Rick Rutter, Director, The Feed, Juniper Networks 

Our AI is Cooler Than Your AI 

Networking for Change AI & ML
Rick Rutter Headshot

A lively discussion about how AI can transform your network into a fast, reliable, self-driving machine.

Is the proliferation of devices, people and data making your IT infrastructure difficult to manage? Rick Rutter talks to Juniper’s Bob Friday and Sudheer Matta to find out how AI-driven operations can help increase speed to resolution and improve user experience. 

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You’ll learn

  • How Mist AI can help address your IT infrastructure issues 

  • The five advantages customers get when they engage Juniper, according to Sudheer 

  • What sets Mist AIOPs apart from its competitors and how it can benefit your enterprise (plus the latest on indoor wayfinding) 

Who is this for?

Network Professionals Business Leaders


Rick Rutter Headshot
Rick Rutter
Director, The Feed, Juniper Networks 

Guest speakers

Bob Friday Headshot
Bob Friday
GVP, Chief AI Officer, Juniper Networks 
Sudheer Matta Headshot
Sudheer Matta
GVP, AI-Driven Enterprise, Juniper Networks 


0:01 [Music]

0:07 the proliferation of devices data and

0:09 people have made it infrastructure more

0:12 complex than ever to manage

0:13 troubleshooting and information overload

0:15 threatens to slow down apps and

0:17 interfere with user experience

0:18 i'm rick roter from juniper a few days

0:20 ago i had the opportunity to sit down

0:22 with a few of my colleagues to discuss

0:24 ai and how it increases speed resolution

0:26 with the direct correlation user

0:28 experience let's take a look

0:31 gentlemen thanks for coming if you could

0:33 quickly introduce yourselves yeah rick

0:35 bob friday cto of juniper's enterprise

0:37 business

0:38 and thank you rick uh sudhir mata uh vp

0:40 of products here at the juniper's ai

0:42 driven enterprise right on well to kick

0:45 things off what are some of the concrete

0:46 areas that juniper and mist ai are doing

0:49 to address some of these challenges

0:50 you know i think you know there's

0:52 several good stories but i think one of

0:53 the most famous one for me was really a

0:55 big retailer where we were basically

0:57 trying to solve some little needle in

0:59 the haystack problem where they were

1:00 having these devices that couldn't

1:02 actually connect to the network randomly

1:04 coming going offline it was really a

1:06 good example where you know ai mutual

1:07 information was actually able to get to

1:09 the point of letting you know it was

1:11 really due to a particular os model type

1:13 that was having authentication problems

1:15 so that's really the power of ai is to

1:17 find these needle in the hey tax stack

1:19 problems that makes sense yeah and and

1:21 you know for us at juniper we measure

1:24 the impact of ai right so so there are

1:27 five things rick i tell every customer

1:29 you can take it to the bank if you come

1:31 to juniper you'll get these five things

1:34 the fastest deployment the fewest

1:37 tickets right imagine going into a

1:39 network and saying you know you know

1:41 service now as an example cut their

1:44 number of tickets coming into it

1:46 more than 90 fewer tickets right so

1:49 fastest deployment fewer tickets fastest

1:51 time to resolution any problem let's say

1:54 you know a doctor walks out of surgery

1:57 call drops now

1:59 and says hey why does my call drop

2:02 we're using ai and we're using machine

2:04 learning to go back and study sort of

2:06 recreate reality

2:08 and say what what really happened there

2:11 and what's all the you know the the

2:13 correlations and the causation of what

2:15 happened there compile all of that and

2:17 reduce the mean time to repair for for

2:20 solutions so first fast deployments

2:22 fewer tickets fast time to resolution

2:25 for tickets as they come in and then

2:27 last but not least is is dramatically

2:30 simplified operations these are the four

2:32 i.t outcomes you get in everywhere and

2:34 then bob's passion has always been is

2:37 how do we digitize the experiences for

2:39 employees guests patients passengers and

2:41 all these verticals and so we built a

2:44 platform for digitization and even in

2:47 that bob you've used a lot of ai even

2:49 the digitization yeah and this is really

2:51 around the location component

2:53 you know if you look at their

2:54 how are people connected to the internet

2:57 but when we get to our b2c customers

2:59 this is hospitality retail healthcare

