The Inclusive City: Building Smart Cities That Serve Everyone

Most smart city initiatives focus on sensors and efficiency—but what about the people left behind? In this eye-opening episode, Harpal Mattu, a social science and data expert, reveals the real promise of building inclusive, human-centred cities with AI. Discover why the future of urban living depends not just on technology, but on safeguarding vulnerable populations and ensuring equity at every step. You’ll learn how data is transforming city services—from China's predictive health programs to UK foster care systems—and why these innovations sometimes unintentionally exclude the very communities that need them most. Harpal shares powerful stories and practical frameworks to prevent bias and ensure responsible governance in AI-driven urban environments. We break down the pitfalls of biased algorithms, highlight the importance of community feedback, and explore how cities can develop continuous, living governance models that prioritize human rights.

This episode is essential for city leaders, policy makers, and tech founders committed to creating smarter cities that truly serve everyone. As AI and data become integral to urban life, the question isn’t just how to innovate—but how to include. If you’re building or guiding city initiatives, missing this insight could mean leaving millions of citizens behind. Don’t miss the chance to learn how to harness technology for a future that is equitable, safe, and human-centred.

Hosted by Magdalene Amegashitsi, an expert in designing intentional, inclusive smart city strategies, and featuring insights from Harpal Mattu, a trailblazer in social sciences and data ethics. Harpal’s experience ranges from global smart city deployments to UK social care—bringing a vital perspective on safeguarding and human-centric innovation.

Why does this matter? Because the true measure of a city’s intelligence isn’t just data points or sensors—it’s how well it improves lives for ALL residents. The risks of neglecting inclusivity grow every day, but so does the opportunity to lead with purpose and empathy. Whether you’re involved in urban planning, public policy, or tech development, this conversation challenges you to think bigger about what a truly smart city looks like.

Connect with us, and learn how to build cities that are not just smarter—but fairer. Because adding technology alone doesn’t make a city wise; using it to uplift everyone does.

Transcript

SPEAKER_00 0:18

What if the city you live in could understand you better? The promise of the smart city is to use data and AI to create cleaner, more efficient, and more livable urban spaces. But as we build this data-driven future, who gets left behind? Welcome to the Anaya Algorithm, the podcast for leaders who want to build the future intentionally. I'm your host, Magdalene Amagasty. Today, we are exploring the human side of smart cities with our guest, Harpal Matu. Harpal is an expert in the social science and data-driven decisions that shape our urban environment. And he's here to talk about the critical challenge of digital inclusion. In this episode, we discuss how to use data to serve people, the risk of creating a digital divide, and the urgent need for governance to safeguard our most vulnerable citizens in an AI-powered world. Please join me in welcoming Hapo Matthew.

SPEAKER_01 1:23

Thank you, Magdalena. That's a great introduction, and I'm proud to be here actually. So I'm going to put my caveats out there. I've not done many podcasts. Actually, no, it's my first podcast, but um this is quite an exciting experience for me. So uh so to the listeners out there, please do uh forgive me if I do make some mistakes on this, but I'm sure I won't. So I'm sure you won't.

SPEAKER_00 1:44

So, Harpal, with your deep expertise in the social science and data-driven reality of smart cities, you bring a vital human perspective to a very technical topic. So thank you so much for being here. Welcome to the Anaya algorithm.

SPEAKER_01 2:01

Thank you. Thank you. So just on that, um, look, I I am a technologist to a point. So I see things from a technology perspective, technology first. But the ethos that we've always worked with when we developed agilix, agilix is our sort of tech services business. Um, Universe is our tech, I'm gonna say our platform, that we're working more towards solving some real sort of point problems within within municipalities across the globe. But also it lends itself quite quite well towards smart cities and and data. But what we've always been sort of um clear on in between myself and my partners and the rest of our organization, that we're we're a we're a people-first organization. We build technology to empower people and be that employees, be that citizens, and we really like to look into the lens of our users and what users are trying to get out of it and get technology to be the enabler. Uh and that that's kind of you know, working at it from that lens, you start to see things as slightly differently than you would if you were technology versus hopefully that makes sense.

SPEAKER_00 3:02

It absolutely does. Did you want to do a quick intro just to help our audience?

