James Maguire, Editor-in-Chief, eWeek

Juniper’s Bob Friday: Preparing for AI in Your Business

Leadership Voices Bob FridayAI & ML
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The still is a title page from the video series, eWeek eSpeaks. Host James Maguire’s picture is shown on the left and guest Bob Friday’s picture is on the right. Underneath Bob’s picture it reads, CTO Juniper.

AI is accelerating. Is your organization ready for it?

Tune into this eWeek interview, starring Juniper’s own leading AI expert Bob Friday, who shares the steps your business needs to take to successfully deploy AI, including the need for quality data that can be processed in real time. Listen as Bob shares his thoughts on the rollout of AI and how AI will change the industry as it expands. 

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You’ll learn

  • One reason why Bob left Cisco to start Mist 

  • How AI is the next step in the evolution of automation 

  • What Bob thinks he’ll see happen with AI before he retires 

Who is this for?

Network Professionals Business Leaders


James Maguire Headshot
James Maguire
Editor-in-Chief, eWeek

Guest speakers

Bob Friday Headshot
Bob Friday
CTO, Juniper AI-Driven Enterprise Business


0:01 [Music]

0:03 hi i'm james mcguire

0:05 and today we're talking about getting

0:07 your organization ready for artificial

0:09 intelligence

0:10 which of course includes making sure

0:12 your ai system is fed

0:13 enough data enough quality data to

0:16 discuss that i'm joined by a real expert

0:17 in the field

0:18 with me is bob friday cto of juniper's

0:21 ai driven enterprise business

0:24 bob very good to have you with us today

0:25 yeah i know james thank you for having

0:27 me here

0:27 you know ai's you know a topic dear to

0:29 my heart so it's really great to be here

0:31 with you today

0:32 great you know there's a there's a

0:34 really interesting uh report about

0:36 the state of artificial intelligence it

0:38 seems like there's a there's a bit of a

0:39 gap between

0:40 some of some of the hopes and some of

0:41 the optimism for ai and some of the

0:43 reality of how it's actually getting

0:44 deployed

0:46 especially a report i think it's either

0:48 sponsored or put together by juniper

0:50 uh it'd be nice for me to do a little

0:51 screen share and show the stats for

0:53 which i think it's

0:54 let me do this kind of advanced

0:56 technology the

0:57 zoom screen sharing uh here we go let's

1:00 do share

1:02 yeah and if i if i look at this it's

1:04 interesting so we look at the figures

1:07 the survey found that 95 of 700

1:10 respondents

1:10 believed that their organizations would

1:12 benefit from embedding ai into their

1:14 daily

1:14 operations so you know everyone really

1:16 wants to do it on the other hand

1:18 this compares with only six percent of

1:21 c-level leaders

1:22 who have reported adoption of ai power

1:24 solutions across their organization so

1:26 there's a real disconnect between uh

1:29 you know the optimism the hope for ai

1:31 and and what people want it to do or

1:33 think it can do

1:34 and the reality of how it's deployed

1:36 what what's going on there

1:38 to create that gap yeah you know james

1:40 when you look at it

1:41 you know i used to be the mobility cto

1:43 at the at cisco right you know

1:45 it would really start you know what i

1:47 heard there is really from customers

1:49 there's a couple things those customers

1:50 really were looking to do one is they

1:52 really wanted to get the

1:54 they wanted to basically get their

1:55 systems to stop crashing uh but they

1:57 really wanted

1:58 end-to-end visibility you know from the

2:00 client to the

2:01 to the cloud and that's really when ai

2:04 ops started to become more critical

2:05 and what i really noticed was you know

2:08 and that's one reason i

2:09 i left cisco to start mist right is

2:11 because what was really happening was to

2:13 build an ai

2:14 ops solution you really needed really to

2:16 have kind of a real-time pipeline

2:19 to deal with the data right it was

2:20 basically an architectural change what

2:23 was happening

2:24 you know from what i did 20 years ago at

2:26 airspace we built systems that were

2:27 helping enterprises manage

2:29 network elements you know really the

2:31 paradigm shift is really now helping

2:33 enterprises

2:34 really manage the end-to-end user

2:36 experience and that really requires you

2:38 to change two things

2:40 technically it requires something in the

2:42 cloud that can actually process this

2:44 data real time

2:45 right interesting the other interesting

2:47 twist i found in this

2:48 is really it requires almost an

2:50 organizational change

2:52 right culture a cultural change i'm

2:53 assuming a cultural change in both the

2:55 customer and in the vendors right so so

2:57 i missed

2:58 what we ended up doing was i ended up

3:00 really tying my

3:01 data science team up to my customer

3:04 support team

