Dr. Sally Eaves, Senior Policy Advisor at Global Foundation for Cyber Studies & Research

AI Skeptics: Is AI Going to Take Your Job?

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An image of the title slide with the title, “Is AI Going to Take My Job?” in white text on a green background. The Juniper logo is at the top. 

No, AI is not coming for your job.  

AI isn’t going to take your job away, but it will make your job better. In this AI Skeptics event hosted by Juniper, a panel of experts busts some of the myths and misinformation around AI, particularly focusing on networking. 

Show more

You’ll learn

  • The fundamentals of AI: what it is, what jobs it can do, and how it is changing our work and roles 

  • The panel’s thoughts on the value AI is going to bring to IT ops and networking organizations 

  • The impact and implications of AI on skills and education 

Who is this for?

Network Professionals Business Leaders

Host

Sally Eaves Headshot
Dr. Sally Eaves
Senior Policy Advisor at Global Foundation for Cyber Studies & Research 

Guest speakers

Darryl Alder Headshot
Darryl Alder
Network Architect, Aston Martin Lagonda, Ltd.
Shamus McGillicuddy Headshot
Shamus McGillicuddy
VP Research, Enterprise Management Associates (EMA)
Tom Hollingsworth headshot
Tom Hollingsworth
Networking Analyst, Foskett Services
Transcript

0:00 [Music]