3:01 wherever there is a consumer involved in

3:03 the business this is your b2c you know

3:05 what we really built as a platform that

3:07 allows you to basically engage with

3:09 those customers you know whether i'm at

3:10 a hospital i work with the hospital

3:12 really around uber for wheelchairs you

3:14 know like when a vet comes in right you

3:16 want to be able to find that uber they

3:18 want to get that wheelchair and get it

3:19 to the uber that requires location to

3:21 actually start working you know here at

3:23 juniper cova tracking during the covet

3:26 right you know you want to be able to

3:27 attract when people came in contact with

3:29 each other you know so that's the other

3:30 kind of use cases we're seeing around

3:32 this digital transformation of helping

3:34 our customers both be to see and even

3:36 carpeted enterprise customers who are

3:38 trying to facilities management now in

3:40 this new hybrid working space

3:42 that's amazing guys very cool stuff i

3:44 mean everyone is talking about ai ops

3:47 right so what do you think sets juniper

3:48 apart from the competition

3:51 well it's there there's no place in this

3:53 one right it's youtube guys right yeah

3:55 this one here is cloud and ai

3:58 right if you look at miss and what we're

4:00 doing now that differentiates us and

4:02 this is one reason why i left cisco to

4:04 start myth because i saw there was an

4:06 architectural opportunity that really

4:08 required a blank sheet of paper you know

4:10 that i wasn't going to be able to do at

4:11 a big company and it started building a

4:13 cloud that can actually do data science

4:16 and pipelines right

4:17 and that's basically solving that

4:19 problem around i'm not going to deliver

4:21 things every year i'm going to deliver

4:23 things every

4:24 week you know

4:26 as seder tells you christmas comes at

4:28 mist every week right you know every

4:30 week we get new features for our

4:31 customers the other big differentiation

4:33 is really around ai ops and this is

4:35 really that day two operational thing

4:38 and that's really what's changing the

4:39 industry we've seen ai ops change you

4:42 know how cars drive you know how doctors

4:44 diagnose cancer and image detection you

4:46 know in networking aiops is

4:48 fundamentally going to change how we

4:49 manage these networks of the future it's

4:51 really cool so this is a fundamental

4:54 question right

4:55 why what separates juniper mist rai ops

4:59 and ai platforms from any everybody else

5:01 in the industry right so tying off of

5:04 bob's uh answer there right bob said

5:06 it's cloud right so first you know it's

5:09 so important everybody has cloud meraki

5:11 has cloud aruba has cloud aerohive has

5:13 cloud everybody has cloud why is juniper

5:16 miscloud different it's as bob said it's

5:19 the pipeline to handle data on a per

5:22 user per minute basis when everybody

5:25 else's cloud

5:27 manages their network

5:28 we operate based on users experience so

5:32 every user every minute we're getting

5:34 that data into the cloud being able to

5:36 process that being able to you know

5:38 collect collate and curate that to sort

5:41 of make meaning on are we delivering a

5:43 great experience so it starts with the

5:45 cloud no question right and and what is

5:48 proof point very simple proof point bob

5:50 said you know uh like

5:52 when aruba central doesn't upgrade for

5:54 their cloud literally there's a 12-hour

5:57 outage on the cloud right you know the

5:59 the agility on some of our competitors

6:01 is terrible

6:03 christmas comes every wednesday at

6:04 juniper mist right literally we are

6:07 bringing new features to the cloud every

6:09 week every other week and this is

6:11 foundational agilities is important so

6:14 user experience every user every minute

6:16 and then an agile cloud that can

6:19 actually grow with the customers as

6:20 their need grows right and then so

6:23 that's on the cloud side on the platform

6:24 side fundamentally foundationally

6:26 different

6:27 yeah sorry bob you were great

6:29 i mean doing that wednesday christmas i

6:31 think is doing that with reliability and

6:33 so that's that micro service

6:34 architecture you know that's the

6:36 fundamental change going from an

6:37 embedded architecture that we were doing

6:39 at cisco 20 years ago to what we're

6:41 doing in the cloud that lets us do that

6:43 release every wednesday reliably right

6:45 without breaking anything speed and

6:47 flexibility flexibility with reliability