3:05 you know because one of the challenges

3:07 is you know you know we have data

3:08 scientists who

3:09 you know they understand the math and

3:11 the science behind the

3:12 ml algorithms but they're not

3:14 necessarily network domain experts

3:16 so you look at the challenges here you

3:18 know you got the vendors who've got to

3:20 change their culture

3:21 you know we got to move to architectures

3:23 that can actually do

3:24 aiml and you got to organizationally

3:27 change on the vendor side

3:28 and then on the customer side you know

3:30 the customers have to basically start

3:33 changing their it teams right you know

3:35 they're going through a bunch of changes

3:36 on

3:37 the it side you know you look at it

3:39 teams you know we were asking it teams

3:41 to do a

3:43 almost an exponential change right how

3:45 they operate their networks

3:47 yeah because because of the level of

3:49 data in other words well the complexity

3:51 right i mean you look at what's

3:52 happening on that site you know their

3:53 networks are getting more complex

3:55 and we're asking it guys to basically go

3:57 from being cli

3:59 programmers to being python programmers

4:01 right you know so they're already making

4:03 that shift to becoming

4:05 python programmers and now we're asking

4:07 them to become

4:08 ai data scientist experts right so

4:10 that's a lot of pressure on our

4:12 you know our customers right now and

4:13 getting their it team skill sets

4:15 up to the next you know next stage you

4:18 may you mentioned ai

4:19 ops so this is really is is that a key

4:21 area of yours in particular especially

4:22 in terms of you know dealing with the

4:24 complexity and

4:25 data and the data in the it operations

4:27 is are you are you big at ai ops in

4:29 particular

4:30 yeah that i mean that is what we do here

4:32 right now and that's really

4:33 that's really the change right ai ops is

4:35 really that paradigm shift from

4:36 managing network elements to managing

4:39 client to cloud right

4:40 you know i really don't care what's

4:42 between the client the cloud whether

4:43 it's

4:44 juniper equipment or someone else's

4:45 equipment my mission in life is really

4:47 to help those

4:48 help them figure out why their devices

4:50 and customers are having problems

4:51 internet poor internet connectivity

4:55 let's talk about the the ai uh ai ops

4:58 market for a second

4:59 how do you think is it is it close to

5:00 mainstream or is it still

5:02 in its infancy in terms of adoption what

5:04 would you say

5:05 you know i think right now we're really

5:07 in the early early days of aiops

5:09 adoption right

5:10 you know you know you look at enterprise

5:12 businesses you know like we mentioned

5:14 before

5:14 i mean culturally they're trying to get

5:16 their it teams you know with the right

5:17 skill sets to really embrace ai

5:20 and they're really really trying to

5:21 understand you know what is what's the

5:23 difference between ai

5:25 and automation right and from my

5:27 perspective

5:28 you know ai is really just the the next

5:30 step in the evolution

5:32 of automation right what we're really

5:34 doing

5:35 is we're really building things

5:37 solutions on par

5:39 they can do things on par with humans

5:41 now right it's kind of like a

5:42 self-driving car

5:43 diagnosing cancers you know and if you

5:45 look really one of the inspirations that

5:47 missed right you know one of the reasons

5:48 i started this was

5:50 you know when i saw watson play jeopardy

5:52 i don't know

5:53 right yeah it was it was a big deal when

5:56 yeah absolutely

5:57 you know that that's when i kind of

5:58 realized hey you know if they can build

6:00 something that can play jeopardy

6:02 right we should be able to build

6:04 something that can basically answer

6:06 questions

6:06 and manage networks on par with

6:10 network domain experts right like you

6:12 know the technology is real

6:14 we've gone from marketing hype to that

6:15 there is real technology here that we

6:17 can actually build something that can do

6:18 something on par with a human

6:21 well it's true but in terms of the the

6:22 you know the weaning of jeopardy that's

6:24 that's spitting back out information

6:28 uh which of course is going to be and

6:29 handling information processing

6:31 information which is going to be one of

6:32 the cores of aiops

6:34 but it's also i think about those i.t

6:36 experts

6:37 they're making nuanced decisions uh

6:40 they're they're

6:41 they're actually having to forecast

6:43 things and it says we need

6:45 we need aiops to do more than just spit

6:47 out the information we need we need

6:48 prediction and forecasting

6:49 we also we'll almost need the system to

6:51 be giving us a little bit of advice am i

6:53 am i correct for that

6:55 well yeah and that's what we're starting

6:56 to see right i mean we're starting to

6:58 see

6:58 systems now that can actually predict

