability require an operating model that learns quickly and responds flexibly.
Leading companies use near- and friendshoring, dual sourcing, and more resilient supplier networks; they proactively adjust to tariffs and compliance requirements and reduce dependencies through alternative routes and capacities. They work with scenario planning and S&OP/IBP, manage inventories in a risk-adjusted way, and anchor CO₂ transparency in categories and supplier evaluation.
The result: shorter response times, more stable service levels, and secured competitiveness—without compromising on quality. The prerequisites are clear decision rights, clean data, technology integration (e.g., risk and network analytics), and governance that enables continuous adjustment.
The role of technology and AI in the transformation
In procurement and supply chain management, technology makes the difference between a reactive and a proactive approach. AI in particular helps make operations more efficient and get more out of your data. Imagine a company that works with hundreds of suppliers worldwide: here, AI can help not only in evaluating large amounts of data, but also in optimizing supplier relationships.
One example: deviations in delivery performance can be detected early and alternative suppliers suggested automatically. Transformation through technology is therefore comprehensive and far-reaching—and it requires new skills and roles in order to manage change and derive maximum benefit from it. This is where the necessary skill sets come in, which are discussed in more detail in the next section.
Key skills and roles for AI and technology
Technical skills:
This requires in-depth knowledge of data analysis and business intelligence, an understanding of programming languages and specialized software, and the ability to handle large amounts of data with confidence. It includes developing and implementing algorithms for data-driven decisions as well as using cloud technologies for scaling, process optimization, and real-time analytics.
Statistics and data visualization: Complex data sets are translated into actionable insights. Tools such as Tableau and Power BI enable dynamic dashboards and reports that accelerate decisions and make their impact transparent.
Soft skills:
Clear communication and convincing storytelling, strong collaboration with active stakeholder engagement, as well as negotiation skills and confident conflict resolution in complex supplier relationships are required. Agility, prioritization, and sound decision-making under uncertainty meet critical thinking and pragmatic problem-solving.
Empathy, intercultural competence, integrity, resilience, and a willingness to learn build trust—and thereby improve results.
Role profiles for effective implementation
Supply Chain Analyst:
Analyzes end-to-end processes, identifies patterns and bottlenecks, and derives actionable measures to increase efficiency. Core competencies: business intelligence, advanced data analysis, reporting. KPIs: lead time, OTIF, inventory coverage.
Digital Transformation Lead:
Plans and scales the introduction of AI, automation, and cloud solutions along the supply chain. Responsible for roadmap, implementation, and change management. KPIs: time-to-value, adoption rate, process costs.
Data Scientist/Architect:
Develops forecasting, optimization, and classification models for demand, inventory, risk, and quality. Translates models into productive use cases with measurable business impact. KPIs: forecast accuracy, service level, cost per order.
Supplier Collaboration Manager:
Builds digital collaboration models with suppliers and promotes transparency, quality, and resilience. Manages platforms, SLAs, and joint improvement programs. KPIs: on-time delivery, PPV/total cost, sustainability metrics.
Category Manager:
Negotiates strategic contracts, drives total-cost and sustainability targets, and orchestrates supplier development. KPIs: savings, risk index, CO₂ footprint per category.
S&OP/IBP Lead:
Synchronizes sales planning, production, and inventories, creating reliable plans and clear decisions on scenarios. KPIs: bias/MAE, service level, working capital.
Practice that delivers: developing the operating model and achieving impact
An established industrial company modernized its operating model end-to-end:
S&OP/IBP was introduced, source-to-pay digitized, AI-supported demand and inventory models rolled out, and a supplier platform for collaborative quality and risk management established.
At the same time, the organization was realigned: cross-functional category squads in procurement, a supplier collaboration team for quality, risk, and sustainability, an inventory optimization squad with responsibility for inventories and working capital, an S&OP/IBP core team for end-to-end planning, a demand planning team with data science support, and a logistics control tower for transparency and responsiveness.
In addition, a Data and AI Center of Excellence and clear end-to-end ownership for source-to-pay and plan-to-produce were established—steered through a lean governance model with clear decision rights and RACI.
After 12 months, service levels (OTIF) increased by 6–8 percentage points, inventory coverage was reduced by 15%, planning cycles were shortened by 20%, and CO₂ data per category became transparent. Measurable successes were also achieved in procurement: process costs in source-to-pay were reduced by 12–20%, savings of 5–8% per category were realized, maverick spend was reduced by 30–50%, and contract coverage increased by 15–25 percentage points—ultimately even exceeding the industry average.
The way to get there: clear ownership, focused use cases, consistent stakeholder engagement, team training, and KPIs that make the benefits visible. In this way, technology integration becomes a lever for greater efficiency, resilience, and sustainable growth—and change becomes scalable.
PROCUREMENT reWIRED provides pragmatic, partnership-based support—from diagnosis, organizational design, and roadmap to implementation and scaling.