0:06 hi everyone i'm dr sally eaves chair of

0:09 global cyber trust at cf cyber and ceo

0:12 of aspirational futures a huge warm

0:14 welcome to this juniper networks ai

0:16 skeptics event is ai going to take my

0:19 job we're here to bust some of the myths

0:22 and miss some misinformation around ai

0:24 particularly focusing on implementations

0:26 within ai and networking to do so i'm in

0:29 great company so first of all i'm going

0:31 to ask our stellar panel just give a

0:33 brief intro into their role darryl can i

0:35 start with you first yeah hi sally great

0:37 to be here um my name's daryl i work for

0:40 aston martin as the league network

0:42 architect and the team leader i'm in

0:45 charge of all the wired and wireless

0:47 connectivity right the way from your

0:49 desk all the way up to the main main

0:51 site so uh yeah that's me brilliant

0:54 stuff thank you baby sheamus over to you

0:56 next

0:56 hi my name is james mcgillicuddy i'm the

0:58 vice president of research and

1:00 enterprise management associates an

1:01 analyst firm and based in the united

1:03 states and

1:05 i've i do a lot of market research and

1:08 consulting

1:09 on

1:10 network infrastructure and operations

1:12 teams how they design build and operate

1:14 their networks brilliant thank you so

1:16 much for joining us today and next week

1:17 to you tom

1:19 my name is tom hollingsworth and i am an

1:21 independent analyst in the networking

1:23 community i work with a lot of other

1:25 independent analysts through an event

1:27 series called tech field day

1:29 but i do a lot of research into emerging

1:31 technologies such as artificial

1:33 intelligence you can see some of my

1:35 thoughts on that subject at my blog at

1:37 networkingnerd.net fantastic that's

1:39 brilliant i've attended one of those

1:40 days as well really really enjoyable so

1:42 thank you for that so perhaps we can get

1:44 straight in now to some of our key

1:46 questions and maybe let's start first

1:48 was the fundamentals so looking at what

1:50 ai is what jobs it can do within it and

1:53 networking and changing changing the

1:55 work that we do and has it changed you

1:57 and your roles and your organizations so

1:59 far now for me i'll come at this quickly

2:01 from two hats so one is a cto by

2:04 practice but also as a change manager as

2:06 well so particularly in telecoms i've

2:07 seen this in kind of three main pillars

2:09 so optimizing user experiences

2:12 simplifying operations and also this

2:13 move from reactive to more active

2:15 intelligence and end-to-end insights if

2:17 you will so i'd love to kind of go

2:18 around the table and kind of explain

2:20 those key fundamentals to your audience

2:22 today perhaps gerald can i go to you

2:24 first

2:25 yeah sure um so ai for us we're still

2:27 quite new into evaluating ai and we're

2:30 starting to deploy that into our

2:31 infrastructure

2:33 ai for us is not about kind of replacing

2:36 anyone it's about and this is a great

2:38 quote i heard the other day in a

2:39 training course ai is not about

2:42 replacing humans with robots it's about

2:45 taking the robot out of the human um and

2:48 it leaves you to get on with things like

2:50 strategy and high level things that the

2:52 business can can value i i use the

2:55 analogy of uh of a car then we we have

2:58 car sat navs um satnav's never replaced

3:02 the driver it just gave you a tool to be

3:05 a better uh more alert driver um so i

3:09 think that's uh that's how we should

3:11 view ai at the moment so yeah i love it

3:13 it's that complimentary partnership

3:14 coming to the forum what you were

3:16 discussing there i love that i love it a

3:17 good analogy that's great daryl thank

3:19 you and tom could i ask for your

3:20 perspective well i actually want to jump

3:22 on daryl's analogy there because one of

3:24 the things that i think that ai has been

3:26 very critical in doing is helping us

3:29 understand

3:30 how to modify systems and how to kind of

3:33 roll with the punches if you will part

3:35 of what we have spent so many years

3:37 doing in the networking space is kind of

3:40 dealing with the little fires that pop

3:42 up and if you think of it kind of using

3:45 daryl's analogy as a sat nav

3:47 i remember the days when satnav was

3:49 effectively going online

3:51 and downloading a list of directions

3:53 from a website like mapquest and lord

3:55 help you if you took the wrong turn at

3:57 step four because from that point

3:59 forward you were completely lost but a

4:02 sat nav not only gives you the overview

4:04 of what you're trying to accomplish but

4:05 it can course correct in the middle of

4:07 your journey so if for example there is