SPEAKER_01 3:08

Yeah, yeah. Happy to. So a bit about me. So I've been in the technology space for technology transformation programs for most of my career. I'm a qualified accountant. Uh and I started off really um from a perspective of implementing ERP solutions, um, and that sort of lent itself quite well towards working in the ERP space within public sector and government spaces. So to give you some examples, I worked on a uh lead on transformation program to um revolutionalize revolutionize the way in Grant Thornton, uh, done a lot of their billing. Uh, then I worked on a transformation program at House of Commons for quite a number of years. Um uh sort of vis-vis the the whole sort of um uh the the the the expenses scandal that sort of blew up many years ago. That was actually at the House of Commons when that actually happened. So from from a technology transformation perspective, digital transformation program or digital transformation perspective, I'd like to say I was at the early days of sort of trans digital transformation 1.0, uh, where organizations were getting to a space of having one single platform to understand their finance needs, understand their HR needs, understanding sort of payroll needs, and getting to a point of actually using sort of very primitive IoTs, if you like, and not who they are right now. So, and then you kind of get to a position and you started to gather lots of data or lots of information. So the first bit was actually getting the data, and I think that's where we sort of first came in. Now you all this rich data, what do you do with it? Uh and this is where the data science is and all this other thing. This is where the the whole thing of you know data-driven decisions or data-driven networks really come to play now, which is where I see digital transformation 2.0 um in in the sort of grand scheme of things.

SPEAKER_00 5:00

Awesome. I love how you called it digital transformation 1.0 and 2.0. It says good clarity. So thank you. And let's start by defining our terms. So the phrase smart city is everywhere, but it often brings to mind just census and efficiency. Yep. From your perspective, what is a smart city really about and what is the human promise at its core?

SPEAKER_01 5:26

This this is a this is a great thing. So I've been privileged to uh let's say, sort of look at what I mean, look at smart cities and sort of see them in action. So if you think of I was at a as a delegation about a year ago, we went to Shenzhen in China, which I think is probably the in terms of smart cities, in terms of kind of what the future could look like, it's it's probably up there in terms of everything you could think you'd imagine. So deliveries by drone for argument's sake, um uh subway trains turning up as soon as you sort of as soon as the citizen arrives, uh digital empowerment where people, you know, everything uh there's there's a sort of a series of sort of platform apps called WeChap. Uh and within China that that sort of you know everything is organized via that. So in that respect, you know, digital empowerment, digital engagement is is quite big in those areas. That that sort of lends itself to what is known as smart cities, and that's that's sort of digital empowerment for citizens using technology to to to to for deliveries, and we talk about drones and other things. And that that kind of works, but the thing I I found that was missing from there, it it's that was smart city from a perspective of using technology. Uh yes, data's obviously driven there, but I think that's from a from a world that we live in on this side of the world. And I think about the social sciences and social aspects of of sort of uh digital or digital data. I still feel there's something missing. So, in other words, digital for me isn't about about empowering everybody with with apps and smartphones, is actually using the data to make decisions on behalf of people, make better informed decisions on behalf of citizens, and getting better set as an outcomes. So I I use an example right now. My my wife works in social care in the UK, uh, with a very I won't mention who the authority is, but it's it's a very significant authority in the UK. Um so one of the big challenges that they have right now is around foster care even adoption. But the amount of times you know she's there on a Friday afternoon where she's getting a panic phone call because a child is in foster care and they they don't have another foster sort of home to take them to, the the you you sort of have to question how do you get to that position? The data's predictable, the outcome is predictable. So, you know, why are our sort of foster care and solutions having to rely on emergency needs every single time? Albeit it's not smart cities and stuff, but it's it's decision, data-driven decisions. And if you don't get that right, you know, it's we're we're a world from drones and sort of drone deliveries. There's a there's a stage we've got to get fixed before that. Otherwise we have a danger of leaving people behind.

SPEAKER_00 8:05

I love that. I love that definition. It reframes the goal from creating a smart city to creating a more livable city for its people.

SPEAKER_01 8:15

Yeah. Yeah. Yeah. Yeah. And look, yeah, cities will only get wider. You've already got to think of the big sort of uh metropolitan cities within the UK. But that's that's that's going across Europe. Um off to India next month to visit some of the big sort of sort of mega cities over there. So there is an attraction to cities, a bigger attraction to cities than ever before, especially as people, you know, because the next generation sort of growing more towards and say tech careers, but this the cities need to be in a better position to serve those citizens efficiently um and effectively.

SPEAKER_00 8:49

Nice, love that. And you talk about making better decisions for people based on people. So can you give us a real-world example of how a city has used data to make a tangible positive impact on a citizen's lives, particularly in a way that promotes inclusion?

