AiThority Interview with Robert Figiel, VP of Centric Market Intelligence R&D at Centric Software

Robert Figiel, VP of Centric Market Intelligence R&D at Centric Software talks about AI for pricing and inventory strategies optimization, key data privacy concerns, future developments in PLM, and more in this conversation…

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Hi Robert, share a brief introduction to yourself, including your key responsibilities at Centric.

Hi, I am the former co-founder & CTO of StyleSage, founded in 2013, now known as Centric Market Intelligence (CMI). CMI is an AI-powered retail analytics solution that enables fashion and beauty brands & retailers to increase their speed to market, with real-time insights across 4 key business areas: Pricing, Assortment, Promotions, and Trends. Leveraging image recognition and machine learning as a core competency, CMI also powers product matching and auto-attribution solutions that help automate key retail processes.

My current role as VP CMI R&D is to lead the Market Intelligence R&D team of about 100 engineers, data scientists, data analysts and QA reviewers – spread across Europe and Asia. I work with a wonderful team that I am very proud of, and I see my key responsibility in aligning priorities & roadmaps and making sure that the team can work efficiently.

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Tell us how Centric Software is leveraging AI to optimise pricing and inventory strategies, and what impact has this had on retail margins and revenues.

 Centric Software is focused on helping retailers tackle complex challenges such as inventory management and pricing strategies in an increasingly competitive market. With massive amounts of data flowing from various sources, it is essential for brands and retailers to maintain efficiency.

Through Centric Pricing and Inventory, the latest addition to Centric’s solution portfolio, AI models analyze historical and real-time data to optimize pricing decisions based on demand fluctuations, competitor pricing, inventory levels and market conditions throughout the pre-season and in-season processes.

Centric leverages AI in many ways here: auto-tagging of products from text & images; finding similar historic products that share similar sales patterns; matching products across retailers to understand competitive pricing; determining demand factors and price elasticity models; understanding and predicting market trends; building informed demand forecasts and scenarios; recommending optimal pricing decisions to maximize a business objective; executing confident pricing decision automatically, and many more. All of that automated and at scale for thousands or millions of products across markets and stores – on a daily basis.

This also extends to inventory management, where AI is used for example to recommend optimal initial stock allocations and stock rebalancing operations. By aligning inventory with demand and dynamically adjusting prices, Centric Pricing and Inventory helps retailers boost their margins and revenues, ensuring the right products are in stock at the right time.

What do you predict will be the future developments in Product Lifecycle Management (PLM) systems, particularly regarding AI integration?

 AI is poised to revolutionize PLM systems by automating complex tasks and improving product development workflows. At Centric, the introduction of Centric AI Fashion Inspiration is already transforming the design process. This AI-powered tool shortens time-to-market by automating design tasks and allowing users to upload sketches or photos, which are then refined by AI into complete digital designs, allowing users to rapidly generate and iterate over design ideas.

Centric AI Fashion Inspiration is just one example of many. Taking this further, soon AI will also support other PLM processes such as creating BOMs, 3D models, tech packs, supplier collaboration and much more with the objective to support businesses in brining the right products to market faster.

Centric first introduced AI capabilities in Centric PLM over 6 years ago and is continuing to invest in these AI-driven capabilities to ensure that brands, retailers and manufacturers can innovate faster while maintaining high levels of creativity and control over their designs.

What are the top challenges organizations face when adopting AI solutions in retail, and how can they be overcome?

One of the primary challenges in adopting AI in retail is data quality. For AI to be effective, it requires accurate and clean data, which means companies need to invest in data governance and management practices.

Integrating AI with existing legacy systems is another hurdle. Many retailers struggle to make their current infrastructure compatible with AI tools, and overcoming this requires flexible and scalable AI solutions that can work seamlessly within existing ecosystems.

Lastly, resistance to change. Many teams worry that AI will replace human decision-making, leading to fears about job loss or an inability to control AI-driven processes. To overcome this, it’s essential to communicate the complementary nature of AI—it enhances decision-making rather than replacing it.

According to you, is interoperability between AI systems and existing enterprise software for maximizing value in retail analytics vital? If so, why?

Yes, interoperability between AI systems and existing enterprise software is crucial for maximizing the value of retail analytics. AI can generate insights only when it has access to the full range of data flowing through a company’s systems. Retailers rely on ERP, CRM, and PLM systems to manage everything from supply chains to customer interactions. If AI tools aren’t integrated with these systems, their effectiveness is limited, leading to disjointed data insights.

By ensuring AI systems are fully interoperable, companies can leverage a unified view of their operations, improving decision-making and delivering more accurate analytics. This is key for delivering tailored customer experiences, managing inventory, and adjusting pricing strategies based on a complete understanding of both internal processes and market conditions.

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As AI tools become more prevalent, what are the key data privacy concerns that organizations should address?

 Indeed, it is very tempting to just upload a lot of sensitive data to an AI. However, it’s paramount that sensitive data will be kept confidential and is not used to train the AI in further iterations and thereby indirectly leaking confidential business information.

For use of personal AI assistants, companies need to make sure to adopt strict policies and ensure their employees are sensitive around data privacy issues.

For AI-powered enterprise software it’s important to review IT Security certifications of vendors and understand the vendors’ measures to avoid leakage of sensitive data.

Finally, share about five common pitfalls in implementing AI-driven pricing and inventory strategies that you would advise retailers to avoid.

1. Poor Data Quality: AI systems are only as effective as the data they receive. Inaccurate or outdated data leads to incorrect predictions, so it’s vital to maintain high data integrity.

2. Lack of Human Oversight: AI should assist, not replace, human decision-making. Over-reliance on AI without human oversight can lead to missed opportunities or errors.

3. Ignoring Customer Behavior: Focusing solely on internal metrics and forgetting customer sentiment can cause pricing strategies to alienate buyers. Use AI to consider both sales data and customer feedback.

4. Neglecting System Integration: AI must work within a company’s existing systems. Failing to integrate AI properly can lead to inefficiencies and disconnected operations.

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