Is the water industry ready for AI?

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Nexus, Publish By GHD.

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Where ideas connect.

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Collaboration is

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important when you're going through this

AI transformation.

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You need an ecosystem of partners.

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Technology partners bring in innovation.

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The regulators, you know, ensure

compliance and public trust.

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But more importantly,

you know, City's interfaces

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with with other utilities

in Texas and across North America

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because they can share lessons

learned and help scale

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smart systems

across the entire water sector.

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That's Freddy Guerra,

digital water market lead for GHD.

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In this episode, we dive in for a clear

look at the value AI can deliver

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and what's needed for its responsible

and ethical integration

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into water management.

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Joining Divya and Freddy is Satish

Tripathy, managing engineer at City of

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Houston, as they explore what it will take

for the water industry to be AI ready.

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Divya begins by asking Satish

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what are the essential components

of AI readiness assessments?

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We are in this water sector.

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Water industry is in the early stages.

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I would like to say Defender East is where

people have a lot of, excited winds.

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They want to trade.

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They have a lot of expectation

and also fear.

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I think that is where we are.

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We are in a fantasy state.

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Is in comparison to the other industry

like finance, health.

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We are a little bit behind

because they are in advanced

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is usually water utility sector.

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This is a risk averse sector

and we have a legacy data data

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infrastructure and do not have the right,

formatted data source.

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So that is where,

our industry is struggling right now.

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But again, we are in a very early stage,

but there are lots of promising sign

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that people are showing interest on,

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how can we integrate this into our life?

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That is a very positive sign.

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So let's go back to the city's journey.

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How did it all start?

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I know there's some exciting things

happening, like the Digital Twin project.

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Could you tell us more about that?

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Of course.

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Houston is a coastal city

and is First City as well.

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So being a coastal city

since last ten, 15 years,

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we have been facing a lot of,

weather challenges.

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What happened is when there is a 500 year

heat and events like drought, flood,

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hurricane, winds from then freeze

everything one after another, every year.

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Then your focus goes to the emergency

support, then the plan program.

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So that's what we faced

in the last 15 years.

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And that exposed our capability

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to be resilient

and to be more sustainable.

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And how can we support

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better to our customer for the now need

and for the future need?

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We saw that opportunity

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to bring technology to use in our decision

making process to help

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during the emergency preparedness, during

the events or for the future planning.

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That's when we identified

that we need technology like digital twin

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AI to bring into our decision

making process.

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That's how we recognize the need.

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And since then,

we started working on the data.

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Data is the basis of everything.

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So we identified

most frequent data in our area.

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Which data set

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we need the most frequently to be used

in our decision making process.

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We built data catalog

which allowed us the how

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we transform data

to make it usable for the data analytics.

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That's how we started,

first working on the data

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and then we identified our low

hanging fruit.

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Where can we first attack and get the,

high ROI runoff investment?

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Working on that.

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And then we started demonstrating

those in-house.

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We were successful,

quite successful on that.

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And when we we use

some of these technology for decision

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making, like five failure

prediction, developing our smart ship,

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capital improvement projects,

then people started

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looking at our recommendation and

they started trusting on this technology.

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We've mentioned data and the term

digital twin quite a bit for our audience.

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Let's unpack that a little bit more.

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Freddie, you've contributed

significantly to this area.

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Could you explain what a digital twin is

and discuss

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the importance of data in the industry?

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So one thing I wanted to mention

with the digital twins, obviously

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it's a part of what we consider

AI readiness.

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So when we look at AI readiness

for utilities,

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it's not about buying the latest software

or hardware, it's only about preparing

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your organization for the actual

use of it in a meaningful way.

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And really, it's there's five core areas

that we should be focusing on.

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The first cause is strategy.

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And like cities that touched on this,

you know, is there a clear reason

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why you want to use AI

or are you trying to solve a problem

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like the failure of assets

like he had mentioned about pipelines?

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Or are you trying to address

customer complaints?

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AI is really driven by business needs.

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It's not just about technology trends.

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When we talk about, again, AI readiness,

the second core areas about data.

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And here to talk about that

I need is good data

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that means having enough data,

making sure it's accurate, and being able

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to bring it all together from different

sources like Army skater GIS.

