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Future of AI in jewellery industry lies in customized, value-driven solutions

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Aniruddha Pal, Co-Founder and CEO, Algoneering  speaks to JewelBuzz on the present and future of AI in the jewellery industry . He underscores that success of AI  hinges on collaboration, data sharing, and a deep understanding of the specific needs of each organization. By embracing these principles, the industry can unlock the full potential of AI to enhance creativity, streamline operations, and ultimately drive greater success.

AI’s Role in the Jewellery Industry

AI is revolutionizing the jewellery industry across disciplines such as designing, manufacturing, streamlining operations, and even ERP and customer solutions. Its presence is reshaping the way operations are conducted, offering new efficiencies and possibilities.

AI Integration Across Industries

AI is making its mark in every industry, from fashion to daily life operations. The jewellery industry, being layered and complex, presents unique challenges. As tech entrepreneurs, we started exploring its potential three years ago, even without a deep jewellery background.

Early Challenges in AI for Jewellery

Initially, the idea of AI designing jewellery seemed far-fetched. Today, the focus has shifted to refining and improving AI-generated designs. The industry is ideating and exploring solutions to its daily challenges, though AI adoption requires clarity on specific problem statements.

AI’s Efficiency Over Perfection

While AI may not yet meet 100% accuracy, its ability to perform even 20% of tasks with high efficiency is valuable. For instance, tools like generative AI simplify processes like content creation, saving significant time on repetitive tasks.

Customization as a Core Requirement

Every organization in the jewellery industry has unique needs and perspectives. One-size-fits-all solutions don’t work. Customization, value-driven approaches, and understanding industry-specific nuances are essential for successful AI integration.

Operational Challenges in Jewellery Manufacturing

Jewellery manufacturing involves significant manual effort, leading to potential errors and missed deadlines. Streamlining operations with AI and implementing strong SOPs can address these issues effectively.

Generative AI in Jewellery Design

Generative AI offers inspiration by creating original jewellery concepts. However, the jewellery industry requires tailored platforms that focus on creating industry-specific designs rather than relying solely on generic tools.

Building a Strong AI Ecosystem

Establishing a robust AI system requires resource allocation, team upskilling, and infrastructure development. Solely relying on AI without supporting resources is not sustainable for long-term growth.

Industry Excitement vs. Realistic Adoption

While there is significant excitement about AI in the jewellery industry, the pace of adoption is slower than expected. Awareness, resource constraints, and organizational protocols are key factors impacting progress.

Importance of Data and Democratization

For AI to thrive, the industry must focus on structured data collection and sharing while maintaining security protocols. The example of Tata Memorial Hospital democratizing MRI data highlights the potential benefits of such an approach.

AI Penetration and Resource Constraints

Penetration of AI in the jewellery sector is slow due to limited awareness and specialized resources. While large brands may have dedicated AI teams, smaller manufacturers face cost and expertise challenges.

Customization and Collaboration as the Future

The future of AI in jewellery lies in customized, value-driven solutions. It hinges on collaboration, data sharing, and a deep understanding of the specific needs of each organization. Collaborative efforts between organizations and AI developers are crucial to training machines with relevant data and building effective models.

AI’s Role in Workflow and SOPs

While AI fits well in product development and reducing design timelines, a strong SOP framework is essential to complement its capabilities. This ensures precision and efficiency in the manufacturing process.

Overcoming Initial Resistance

Early resistance to AI stemmed from data-sharing insecurities and doubts about its ability to understand jewellery-specific nuances. Gradual adoption and collaboration have improved trust and usability.

The Journey Towards Realistic AI Designs

Significant progress has been made in training AI to design realistic jewellery pieces by addressing challenges like material composition, design elements, and client customization needs.

A Vision for the Future

Customization will continue to be the key driver of AI adoption. Structured data, team skill development, and a strong AI vertical are essential for achieving efficiency and long-term success in the jewellery industry.

Collaboration for a Better Future

To unlock AI’s full potential, industry bodies and councils must support collaborative efforts. This collective approach will help create successful models and drive the jewellery industry towards a promising future.

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JB Insights

From Customer Intelligence To Autonomous Growth Intelligence: Deepansh Bhargava Senior Vice President & Head Marketing at VBJ

From Customer Intelligence To AI-powered Growth Decisions, The Next-Gen CDP Is Built To Create Measurable Incremental Value.

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For the last 6 years, Customer Data Platforms have transformed how brands understand their customers. They have brought fragmented data together, created unified customer profiles, enabled segmentation and made personalisation more intelligent. But the next evolution of CDPs will not be defined by how much customer data they can organise. It will be defined by how intelligently AI can convert that data into consumer value and incremental business outcomes.

The shift is from Customer Intelligence to Growth Intelligence.

Businesses today know more about their existing customers than ever before their purchase history, frequency, preferences, lifetime value and likelihood of buying again. Yet the larger opportunity lies in moving beyond what customers did to understanding what they need next, where their behaviour is changing and where the next phase of growth will come from. This also requires a sharper focus on incrementality.

Revenue, ROAS, conversions and engagement tell us what happened. But they do not always tell us how much of that outcome was created because of an intervention and how much would have happened anyway.

That difference is the Delta. AI can help take CDPs beyond systems of customer intelligence and turn them into systems of consumer and commercial decision intelligence. Instead of simply identifying customers with a high propensity to purchase, an intelligent growth system could identify intent, unmet needs and moments that matter then evaluate opportunities across acquisition, retention, frequency, basket size, category penetration, pricing, media, events and geography to identify where the highest incremental growth opportunity lies.

Growth intelligence should also help businesses identify where growth is likely to disappear. Early signals from customer behaviour, categories, geographies or spending patterns could reveal risks long before they become visible in the P&L. AI could identify the source of the risk, estimate its potential commercial impact and recommend interventions to change the trajectory. This is where the future becomes particularly interesting.

AI should not just predict customers. It should increasingly help predict the business.

The next generation of AI agents could continuously monitor customer behaviour, sales, categories, inventory, geographies, media and store performance. They could identify anomalies, form hypotheses, estimate commercial impact, recommend interventions, test them and measure the incremental outcome.

The role of the marketer would evolve from simply managing campaigns to managing the intelligence, strategy and guardrails around a more autonomous growth system.

This evolution can be built around three principles: Anticipate, Act and Account.

1. Anticipate where consumer needs and growth will emerge and where they may disappear.

2. Act by identifying and eventually executing the most relevant intervention for the consumer and the business.

3. Account for whether that intervention genuinely created incremental value.

The CDP of the future, therefore, should move beyond being a platform that simply manages customer data. It should become a Growth Decision Platform one that helps businesses understand consumers better, identify opportunities, detect risks, recommend actions, measure incrementality and continuously learn from every intervention. The ultimate measure of such a platform should not be the volume of data it manages.

It should be much simpler:

Show me the Delta you create.

Because ultimately, consumer centricity is not the opposite of growth. It is where sustainable growth begins.

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JewelBuzz is Asia’s First Digital Jewellery Media & India’s No.1 B2B Jewellery Magazine, published by AM Media House. Since 2016, we’ve been the trusted source for jewellery news, market trends, trade insights, exhibitions, podcasts, and brand stories, connecting jewellers, retailers, and industry professionals worldwide.

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