Tapestry Embeds AI-Enabled Decision-Making Across Enterprise
Global fashion house Tapestry, which manufactures such brands as Coach and Kate Spade New York, is using a proprietary AI platform to elevate decision-making across its enterprise.
The company was just awarded a U.S. patent for the Mira platform, which connects data across all areas of Tapestry's operations. This is the organization's first AI patent and second tech patent overall.
The platform is used alongside Tapestry's patented Global Data Fabric, allowing employees to access rapid business insights and replacing a process that consisted of multiple dashboards and several days of manual legwork.
With the consolidated structure, Tapestry employees can now make decisions in seconds to minutes, using the tool across assortment planning and inventory management to more quickly respond to emerging consumer trends.
“Our teams bring deep expertise, human judgement and creativity; Mira provides business intelligence to help our teams move with speed and agility,” Fabio Luzzi, chief data and analytics officer for Tapestry, said in a statement. “Together, that combination becomes a structural competitive advantage.”
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The company's data and analytics team designed the internal tool with the help of business partners, ensuring it aligns with brand strategy, operational priorities and financial goals. It embeds institutional knowledge within an enterprise ecosystem with role-based access controls.
“We’re an 85-year-old fashion company harnessing cutting-edge innovation and technology. Mira is a powerful tool that puts business insights into the hands of decision-makers across the company,” Joanne Crevoiserat, CEO, said in a statement. “This is one more way we are moving with agility to deliver for our consumers and drive durable growth.”
This has been a multiyear process. At last year's NRF conference, Tapestry's director of planning systems and RPA, Karthik Ramakrishan, shared details about the foundational work it was doing to evolve data within the organization.
This included separating insights into either private or public pools to protect sensitive data while allowing employees to easily access public information through form auto-completes and AI-enabled chat responses.
