Question 1: How is AI transforming traditional category management practices and what are the most significant technological disruptions?

Santosh Nair: Traditional category management has long been reactive and fragmented, relying on historical spend data, manual processes and limited market intelligence. 

AI is redefining this function by introducing real-time data synthesis, predictive analytics and automation, enabling procurement teams to shift from tactical execution to strategic orchestration.

Key technological disruptions in category management include:

• AI-driven market intelligence: Aggregates internal spend data, supplier insights and external market indicators to create a dynamic category strategy.
• Predictive and prescriptive analytics: Helps category managers anticipate pricing shifts, supply disruptions and risk factors before they materialise.
• Scenario modelling and what-if analysis: Enables procurement teams to evaluate multiple sourcing strategies under different economic and market conditions.
• AI-powered negotiation assistants: Automates supplier benchmarking and contract optimisation based on AI-generated insights.
• Generative AI in category planning: Automatically suggests sourcing levers, risk mitigation strategies and negotiation tactics tailored to each category’s dynamics.

By embedding these AI capabilities, organisations can proactively manage costs,

supplier relationships and risks while aligning procurement with broader business goals.

Question 2: What specific benefits do AI-powered analytics and scenario modelling provide for strategic procurement decision-making?

Drasko Jelavic: I am not sure we are there yet to use the benefits of AI-powered analytics and scenario modelling. The reason is not the AI, but the access to and the quality of the data that are needed for drawing the right analytical conclusions. For scenario building, all inputs need to be well prepared and understood, especially the business requirements at all levels in a company. The lack of that procurement capability reflects on AI usage. Ultimately though,

AI analytics should help us focus more on making informed decisions rather than hunting down information.

Question 3: 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. 

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.

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:

• Establish a single source of truth: AI solutions must be seamlessly integrated with ERP, supplier management and sourcing platforms to centralise procurement intelligence.
• Enable AI-driven spend analytics: Organisations should leverage real-time, 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

Question 4: What emerging skills and organisational capabilities are required for category managers to successfully leverage AI-driven procurement strategies?

Drasko Jelavic: Shifting the focus to the quality of stakeholder engagement, storytelling and execution through project management. AI will do a lot of heavy lifting in the future in regard to analytics and strategy development. These skills are not emerging, but simply lacking for decades. I urge leaders to do the first things first. 

The big suites for P2P or S2C are not adding value beyond efficiency and compliance and not improving the value contribution perceived by procurement stakeholders at all. Helping stakeholders become better at managing relationships and projects will be key in the age of AI.

Question 5: What are the potential challenges and ethical considerations in implementing AI technologies within procurement frameworks?

Matthew Kippen: The fear of job displacement due to AI cannot be downplayed, especially due to the lengths it has been championed across the media as a replacement for human roles. Unfortunately, this has created a massive challenge in getting adoption of AI technology internally, not just in procurement but across all businesses.

AI needs to be seen as an enablement tool first and foremost. It can assist employees by handling repetitive tasks, allowing them to focus on more strategic, value-added activities. In retail, where complexity is increasing due to growing store networks and diverse customer demands, AI can streamline processes, manage vast data points and empower planners to create more tailored in-store experiences. The key is ensuring AI complements human work rather than undermining it.

Question 6: How do AI and advanced modelling techniques enable more agile and predictive category management approaches?

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.
• 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:

• Cost-saving potential
• Supply chain risk
• Supplier capacity and alternative sourcing options

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.

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.

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

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. 

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. 

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. 

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 ensuring more efficient operations. This dynamic approach enhances decision-making and boosts responsiveness to market trends.

Question 7: What will category management look like by the end of 2025?

Drasko Jelavic: I have been in this space for 25 years and can tell you the development of procurement is super slow. “We are not mature enough for category strategies” is something we hear quite often. 

There is no qualitative change expected by the end of 2025. Our mission is to increase the awareness of category strategy being key for driving business decisions in a complex business environment. 

At Cirtuo, we are working on the next stage of incorporating AI agents into our product to further simplify the approach and reduce barriers to adoption. If we succeed in improving it a bit with our work, I would be very happy. 

Matthew Kippen: While category management has traditionally been treated as synonymous with planograms, it will be given proper recognition as a core retail process in 2025. Category management is the very essence of the in-store experience – combining assortment and space planning together to deliver the “ideal” end result to the customer. 

Everything that flows in and out of retail – from fulfilment to allocation and replenishment and more – is driven by the strategies and data gathered by effective category management. None of this should come as a surprise to retailers, who know how important category management is to their day-to-day business. However, from a market perspective, it sees less coverage compared to other foundational retail planning topics. This will change in 2025, especially with centralised data becoming so important alongside new AI initiatives.

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