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CATEGORY MANAGEMENT

How can organisations effectively integrate AI tools to optimise spend analysis, demand forecasting and supplier relationship management?

MATTHEW KIPPEN: We are at the beginning of that journey. All spend analytic tools integrate AI to enhance access to data and boost displays of different analysis types. Classical dashboards are probably outdated, as the information you need right now can often not be found in the right way.

Again, AI tools cannot help to cure bad procurement practices, but they can help us cleanse and enrich data, understand and predict patterns and focus supplier relationship management on the relationship – not on tracking questionnaires and certificates.

SANTOSH NAIR: AI integration requires a structured approach that aligns technology with procurement goals, ensuring seamless adoption and value realisation. I would recommend the following steps for effective AI integration:

I trust asking for a specific piece of information and receiving it immediately in the best possible format in the future. Demand forecasting again depends heavily on the quality of the inputs, transparency and commitments of all parties in the supply chain. Supplier relationship management needs to be redefined as such. Beating suppliers and expecting to develop a fruitful relationship is not the right approach.

• Establish a single source of truth: AI solutions must be seamlessly integrated with ERP, supplier management and sourcing platforms to centralise procurement intelligence.

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CATEGORY MANAGEMENT

• Enable AI-driven spend analytics: Organisations should leverage realtime , AI-powered dashboards that continuously cleanse, classify and enrich spend data, providing instant visibility into cost drivers and savings opportunities.

• Adopt predictive demand forecasting: AI models analyse historical consumption patterns, economic indicators and supplier behavior to predict demand fluctuations – helping procurement teams anticipate inventory needs and optimise purchases.

• Enhance supplier relationship management (SRM): AI automates supplier assessments, continuously tracking risk, compliance and performance metrics while proactively recommending supplier collaboration and innovation opportunities.

• Empower teams with AI-powered decision support: Deploy conversational AI interfaces that allow category managers to ask procurement-related questions in natural language and receive instant, data-driven insights.

By embedding AI at every stage of procurement, organisations enhance visibility, streamline operations and improve supplier engagement, driving long-term value creation.

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How do AI and advanced modelling techniques enable more agile and predictive category management approaches?

Risk-responsive procurement AI continuously scans for external risks, such as geopolitical events, financial instability and ESG compliance failures, alerting procurement teams to proactively mitigate disruptions.

SANTOSH NAIR: AI and advanced modelling techniques empower procurement teams with unprecedented agility by enabling:

AI-driven predictive category strategies Dynamic cost modelling:

• AI continuously monitors cost drivers, forecasting pricing fluctuations across commodities and supplier markets.

Seamless AI-augmented execution AI automates the execution of sourcing events, supplier evaluations and contract negotiations based on predefined business rules, allowing procurement teams to focus on strategic initiatives rather than administrative tasks.

• Demand-supply alignment: AI predicts demand surges and identifies optimal procurement strategies based on market conditions.

Scenario-based what-if analysis Procurement teams can run multiple what-if scenarios for different supplier strategies, assessing:

By leveraging AI, predictive analytics and automation, procurement teams can achieve realtime adaptability, making category management faster, smarter and more resilient in today’s volatile business environment.

• Cost-saving potential

• Supply chain risk

• Supplier capacity and alternative sourcing options

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DRASKO JELAVIC: Procurement always dreams of innovation, something advanced and predictive, but is not yet able to deliver the basics of category management. Do your homework! Identify the stakeholders – primarily the more senior executives – you have not reached out to in the past because you felt uncomfortable in discussions. Understand their needs and wants.

MATTHEW KIPPEN: At its best, AI uses up-to-date data to predict and plan for changes in the market. Utilising a centralised repository of information, AI can even run its own trend analyses. This ultimately creates better and more thorough baselines upon which planners can quickly build assortment and spacing plans, while also offering important insights and recommendations that improve long-term results.

Prioritise and de-conflict them. The best prediction is to understand the future development of the business. Most of the business requirements per category are often not found in writing. As mentioned earlier, AI can cleanse data, track real-time developments and even trigger reviews and actions, but it won’t build a trustful relationship with the people that run the company. This is where AI will not replace them and they can carve out a new niche.

By identifying where certain products are popular or underperforming, AI can further enable businesses to adjust inventory and stocking strategies in real time, shifting products to regions with higher demand and ensurin more efficient operations. This dynamic approach enhances decision-making and boosts responsiveness to market trends.