SPEAKER_01 9:10

Yeah, it's a good question. Uh yeah, I mean, I I I take the view of some of the things in China that I saw recently in terms of the data that was used towards health conditions and health outcomes. So for argument's sake, uh they seem to be using data towards sort of preventative or or or sort of um early diagnosis of sort of issues. A lot of it's wearables, for instance. Um and you you see that sort of in in a lot of the elderly community in China. And that's that's great. But I think the the problem we have in is we the problem we have in in in the UK right now is from a citizen perspective, we struggle to have a single view of the citizen, which makes it very difficult for our authorities to sort of serve their citizens so far. We have NHS data, we have sort of you know local authority data on various points of data. So the last time we count, I think there's about 80 to 20 data points on a citizen in the UK, and those data aren't connected. And if you compare that to say, you know, the you know, the big internet providers, the Googles or what have you in the world, they hold 50 to 60 data points on any citizen any one time, and they can use that data more effectively, but that doesn't sort of sort of gel well with sort of our our public services right now. So, you know, NHS, uh Department of uh Department of Worker Pensions as an example, council tax, uh sort of parking, you know, etc. etc. etc. So it's it's there's a complete distinction or there's a complete gap between how that data's been used at the moment. So so again, to answer your question, wearables was quite interesting things. That that sort of works quite well in in in China, where preventative outcomes are sort of more akin towards actually sort of you know fixing emergencies all the time.

SPEAKER_00 10:57

Awesome. Well, that's incredibly powerful. So on the flip side, how can this same data approach, you know, the data-driven approach accidentally lead to exclusions? You know, what's an example of a well-intentional city, you know, smart city project that ended up leaving a segment of the population behind?

SPEAKER_01 11:21

Good question. That's a very good question. So there's I can't think of any examples of that sort of straight away. Um but I I'm we're aware, we we see this all the time, that that people are being left behind, the elderly are left behind, sort of vulnerable sort of children in care seem to be left behind quite a lot right now. So it gets back to the you know, they call it the smart cities or the data-driven decisions, um are kind of not working very well for those, you know, so those are excluded from those sort of patterns of data or pattern analysis. Again, we see this quite a lot, uh, living in a you know, I have the the benefits, if you like, of sort of seeing things firsthand from a you know, living in a sort of, you know, a large metropolitan city uh with with diverse needs, diverse sort of challenges, and for sure those communities are but certainly excluded from data or making those better data-driven decisions.

SPEAKER_00 12:19

I think what you've said is really telling because usually when the data set is biased, or if you only measure what's easy to measure, your smart decision will just be a faster way to create an unfair outcome. Because you've missed out on, you know, aspects of the society that should be considered in that algorithm. Yeah. Yeah. And it highlights the need for a robust governance framework before you even begin.

SPEAKER_01 12:51

Yeah, for sure. Yeah, yeah. And this is it, and it's just a bit of wild west right now, I I feel. Um, so you know, everybody's trying to get to an outcome without any framework, as you said, governance. Uh, and that that worries you a little bit in terms of um how that data is going to be used and sort of fall into the wrong hands. We see this already with with to a point, you know, we we we see this with sort of big data providers right now and how that data's been used. And as citizens, we kind of accept that because a lot of that data's been used for commercial reasons and they, you know, there's there's a monetization to a point. Again, as citizens, we sort of accept that. However, when we're talking about those stakeholders from a citizen perspective, and where municipalities, local authorities have have a duty of care, that that that governance needs to be very different to what's right allowed in the commercial arena. This is my view.

SPEAKER_00 13:47

Yeah, I agree. Totally, you know, it takes me back to the whole conversation around the people element of things when it comes to smart city. How do you ensure this human element is at the center of the design process? And how do you get real community feedback and not just data points? Because we're looking at the quantitative and the qualitative data. How do we get all this together into you know that design process?

SPEAKER_01 14:17

Yeah, that's that's half that's that's that's surely a battle. Um, because when you when you think of getting data together, you think of sort of uh pulse surveys or or pulsive, and that's to a point that's not is that really the right way of doing things now in in in the you know in 2026 and beyond. So so the emotions, the sentiments have to be I don't it's a good question. I don't know to understand it, but that's that's there's a challenge. There is a challenge then. Uh it's probably going to be out there for the data scientists to understand this. So do you wait for do you do you do you gather the information by sentiments? Uh does the information come from from from dissatisfaction? I don't know what this looks like really, because yeah, I that's that's a challenging question. You know, I've sat on a few things now. We we sort of had a recent sort of seminar with the police recently, and they they tried to use agents, for instance, to to sort of deal with with um let's say sort of crime or sort of crime predictions, but that's that's at the start of the event. But it but there's nothing to sort of gather, go back and sort of say, what's the what's the feedback? How do you how do you improve this? So that's a good question. I I don't know the answer to that question.