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Because without this, AI will not work.

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The third area is digital infrastructure.

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And again,

this kind of leads up to the digital twin.

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But your systems need to be cloud ready.

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They need to be able to work together.

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If your tools don't talk to one another,

AI insights

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won't flow to where they're needed.

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And then the two remaining areas.

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It's really about people and skills.

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When you look at AI readiness, do you have

the managerial skills to work with AI?

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Can your teams understand the insights

and act on them?

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And then the last one is about change

management.

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Is leadership bought into this?

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Are they ready to shift?

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Our decisions are being made.

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And then really AI affects processes.

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So you need some type of governance

and buy in to user responsibly

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in the water industry.

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What are the biggest opportunities

you've seen?

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Is there enough data

to create truly meaningful tools?

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I'd love to hear about the challenges

and opportunities

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and what people in the industry

should expect.

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So in the next two years,

when you look at AI,

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the most, I guess important opportunities

are going to be related to four things.

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One is predictive asset management.

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They can.

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You heard Satish talk about this

forecasting failures before they happen.

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The other is operational optimization

improving the efficiency of pumps

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pressure zones,

chemical use and other things.

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And then the remaining two are water

quality forecasting anticipating issues

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related to chlorine decay

or or disinfection byproduct formation.

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And then the last one

is about customer engagement.

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How do we use AI to detect leaks

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and improve

billing accuracy for our customers?

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So are you seeing these opportunities

in your day to day work?

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Could you tell us more about the City of

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Houston's initiatives

and what you've observed on your end?

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No, I think, very fixed

all of our initiatives already,

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and we have now, demand prediction,

system vulnerability

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prediction tool, which actually predicts,

based on the weather,

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what would be the weather next week

and which area of our system might fail.

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We have not integrated it into the real

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operational world, but,

it is in the planning side.

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At least we can have that time

to run some modeling scenario that,

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okay, this area might fail

or might have the low pressure,

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low water quality,

then how can we improve that

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so we can simulate that

in the modeling side of the planning side

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on top of that, actually,

very broad, very good point.

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That customer engagement part,

actually these tools

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and digital transformation,

it helps us to make the more transfer

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and in our process to be closer

with the customers.

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On top of that, one benefit,

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which I am seeing right now in my team

and within organization,

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even though you are not using AI tool

for the whole project, it's,

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it might not be a full project or program,

but you can use this

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for the daily activities

like if you have to transform data

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from one and data format

to another data format before

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you used to work in the Excel and you

now you don't need to do that.

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You just upload your data set

and you can transform it.

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Or if you feel secure it.

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And if you think that the data

is not critical data set, which cannot be

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shared to the open platforms,

you have, other way to use generative AI.

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Most of this data work is repetitive work.

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Like when you have that data,

we will produce the data

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or collect the data

from that point to the user's point.

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That data needs to travel

a lot of that and process

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and that process

you can automate using as indicator.

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That is where I am

seeing a lot of advances,

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and we have already started piloting that.

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Looking at pilot programs,

I am sure people are wondering

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how to get started and probably not sure

how to set up these initiatives

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like automation, data transformation,

or larger public engagement projects.

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What advice would you give or what

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was your process for getting leadership by

and for the pilot and the program?

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We have to understand

what are we trying to do,

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and do we really need to use

AI for the work

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which we are trying

to bring that technology?

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First, you need to be convinced

that is the first step,

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because whoever is thinking about it,

it just would not be okay

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that utility has used it.

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Why not me?

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That needs to be determined first.

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And then, second step is where this

AI tools adds value in your process.

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Do you really need,

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machine learning prediction

for the pipe replacement program or not?

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Do you really need some AI technology

to do your regular job or not?

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And if you are convinced,

then you can find the quick wins

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which programs you really need to deliver

within insert time.

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And, I can help you

that part identify, rank it,

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and then try to find out the cost savings

like efficiency.

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So one thing I wanted to add

to what I was talking about,

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that's one thing to focus on, is really

getting that buy in from leadership.

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And it's really about framing AI

as a business enabler.

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And again, it's not a technology project.

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And again,

it's about tying into these pain points.

