AI & promotional products: the anti-disintermediation playbook

The European market for promotional-products distributors is worth $14.24 billion in 2024 (+1.22% year on year). But behind this façade of stability, France (the 3rd-largest national market, at $1.67 billion) is declining by −1.7% in 2024, after −1.7% in 2023 [1]. Two consecutive years of contraction.

And cost pressure is intensifying: in the second quarter of 2025, 70% of the sector’s suppliers raised their prices, driven by customs duties, while average sales declined (ASI, State of the Industry 2025) [6]. The context is North American, but the mechanics (sourcing under strain, margins eaten away) apply to the French industry too.

This figure changes everything. In a market that is no longer growing and where costs are rising, AI is not an engine of growth. It is a lever for margin, efficiency and retention. And the real story is not even there. It lies in a more structural threat: disintermediation.

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The threat is not the technology, it is the loss of the upstream end of the relationship

The promotional-products buyer (a communications, marketing or HR manager for whom this purchase is secondary) is changing their starting point. Increasingly, they begin their search in a conversational assistant such as ChatGPT, Perplexity or Copilot, rather than on a search engine or by calling a supplier.

The direct consequence: the distributor that is no longer cited by these assistants loses the upstream end of the relationship. This is the “zero-click” risk, and it is precisely the challenge of AEO/GEO (Answer/Generative Engine Optimization): being findable and recommended by AI becomes a matter of commercial survival, not an SEO nicety.

A second force adds to this one: platform capture. The large sourcing hubs, coupled with AI matching, concentrate standardised catalogue data and audience. The “order-taker” distributor, with no added value of its own, risks becoming the mere front-end of a platform that owns the data and the algorithm. The margin migrates to whoever owns the data and the relationship.

The response can be summed up in a single sentence: shift value away from transactional intermediation (finding the product, which AI commoditises) towards what AI does not commoditise: creative advice, CSR compliance, marking-quality assurance, logistics, and proprietary data (proof history, supplier compliance, client preferences).

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The paradox of choice: why an overabundant catalogue drives the buyer away

The industry is extremely fragmented and piles up catalogues of tens of thousands of references. Yet research in decision psychology is unambiguous: option overload paralyses the buyer, especially when the task is complex, preferences are uncertain, and they are seeking to minimise their effort. That is exactly the profile of the promotional-products buyer (meta-analysis by Chernev, Böckenholt & Goodman, 2015) [2].

AI, used well, acts as a cognitive filter: it turns a natural-language brief into a justified shortlist. But the underlying data must be clean. Hence the order of priorities.

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The 4 priority levers (extract from the MATIA Method™ playbook)

1. The catalogue first: AI-augmented PIM. This is the non-negotiable foundation. Ingesting heterogeneous supplier catalogues, cleaning up descriptions, standardising attributes (material, MOQ, marking techniques, CSR labels), completing missing data, deduplication. Without this step, everything else hallucinates.

2. Self-service generation of proofs and visuals. Applying the client’s logo to the product, colour consistency (avoiding the gap between the digital rendering and the real colour of the fabric), automated pre-flight checking (DPI, bleeds, marking area). The bottleneck of back-and-forth with designers eases.

3. Quote automation. A brief → product proposal + margin calculation + shipping + marking grid → quote. To be connected to the standardised catalogue.

4. Conversational sourcing and CSR compliance. An agent that produces explainable recommendations, and a per-order carbon calculation connected to the ERP, with automated auditing of supplier certifications (GOTS, Oeko-Tex, FSC).

These building blocks are not theoretical: the sector’s business platforms are already deploying them. AI product search is in production at commonsku, mock-up generation is in beta there, and PIMs such as Afineo are integrating agents that write product sheets. Maturity varies, and most of the promised gains remain vendor figures to be verified against your own scope.

The trap to avoid: believing that “giving the sales team access to ChatGPT” is enough. That is precisely what produces zero return: the tool without the process redesign or the clean data.

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Two regulatory points to address right now

The AI Act, Article 50, without overplaying it. The obligation to disclose “deepfake” content (applicable from 2 August 2026) has a narrow scope: a standard AI product visual, a background, an upscaling or a typographic banner are not deepfakes [3]. The real precaution is contractual: setting out in the terms and conditions of sale the use of generative-AI tools and liability for the elements supplied by the client. The real risk is to “sell rights that do not exist” on AI creations [4].

Greenwashing: enforcement is tightening. The DGCCRF inspected more than 3,000 establishments in 2023-2024, with more than 15% serious breaches, resulting in 430 injunctions, 500 warnings and 70 formal notices [5]. Environmental claims on promotional products are squarely in the crosshairs. AI must help to prove CSR performance (traceability, carbon calculation, anticipating the Digital Product Passport), not to embellish it.

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The roadmap, in five phases

The MATIA Method™ structures the transformation over 12 months: 360° diagnostic (audit of the catalogue and the quote/proof cycle) → Scoping the use cases (2-3 priorities, business champions) → Preparing the foundations (catalogue standardisation, sovereign technology stack, governance) → Pilot deployments (restricted, measured scope) → Consolidation and governance (extension, AI committee, AI Act register).

The objective is not to leap to the “Pionnier” level in a single bound, but to move the organisation from the Artisan level (individual Shadow AI) to the Orchestre level: AI embedded in processes, steered and measured.

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In summary

In a contracting market where AI threatens intermediation itself, the question is not “which tool to buy”. It is: how to own what AI cannot commoditise (the data, the relationship, compliance, advice). The distributor that structures its data and shifts its value towards advice and CSR proof does not suffer disintermediation: it becomes the trusted third party for it.

To position your organisation on the 5-level maturity Scale and build your roadmap, see the MATIA Method™, the SME AI Maturity white paper and the AI Express Audit & Roadmap.

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Sources

1. ASI Research, “European Distributors’ Annual Sales… topping $14.24 B” (June 2025) and “Europe’s Distributors Top $14B” (August 2024). French estimates in euros: C-Mag/2FPCO (€1.6 billion), PPAI (€2.09 billion), different methodologies, same rank and trend.
2. A. Chernev, U. Böckenholt, J. Goodman, “Choice overload: A conceptual review and meta-analysis”, Journal of Consumer Psychology (2015).
3. Regulation (EU) 2024/1689 (AI Act), Article 50, transparency obligations applicable from 2 August 2026; “deepfake” scope restricted to realistic content that is falsely authentic.
4. Maddyness, “Création & IA : le piège de la vente de droits qui n’existent pas” (16 October 2025).
5. DGCCRF, press release “Lutte contre l’écoblanchiment” (1 October 2025).
6. ASI, “2025 Counselor State of the Industry” (7 August 2025), price increases linked to customs duties, changes in sales; PIWorld summary (12 August 2025). North American data.