AI in Category Management: Move from Experimentation to Value Realization

Pressure is rising for procurement professionals and category managers to deliver strategic value. Supply chains are growing in complexity, and intelligence has become a critical enabler. AI capabilities are no longer a distant concept, but a practical tool that will help you analyse markets, manage suppliers, and make insightful decisions.

But if you focus on the tools themselves, rather than the outcomes they’ll enable, you won’t achieve their full value. Workflows and objectives must come first to give a clear view of how outputs will inform category management strategy. Then, you can find the tools needed to make those outcomes reality.

More than 75 procurement professionals joined us in a recent workshop to explore what challenges organizations are facing when adopting AI capabilities, and how procurement teams can move from experimentation to value realization.

While enthusiasm is high, nearly 80% of the procurement professionals we spoke to are still early in their journey of AI adoption in procurement. Most organizations are working out where AI could deliver the most value, but not yet realizing those benefits.

The primary factor holding them back isn’t the AI tools themselves, but the data foundation required for successful AI adoption in procurement. Trustworthy and accessible data is integral to successful AI adoption, and many organizations struggle to generate reliable insights and scale AI effectively.

While data is the biggest entry barrier to AI value, operationalization is the biggest scaling barrier. AI tools cannot deliver their full value in isolation. And piloting them without a clear understanding of how they’ll impact category strategies creates a series of disconnected experiments that don’t actually contribute to strategic capability. You need to integrate tools into processes, and upskill your people to translate outputs into actionable observations that progress you toward your goals.

Establishing a robust data foundation must be the first step of any effective AI procurement strategy. Once you’ve done that, it’s important to continue taking gradual steps and identify key areas of focus based on your most pressing goals. This could be faster analysis, automating repetitive activities to reduce manual effort, or improving how you gather information.

If you can successfully achieve these things, you’ll be well on the road to creating long term business value. Then, by embedding AI into procurement workflows, category strategies, and decision-making processes, you’ll extend the benefits even further.

In general, organizations that are leading in this space:

1. Fuel AI with trustworthy data. The right AI procurement strategy begins with clean standardized data, integrations across systems, and well-established governance, helping organizations create a comprehensive data landscape they can use with confidence.

2. Prioritise adoption and change management. AI tools only add value when people trust them — and use them. Leaders in this space will focus on upskilling and onboarding their team onto new tools while providing clear mechanisms for gaining extra support.

3. Treat AI as augmentation, not replacement. The most mature organizations understand the immense value AI can deliver, but they also understand it’s limits. With human validation, the insights gained from AI can inform decision-making and support strategic goals.

   To see what this looks like in your specific area, let’s dive into what professionals are seeing across five categories: maintenance, repair, and operations; facilities management; marketing; travel; and IT.

   1. Maintenance, repair, and operations

      Maintenance, repair, and operations professionals agree that data quality remains a core obstacle to strategic category management when harnessing AI. The key challenges they’re facing include poor data quality and visibility, inconsistent item descriptions and supplier information, and difficulty consolidating spend and volumes across suppliers.

      But they also see AI’s significant potential in:

      1. Data cleansing and standardization

      2. Improved benchmarking

      3. Supplier recommendation capabilities

      4. Better spend visibility and category insights

      Alone, each of these opportunities could significantly improve procurement outcomes. And together, they could drive value-driven strategic category management.

   2. Facilities management

      Facilities management professionals see centralizing procurement across manufacturing and facilities environments as one of their main challenges, alongside limited visibility, fragmented supplier and spend data, and coding inconsistencies.

      AI is a valuable tool for cleansing historical data and creating the visibility needed for strategic category management. It can:

      1. Automate spend classification

      2. Detect duplicate suppliers and data anomalies

      3. Improve reporting and spend visibility

      4. Reduce manual data management

   3. Marketing

      Marketing professionals are more focused on organizational readiness than specific AI tools. Their biggest challenges include limited AI knowledge among end users, IT security and governance requirements, and a lack of operating models and governance frameworks.

      Governance, education, and change management will be critical for successful AI adoption. But the right approach can transform marketing in procurement. This includes:

      1. Procurement-led AI strategies

      2. AI governance and adoption frameworks

      3. Education and enablement programs

      4. Stronger cross-functional collaboration

   4. Travel

      Rather than focusing on small, isolated use cases like automating bookings, travel specialists view AI as an opportunity to improve decision-making across the travel lifecycle. The key challenges they’re facing include traveler safety requirements, a highly fragmented supplier ecosystem, strong behavioral and emotional influences on travel decisions, ongoing transformation initiatives, and competing priorities.

      But implemented correctly, AI could improve travel specialists’ ability to:

      1. Determine whether travel is necessary vs virtual alternatives

      2. Optimize who should travel and when

      3. Improve booking decisions and policy compliance

      4. Provide greater visibility across the end-to-end travel journey

      5. Support operational decisions before, during, and after travel

      And it can support a smoother end-to-end traveler experience overall.

   5. IT

      IT professionals are focused on practical use cases for AI that are already delivering measurable benefits such as in accelerating contract review processes and reducing manual burden. AI also shows clear benefits for handling large volumes of sourcing and contracting activity and simplifying complex software and license management.

      IT professionals also see clear opportunities for AI to support:

      1. RFP, RFQ, and RFI creation and evaluation

      2. Contract comparison and redlining

      3. Software license optimization

      4. Renewal forecasting

      5. • Benchmarking and negotiation

      Alone, each of these opportunities could significantly improve procurement outcomes. And together, they could drive value-driven category strategies.

The next step for implementing AI in category management will depend on your current position along the path to value realization. Take an honest look at where you stand and what you need to do to move your maturity forward.

1. Firm up your data foundation. This first step is fundamental to extracting real value from AI capabilities. Without trustworthy, consistent, and accessible data, AI cannot generate reliable insights.

2. Focus on a key set of high-impact use cases. Start small by identifying a few high-value opportunities where AI can deliver immediate benefit. But don’t select these based on tools you’re interested in trying. Instead, start with the end goal you want to achieve, then experiment with tools that can help you get there.

3. Embed outputs into procurement workflows. To create strategic value, AI outputs must have strategic relevance. Embed AI-generated insights into processes including strategy development, supplier reviews, and negotiation planning to turn outputs into insight that gets used.

4. Focus on adoption and change management as you transform. With new AI capabilities in place, the role of category managers will shift. You need to make sure you’re bringing your team along with you by providing training and upskilling opportunities and clearly communicating the new value they’ll now be free to deliver.

Download the AI Category Management Readiness Framework

Organizations that can align people, process, data, and technology will get the most value from AI tools. On their own, AI capabilities could never run categories, but alongside human expertise and procurement intelligence, they can transform the way you work.

AI can accelerate analysis, improve visibility, surface recommendations, and automate repetitive activities, while category managers continue to make decisions, drive strategy, and manage critical supplier relationships and stakeholder relationships.

In short, the right tools — implemented effectively — will give category managers the right procurement intelligence and the time to become vital players in shaping business outcomes .

If you’re ready to identify high-value AI use cases, prioritize investments, and embed AI into procurement workflows, we're here to help. We offer category opportunity assessments, transformation support, and change management consultancy designed to help you move from experimentation to value realization.

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