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It's not just talking about AI, but again,

talking about fixing problems.

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Whether you want to identify leaks quicker

or you want to reduce unplanned outages

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quicker, again, it's tying into real

pain points and not just talking about AI.

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A lot of utilities will try to use AI,

AI simply by automating reports.

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And again, that helps build momentum

within an organization.

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And then the last key point, think it by

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leadership

is to speak in in business terms.

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And you know, we talked about this about

how AI can reduce cost, improve service,

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or defer, you know, capital expenditures

or extending the life of an asset.

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As someone with a background in this

field, these topics are music to my ears.

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But I'd like to delve deeper

into what innovation looks like

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in the water industry.

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You've mentioned a few things already,

but could you talk about

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some of the innovations

you've seen in the last

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5 to 10 years,

or what you expect to see in the future?

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Yeah, one of the biggest areas that we see

innovation

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is, is how AI is improving

water quality within a utility.

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So when you look at how AI is helping

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with water quality within a utility,

I'll give you a quick example.

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The census altimeter, records

temperature, and it's a water meter

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which can be used to estimate water age,

which is a key factor in water quality.

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But I can link higher temperatures

to longer residence times,

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helping identify decays and disinfectant.

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But also it helps the utility

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by allowing them to understand

what's going on in their system

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so that now they can adjust

flushing schedules, they can fine tune

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their chemical dosing and really

prioritize areas for pipe replacement.

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So again, this is one of the main areas

I see where AI is helping.

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Utilities is focused on on water quality

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with the data being collected

for these developments.

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Is it publicly available

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or is it a combination of data

from the utilities and other sources?

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Most of the data that we don't

make it public, but we submit our report

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to our regulatory agency,

which is, TCU for the Texas.

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And we have to submit it's a requirement

and that is publicly available.

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So that can be analyzed.

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And research that is one side

other side is inside the organization.

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And we are actually working

with different research organization

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and also with the academic research

institutions where we are

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sharing these data with the Indian

underscore closer to agreement

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because it has some of the data

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when you use it that can be used for,

against the system as well.

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So we need to be a little bit

careful on that.

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But it's publicly available

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in terms of report,

once we submit to the regulatory agency.

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But most of these time series data

or the more higher resolution data is not

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being shared right now.

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One thing I wanted to add

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to this I know we're talking about data

privacy, but, you know, this really speaks

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about how you start a

AI project in really years.

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To talk about this, you want to start

small and what does that really look like?

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So again, we want to be able to use data

that we currently have.

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And you know you heard Satish talk about

you know am I information skater GIS.

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So really you want to choose a use case.

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It may be about billing accuracy

or efficiency.

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But you know you want to partner

with somebody internally or somebody

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externally, like a consultant that knows

that utility space and run this pilot

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maybe three six, 12 months,

just depending on what you're focusing on.

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But this helps build

that internal knowledge that you hear,

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talk about, but also de-risk the future

scaling of that.

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I implementation.

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Of course,

utilities should not be waiting

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until the data becomes 70% or 80%

confident.

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Data said no, we have to start

using it in a pilot or demonstrating it.

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First benefit of all these pilot is

you will know that whether your data set

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is ready for the real user or not,

and you will know

00;14;29;05 - 00;14;32;17

what's the limitation in your data

starting from the data collection.

00;14;32;28 - 00;14;37;03

How you can change your data

collection process, how you can optimize,

00;14;37;06 - 00;14;41;29

or how you can reform your whole quick

process data validation process.

00;14;42;09 - 00;14;47;13

It helps, that I when you pilot it,

you have to go through all those process.

00;14;47;19 - 00;14;51;10

Like for example,

if we have a lot of legacy infrastructure

00;14;51;17 - 00;14;55;02

and that's

why it's very tough to directly go

00;14;55;05 - 00;14;58;23

and, integrate our new technology

to our system.

00;14;58;27 - 00;15;03;07

Now, when you are replacing, you are about

to replace the legacy infrastructure.

00;15;03;10 - 00;15;06;17

You have to have it in your mind

or in your contract that

00;15;06;25 - 00;15;10;28

that new infrastructure

should be able to integrate

00;15;11;01 - 00;15;15;10

to the AI system or new IoT device system,

or the cloud system.