4:10 a car accident that you must

4:12 route around or in modern sat nav with

4:16 capabilities to include more

4:18 intelligence if there is a congested

4:21 route that has become congested since

4:23 the start of your journey it can

4:25 automatically reroute you based on

4:27 certain factors like least amount of

4:29 time traveled or

4:30 whether or not you're going to have to

4:31 pay a toll on the road so it gives you

4:33 this capability to be smarter about what

4:36 you're doing instead of just relying on

4:38 the same old tried and true directions

4:40 that we've been following for a number

4:42 of years i love us great example also

4:44 what i love there is the ability to

4:46 personalize to what matters to you

4:48 whether that's that cost of the toll or

4:50 the shorter journey or environmental

4:52 impact even as well it's a great example

4:54 and james did you have any final

4:55 thoughts on that yourself

4:57 yeah sure um one thing

4:59 from my perspective one thing i see is a

5:01 lot of network operations tools and the

5:03 network monitoring network performance

5:05 management tools they excel at uh

5:07 presenting data to

5:10 admins engineers

5:12 they don't always

5:13 do a very good job of providing insights

5:15 they they point you to indicators of a

5:18 problem don't really tell you what the

5:19 problem might be

5:21 all the time

5:23 ai technologies are

5:26 sort of providing

5:27 those first

5:29 intelligent insights into the data that

5:31 those tools

5:33 typically provide

5:34 absolutely excellent thank you so much

5:36 for sharing that so let's go back to the

5:38 heart of our kind of question our title

5:40 for our event today is ai going to take

5:42 my job let's drill into that for the

5:44 audience because again it's something

5:46 that's grabbed a lot of headlines over

5:47 recent years although i think the

5:48 narrative is changing certainly for me

5:50 it's not about this replacement it's

5:52 that complementary strengths i mentioned

5:53 at top and also robots kind of taking

5:56 things out of humans for example and you

5:58 know making more time for meaningful

5:59 activity so shame if i could go back to

6:01 you and some of your research in this

6:02 area yes um i uh did some market

6:06 research on

6:07 network operations team interest in uh

6:10 ai uh last summer we surveyed a few

6:13 hundred

6:14 people who are actively engaged in using

6:17 this technology there wasn't a lot of

6:19 concern about

6:21 losing your job uh

6:23 there's a lot of

6:25 there was a sense that this can deliver

6:27 a lot of value to to make things run

6:30 better like one of the one of the early

6:32 use cases we were seeing uh

6:34 with a lot of traction was just the idea

6:37 of

6:38 more intelligent alerting and

6:39 escalations of events so that um

6:44 the alerts um connect the dots uh you

6:47 know there's a lot of contextualization

6:49 of data in those alerts um

6:52 more so than you may have seen in the

6:53 past and and also

6:55 uh the system gets smarter about routing

6:58 it to the right person that has the

6:59 expertise to take action on those alerts

7:02 although

7:03 when the alerts get uh smarter uh and

7:06 more contextualized uh people with less

7:08 sophisticated engineering skills can

7:09 actually take action more often so it

7:12 empowers people with with lower skills a

7:14 lot of the time that typically would

7:16 have just escalated something to a tier

7:18 three engineer rather than trying to

7:20 deal with it themselves so that's one

7:22 big thing that we saw in our research

7:25 fantastic really interesting i love that

7:26 one around that democratization of the

7:28 ability to take agency and to take

7:30 action um to more and more roles that's

7:32 really really interesting thank you and

7:34 daryl what are you seeing

7:36 yeah so um i can take that a step

7:38 further and and go on from what shane's

7:40 just saying there in some real world uh

7:42 uh examples there we've been deploying

7:44 and testing the the ai platform at the

7:47 moment um and it's um it's starting to

7:49 identify things that we might not have

7:51 known about and one particular one is uh

7:54 bad cable about network cable now

7:57 historically you wouldn't have

7:59 necessarily known that straight away um

8:01 it might manifest itself in a site is

8:05 you know poor performance or something

8:07 um but in order to identify that you

8:09 need to trawl through an awful dollar

8:11 logs and look at errors on interfaces

8:14 and and things like this so ai can pull

8:17 all that log files and alerting all into

8:20 one place and say hey you know what i i

8:23 think this is a bad cable on this

8:24 particular interface here um you might