7:02 hardware failures or find rma failures

7:04 inside the network right

7:05 you know so the days of you know where

7:07 you used to have to argue with your

7:09 vendor about you know whether or not

7:10 you're having a hardware software

7:11 problem

7:12 those days are going away because these

7:14 more intelligent systems they

7:16 know whether or not it's a hardware

7:17 problem software problem you know so

7:19 my transition from what i did 20 years

7:21 ago at airspace you know where

7:23 basically a customer would have to you

7:24 know send all bunch of information to

7:26 convince me it was a hardware problem

7:28 we are now we're sending rmas out right

7:30 you know customers don't have to wait

7:31 for me we can basically tell them yes we

7:33 know that you have a hardware problem

7:35 please send it back or other use cases

7:38 like bad cables

7:39 you know we've gotten down the point now

7:41 we can actually find these needle in the

7:42 haystack

7:43 bad cable problems where we're 90

7:47 or better accurate where i t departments

7:49 now i can say hey i trust you

7:50 you know don't don't bother me or if you

7:53 find a bad cable issues a support ticket

7:55 and and move on so those are kind of

7:57 cases where

7:58 we're starting to see ai and ml really

8:01 take on the this next level of

8:03 automation right

8:04 helping helping offload i t task onto

8:07 these ai solutions

8:09 so so what what is the name of the ai

8:10 ops solutions that actually can find a

8:13 bad cable

8:14 amid all those cables in in the data

8:16 center is there a specific solution that

8:17 does that

8:18 well i mean so so juniper we call it

8:20 marvis uh exactly

8:22 if you dig under the hood and actually

8:24 look at it you'll see something called

8:25 logistic regression

8:26 so that's kind of the actual ml

8:27 algorithm that's down

8:29 in the uh under the hood there is

8:31 actually making the determination

8:32 whether or not you have a bad cable or

8:33 not

8:35 interesting let's talk about uh the data

8:38 challenges what what data challenges

8:40 do organizations need to address in

8:43 order to quickly inform ai systems

8:45 because of course they need to be fed

8:46 the right the right data how do

8:49 companies do that

8:50 yeah and to be honest i mean like i said

8:52 back when we started mist right

8:54 i mean one of the questions when we

8:55 started this was you know should we

8:57 actually build an access point

8:59 or not right you know and when i made

9:01 the decision

9:02 back then was i wanted to build the

9:04 access point not because i thought the

9:05 market needed another

9:06 wireless access point it's because i

9:09 really want to make sure i can get the

9:10 data i need to answer a question

9:13 when i look at most enterprises right

9:15 now the mistake they're making is

9:18 they tend to focus all their energy on

9:20 trying to build big data lakes

9:22 data warehouses without really starting

9:24 with the question

9:25 right and so usually the advice i give

9:28 people is like hey if you're going to

9:29 work on an ai

9:30 ops ai type of problem you know you

9:33 really start with the question you're

9:34 trying to answer

9:35 you know in our case it was we're trying

9:37 to answer the question of why are you

9:38 having poor internet connectivity

9:41 and so that really starts with making

9:42 sure you get the data from the edge of

9:44 the network you know if you

9:45 start with that then it starts to drive

9:47 to make sure you get the data you need

9:49 to answer the question

9:50 you're trying to answer so the idea is

9:52 make make sure you know your question

9:54 you're sort of

9:55 guiding leading question as opposed to

9:56 merely buying technology

9:59 right or trying to pile data in a day

10:01 like right you know you see a lot of

10:03 enterprises so the first thing they try

10:04 to do is they try to take

10:05 every piece of data they have and start

10:06 you know you know sticking into splunk

10:09 or sticking to some big data warehouse

10:10 and then

10:11 then they find out they're not using 95

10:14 of the data never gets used for much of

10:16 anything right

10:18 it is amazing that the small amount of

10:21 data that is actually mined for

10:22 competitive advantage i mean that's i

10:23 think it's sort of the story of our of

10:25 our time is that

10:26 we have an ocean of data but we're using

10:28 just a small portion of it to really

10:30 navigate right and i'm going to add you

10:34 know if you look at the question right

10:36 you know as we've got you know what

10:38 we're doing now is we're starting to

10:39 expand

10:40 across different data sources right you

10:41 know when we started it was basically

10:43 getting data from access points to

10:46 answer the connectivity question

10:47 now we're getting data from the clients

10:49 you know their visibility of the network

10:51 we're getting data from the switches