00;15;15;13 - 00;15;17;19

It needs to have that approach.

00;15;17;22 - 00;15;20;19

Every project

needs to go through this thought process

00;15;20;19 - 00;15;25;11

and needs to align with this digital space

or digital, future.

00;15;25;14 - 00;15;31;22

And then only you can transform

your utility into a true digital utility.

00;15;31;25 - 00;15;35;28

Then only you can really benefit, from AI.

00;15;36;23 - 00;15;40;09

A lot of utilities are concerned

about the cost and return on investment.

00;15;40;19 - 00;15;42;26

Doing these AI projects,

00;15;42;29 - 00;15;46;17

and frequently they'll ask,

what is the cost of doing this?

00;15;47;02 - 00;15;50;01

But, you know, really

the bigger cost is doing nothing.

00;15;50;04 - 00;15;54;07

Now, sticking with the manual processes,

reacting to emergencies.

00;15;54;10 - 00;15;56;18

And these are really missed opportunities.

00;15;56;21 - 00;16;01;09

And in order to really manage that risk

and increase the return on investment,

00;16;01;18 - 00;16;06;00

you know, we talked about starting

small pilot project with existing data.

00;16;06;15 - 00;16;10;21

Also, the other thing too

is tracking the metrics on the project.

00;16;11;03 - 00;16;12;28

And, you know,

you can't show that the return

00;16;12;28 - 00;16;16;07

on investment quickly,

but you can show the value of it, like

00;16;16;10 - 00;16;19;24

detection rate, for failures

or customer callbacks.

00;16;19;27 - 00;16;21;21

Are they are they being reduced?

00;16;21;24 - 00;16;22;07

But again,

00;16;22;07 - 00;16;25;24

all this builds up to showing that return

on investment on these projects.

00;16;26;11 - 00;16;29;00

Right. Let's talk about the people aspect.

00;16;29;03 - 00;16;33;04

We've mentioned tools like ChatGPT

or Copilot to supplement talent.

00;16;33;15 - 00;16;35;00

But what do utilities need to do

00;16;35;00 - 00;16;38;20

to level up their talent pool,

especially with the next generation

00;16;38;23 - 00;16;41;25

that's digitally native

and expect to operate in a digital world?

00;16;42;16 - 00;16;45;08

It's not just about hiring data scientist,

00;16;45;11 - 00;16;48;11

it's really about building

this culture of digital learning.

00;16;48;19 - 00;16;49;26

And there's two sides to it.

00;16;49;26 - 00;16;51;04

One is technical.

00;16;51;07 - 00;16;55;08

You know, you train your team

on the basics of AI and data literacy.

00;16;55;22 - 00;16;57;25

Then you start creating these hybrid

teams.

00;16;57;28 - 00;17;01;00

Utilities are used to working in silos

where you have

00;17;01;02 - 00;17;04;02

engineering, you have planning,

you have operations, you have maintenance.

00;17;04;10 - 00;17;05;18

You know, it'd be nice to start

00;17;05;18 - 00;17;08;18

bringing those siloed teams

together as a hybrid team.

00;17;08;26 - 00;17;10;10

And they're all at the table.

00;17;10;13 - 00;17;13;13

And then also you start using pilots

to learn by doing,

00;17;13;26 - 00;17;16;20

and then not only focusing

on the technical side,

00;17;16;23 - 00;17;19;04

there's also that shift in the mindset.

00;17;19;07 - 00;17;23;03

How can I support

work versus replacing people?

00;17;23;16 - 00;17;27;20

Also, like I mentioned before, it's about

breaking down silos between departments.

00;17;28;04 - 00;17;30;24

And then more importantly,

it's about celebrating early wins.

00;17;30;27 - 00;17;35;06

No city has done a lot within the city,

Houston, and it's really about celebrating

00;17;35;09 - 00;17;38;27

those early wins and getting by

and by his leadership to do other things.

