Host Jim Whittaker sits down with ISO TC 267 Work Group 6 members Mahnaz Ensafi and Dipin Kasana to discuss how international standards and high-quality data drive successful digital transformation in facility management. They explore the core principles of data quality, the necessity of responsible AI governance, and why FM practitioners must focus on defining business outcomes before deploying new technology solutions.
For years, facility management relied on local habits, tradition and gut feel. Today, international standards like ISO 41001 and the work of ISO TC 267 Work Group 6 are creating a shared global language that connects data, technology, and business outcomes.
In this insightful conversation, host Jim Whittaker leads a deep dive with Planon Solution Architect Mahnaz Ensafi and JLL Director of Data Insights Dipin Kasana on how FMs can navigate the changing digital landscape.
Together, they discuss:
Whether managing a single building or a global portfolio, discover how trustworthy data and standardized frameworks help quantify value and drive strategic decision-making.
Dipin Kasana: [00:00:00] in this changing environment, there's no standard way of documenting or doing one time fix of the data and we really need to have systems and governance in place so you are continuously monitoring the information, but at the same time updating it to meet our desired outcomes and goals.
Host: Welcome to ConnectedFM, a podcast connecting you to the latest insights, tools, and resources to help you succeed in facility management. This podcast is brought to you by IFMA, the leading professional association for facility managers. If you are ready to grow your network and advance in your career, go to IFMA.org to get started.
In today's episode, host Jim Whitaker is joined by digital subject matter experts, Mahnaz Ensafi and Dipin Khasana, who serve alongside him on ISO TC 267 Work Group 6. Together, they unpack [00:01:00] why the FM profession needs a unified voice in digital data standards, how to establish trustworthy data frameworks, and what it takes to effectively govern AI adoption across built environment technology.
Now, let's get into it
Jim Whittaker: Welcome. I'm Jim Whittaker, the global lead for innovation and optimization on JLL's technical services operations platform. Joining me here today are two talented digital subject matter experts, Mahnaz Ensafi and Dipin Khasana. Mahnaz is solution architect for Planon Software, and Dipin is director of data insights and performance with JLL.
Both of them serve with me on Work Group six of ISO TC 267. It's ISO's technical committee responsible for developing global [00:02:00] FM standards. WG6 is focused on digital data and technology. Ensafi and Dipin have a wealth of knowledge and experience in AI, data analytics, automation, built environment, technology.
So I'm excited to have this discussion to help FM practitioners understand why the profession needs its own voice in digital data and technology standards, and why outcomes and trustworthy data should guide technology strategies. Here's the thing. For years, we ran on local FM habits, experience-based traditions, muscle memory, gut feel.
Then came ISO 41001, the first global FM standard. Suddenly, FM teams across the globe had one shared language for how to plan, deliver, and measure their work. In 2024, we published a technical report that maps Tools transforming our buildings from smart buildings and IoT sensors to [00:03:00] platforms that turn raw data into better decisions.
Standards are what make data consistent and comparable across every site. They help create data topologies that quantify outcomes, enable benchmarking, improve the value FMs deliver every day. So whether you look after one building or a thousand, this is where good decisions start: high-quality data. So let's get into it.
I'd like to start by framing digital data and technology as an FM management ecosystem. The aim of WG6 is to develop international standards to support digital transformation of facilities management. So the first question is to both of you, what does your involvement in WG6 look like, and what are some of the outcomes you're aiming for?
Dipin, let's start with you.
Dipin Kasana: Thanks, Jim, and delighted to be part of this podcast here today. From my perspective I'm a young professional just getting introduced and involved with the [00:04:00] work of ISO, and I've read the technical report of 41016, which puts out a great foundation of what we are planning to do with the WG6 group.
Personally, the way I see my involvement is I come from a strong data science background, working very closely with FM professional. And as we all know, the, the speed at which the technology is moving and the data is a very integral part of that technology movement, I believe we need to communicate better how FMs should be involved in this entire process or in this change or in this journey.
So I plan to communicate some of the technical data science concepts and, and lingos that can be easily understood by FM professionals, not only that they can understand and relate, but start applying some of, some of those concepts in their day-to-day job. So I'm coming from an angle, how do I make it easy for our FMs in the field?
Jim Whittaker: Great. Mahnaz?
Mahnaz Ensafi: Thank you. It's a pleasure to be here today. My [00:05:00] involvement with the working group is more focused on AI and AI governance, and that's because of my background and the dissertation, the topic I worked on. We're focused on how we can help a firm organization adopt AI in a way that would add to their outcomes and help support them with their operational needs.
There is obviously a lot of attention right now on AI, but adopting AI shouldn't be the outcome itself. We need to understand the problem and the outcomes we're looking for, the users that we-- that will be interacting, and the decisions that will be supported by AI. And then we will focus on the governance, which is also an important pa- part of this successful process.
