Gap, Inc Transforms In-Store Experience with AI-Driven Networking

AI & ML
Video capture of Snehal Patel, Director of Global Infrastructure Architecture, Gap, Inc.

Game-changer Mist AI lets clothing innovate

Wireless is no longer a nice-to-have — it’s a necessity. Discover how Gap, Inc. embraced and deployed Juniper’s AI-driven networking solutions across their entire network, changing forever how shoppers interact with their brand and giving customers and employees the best in-store Wi-Fi experience possible. 

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You’ll learn

  • How Gap started from scratch to rebuild their infrastructure with Mist AI

  • How the Juniper Marvis Assistant helped troubleshoot and solve problems fast

  • Why Gap considers the AI implementation a success for its business and its customers 

Who is this for?

Network Professionals Business Leaders

Guest speakers

Snehal Patel Headshot
Snehal Patel
Director of Global Infrastructure Architecture, Gap, Inc.

Transcript

0:00 [Music]

0:00 gaap has long been a leading retail

0:03 brand which was why they were one of the

0:05 first to really embrace ai-driven

0:07 networking solutions the retail

0:09 environment is very dynamic

0:11 to stay at the forefront of technology

0:13 we have built a strong foundation of

0:15 mist ai-based wireless

0:18 the wireless is no longer a nice to have

0:21 as it used to be and actually a

0:23 necessity in our stores

0:25 our strategy is to continue optimizing

0:27 our wireless network and relying on it

0:30 to deploy more and more services as part

0:32 of a more our modernization effort but

0:35 all projects bring their problems

0:37 especially with an operation the size of

0:39 gaps

0:40 adoption of a new solution can be very

0:42 challenging at times especially if

0:44 you're deploying it in thousands of

0:46 stores like us so with the missed

0:48 deployment we took an opportunity to

0:50 start from scratch and get rid of all of

0:52 our tech debt that we had accumulated

0:54 over a period of time

0:55 we not only deployed an ai-based

0:57 wireless network but also started

0:59 getting lost lot of visibility from the

1:01 mist

1:02 in terms of sles the service level

1:05 expectation uh for the entire fleet of

1:07 stores

1:09 the marvel ci assistant added cherry on

1:11 top of it by helping us troubleshoot the

1:13 problems faster and using marvis we were

1:15 able to push the

1:18 escalations and resolutions to our tier

1:20 one operations team where they were able

1:21 to troubleshoot the problems much faster

1:24 now we not only can troubleshoot the

1:27 issues faster but also have data to

1:29 check if

1:30 it is a network problem or something

1:32 else

1:33 deploying juniper's ai technology across

1:35 the network has brought gaps some very

1:37 tangible results overall the visibility

1:40 the sle and marvis ai has helped us a

1:43 lot to analyze and troubleshoot network

1:45 problems and reduce store visits and

1:47 those are the factors that we consider

1:50 as the main factors to call our project

1:53 successful

1:54 using mist we were also able to reduce

1:56 the store visit by about 85

1:59 and this is just one way that gap are

2:00 using the latest networking technology

2:03 to drive their business forward

2:05 at gaap our primary focus is always to

2:07 meet business requirements and innovate

2:09 in that process to meet customer needs

2:11 as well and our wireless deployment is a

2:14 prime example of that

2:16 that approach

2:23 you

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