SPEAKER_00 15:29

Yeah. Well, I think it's a good question for us as a society to be thinking about, especially for us, those in the technology field, who are leading the technology that would impact our society. And again, it's about supplementing the quantitative data with qualitative human stories. And and it's also a reminder that the data, you know, tells you the what the what of a situation, but you need to talk to people to understand the why. And that information is is really important to ensure that your your story is complete and it's also a lesson that applies to any industry, not just city planning.

SPEAKER_01 16:15

Yeah, yeah, yeah, exactly. I mean look, ultimately, you know, when you when you bowl back human sentiments, does it really change that much, really? Because people, you know, we we want, you know, in terms of citizens, if you like, or or consumers, you're after the same thing. You're after that, you know, some you know, you're after a good product, service, quality what you want for us at the right price, delivered efficiently and delivered effectively. So ultimately, when it boils down to that, it it it's it's that's kind of the the the core of any these decisions, innit, or the core of what a citizen or a consumer needs are. So it shouldn't be too difficult really to sort of differentiate between a consumer and a citizen, because ultimately it's the same person, but you know, it's just really different help.

SPEAKER_00 16:58

Absolutely. Wow, okay. So, Hapo, a couple of weeks ago when we spoke, I remember you shared a chilling story with me about an AI savvy person's come in, a vulnerable woman, which is very sad. And it highlights the new risk that we all face.

SPEAKER_01 17:18

Yeah.

SPEAKER_00 17:19

As we've built AI into the fabric of our cities, what is the single biggest security or safeguarding challenge we need to address?

SPEAKER_01 17:29

Single piece or or multiple pieces?

SPEAKER_00 17:31

It's I'm happy for you to give me more.

SPEAKER_01 17:35

It's the whole thing of bad actors, and that's always going to be there, isn't it?

SPEAKER_00 17:39

Yeah.

SPEAKER_01 17:41

The whole thing is, you know, how does, let's say, how do how does Again, putting aside AI, sort of if you go back through sort of, you know, back to medieval times and all the way through, how do people get duped? How do people get scammed? Call it what you want. It's basic because it it's acting on sort of a you know, you know, acting on it's all acting on emotions. So many people are acting on emotions, people act on when when it's emotions, uh a call to action at a timely sort of event, if you like. Yeah. Or the other thing is the classic thing is if it's too good to be true. It's so so it's it's it's no different to what's always happened, or you're doing now is using an AI agent to do that quicker, more sort of you know, more you're doing it quicker, there's more of sort of, you know, uh sort of personalization, if you like, that was evident for. So that's no different. So so how do we sort of protect people? How do we get sort of better at that? That's gotta be education. I can't think of any other way right now. And sort of it's just a double check. And I think it'll get harder and harder. You see these AIs now, the whole thing's around. We've seen what's happening in Grock right now, for instance, it's been used for for interesting things, let's say.

SPEAKER_00 18:49

Yeah.

SPEAKER_01 18:50

But if that's you know, and that it's it's things will they'll that's only gonna get more and more now, isn't it? It's only gonna get more and more convincing, isn't it? And um, you know, is it really me that you talk to right now? Am I really talking to you right now? I don't know. But this is it. So this holograms is it's it's yeah, it's you know, I I would sort of say, I think, you know, I think we all have to accept that at some point we are, you know, as as even as you and I sit here as a as technical savvy, we are as worldly wise as we are, we are also vulnerable to these threats as well. That's just that's just the way it isn't.

SPEAKER_00 19:26

It is a is a reality that we need to face, that those who are on the other side are also getting equally tech savvy and smarter in how they address or continue to extort from the innocent. And and this story is so important because it makes the risk personal and real. It could be you, it could be me, it could be anyone. It's not it's not an abstract data breach. It's actually a human being getting hurt.

SPEAKER_01 19:54

Well, exactly. It's like, you know, you can protect yourself for the risks, you know, for instance, so uh you you will lock your door at your home at night, for instance, or during the day, you will, I don't know, uh just be careful of your surroundings as you walk through the streets, uh, you will, you know, do all the things you should be doing, you know, uh lock your car and all these things because you know those risks, you know what those risks are. There's risk patterns, there's there's there's you know, there's uh there's these things that happened before, so you could not so much do you, but you know society, these things happen in society. So so on that basis, you you could protect yourself against what you know, what you can see, but when you're up against things that you can't see and also don't exist right now, how do you get better at that?