We need to consider what needs to be in place for AI to be used effectively and responsibly, and that is in terms of accountability, transparency, oversight, and the people who interact with it understands the limitations and the capabilities together. So I think one of the outcomes we're hoping to get from this working group is a [00:06:00] guideline helping FMs to understand where AI can add value, what needs to be in place for the adoption, and how it should be governed over time
Jim Whittaker: Great.
Manas, obviously, AI is a huge hot topic these days, and you mentioned governance. Dipin, the importance of data quality. Explain data quality, ownership, and flow. What, what does good data actually look like in process? And why, why is it something organizations have to continuously manage rather than fix once and move on?
Dipin Kasana: Oh, great question, Jim. When I look at the data, I try not to get into very technical side of data, which we can go into a rabbit hole. But thinking from a very higher perspective or higher level, think of data as something that provides us information. Now, if we're getting information, any kind of information from data, we wanna make sure that it's trustable and usable.
And in order for it to be trustable, we have certain [00:07:00] principles that we can apply and then kind of understand whether this information is trustable or not. So there are four key principles that I would like to share with the audience today starting with the completeness, right? Like, we wanna make sure that the data is available before we even check for accuracy or, or anything else around that information.
Once we know that we have the desired or necessary information, we wanna make sure that it is valid, and that's where the data standards come into place. I know we'll talk a little bit more about why standards are very important, but basically, standards help us to bring together, help everyone to communicate the same shared messaging around what we are talking about.
A simple example I would say, I was thinking about last night is think of boot. When we think of boot, based on which country you're speaking, you will have different interpretation of boot. In England, the boot will be a trunk. Over here, a boot might be the car the normal shoes. But depending upon the individual and organization you're speaking to, there's a different meaning to the same [00:08:00] terminology.
And that's where standards become very important because how do we bring everyone together to talk about the same thing and define what that thing really means? So that's the second piece around the validity. We need to be make sure we're using standards so that we're talking about the same thing Third is consistent consistency, which is where we wanna make sure that the data is consistent across systems.
As we know in today's multi-model system, we have to operate in different technologies and different timelines depending upon where you're working at. We have data present at different stages and different systems, but they need to communicate and talk to each other. In order for that to happen, we need to have consistent data so that we can tell a cohesive and a comprehensive story.
And finally, last but not least the data needs to be current and timely. We... Depending upon the application, we often look at the historic information, we look at the current information, and make future decisions or prediction models. But that's where we need to understand that your [00:09:00] data is current, your data is up to date and meeting the desired standards.
To add to that last point, why we need to continuously manage and monitor data in today's world is the reason we are dealing with big data. When we talk about big data, we talk about high volume of information that is changing rapidly. So not only is the information changing, but the way we define our standards are also evolving as our outcomes are changing.
So in this changing environment, there's no stagnant or standard way of documenting or, or doing one time fix of the data and getting over, over with it. We really need to have systems and governance in place so you are continuously monitoring the information, but at the same time updating it to meet our desired outcomes and goals.
I hope that kind of helps our audience understand how do you manage the journey of the data.
Jim Whittaker: Yeah, it really does, Dipin. I love those points. Great points. I wanna pull on that thread of consistency of data across [00:10:00] multiple technologies. I think this is really important. Manoj, this is your specialty. So for FM professionals who are looking at new technology to manage all this data,
AI enabled or otherwise, what should they figure out before they even start evaluating those tools and technologies?
Mahnaz Ensafi: Great question. I think before evaluating any tools, organizations need to be able to define what is their actual problem without even talking about the tool or the product they're using or they're willing to be-- to use.
What they need to start with is what's, what, what is it that they're trying to accomplish? What is the outcome? Are they trying to create a new capability, or are they trying to improve a existing process? Do they understand how their existing process works? Can they map it out and then explain what's the actual problem?
What are the constraints and dependency? And this practice and exercise is very important, not just for buying work, going for a new [00:11:00] technology, but also for configuring it properly. Because a lot of platforms that you can use these days, they allow you to configure it based on your needs. And if you even buy a perfect technology and you don't understand your process, the configuration wouldn't support it.
The exercise can also help you to understand even if the technology is the answer to your issues or the problems you're trying to solve. Maybe the problem is with your process, that you need to simplify it or clarify your existing process. And an example I can think with facility management right now is the issue that a lot of facility may have with their service request taking a long time to process.
And so they decide to get a new product and with the great features, let's say automation, dashboards, everything, without going the, through the process of checking what steps they take currently. And then what ends up being is the same issue, long processing time, because they didn't understand that the actual problem was [00:12:00] unnecessary approval steps or categorizing inconsistently with their data.
