Released in The Interline’s AI Report 2025, this executive interview with Centric Software is one of an eight-part series that sees The Interline quiz executives from companies who have either introduced new AI solutions or added meaningful new AI capabilities into their existing platforms.

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Key Takeaways:

  • Centric Software asserts that AI is transitioning from a speculative technology to a practical, virtual companion capable of measurably impacting enterprise performance. Their focus is on embedding AI into real business processes, not as a standalone phenomenon, supporting over 100 atomic AI use cases across the product lifecycle.
  • AI’s value is directly tied to the underlying digital infrastructure. Centric emphasises that AI without a strong digital foundation is just noise, stressing the need for high-integrity, well-structured data. They position PLM as the evolving operational backbone, grounding AI agents to enable context-aware decisions that comply with corporate policies, as seen in projects automating design-to-BOM workflows.
  • To foster successful AI adoption, Centric advocates for a “human-in-the-loop” model, where AI augments human decisions and automates repeatable tasks rather than replacing roles. They recommend a phased approach, starting with high-impact, low-risk domains like PLM, which has delivered up to a 60% reduction in time to market and a 15% increase in margins for their customers.
Where do you believe we currently are on the progression curve from AI as an extremely broad set of capabilities and promises, to AI as the foundation for applications and services that can deliver a measurable return on investment in well-defined areas?

We are entering a phase where AI is no longer a speculative technology; grounded in proper guardrails it is becoming a practical, virtual companion that can demonstrably impact enterprise performance. At Centric Software, we are focused on embedding AI into real business processes, not treating it as a standalone phenomenon. Our strategy emphasizes AI agents that work within the structured, validated context of our customers’ domain model through Centric’s OOTB semantics comprising over 1,000 business objects, spanning PLM, planning, pricing, market intelligence, PIM and more.

The foundation for ROI lies in applying AI to real enterprise workflows, where AI augments rather than replaces human input. Our implementations — like automated BOM creation based on AI-generated designs, or predictive pricing recommendations — are driving efficiency and decision quality in measurable ways. We now support over 100 atomic AI use cases across the product lifecycle, illustrating the tangible progression from AI theory to enterprise value delivery.  

There’s a temptation, across every sector, to look at AI right now as something of a magic wand – a solution to not just long-simmering business challenges, but also to fundamental technology problems like information fragmentation, siloed processes, and enterprise-wide data governance. It’s clear that Centric sees a lot of possibility in the different kinds of AI – from generative inspiration to demand forecasting – being applied on top of existing systems and structures, so how important do you believe it is for organisations to have those systems and structures properly built to begin with? Can AI deliver value if it’s layered on top of a disconnected technology estate?

AI, without a strong digital foundation, is just noise. We’ve consistently found that the most impactful AI applications emerge when they are grounded in high-integrity, well-structured data and process environments. Our value at Centric Software lies in helping brands integrate AI into a unified architecture — spanning Centric PLM™, Planning™ and Pricing & Inventory™ and into Product Information Management, Digital Asset Management, Content Syndication and Digital Shelf Analytics (Centric PXM™) — where enterprise information semantics, processes and business rules are already codified and thereby augmenting task automation and decision support.

Within an enterprise context, AI must be layered on a cohesive model that captures the nuance of a business. For instance, similarity detection in pricing strategies is far more powerful when it includes functionally oriented data from PLM (attributes, materials) alongside market-driven pricing elasticity of demand. Fragmented tech stacks limit this capability. Our role is to unify silos, enabling decisions that draw from a complete and trusted dataset. Without that foundation, AI is likely to generate misleading or unexecutable or sub-optimal insights.

PLM’s role has evolved — from data management, to process orchestration, to collaborative business concurrency — and now, with AI, into decision support. When AI is integrated at the core of a modern PLM system, it enables dynamic, context-aware decision-making that reflects not only historical data but current trends, business rules and predictive insights within the fabric of a company’s ethos.

Centric PLM is not just a repository; it’s the operational backbone. By grounding AI agents in this domain model, we empower users to execute AI-assisted decisions that are fully compliant with corporate policies and process governance. For example, material recommendations can be validated against approved suppliers and sustainability targets within the PLM and dynamically integrated with World knowledge from LLM models augmented with search, minimizing risk while accelerating innovation. This isn’t theoretical, we’re seeing it in practice through projects with leading retailers automating design-to-BOM workflows with AI support. Navigating tariff complexity is a great example where AI can make a concrete impact.

Obviously, the roll-out of AI is causing friction and uncertainty in creative communities – but it’s also making uncomfortable waves throughout all the different roles and disciplines that contribute to the typical product journey. In general, professionals are concerned that automation means giving up control today and potentially losing their positions in the slightly longer term. This is a difficult foundation for any organisation to build a strategic, full-scale transformation on top of – but if we collectively believe that AI is going to help usher in that transformation, it’s vital that different communities and end users are given the right grounding to understand its value and influence its direction. How do you believe we should be approaching this?

The key is pragmatic transformation, not technological evangelism. We work with customers to ensure AI is introduced in a way that complements, not threatens, existing roles. Our strategy is anchored in a “human-in-the-loop” model, where AI augments user decisions, automates repeatable tasks, and brings new opportunities for creativity and value creation in consonance with prevalent compliance, traceability and accountability demands.

We also recognize the diffusion curve. We’re not chasing AI hype but working to cross the adoption chasm with real, measurable improvements. This requires transparency, explainability and clear ROI. We advocate for governance models that reflect organizational ethos, because successful AI isn’t just functional; it’s cultural, and should improve our operations through continuous learning.

With the right data foundations and the right culture and change management strategy in place, what specific use cases for AI do you see being the most powerful in the next 12-24 months? In a general sense, the vision is obviously for every different job role to be able to contribute to better product outcomes, real-time decision-making, and bottom-line performance, but more specifically how should the companies reading this report be prioritising their investments in AI across planning, pricing, design, content and other domains?

In the near term, we anticipate that the biggest ROI will come from task automation and intelligent task complementarity, areas like AI-assisted assortment planning, predictive pricing, content generation and design simulation. We’re seeing rapid uptake in use cases where AI acts as a co-pilot — accelerating processes like BOM creation, copywriting and trend-driven design — without removing human judgment.

From a prioritization standpoint, companies should start where they have the strongest data confidence. For many, that means PLM. A solid PLM backbone enables AI-powered planning and pricing decisions that reflect real product constraints, market data and customer behavior. Start with high-impact, low-risk domains and scale from there. Our customers have seen up to a 60% reduction in time to market and 15% increase in margins using this staged, grounded approach.

What do you believe are the next steps for how AI is deployed and used? Is it more likely that AI will solidify its place as a new human interface paradigm the frontend of tools and workflows? Or is its future closer to what cloud infrastructure has become today – a quieter commodity that is still the foundation for the next generation of applications, but in a less obvious way than what we’ve seen over the last couple of years? Or is it both?

It’s both. AI will become invisible infrastructure and a visible interface. We’re already embedding agent-like capabilities that navigate workflows, assist in decision-making, and ensure compliance with business rules. These agents are not just chatbots, they are context-aware collaborators capable of executing tasks across Centric’s applications based on voice or text input.

Simultaneously, AI is forming the backbone of new capabilities, like dynamic pricing optimization and market trend analysis. These are not flashy UI features, they’re powerful engines operating behind the scenes. In that sense, AI is taking on the dual role of being both an interface and infrastructure, deeply embedded in the enterprise stack and reshaping how business happens at every layer.

Stories from the fold between fashion and technology, told by industry experts.