SPEAKER_00 20:40

Yeah, that's so true. And it proves that our responsibility doesn't not end with deploying the technology. We also have to manage its societal impact.

SPEAKER_01 20:52

Yeah, exactly. Yeah, yeah. And that was never, if you like, yeah, but if I go back to you know the early days of my career, that was never really a consideration. Because you think of technology, what technology would do, technology is there to build efficiencies, and it still is to build efficiencies, that's why you do it, so you can do things what you've always done better, quicker than ever before. But actually technology's moved on from efficiencies now to to actually performing tasks that probably weren't performed before. So, in other words, what you do before is you know, you you you you took a repeatable pattern and you done this quicker and more efficient effectively. It's it you can take on more than that now. It can actually do things that weren't done before. Uh that was never a with you give, uh, make decisions for you that you weren't making for argument's sake. So that's all good, that's all all all kind of what we want. Because human inertia is we you know, we we always evolve, we always get better at things. If you go back to sort of our ancestors, if you like. But again, you're up against things that we don't know right now. That that's the scary life we live in. Scary, challenging, exciting, all those things.

SPEAKER_00 21:56

That's true, and this brings us to governance. You mentioned the need for safeguarding of AI agents and a whole customer care to manage them. What does a good governance framework for a city's AI systems look like? Who is accountable when an AI system costs home?

SPEAKER_01 22:16

Yeah, and that that's a good question. And this is where in terms of who is accountable, I think this is only going to work where you've got the municipality or the city at the central things, because in theory that they they are sort of responsible or accountable to their stakeholders. So the AI governance has to be citizen first. And that's not, yeah, I mean, you would argue that's no different to a point that's not where where AI should governance should be consumer first as well. But citizens have their own challenges. Whereas a consumer, you have you have a choice to consume services or goods. Whereas a citizen, you kind of have a choice, but you also have a sort of, you know, you you have a trying to think of the right word, but you also have a you have a requirement, you have a need, you know, healthcare services isn't the same as sort of you know ordering something of Amazon, for instance. So that's that's you know, it's it's very different. And it's also going to be it's you know the the the the the driver for it or or the the consumption of the goods or services from your local authority tends to be sort of um reactive as opposed to proactive. So unfortunately, neither of us have the the the the uh the opportunity to sort of decide on when we're gonna be ill, unfortunately. Um so so it's a very different sort of you know, so and also panic or or the the whole distress puts you in a very vulnerable position you might never done before before. So it is it is an interesting sort of point, but again, it goes back to the municipalities or the cities have to take some responsibility, and I don't think we see that much right now.

SPEAKER_00 23:51

You know, it's a crucial point about accountability because it's not enough to say the algorithm did it. You know, you can't blame the algorithm when it comes to the impact, social impact, if something goes really wrong when it comes to AI systems, yeah. Smart CTs.

SPEAKER_01 24:09

Look, I mean, if you if you think of the C-suites within local authorities or or sort of uh sort of local authorities, municipalities, actually from a C C suite perspective, you don't really or very rarely get a you know position of a CIO, and you've got a CTO sort of trying to cover a CIO perspective, and that's that's really sort of data management. But again, there's no governance around that, uh how the data is going to be used. So actually that there's there's a complete layer missing there.

SPEAKER_00 24:36

Yeah. And I agree, there has to be a clear line of human responsibility.

SPEAKER_01 24:42

Yep.

SPEAKER_00 24:42

And it's why we advocate for an intentional governance framework. At at Anaya Group, we have to design the accountability structure with the same care you design the technical architecture.

SPEAKER_01 24:56

Yeah, for sure. Yeah, no, exactly. Uh and again, from the consumer's perspective, you could argue it's very different because consumers have a choice. But there's a there's a question, there's a very blurred line right now, I think, in terms of consumer choice. But that's a different thing. The citizens are very, very different to consumers.

SPEAKER_00 25:15

Absolutely. So to bring it all together, what is the one principle you would urge every city leader to adopt to ensure their smart city journey is also an inclusive city journey?