10:52 and the routers you know and as you

10:54 start to get more data

10:56 it lets you answer questions more

10:57 questions like when we get data from the

10:59 sd-wan

11:00 router it starts to let us answer

11:01 questions about applications and

11:03 services

11:04 and we start to be able to answer

11:05 questions with more granularity

11:07 and then you can start doing proactively

11:09 fix it once you have data from the

11:10 switches

11:11 you know if we see a misconfigured vlan

11:14 we can actually practically correct that

11:15 without

11:16 bothering the i.t teams right right

11:20 you know we we talked about this a

11:21 little bit beforehand the idea of a

11:23 common telemetry

11:24 standard so what why does there need to

11:26 be a common telemetry

11:28 standard to make obtaining data for ai

11:29 easier why is that important in your

11:31 view

11:32 well i think it's important i think

11:33 that's one friction point we see in the

11:35 adoption of ai

11:37 is are we really going to get to a

11:38 common language where every vendor uses

11:41 the same you know

11:42 format for time um i think that's

11:45 probably a little hard

11:46 i think what is probably required in the

11:48 industry is if we get everyone to kind

11:49 of agree to how to store data away

11:52 you know when we look at you know what

11:53 question or what problem we're trying to

11:55 save

11:55 you know you may have to get data from

11:57 your access vendor right and you may

11:59 have to get data from your

12:00 lan router vendor and you may have to

12:02 get some data from your application

12:04 vendor

12:04 you're really to answer an end-to-end

12:06 type of question

12:08 so i think what's more important in the

12:09 industry right now is you know can we at

12:11 least get people to agree

12:12 on how they're going to store the data

12:14 way in these cloud solutions

12:16 because if you look where we're headed

12:17 right you know the industry is moving to

12:19 kind of

12:20 cloud first direction right we're almost

12:23 you know all the data you need is being

12:25 stored away you know

12:26 your different vendors are throwing this

12:28 data for you on your behalf

12:29 in different clouds so it's more

12:31 important about getting the data stored

12:32 away in some sort of descriptive format

12:34 that ai and bi can actually access and

12:37 understand how the data is being stored

12:40 is is it realistic to think that there

12:42 would be an industry standard in terms

12:43 of that or is it

12:44 is it inevitable or is it is it a long

12:46 shot in europe

12:48 i think it's an inevitable i think we're

12:49 in the early days of ai and so i think

12:51 people are just now fully

12:52 starting to appreciate the fact and i

12:54 think a lot of the aai

12:57 efforts are being kind of focused on

12:58 very narrow efforts where the data is

13:01 you know coming from one source or one

13:03 vendor you know so i think as you know

13:05 as ai expands especially iops and

13:07 network and expands

13:08 across networking we'll start to see the

13:10 industry move to more

13:12 industry standards like uh you know on

13:14 the switch side we moved to open config

13:16 right that was kind of a standard

13:18 effort to get the switches managed i

13:20 think so i do think start to see the

13:21 industry move to kind of more

13:23 standard ways of getting telemetry data

13:26 from different devices

13:27 especially on streaming right you know

13:29 pulling and pushing

13:30 you know streaming data yeah streaming

13:33 data you know you look at ai

13:35 uh you know in the past you know 20

13:37 years ago was all snmp right we were

13:39 basically pulling all these devices to

13:41 get stats

13:41 and information out of our devices as we

13:44 move into more of a real

13:45 time ai environment it's more about i

13:48 want things to be streamed to me in real

13:50 time i don't want to go pull things i

13:52 want to have telemetry come at me

13:54 so it's probably more important that we

13:55 get that standardized

13:57 as opposed to actually trying to

13:59 describe each little

14:00 field in the in the in the schema right

14:04 let's let's look to the future for a

14:05 second i i think specifically the future

14:07 not of overall ai necessarily but first

14:10 the future of ai apps in particular

14:13 you know when you look at the definition

14:14 of ai apps it's artificial intelligence

14:16 for it operations and that's really the

14:18 commonly accepted one i think gartner

14:19 made

14:20 that up in 2017. um

14:23 but i think about this obviously going

14:25 to be an ai platform that runs

14:28 the entire enterprise from sales to it

14:32 to you know hr and it's all going to be

14:34 one platform so really the idea of ai

14:36 ops which

14:37 you know our ai for it operations

14:40 is going to be consumed into a larger ai

14:44 framework true false or what's your view

14:47 on that

14:48 well i mean i think there's a lot of