8:27 want to look at this and so it goes back

8:29 to that is it going to replace me and

8:31 the answer to that is is absolutely no

8:34 you you find yourself between two

8:36 extremes at the moment and this is where

8:38 i see things going on the one extreme

8:40 you've got oh we're getting an alert for

8:42 bad cable we'll ignore it because people

8:45 turn off their pcs all the time on the

8:48 other extreme we'll get oh it's a bad

8:50 cable i'm going to go and replace it

8:52 because the ai told me to that cable

8:54 might be the uplink for the site and so

8:57 it's kind of like an extra pair of hands

8:58 just guiding you in that direction to

9:00 say hey you know what you might want to

9:02 go and have a look at this and that

9:04 saves you an awful lot of

9:05 troubleshooting because it's already

9:07 correlated that uh that data so at the

9:10 moment we're seeing ourselves in in that

9:12 balance and um it's definitely going the

9:13 right way so it's really promising at

9:15 the moment

9:16 i love that almost takes us back to that

9:18 analogy and sat nav earlier doesn't it

9:20 you've got a myriad of paths and it's

9:21 filtering out that noise into what's the

9:23 optimum direction and path to take so i

9:25 really like that excellent and tom what

9:26 are you seeing there particularly around

9:28 that kind of single source of truth

9:29 aspect i think it's really interesting

9:31 well it's interesting that a lot of

9:33 people have have looked at this ideal of

9:35 having a single source of truth for your

9:37 network but if you asked most engineers

9:39 or operations teams today what is the

9:42 state of your network they would go to

9:44 some kind of a design document and say

9:46 oh well this is what it looks like and

9:48 my first question when someone does that

9:50 is okay now what does the network

9:52 actually look like

9:53 and that usually throws them for a loop

9:54 because well no no it's supposed to be

9:56 like this and i'm like

9:57 von mulkey said that no plan survives

9:59 contact with the enemy well no network

10:01 configuration survives implementation

10:03 and so there's even if there's a little

10:05 bit of a disconnect between what you

10:07 intended to put out and what was

10:09 actually configured those two

10:11 disconnects create two sources of truth

10:14 desired state and actual state

10:17 and nobody wants to do the investigation

10:20 work to find out what actual state is

10:22 there's a quote that can be attributed

10:23 to kurt vonnegut it's a flawed human

10:25 nature that everybody wants to build but

10:26 nobody wants to do maintenance and

10:28 that's effectively what this is is going

10:30 out to figure out what's going on so

10:32 what you can use an ai platform to do is

10:35 to find out what current state is

10:37 implementation

10:38 and compare it to desired state and to

10:41 try to find out where the disconnect

10:43 happened in those two was it

10:45 unintentional i copied a configuration

10:47 to a switch or i had some kind of a

10:49 staging server that had an error in the

10:50 middle of deployment and now suddenly

10:53 the switch thinks it's the spanning tree

10:54 root for the whole network or was it

10:56 intentional where you have someone who

10:58 has a lot more skill than they think

11:02 they do

11:03 and says oh well i know that every time

11:04 i try to put this on that particular

11:06 model of switch it fails so i'm going to

11:08 modify this command just a little bit so

11:10 that what actually goes under the switch

11:11 works

11:12 even if the intention was pure what

11:15 you've created is a disconnect in state

11:17 where a command later on or some kind of

11:19 a desired uh deployment could fail

11:22 because of something you had no clue

11:24 about what happened because of the

11:26 disconnect between those two and

11:28 part of the reason why humans don't like

11:30 this is because maintenance is boring

11:32 it's mind-numbing

11:33 and an ai doesn't sleep it doesn't get

11:37 tired it will not stop until it does

11:39 exactly what it's been told to do

11:41 and what it returns is the truth as it

11:44 sees it it is not trying to save its job

11:47 it is not trying to get a promotion it's

11:49 not trying to make somebody else look

11:50 good it is telling you what it found so

11:53 you can trust it you may not inherently

11:56 believe it but you can trust that the

11:58 answers that it's returned are accurate

12:01 i love that it's a really really good

12:02 example there and sheamus can i bring

12:04 you back in as well because i know we

12:06 spoke previously you were drilling in a

12:08 bit more as about how it helps find out

12:10 the how you know how things are broken