SPEAKER_01 25:32

Yeah, that's I would have to pause on that question right now because I don't think I I've kind of so so I've got the problem statements, or we know what the problem statements are, but we don't really have all the solutions, so we're still trying to work those out. But it has to be yeah, it it's it's a tough one because I also understand the challenges because there isn't one citizen. There is the avatars of citizens are very varied. So these avatars of a citizen varies from city to city, and unfortunately, so there isn't a sort of a a carte blanch that you can put across all cities, but it it's getting those avatars together first, understanding what those avatars are. There cannot be that many avatars per city, and building framework across those avatars. So I talk about that in terms of sort of children, wonderful children, adults, wonderful adults. So those are the spectrums, but in that there's there's there's varies as well in terms of what those avatars are, and sort of trying to build a framework around those avatars, I think is probably the way it's a starting point. Uh you you may, I mean, I don't know what what your thoughts are. Would you agree, disagree? Just from your point.

SPEAKER_00 26:32

I completely agree with you. I think the conversation needs to be brought to the to the people at the right table and ensuring that there is a governance framework that ensures a continuous review of whatever was used to define the the design process with the for for any AI system. Because because you don't know what you've missed until you've given yourself a chance to review again with fresh pair of eyes. And that governance framework needs to be a living governance framework. This should be one that keeps being reviewed.

SPEAKER_01 27:07

I don't I think but when you so so when when I answer that question, I I sort of or try to answer that question, I didn't I don't think I answered it very well. But then, you know, how do you get into those those the call it the avatars uh or the stakeholders? Because your your window into to municipalities tends to be through governing authorities, councillors, MPs, uh, and there's biases already, depending on sort of, you know, you know, without getting political. So is that framework outdated uh or is that framework not fit for purpose when it comes to sort of AI? I think there is a question to be answered there. Um so so yeah, so so we we need, you know, the the window in probably isn't the traditional sort of counselors or or or MPs. It probably is something different.

SPEAKER_00 27:56

Yes, exactly.

SPEAKER_01 27:57

I think so. I think I think that's the answer. I sorry, I think I'm clear on that. Because just normal council or normal dominating isn't isn't gonna give us what we need.

SPEAKER_00 28:07

No, absolutely. And even on that note, you could argue whether the gov that governance framework is even existing in the first place.

SPEAKER_01 28:15

Oh, big time, yeah, yeah, yeah. Um, I I agree. It doesn't exist right now because there isn't anybody to take the data rot to or ask the question. Or sorry, push down if you like. Think of the push down perspective. The framework, if you like, there isn't any sort of single city. So if I take the research we've been doing right now, for I could say across UK local authorities, we have, I think we count in, and again, it differs from city to city, but sorry, different types of authorities in terms of the size and makeup of the cities, not city to city. Sorry, that I got that wrong. But there's anything between 14 to 18 data points of a citizen. So I uh and I haven't got all of them, but if it if you think of you know, you you pay your council taxes, you may get DWP payments, you may have social needs, you have health needs as citizens, we all do. We have a sort of refuse collected, which which pretty much is all of us, depending on which even that's controversial right now. So by the end of that, you you're up to 14 to 18 data points. And as in if I take the UK for argument's sake, we we we are moving towards devolution now, uh, which will you'll see more and more services delivered by the authority. So police services, uh, ambulance health is going to be more and more sort of you know, even sort of general sort of practice of health, then actually the data points sort of increase more and more, but then they're they're discrete, distinct data points. So when you've got distinct discrete data points, then you have no sort of, let's say, uh sort of data framework or AI framework or a CIO, even let's go with the CIO, it's the chief information officer framework. How do you bring that data together? How do you make any sense of that data? And how do you start to draw parallels from that data to do those data-driven decisions?

SPEAKER_00 29:56

How pal, this is the perfect principle to end on. Really good advice and one that should be taken away, and we can do more to help our cities to become, I would say, much more equipped to support the society. So thank you for bringing such a necessary and human-centered perspective to this important topic.

SPEAKER_01 30:20

Thank you.

SPEAKER_00 30:21

What a vital conversation with HAPHAU. My key takeaway amongst many is that a city does not become smart just by adding technology. It becomes smart when it uses that technology to become more inclusive, more equitable, and more responsive to more responsive to the needs of all its citizens. The human impact is the only metric that truly matters. So to connect with HAPAL, please visit the links in our show notes. And if you are a leader working to build your own intentional data driven strategy, you can learn more about our frameworks at Anaya Group by visiting anaia.org. Until next time, keep leading intentionally. Thank you.

SPEAKER_01 31:12

Thank you.