Why retail content teams are moving from AI tools to AI infrastructure

Executive summary

Between 2022 and 2024, retailers massively deployed AI copywriting tools with the hope of accelerating content creation and improving productivity. What they experienced instead was a rise in operational friction, lower content quality, and a flood of generic, off-brand text that weakened merchandising impact and brand equity. Instead of unlocking performance, many teams ended up generating more content debt and were forced into constant editing cycles just to maintain minimum quality standards.

In an internal Newtone study across 29 enterprise retailers, marketing teams reported spending between 45 and 50 percent of their time on formatting, data transfers, and quality control, even after adopting the latest generation of AI writing tools. One Chief Digital Officer of a four-billion-euro fashion retailer summarized the problem clearly: “If putting ChatGPT in the hands of every team member solved our content challenges, we would already know. Instead, it is making the situation worse. We are producing content faster, but not better.”

This is why the market is shifting toward AI infrastructure. Deep integration with PIM, DAM, CMS, and e-commerce systems enables 70 to 90 percent faster time-to-market, 4 times more content output with the same headcount, and drastically improved quality through brand-trained models. The strategic transition is underway: the era of AI writing tools is giving way to AI-native content operations. The core message is simple. Infrastructure is not a tool. Infrastructure is a system-level transformation that unlocks consistency, control, personalization, and long-term scalability, including the ability to deliver user-level content personalization across channels.

The retailers who understand this shift are building competitive advantage today. Those who delay will face a growing gap in velocity, quality, and search visibility.

The breaking point: when AI tools start slowing retailers down

The post-2023 reality of retail content operations

By late 2023, most large retail organizations were operating with increasingly fragmented content stacks that looked like this: Akeneo for PIM, Bynder for digital assets, Shopify or Adobe Commerce for e-commerce, Smartling for translation, SEO platforms for performance alignment, and an AI tool bolted awkwardly on top. The result was copy and paste workflows, disconnected data structures, and endless reformatting between systems that were never designed to talk to each other.

Worse, generic content produced by standalone AI tools failed to respect brand guidelines, tone of voice, product hierarchies, and market nuances. Instead of improving the experience, teams ended up generating large volumes of low-quality text that undermined consistency and brand trust.

Internal Newtone benchmarks show that retailers using standalone AI tools achieved first-pass approval rates of only 54 percent, forcing teams to spend more time editing than creating.

Symptoms retailers have identified

Retailers using point-solution tools consistently reported:

• Delays in product go-live, especially during peak seasons.
• Inconsistent brand voice across markets and languages.
• Rising operational debt as content volume increased.
• Manual rework every time the PIM updated product data or attributes.

As the volume of content grew, these symptoms became structural blockers rather than occasional inefficiencies.

The tool trap: why point-solution AI does not scale

The integration tax

AI writing tools accelerate text generation, but they solve neither quality nor consistency, and they do not optimize the workflows surrounding content operations. The hidden cost becomes apparent quickly. Retail teams reported spending between 12 and 18 hours every week on data formatting, import and export tasks, and manual cleanup after AI generation. The text may generate instantly, but everything around it slows teams down dramatically.

The context problem

Standalone tools operate without understanding the product hierarchy, category nuances, brand identity, or SEO logic of a retailer. They do not know the difference between technical attributes, selling points, and compliance requirements. They cannot access historical content to ensure consistency. The result is generic, repetitive, or off-brand text that requires heavy editing, especially when applied across hundreds or thousands of SKUs.

The scalability ceiling

Point-solution tools begin breaking down as soon as retailers scale beyond three or four languages or beyond five hundred SKUs. The editing effort grows faster than the generation speed, creating an operational ceiling that makes true omnichannel scale impossible.

What AI infrastructure really means for retail content

System integration layer

AI infrastructure begins with deep, direct connectivity to PIM, DAM, CMS, and e-commerce platforms. Product updates in Akeneo automatically trigger content updates. Asset availability from the DAM informs content generation. Category changes or attribute modifications flow through without manual intervention.

Brand intelligence layer

Instead of relying on prompt engineering, infrastructure trains AI models directly on the retailer’s historical content, editorial guidelines, product taxonomy, and brand rules. Internal Newtone data shows approval rates of 95 to 99 percent when models are trained on brand-specific datasets. This creates a level of consistency, nuance, and product expertise unmatched by generic tools.

Workflow and governance layer

Enterprise-grade role management, approvals, versioning, and audit trails are embedded within the system. Every stakeholder knows the status of each asset, and every change is tracked. The entire content lifecycle is orchestrated, not manually pieced together.

Performance layer

SEO and GEO optimization are directly integrated into the generation process. Performance analytics loop back into the system, allowing continuous improvement. This transforms content from static text into a dynamic, performance-aware asset.