12:12 for example not just how to fix

12:13 something that's a really interesting

12:14 take as well

12:15 i remember having a conversation with um

12:18 a network operations manager a couple

12:21 years ago who he told me like he might

12:23 have 7 000

12:25 interfaces down on his network he only

12:28 cares about the ones that matter um

12:30 if there's applications that are trying

12:32 to run over there's interfaces he wants

12:33 to fix somebody it points to the fact

12:35 that um a lot of

12:37 tools that network operations teams use

12:39 do not provide actionable insights uh

12:41 typical enterprise

12:43 tells in our research a typical net ops

12:46 team less than half of alerts being

12:49 pumped out by their tools are actionable

12:51 like indicators of actual problems and

12:53 so there's a lot of noise that needs to

12:55 be sorted through first before you even

12:57 try to fix something to figure out if

12:59 it's actually

13:00 broken if there's actually something

13:02 broken that needs to be fixed

13:04 and then yeah once once you figure out

13:06 something's broken you need to know

13:08 how it broke uh not just how

13:11 how to fix it um

13:13 because you don't want it to break again

13:14 um

13:15 so

13:17 you know you have to be thinking about

13:18 proactive problem prevention and

13:20 optimization as you're troubleshooting

13:22 ai can sort of help you do that because

13:24 ai kind of connects the dots um look at

13:26 how the network is looking at how the

13:28 network is serving the business and

13:30 how to optimize that it will open up the

13:32 opportunity for more problem solving

13:35 that that improves how it improves the

13:37 network's ability to serve the business

13:39 not just fighting fires but but um

13:41 preventing those fires from ever

13:44 happening over the last like 10 years

13:46 our research has shown that

13:49 between 30

13:51 30 and 40 of all it service degradations

13:55 are detected by end users before it

13:57 operations is aware of them

13:59 that means they're reporting them to the

14:01 help desk and they're not being not

14:03 being productive because of those

14:05 problems

14:06 to some extent

14:07 before net ops is even responding uh you

14:11 need to you need to turn that around you

14:12 need to get that closer to as close to

14:14 zero as possible i think

14:16 ai can can help you get there to some

14:18 extent

14:20 absolutely there's a key point there i

14:21 think two pillars of what you were

14:23 saying really struck me a that move to

14:25 be more proactive as you were just

14:26 talking about there that active

14:28 intelligence i said at the top but also

14:30 the ability to filter out the noise and

14:32 one thing i read some research i've been

14:34 involved in it was looking at for

14:35 example burnout in a lot of operations

14:37 team and that was one of the biggest

14:38 contributors to that and also to churn

14:40 as well so again making a huge

14:42 difference in that respect as well so

14:44 really interesting points all around

14:45 there fantastic

14:46 and perhaps we can now drill into that

14:48 impact point so around your

14:50 organizations what have you seen ai

14:52 delivery in particular how have you been

14:54 able to demonstrate that value across

14:55 the organization and what other

14:57 implications is this brought about

14:58 particularly around skills and education

15:00 which implicit first acknowledged that's

15:02 a real strong point area for me i really

15:04 love looking at that area too so perhaps

15:06 we can bring all those points together

15:07 look at the impact and the implications

15:09 and perhaps shameless if i could go back

15:10 to you first well as you're bringing ai

15:12 in the organization uh one thing that

15:16 you're going to see is um

15:19 many

15:20 people in your your it ops organization

15:23 are

15:24 not confident in their ability to

15:25 evaluate ai they have multiple vendors

15:28 telling them that they've developed ai

15:30 technology machine learning technology

15:32 that can transform operations they don't

15:34 know how to

15:36 determine whether

15:38 what they're being sold is is just a

15:40 bill of goods or not

15:42 um

15:43 they also want to know that there's a

15:46 variety

15:47 of um of network data that's being used

15:50 to train those algorithms like are the

15:52 is the is the training that that this

15:55 vendor

15:56 does with its algorithms going to you

15:59 know be representative of what i see in

16:00 my network like are they are they are

16:03 they training this in a realistic way so

16:04 it's actually going to understand

16:05 networking um so that's uh something

16:08 that we've seen uh early on

