Operating Model Upgrade 2025: Using AI with the right skills and key roles
- Erik Esly
- June 17
- 4 min. reading time
Updated: Aug. 21
Procurement and supply chain managers need a dynamic operating model: clear ownership, data-driven decisions, technology integration along the end-to-end processes. This makes uncertainty manageable and improvements measurable.
Understanding and further 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 to inventory control and supplier management through to delivery.
A dynamic operating model reacts 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. To achieve this, you need consistent stakeholder engagement, clear roles, responsibilities and decision-making rights, lean governance and defined KPIs.
Challenge: Dynamics of global markets
Volatile markets, geopolitical tensions, new trading rules and the pressure for greater sustainability require an operating model that learns quickly and reacts flexibly.
Leading companies use near & friendshoring, dual sourcing and more resilient supplier networks, proactively adjust tariffs and compliance and reduce dependencies through alternative routes and capacities. They work with scenario planning and S&OP/IBP, manage inventories in a risk-adapted manner and anchor CO2 transparency in categories and supplier evaluation.
The result: shorter response times, more stable service levels and guaranteed competitiveness - without compromising on quality. The prerequisites are clear decision-making rights, clean data, technology integration (e.g. risk and network analytics) and governance that enables continuous adjustments.
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 to make operations more efficient and get more out of your data. Imagine a company that works with hundreds of suppliers worldwide: AI can help here not only in the evaluation of large amounts of data, but also in the optimization of supplier relationships.
One example of this is that deviations in delivery performance can be detected at an early stage and alternative suppliers automatically suggested. 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 skillsets begin, which are discussed in more detail in the next section.
The 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 as well as the ability to handle large amounts of data with confidence. This includes the development and implementation of algorithms for data-driven decisions as well as the use of cloud technologies for scaling, process optimization and real-time analyses.
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 strong decision-making under uncertainty meet critical thinking and pragmatic problem-solving.
Empathy, intercultural competence, integrity, resilience and a willingness to learn create trust and thus 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 reach.
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, promotes transparency, quality and resilience. Management of 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, orchestrates supplier development. KPIs: savings, risk index, CO2 footprint per category.
S&OP/IBP Lead:
Synchronizes sales planning, production and inventories, creates 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 modernizes its operating model end-to-end:
S&OP/IBP introduced, source-to-pay digitized, AI-supported demand and inventory models rolled out, 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 - controlled via a lean governance model with clear decision-making rights and RACI.
After 12 months, service levels (OTIF) increase by 6-8 percentage points, the inventory range is reduced by 15 %, planning cycles are shortened by 20 % and CO2 data per category becomes transparent. Measurable successes are also achieved in procurement: Process costs in source-to-pay are reduced by 12-20%, savings of 5-8% per category are realized,
Maverick spend is reduced by 30-50%, contract coverage increases by 15-25 percentage points and ultimately even exceeds 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 increasing efficiency, resilience and sustainable growth - and change becomes scalable.
ERIKESLY Advisory & Consulting provides pragmatic support in a spirit of partnership - from diagnosis, organizational design and roadmap to implementation and scaling.