00;17;39;13 - 00;17;43;20

Yeah, I fully agree with Freddie

and I just want to add one thing on this

00;17;43;23 - 00;17;46;26

that we need to first change our mindset

00;17;47;01 - 00;17;50;19

and then, culture

in, organization, that it's

00;17;50;22 - 00;17;53;24

not about what efficiency

you will get from you.

00;17;54;04 - 00;17;57;06

We have to prepare our people

to accept it.

00;17;57;09 - 00;18;02;24

It's about the acceptance and to believe

that it is not going to replace you.

00;18;02;27 - 00;18;05;20

It is here to make us better.

00;18;05;23 - 00;18;07;16

We have to change that mindset.

00;18;07;19 - 00;18;12;24

Why? Most of the people are now

talking about the oh, I might replace us

00;18;12;27 - 00;18;15;29

and why they are not much, in favor

00;18;16;02 - 00;18;19;02

of fully deploying the AI right now is

00;18;19;10 - 00;18;22;23

there are many other factors,

but one factor is fear factor as well.

00;18;23;03 - 00;18;24;29

We have that fear environment here

00;18;25;02 - 00;18;30;12

because we need to first understand

that this helps us without me.

00;18;30;15 - 00;18;32;25

Yeah, you cannot do anything

I have to tell.

00;18;32;25 - 00;18;35;14

First, I have to train this, model first.

00;18;35;14 - 00;18;38;01

And I am the one who is subject

matter expert.

00;18;38;04 - 00;18;41;25

I can tell before even model runs

and gives me results.

00;18;42;07 - 00;18;44;18

I can tell you that, okay.

00;18;44;18 - 00;18;47;17

My result, this number will be this.

00;18;47;17 - 00;18;51;13

But what it means is you can predict

your results as a subject matter expert.

00;18;51;16 - 00;18;52;08

So you.

00;18;52;09 - 00;18;54;26

Your need is always there

to validate the things.

00;18;54;29 - 00;18;56;20

It will make you better.

00;18;56;23 - 00;18;57;25

It will not replace you.

00;18;57;25 - 00;19;00;19

That kind of discussion

we need to have it.

00;19;00;22 - 00;19;03;04

Then second thing is let's look back.

00;19;03;07 - 00;19;05;19

What's the problem in our utilities?

00;19;05;19 - 00;19;10;07

Let's find that the reason, the reason

main reason is the generation which are

00;19;10;09 - 00;19;14;11

working here mainly in the leadership

or in a mid-level management,

00;19;14;15 - 00;19;18;18

and the people who are who are in work

first since last ten years plus

00;19;19;04 - 00;19;22;01

we have never trained or the no school

00;19;22;02 - 00;19;26;09

curriculum or any trainings in the past

they have gone through

00;19;26;12 - 00;19;30;13

which has actually trained them

and thought about the data science

00;19;30;16 - 00;19;33;16

or data structure, structured

any kind of this technology

00;19;33;19 - 00;19;36;07

and how do you feel confident about it?

00;19;36;10 - 00;19;36;26

Interview.

00;19;36;29 - 00;19;40;01

This kind of leads to digital maturity

or or

00;19;40;03 - 00;19;43;26

how do you measure AI readiness

because you heard teach talk about it.

00;19;43;26 - 00;19;45;18

And we focused a lot on the workforce.

00;19;45;18 - 00;19;47;14

But there's a model out there.

00;19;47;14 - 00;19;49;07

It's a five level model.

00;19;49;10 - 00;19;51;05

It's the digital maturity framework.

00;19;51;08 - 00;19;53;24

Cities can expand on this

because he's been working on this.

00;19;53;24 - 00;19;57;06

But we talked about the workforce,

the gaps, identifying that.

00;19;57;16 - 00;20;00;25

But really, you know, taking a step back,

kind of seeing where you are

00;20;00;28 - 00;20;02;23

in that maturity, framework.

00;20;02;26 - 00;20;06;01

So level one is really about utilities

that are reactive

00;20;06;12 - 00;20;08;09

and they're still very analog.

00;20;08;12 - 00;20;13;01

Level two there might be some digital

tools, but again, they're not connected.

00;20;13;17 - 00;20;17;22

And by the time you get to level three

that governance improves of data.

00;20;18;02 - 00;20;20;05

You start doing AI pilot.