16:11 those are the types of things that they

16:12 want to see i've also heard people say

16:14 you know i want to just get past the

16:15 like people specifically told me like

16:17 architects and

16:18 fortune 500 companies

16:20 fortune 100 companies

16:22 who say there's a lot of marketing

16:25 to speak a lot of buzzwords going around

16:28 they just want to know how you're

16:30 solving problems like how you solving

16:31 the problems that i have can you show me

16:33 examples of that so that i know like

16:36 what this can do that's something that

16:38 they want to be able to take back to

16:39 their organization be like hey look what

16:40 i saw you know this is this i think this

16:42 is something that we could use to really

16:44 uh change the way we we manage our

16:46 network here

16:48 at those tangibles those examples so so

16:50 important make it relatable to that

16:52 particular sector and that clarification

16:54 you're spot-on around language i think

16:56 zero trust would be a classic example of

16:58 that uh doing something on that earlier

17:00 on today and again that's somewhere

17:01 where there's quite a lot of confusion

17:02 so getting that right and making sure

17:04 there's not misinformation absolutely

17:06 critical as well so i love that tangible

17:08 focus and tom what do you see in your

17:10 area

17:11 so a lot of the unease about deploying

17:13 ai within an organization comes from two

17:15 different areas the first is that it's

17:17 going to take my job away and and we've

17:19 kind of talked a little bit about that

17:20 throughout this whole thing but more

17:22 importantly there are a lot of people

17:24 that don't feel comfortable with the

17:25 idea that an ai is going to be able to

17:28 determine what the source of your

17:29 problem is instantaneously and give you

17:31 a recommended fix it kind of eliminates

17:34 that that kind of feeling that we get

17:36 when we we figure out a really hard

17:37 problem like working in sudoku or trying

17:40 to figure out a puzzle and we get that

17:41 rush of endorphins when hey this is the

17:43 the secret and i figured something out

17:46 to combat that second problem um you

17:49 kind of have to get into a mode where

17:51 you're you're communicating more

17:52 effectively and you're using ai as a

17:55 tool to do that how many times has your

17:58 average engineer ops person kind of been

18:00 under the crunch because there's a huge

18:02 problem that we need to fix and the

18:03 executives are frustrated and something

18:05 needs to happen and you're sitting here

18:07 literally racking your brain trying to

18:09 find out what the source of the problem

18:10 can be eliminating choices one at a time

18:14 to them you look like an ai because you

18:16 are doing the same kind of learning and

18:18 analysis that an ai would do to spit out

18:21 an answer they trust you because you're

18:23 a living breathing person we should

18:25 trust ai in the same way we should still

18:27 communicate we should still tell people

18:29 what happened and and more importantly

18:31 even if ai is the solution that

18:34 solve that problem we do need to tell

18:36 people that ai came up with that um we

18:38 in my old job as a managed service

18:40 provider we used to have a saying on the

18:42 the wall if you didn't tell your

18:43 customers that you fixed something did

18:45 you actually fix anything in a way users

18:47 need the same kind of reassurance inside

18:49 of your organization

18:51 but the other thing that is very

18:52 important is that people need to look at

18:53 ai not as a way for them to have their

18:56 jobs taken away

18:57 but it will remove tasks from your jobs

19:00 kind of like daryl said we're taking the

19:01 robot out of the person think about all

19:03 the things that you do on an average day

19:05 that are just repetitive and boring and

19:07 mind-numbing but necessary that having

19:11 that taken off of your plate having that

19:12 given to something that will not stop

19:15 will always accomplish the results and

19:16 send you a report telling you what it

19:18 did frees you up to think about other

19:21 things to spend more time building and

19:23 imagining and creating instead of doing

19:25 maintenance and i think that that

19:27 framing helps people understand you're

19:30 not going to lose your job in fact

19:31 you're probably going to get effective a

19:33 promotion to something more exciting and

19:36 new

19:37 and that's a good thing overall

19:39 absolutely it's that changing of the

19:41 narrative isn't it and the word

19:42 enablement is kind of ringing through my

19:43 eyes when we were describing these

19:45 different aspects around around this