14:49 concepts here you know one of them is

14:51 this ai for me personally like i said ai

14:54 is really about

14:55 trying to do something on par with a

14:56 human you know whether it's

14:58 driving cars or detecting cancer and

15:00 medically

15:01 for networking it's more about can we

15:03 build something that does something on

15:04 par with network domain experts

15:06 you know and i think the future is you

15:08 know and my common things i think

15:10 you and i probably grew up i don't know

15:11 if you remember watching star trek when

15:13 you were a kid right i do

15:14 yep definitely you know in every almost

15:16 every technology that was in star trek

15:19 30 40 years ago has now separated

15:22 except for the transporter right the

15:23 transporters i'm not sure we're gonna

15:25 live to see that

15:26 yeah computer remember the computers

15:28 like computer tell me

15:29 oh definitely yeah no it's and it's here

15:31 yeah yeah so i think we are gonna see

15:33 that in our future where

15:35 you know network it teams are gonna have

15:37 an ai assistant on their team

15:40 right that's going to make it easier for

15:42 them to diagnose

15:43 and run and maintain these networks just

15:46 like we saw on star trek right

15:47 and this is what i call the transition

15:49 you know to conversational interfaces

15:52 you know you can you kind of remember

15:54 the industry you know

15:55 when you're younger it's all about cli's

15:57 right we're basically programming and

15:58 maintaining all these

15:59 switches and routers you know i mean cli

16:02 jockeys you know

16:03 and then we kind of move to the

16:05 dashboard the gui dashboards for running

16:07 and manage our networks

16:08 right i think we're now in that

16:09 transition to conversational interfaces

16:12 are just a much easier way for network i

16:14 t

16:15 teams to actually manage and extract

16:17 data

16:18 and visibility on other networks so i

16:20 think that's kind of the future of

16:22 what i call ai in networking is the

16:24 virtual system think star track then

16:25 computer

16:26 you know you're going to start to see

16:28 virtual systems become a key element

16:30 in most of these network i.t teams in

16:31 the future so that means that

16:34 that nlp will be really at the center of

16:37 it all so that the i.t

16:38 expert can speak to his or her machine

16:40 yeah nlp is a great example right now of

16:42 these conversations and that that's an

16:44 area that's changing as we speak

16:46 right now i mean i can tell you there

16:48 are things that we can do now with the

16:49 conversational interfaces that we

16:50 couldn't do two years ago right

16:52 you know so that technology is evolving

16:54 you know it's still evolving right now

16:56 right so so what about the future of ai

17:00 ops

17:00 specifically if you look at this you

17:03 know

17:04 they're expecting something like 15 to

17:05 20 percent annual growth in in revenues

17:08 from about 17 billion now at about 40

17:10 billion in the year 2026

17:12 based on i think at idc numbers what do

17:15 you see

17:16 we the future of ai up specifically

17:20 going forward

17:21 yeah so specifically you know in in the

17:23 short term i think right now we're gonna

17:25 see you know as i said data you know

17:29 data is gonna be the i think it's gonna

17:30 happen in phases and data is probably

17:32 the first phase

17:34 you know where enterprises are working

17:35 on making sure they can get the data

17:37 that they need to solve some aips

17:39 problem right

17:40 and as we success earlier right so you

17:43 know i make a barrel of wine every year

17:45 and you know names red wine or white

17:47 wine red wine

17:48 yeah okay yeah so but uh you know same

17:52 saying right

17:53 great wine starts with great grapes

17:54 great the great data

17:56 and so that's probably the first phase

17:58 of the journey that most people are

17:59 going to get on is get that down

18:01 and then you gotta really get that data

18:02 into a framework that you can actually

18:04 start deploying

18:05 ml to and that's kind of real-time

18:07 pipeline

18:08 you know so that's kind of the future i

18:10 see the journey

18:12 uh onto the adoption of ai ops into

18:14 ultimately

18:15 you know before we before i retire i

18:18 suspect we're going to start to see

18:20 these virtual ai assistants become a

18:22 mainstream of uh

18:23 the i.t teams of the future yeah it

18:26 doesn't seem like that

18:27 particular one is too far away actually

18:29 that could be in the next couple two

18:31 three years i would think

18:32 yeah before before i retire right

18:35 okay good uh bob i think you've said it

18:38 there's a lot more to say but i think

18:39 that's

18:39 that's we're good for today i greatly

18:42 appreciate you sharing expertise with us

18:44 thank you very much yeah great to be

18:46 here james

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