00;20;20;08 - 00;20;23;15

Level four is where, you know,

AI starts delivering value.

00;20;23;28 - 00;20;28;28

And then level five is where I digital

tools are embedded across the

00;20;29;01 - 00;20;33;02

entire operation, whether it's planning,

engineering, operations and maintenance.

00;20;33;08 - 00;20;36;08

And it's really driving real time

predictive decisions.

00;20;36;28 - 00;20;37;18

Exactly.

00;20;37;18 - 00;20;41;07

And,

let me take you back to the Swans effort.

00;20;41;10 - 00;20;44;07

I am right now co-lead of, digital twin

00;20;44;10 - 00;20;47;27

architecture group in Swan

Sun is a smart water network.

00;20;48;00 - 00;20;49;17

It's a global organization,

00;20;49;20 - 00;20;54;22

and we have a lot of utilities

consultant, engineers, everybody.

00;20;54;25 - 00;20;58;25

We participate and we discuss about

what can be the, future

00;20;58;28 - 00;21;03;22

and how can we increase literacy about

the digital twin digital transformation.

00;21;03;28 - 00;21;06;23

That's, our efforts now, we have just

00;21;06;23 - 00;21;10;00

released

digital twin maturity assessment tool.

00;21;10;05 - 00;21;13;07

You can if you go to this one website,

you can see there

00;21;13;19 - 00;21;18;00

we have prepared a different set

of questions where utilities

00;21;18;03 - 00;21;21;26

or any organization can go

and try yourself that.

00;21;21;29 - 00;21;26;12

Where do you are for every sectors

like data, workforce development,

00;21;26;19 - 00;21;30;24

operational integration, everything

is planning all sorts of diamonds.

00;21;30;27 - 00;21;34;16

And you can see there

and you can choose where you are.

00;21;34;20 - 00;21;38;03

And then it provides you,

not just where you are.

00;21;38;12 - 00;21;40;16

Then what are the advance option.

00;21;40;19 - 00;21;45;22

Like if I, I am in level two or level

three, like, Freddy was stating

00;21;46;04 - 00;21;49;04

what is the level for me

and how do I go there?

00;21;49;07 - 00;21;51;06

How can you rise to that level?

00;21;51;09 - 00;21;53;04

Thanks for the conversation

situation, Freddy.

00;21;53;04 - 00;21;56;01

That was an awesome,

we've covered so many topics.

00;21;56;04 - 00;21;57;29

We covered the positive mindset towards

00;21;57;29 - 00;22;01;16

building prototypes and solutions

driven by business needs and user demands.

00;22;01;25 - 00;22;04;15

We've also covered the influence

of change.

00;22;04;18 - 00;22;07;18

Before we close off,

do you have any final thoughts?

00;22;08;01 - 00;22;10;21

We talked a lot about hardware software,

00;22;10;21 - 00;22;13;24

workforce,

but it really comes down to collaboration.

00;22;14;08 - 00;22;16;25

In collaboration is important

when you're going through this

00;22;16;25 - 00;22;20;11

AI transformation,

because whether it's the city of Houston

00;22;20;14 - 00;22;24;07

or Dallas water utilities,

no utility can transform in a vacuum.

00;22;24;20 - 00;22;26;20

You need an ecosystem of partners.

00;22;26;23 - 00;22;29;04

So obviously you need technology partners.

00;22;29;04 - 00;22;32;15

You need to interface with the regulatory

agencies.

00;22;32;24 - 00;22;36;24

You have to work with consultants and

peers because each plays a certain role.

00;22;37;01 - 00;22;40;09

For example, the technology partners

bring in innovation.

00;22;40;23 - 00;22;43;27

The regulators, you know, ensure

compliance and public trust.

00;22;44;07 - 00;22;47;20

But more importantly, you know, cities

interfaces with with other utilities

00;22;48;02 - 00;22;51;18

in Texas and across North America

because they can share lessons

00;22;51;21 - 00;22;56;05

learned and help scale smart systems

across the entire water sector.

00;22;56;14 - 00;23;00;09

So basically, I just wanted to mention

that that AI is important,

00;23;00;12 - 00;23;03;11

but also you need to look at the ecosystem

that's going to support that.