The economics: why infrastructure beats tools

Time-to-market

According to the Newtone 2024 enterprise study, integrated systems deliver an 83 to 89 percent improvement in time-to-market. In one SMCP deployment, a full catalog update that previously required eighteen days was executed in a single day after migrating to AI infrastructure.

Quality and approval rates

Approval rates climbed from 52 to 61 percent with tools to 94 to 99 percent with infrastructure. The reason is simple. Brand-trained models generate content that already aligns with editorial expectations, drastically reducing editing loops and bottlenecks.

Translation and localisation

Infrastructure reduces translation costs by 80 to 85 percent, not because the AI is cheaper, but because it finally has context. Current standalone translation AI fails for retail because translating a SEO article is not the same as adapting a CTA, a short title, or a microcopy element on a product page. Infrastructure aligns translation outputs to the actual content requirements and product attributes, enabling instant multilingual consistency.

Total cost of ownership

Point-solution tools may appear cheaper on monthly SaaS pricing, but they hide enormous operational costs in manual editing, formatting, and rework. Infrastructure flips the equation by removing friction and centralizing operations. The ROI is driven by reduced labor overhead, faster go-live cycles, and higher performance.

The SMCP pattern: what real enterprise integration looks like

Challenges in multi-brand retail groups

Groups like SMCP face unique challenges: they must maintain distinct brand voices for Sandro, Maje, Claudie Pierlot, and Fursac, while operating through shared PIM systems and unified workflows. Point-solution AI tools struggle to adapt to this complexity, leading to inconsistent output across brands.

The four-phase infrastructure model

Enterprise transitions follow a consistent four-phase pattern:

1. Integration with PIM, DAM, and e-commerce systems

2. Brand model training using historical content

3. Pilot deployment and refinement

4. Scaling across all markets, categories, and languages

Gains observed in SMCP-type deployments

• Three to five times more content produced with the same team size
• Consistency across multiple brands despite distinct tones and positioning
• Real-time updates automatically generated when PIM attributes change

The shift is not incremental. It is transformational.

The competitive shift: why timing matters in 2025 and 2026

Leaders vs followers

Newtone’s analysis of enterprise retailers shows a growing divide:

• 86 percent of the leaders have adopted AI infrastructure
• 67 percent of the followers still rely on tools or manual content workflows
• Leaders publish content 73 percent faster
• Leaders operate in twice as many languages on average

Compounding advantage

High quality content delivered faster generates consistency and higher search visibility. Better SEO and GEO traction drive more traffic, which produces more data, which, in turn, strengthens the model. Point-solution tools cannot replicate this compounding loop.

The SEO and GEO convergence: why infrastructure is becoming mandatory

The rise of AI-generated search

AI-driven search results now appear in 46 percent of commercial queries, increasing at a rate of 8 to 12 percent every month. Retailers must therefore optimize for both SEO and GEO at the same time. In parallel, a new trend is emerging: on-site content personalization, where every customer receives personalized descriptions, selling points, and recommendations based on seasonality, browsing behavior, and intent.

Why tools cannot handle this

Standalone AI tools optimize content page by page. They cannot optimize an entire content ecosystem, nor do they have integrated feedback loops to understand what performs and what does not. They work in isolation and are blind to performance data.

Infrastructure capabilities needed

True performance requires:

• Real-time keyword and GEO intent injection
• Structured metadata integration
• Multilingual GEO consistency
• Continuous performance-driven content optimization

This cannot be achieved with disconnected tools.

Looking forward: the maturity curve of retail AI infrastructure

Emerging capabilities

Several advanced capabilities are already appearing in next-generation systems:

• Predictive content performance based on historical conversion data
• Personalization at scale for every visitor
• Automated competitive content intelligence
• Automated compliance and sustainability disclosure management

These capabilities require deep infrastructure, not isolated tools.

Long-term differentiator

AI infrastructure becomes a compound advantage. The more the system is used, the more intelligent, consistent, and performant it becomes. This creates a structural moat that competitors cannot easily replicate.

Strategic decision framework for retail leaders

Six questions to determine readiness

Retail leaders evaluating the transition should assess:

• How large is the content scale?
• How deep must the integration be?
• How differentiated is the brand voice?
• How critical is time-to-market?
• How many markets and languages are required?
• How sensitive is the brand’s product and pricing data?

Rule of thumb

If the answer is yes to three or more of these questions, infrastructure will yield exponential ROI. If the answer is no to most, point tools may remain a temporary solution, though not a long-term path.

Conclusion: the shift from tactical tools to strategic infrastructure

AI writing tools provide local optimizations, but they do not address the systemic challenges of enterprise retail content operations. AI infrastructure transforms how content is created, updated, localized, governed, and optimized. Retailers embracing this shift are establishing multi-year competitive advantages in quality, consistency, velocity, and discoverability. The window for competitive differentiation is open today. The question is not whether to make the transition, but when. And in retail, waiting has always been the most expensive option.