6:50 yeah and reliability

6:52 the one thing though on

6:53 when it comes to ai right bob is always

6:56 built as a pipeline it starts with data

6:58 it starts with you know great data you

7:01 know great grapes make good what good

7:03 wine comes from great grapes so i make a

7:05 barrel of wine every year you know great

7:07 wine starts with great

7:08 grapes

7:09 great ai starts with great data great

7:11 data right so great data for us and then

7:14 ai primitive framework that can take

7:16 that data and correlate and curate that

7:19 data and then comes complete a full

7:23 toolbox of algorithms we've been working

7:26 on for seven plus years and then

7:29 finally that all gets boiled up into a

7:31 nice little marvelous virtual assistant

7:34 we'll talk about marvis in a minute

7:36 but

7:36 all of this is a journey towards the

7:39 final answer the final answer for us is

7:42 a truly self-driving network right a

7:45 self-driving network meaning when you

7:48 deploy a juniper miss network i just

7:50 said we have fewer tickets how do we

7:52 have fewer tickets just because we threw

7:54 some ai algorithm that it no

7:56 we have the fewest tickets because we

7:59 use ai to design the network to deploy

8:02 and operate the network right channel

8:05 partners go in design they do site

8:07 surveys they deploy the network great

8:09 but the main the moment they walk off of

8:11 that premise that network is changing

8:13 the wi-fi is a living breathing thing so

8:16 we use reinforcement learning ai driven

8:18 radio resource management to sustain

8:21 that network at a strong point over a

8:23 long time right and i think the other

8:25 thing we've talked about is you know in

8:26 addition to that architecture is really

8:29 organizationally

8:30 what people don't appreciate with marvis

8:32 is in addition to the cloud architecture

8:35 is organizationally we have tied that

8:36 support team right to our data science

8:38 team and so people who are trying to

8:41 solve this problem that's probably the

8:42 other key secret sauce into marvis

8:45 is organizationally is making sure those

8:47 two groups are like acting as one

8:49 you know another thing that i hear a lot

8:51 about location data you brought it up uh

8:53 you know a little bit earlier but what

8:55 are some of the coolest ways that people

8:56 are using location data to to change the

8:59 way we live and work

9:01 yeah i mean so location is one of these

9:02 topics as dear seder knows has been dear

9:05 to my heart ever for the last 20 years

9:06 or so is really bringing indoor location

9:09 on par

9:10 with connectivity and making it a

9:12 must-have in the enterprise space you

9:14 know so what i said before is b2c

9:17 customers right retail after all these

9:19 customers are trying to digitally

9:21 transform either a customer experience

9:23 or an employee experience and we're

9:25 starting to see it on both levels you

9:26 know on the customer side it's about

9:28 wayfinding you know either i'm in a

9:30 complex hospital or a maze of things

9:32 they're trying to bring indoor

9:33 wayfinding on par gps

9:35 right you know the wheelchair example

9:37 right the uber right they're trying to

9:39 basically build applications you know to

9:41 help a doctor

9:42 can get a wheelchair to a uh a vet or

9:45 something right you know same thing you

9:47 want with your car right i want to be

9:48 able to sit there and i want to be able

9:49 to see you know how long am i waiting

9:51 are they coming they're making progress

9:53 that same experience you want indoors so

9:55 these are all examples that we're

9:56 starting to see

9:57 inside of our b2c customers and we're

9:59 also starting to see inside of our inner

10:01 carpet enterprise customers and ricky if

10:03 i could add a couple more interesting

10:05 examples right

10:06 april 2020 in march 17th uh 2020 so the

10:11 bay area here got shut down with covid

10:13 right

10:14 literally with under six weeks we turned

10:16 the juniper network right the same

10:18 network no new aps were added the same

10:21 network for complete digital contact

10:24 tracing right and so the the power of

10:27 the platform that sort of bob has

10:28 envisioned and built for us here is that

10:31 we are truly uh

10:33 truly today juniper networks is the only

10:36 networking player who's a leader in the

10:39 indoor location magic quadrant and so we

10:42 truly are the only player that can

10:44 combine best of breed wire and wireless

10:47 with this best of breed location