19:46 today so couldn't agree more strongly

19:48 tom absolutely daryl did you have any

19:50 final thoughts on this around that value

19:53 also the skills implications of this

19:54 piece too sure yeah i mean we're

19:57 starting off quite small with ai at the

20:00 moment we are a small team and so we

20:02 can't bring in something in big bang

20:04 because you know there's a there's only

20:06 there's only three of us now um so we're

20:08 starting off very small where we see a

20:10 little bit of not so much resistance but

20:12 um our operations teams uh our service

20:15 desk network operations and so on um

20:18 they don't um they don't dislike ai they

20:21 think you know everyone kind of accepts

20:23 okay this is a great tool where we do

20:26 find um maybe a little bit of pushback

20:28 is with management and they say oh hang

20:30 on a minute is this is this just another

20:32 one of the toys that it's buying um so

20:35 we kind of say well actually no look

20:37 what we can do

20:39 before i had to count you know how many

20:42 mac addresses were on the network or how

20:44 many ip addresses were in use now i can

20:47 just ask

20:48 a high-level like language question like

20:51 how many users are on this site and it

20:54 gives me an answer and so management is

20:56 sort of seeing this and i'm demoing this

20:58 very informally it's almost like a

21:01 oh hey while you're here just have a

21:03 look at this you know it's just kind of

21:04 just as somebody passes by and uh and

21:07 we're seeing a lot of uh value from that

21:09 so um just having that very small um

21:11 focused uh uh try not so much trial but

21:15 um small focused deployment and growing

21:17 from there is uh it's hugely beneficial

21:20 yeah i really like that i like the fact

21:21 you're bringing to the floor as well

21:22 this could be an incremental change as

21:24 well so i love that that's really

21:26 important for a lot of businesses so

21:27 that's a great point thank you for

21:28 sharing that and with my telco hat on as

21:30 well certainly things that i've seen up

21:32 close and personal is around you know

21:33 faster problem solution that resolution

21:36 um fewer on-site visits for example as

21:38 well so you've got that big operator

21:39 benefit and equally from the consumer

21:41 you've got a better reliability

21:42 measurability huge point as well

21:44 unpredictability too so again that kind

21:46 of shared value proposition through ai

21:49 so perhaps as we start to bring all

21:50 these different elements to a close we

21:52 do a bit of a round table again and kind

21:54 of just bring each of you like a closing

21:56 thought to take away to share with the

21:58 audience it could be something that's

21:59 come out from this conversation or kind

22:00 of a top tip that might be relevant for

22:02 them wherever they are in their journey

22:04 towards implementing ai at the moment

22:05 and sheamus got to go for you first

22:08 one thing is um

22:09 most network operations teams have too

22:12 many tools um

22:14 they're using 10 15 20 tools monitor and

22:17 troubleshoot their infrastructure

22:20 ai

22:21 can

22:22 consolidate that i think quite a quite a

22:25 bit they can make some tools less

22:26 relevant they may not go away but that

22:28 you know the insight you're getting out

22:29 of the ai will

22:31 will lead to your people spending less

22:33 time going from tool to tool to tool to

22:35 get the answers they need because the

22:36 answers are in the ai

22:38 solution um depending on where it lives

22:40 i've heard from people saying i'd love a

22:42 ai solution just pulls all the data from

22:44 all my tools and presents it as a single

22:46 view with insight um

22:48 i think and once

22:50 once you see that um you see the value

22:53 um i also find you know people spend a

22:56 lot of time

22:58 just generating reports you know like

23:00 hey we got to audit this we need to know

23:02 what you know what what's what

23:03 is everything in compliance you know

23:05 they're golden configs

23:06 like i talked to people recently who who

23:09 devote 10 15 20 hours a week

23:11 generating reports

23:13 uh

23:14 i think

23:15 an intelligent solution can can optimize

23:17 that so

23:19 if you feel when you're doing stuff like

23:21 that like swiveling from tool to tool

23:23 the tool or generating reports over and

23:25 over again

23:26 not really delivering a lot of value to

23:27 the business like you're spending most

23:29 of your time just

23:31 doing repetitive tasks as everyone's

23:33 been talking about here you're not

23:34 demonstrating your value and when i'm

23:36 trying to understand the return on

23:38 investment in the tool like an ai