00;23;03;24 - 00;23;04;03

Yeah.

00;23;04;03 - 00;23;09;24

For me, what I would say is, for the

AI enthusiast in the water utilities

00;23;10;02 - 00;23;15;04

trust, the technology, it's,

not just that word,

00;23;15;07 - 00;23;21;02

just here for some year, it will disrupt

all industries, not just water utility.

00;23;21;05 - 00;23;24;17

So whether we choose it

to accept it or not, it is here.

00;23;24;20 - 00;23;28;05

And it will come to your desk one day,

so it's better.

00;23;28;16 - 00;23;29;18

Start early.

00;23;29;21 - 00;23;33;06

That is, one suggestion

to all utility friends.

00;23;33;09 - 00;23;37;21

And another thing to add, and this is

the let's create a trust environment.

00;23;37;25 - 00;23;40;23

We have to be aware about the ethical side

of how to use it

00;23;40;26 - 00;23;45;05

and how not to impact our customers

or service delivery.

00;23;45;08 - 00;23;50;17

Because our industry is a service

industry, we are here to provide

00;23;50;20 - 00;23;55;00

better service to our customer,

to our citizens, to our constituents.

00;23;55;08 - 00;23;58;10

That means to provide

that level of service to our folks.

00;23;58;20 - 00;24;02;25

We need to be bold

enough to improve ourself.

00;24;03;04 - 00;24;06;14

We need to find our version

to all softwares,

00;24;06;17 - 00;24;09;20

or every device needs to be upgraded

right now.

00;24;09;28 - 00;24;13;07

This is the time to upgrade ourself,

trusting on ourself

00;24;13;15 - 00;24;16;12

and then come up with a version

two of ourself.

00;24;16;15 - 00;24;19;10

This is the time to have that conversion.

00;24;19;13 - 00;24;24;17

And third thing is there are a lot of

information available right now.

00;24;24;20 - 00;24;26;18

Many use cases around the world.

00;24;26;18 - 00;24;29;25

You can see collective intelligence

always wins.

00;24;30;02 - 00;24;33;01

And that's why what I recommend,

and I agree with Freddy,

00;24;33;04 - 00;24;36;21

that we have to have

a collaborative environment.

00;24;36;26 - 00;24;40;14

And on top of that, regulatory

agencies, federal government,

00;24;40;24 - 00;24;42;06

they need to push more.

00;24;42;07 - 00;24;44;24

They need to create the reward

environment.

00;24;44;27 - 00;24;46;01

Why utilities?

00;24;46;01 - 00;24;48;05

People,

they do not want to try innovation.

00;24;48;05 - 00;24;52;27

They are not much encouraged

to do the innovation work in the work

00;24;53;00 - 00;24;57;14

is if you have a problem using that,

you will get punished.

00;24;57;18 - 00;25;01;28

But if you have, something

if you can bring value from that,

00;25;02;09 - 00;25;05;18

most probably,

very few people will notice.

00;25;05;24 - 00;25;09;08

So what it means is

we do not have a rewarding environment

00;25;09;15 - 00;25;13;04

to transform

or to upgrade our work process.

00;25;13;07 - 00;25;15;29

That's why it is all collaborative effort.

00;25;15;29 - 00;25;20;05

It's not just a event or a project,

it's a journey.

00;25;20;27 - 00;25;24;12

And I know we'll probably continue yapping

for hours on this around the product

00;25;24;15 - 00;25;30;00

culture change process, technology,

and more importantly, the water industry.

00;25;30;04 - 00;25;32;27

I appreciate both of you

so much for joining me today.

00;25;32;27 - 00;25;34;28

I really hope our listeners learn so much

00;25;34;28 - 00;25;37;28

because there's so many great insights

in this conversation.

00;25;38;04 - 00;25;39;03

Thanks again Freddie.

00;25;39;03 - 00;25;41;13

Thank you Satish and have a great day.

00;25;41;16 - 00;25;42;23

Thank you and thank you.

00;25;47;10 - 00;25;50;10

Brought to you by Nexus, Publish By GHD.

00;25;50;18 - 00;25;51;22

Where ideas connect.