10:48 services so contact tracing especially

10:50 as come as employees are coming back

10:52 another very important use case as it's

10:55 so relevant now

10:56 is is during

10:59 and and even before that

11:01 companies were consolidating real estate

11:03 out right real estate is one of the

11:04 largest line items on on everybody's

11:07 uh you know expenditure and companies

11:09 are actually trying to optimize that and

11:12 how are they optimizing they're going to

11:13 digitize workspaces right so one of the

11:16 largest wins juniper we've had is a

11:18 financial services company and there

11:21 the the uh uh the facilities team

11:24 contributed a substantial money piece of

11:27 money for the infrastructure i.t project

11:29 because we're digitizing their employee

11:32 experience as employees are coming back

11:33 from kobe that's a remote working thing

11:35 right so we had a very large customer

11:37 that built a smart building you know

11:39 open workspace right hybrid workspace

11:41 hoteling right now but what they have to

11:43 figure out is why is no one going to

11:45 that side of the building you know is it

11:47 the temperature is it the furniture you

11:49 know so these are the type of problems

11:51 that we have to you know big companies

11:52 have to deal with when they move into

11:54 this new hybrid working model right now

11:56 making sure the space is being utilized

11:58 efficiently this is cool and it's almost

11:59 like even with iot i would imagine this

12:02 even becomes more important absolutely

12:04 absolutely

12:05 you know it's interesting

12:07 so so we talked a lot about digitization

12:09 location

12:10 but this conversation is about ai

12:12 how did we use ai for enabling this

12:14 digitization so let me give you sort of

12:16 a a very simple but very you know uh cut

12:21 and dry

12:22 you know comparison with our competitors

12:25 100 of our competitors every single one

12:27 of them if they need to deploy location

12:30 they need to go like uh the old verizon

12:32 ad can you hear me now can you hear me

12:34 now they need to go to every point in

12:36 the building and do manual calibration

12:40 bob you've used ai for that so explain

12:42 that yeah so at miss actually our very

12:45 first patent

12:46 got issued in less than six months was

12:48 for virtual ble you know in the very

12:50 first use case for aiml was basically

12:52 trying to learn the path loss model in a

12:54 building for every device every model

12:57 inside the building so that was actually

12:59 our first example of aiml was really

13:01 around location and what that really

13:04 solved is the problem of making sure

13:05 people didn't have to do fingerprinting

13:07 and a lot of setup you know because when

13:09 you look at location the biggest problem

13:11 with it has been the complexity of

13:13 getting it up and running it's an

13:14 overlay network

13:15 right if you look what miss has done

13:18 every access point we ship has a virtual

13:20 bla antenna built in already so that

13:23 totally eliminates the overlay problem

13:25 right which is what seder is pointing

13:26 out yeah you know the cloud is what let

13:28 us get to you know in six weeks we had

13:29 covet up and running covert tracking up

13:31 and running that was the cloud

13:33 but the fact that we had virtual ble was

13:35 the other part of it that everything was

13:36 enabled for location coming out of the

13:38 bat coming out of the gate you put that

13:40 in context if i'm a retailer let's say i

13:42 have a thousand retail stores

13:44 if i choose if they choose our

13:46 competitor they choose an aruba cisco

13:49 meraki

13:50 they actually have to send people

13:52 trained people to a thousand stores to

13:55 calibrate them right millions of dollars

13:58 versus a juniper network that is using

14:00 ai for calibration think of applying ai

14:03 in a dramatically different place well

14:05 in that case the virtual bla eliminated

14:07 the overlay aiml eliminated the

14:10 calibration factor and so that was we

14:13 were the first ones to basically get rid

14:14 of the need to calibrate these networks

14:16 for location

14:17 that's crazy it's crazy it is

14:20 what a great opportunity to hear from

14:22 sudhir and bob to learn about juniper's

14:24 ai capabilities and hear how great ai

14:27 starts with great data thank you for

14:29 joining and be sure to check out all the

14:31 great content that we have about ai on

14:33 the feed

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