23:40 solution it's about

23:42 freeing up labor and allowing that labor

23:44 to do something that's more important to

23:46 the business

23:47 and that's what i think people should be

23:49 thinking about as they're looking at ai

23:51 is like how do i take my people who are

23:54 really knowledgeable and skilled and

23:56 empower them to support transformation

23:59 of our business not not keeping the

24:00 lights on but like you know spreading

24:02 the light elsewhere

24:04 i really like that bring up that

24:05 capacity for higher order work against

24:07 that changing of the narrative isn't it

24:09 so so important thank you so much and

24:11 tom over to you for your closing

24:12 thoughts

24:13 i really like a lot of the things that

24:14 sheamus said and i think that one of the

24:16 reasons why we kind of have this tool

24:20 aversion is because the tool is the

24:22 solution to whatever problem we have

24:24 so every time we come into a new issue

24:26 that we need to solve we we just go buy

24:29 something and make the problem go away

24:31 for a little while longer

24:32 and i think that people are looking at

24:34 ai like that oh great this is just

24:36 another tool that goes into the box that

24:37 i never use

24:39 change the thinking about what you're

24:40 looking to use ai for you're not buying

24:43 a tool you're hiring someone no it may

24:46 not be a flesh and blood person but you

24:48 are going to invest training in that

24:49 person you are going to give them tasks

24:52 and job roles that will multiply the

24:55 ability of your other

24:57 assets to get things done if you look at

25:00 it that way if you look at it as you are

25:03 hiring someone then you'll appropriately

25:05 invest in that person you'll give them

25:08 the capabilities they need to accomplish

25:10 the tasks and roles and responsibilities

25:12 and kind of like sheamus said then that

25:14 elevates the rest of the team because

25:16 now they're not spending hours of their

25:18 day tracing down log messages or

25:21 generating reports for the executives i

25:24 mean with a properly configured ai the

25:26 executives can request the report

25:27 whenever they like to see it and they

25:29 can verify that the information that's

25:31 in the report is good information that

25:34 they wanted and those are the kinds of

25:36 things that give you the capability to

25:39 spend more time doing more impactful

25:41 things generating revenue being more of

25:44 an asset to the company in the bottom

25:46 line as opposed to just doing the

25:48 regular maintenance task that nobody

25:50 wants to do

25:52 absolutely very well seth thank you so

25:54 much and daryl over to you for final

25:56 thoughts

25:57 great thanks a lot i can carry on from

25:58 what tom is saying there really i mean

26:00 i'm always telling the uh the operations

26:02 uh staff that uh progress is progress

26:05 whether you're fixing a vault or

26:07 deploying something progress is progress

26:10 sometimes it's small progress sometimes

26:11 it's big ai is not going to fix

26:14 everything all at once

26:16 and so i say

26:18 on tom's note there about think of it as

26:21 hiring another person it's higher in

26:22 that pair of hands to just progress your

26:24 fault along the right lines you don't

26:27 you're not throwing it all against the

26:28 wall and seeing what sticks ai really

26:31 focuses you and it just progresses you

26:33 forward in that incremental step that we

26:34 talked about earlier i don't mind if a

26:36 fault takes a long time to fix as long

26:38 as there's progress in that in that way

26:41 so then i can report to management and

26:43 say

26:44 it is taking a while but we're seeing

26:45 this progress and i think that's

26:46 definitely what ai is bringing us at the

26:48 moment

26:49 absolutely fantastic i think we brought

26:50 in you know a lot of themes for the four

26:52 here you don't need to know everything

26:54 you know all the plumbing so to speak

26:56 it's how you can work with ai it's

26:58 changing this narrative is this

26:59 complementary strengths aspect and going

27:02 from menial tasks to the meaningful how

27:04 we can get that higher order works i

27:06 think so many takeaways here a lot of

27:08 practical examples as well how we're

27:10 seeing organizations at different stages

27:12 using this within itn network so

27:14 fantastic thank you all three of you so

27:16 shameless tom darrell thank you so much

27:18 for joining us today and thank you all

27:20 for joining our webinar as well it's

27:21 been fantastic and a lovely discussion i

27:23 think one we can continue

27:25 great thank you very much

27:27 [Music]

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