So, I think that's a very important thing because otherwise you will just keep digitizing the same problem. The next step is the readiness. So Dipin talked about data readiness, which is very important, having good quality of data, but also the integrations and the securities. You need to collaborate with your IT team, and this is not this team or the other team task.
It's something that needs to be identified with a collaboration. And at the end, does your organization even have the capability to support this solution? So once you have the understanding of all these requirements, then you can translate them to, to points that would help you evaluate the technology.
And I think that's where the ISO or any standard and guidance can help to navigate this process to be able to identify what's next
Jim Whittaker: Well, I couldn't agree more, Manoj. Y- as somebody that's [00:13:00] deployed a lot of technologies in the past, the importance of having a plan, understanding the outcomes, and the right configuration is absolutely critical.
So Dipin, I wanna pivot back to how do you, how do you plan to address all of these issues and the importance of standards and these strategies in the Work Group Six given how closely it ties to the broader FM systems process? Kinda there's a lot there to unpack, but very quickly, you know, how are you gonna address all that?
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Dipin Kasana: When I was reading the past report, I think our previous experts have done a great job at putting together some of the foundational layout, and I think we need to build on what already exists. What I-- The way I see it, we need to define these data strategy platforms model within the space that have already been developed.
Talking about one of the, I think you might know, Jim, there are four foundational pillars that have been laid out in the technical report, which starts from the phys- very physical side of the facilities management going all the way to the virtual side. And there's a journey how may be talking about asset management or facilities management.
There's a journey from physical to the virtual world. But within that, how does the data platforms, data strategy should be laid out? That's where this ISO WG6 group will be very, very integral in defining [00:15:00] those strategies. So within each space are the foundational pillars that are laid out already.
The way I see it is we need to further develop them and define them within the context of data science, but as well as that is something relatable for FMs. Because we have to somehow communicate all of this in a way that is resonated by facilities management professionals. So that is the perspective that I'm trying to apply to this Work Group 6 documentation.
Jim Whittaker: Great. Awesome. Hey, I'm gonna throw a challenge in here i-in that, you know, that foundation is solid and standards are absolutely critical. Things change rapidly, and we see that with AI. So when you think of the new world and how AI is changing things that we do in FM, Manoj, I'd love to know this kind of the same kind of philosophy.
What actually needs to be in place for AI to drive real success and change in FM? And what steps [00:16:00] would, would organizations take that they often overlook it in a new consideration with AI?
Mahnaz Ensafi: So I think for AI to drive success in FM practices, there are a few layers that need to be addressed. The first layer is the foundation. That's what we discussed for the technology.
That applies to any product, any technology, that you need to know what's your problem, what you're trying to solve, what is the outcome you're looking for, understanding your processes, making sure you have the right data in place, and the competency and capacity for change. But the second layer is specific more to AI, and that's for how AI is being used and governed.
AI is moving beyond just providing data and makes recommendations, take action, and it can even operate with a great autonomy. So what becomes very important here is that we-- what governance do we have to establish around those and, and define those boundaries. So, the governance in terms of AI are [00:17:00] multiple factors.
One is the privacy and security, and that's what data it access and how does it store the data. The other item is the transparency. The people working with it, can they understand how it-- the AI solution is reaching a, a decision? Can they explain it? And it's very important for you to trust it if you don't understand it.
And then it's the biases and the fairness. Both the model or the data can systematically influence the outcome, and so you need to make sure there is no bias or at least you minimize those impacts. And the accountability. Whether the AI is making the decision or it's supporting the decision, there needs to be some sort of accountability in place.
And lastly, human oversight is very critical, especially when the risk and consequences of the outcome are important. So this is the s- the second layer, and I think the third layer is the ongoing oversight. Sometimes the organization focus so [00:18:00] much on the AI deployment that they just see that as a finish line, and it's just like the end.
But you have to have some criteria in place to constantly measure the performance of your solution over time, monitor the outcomes it's producing, and update if needed. You may need to update the data, update your operating environment. And even the AI changes over time, that may force you to make some adjustments based on the processes and what it's taking on.
So I think all of these are steps that can be skipped if you are focusing on what AI can do rather than what do you need, what are you trying to target, and what are the outcomes you're looking for.
Jim Whittaker: Wow, that's a whole nother podcast. What I heard in transparency, security, governance, accountability, over-- human oversight and interaction clearly a, a lot there.
Thank you. Well, in the couple minutes remaining, I wanted [00:19:00] to close with a couple actionable insights and a- advice. So for both of you, Dipin first, what's the simplest first step for someone just getting started should do? Picking one decision, tracing the data that it supports all the way through.
Dipin Kasana: Yeah. In today's world, with so many problems and so much data, it's often hard to pinpoint where to start, and I think that's where the first and the most important step is defining the scope. What are you trying to solve? So before even building everything, lay out what is the outcome, what potential data do you need to drive those outcomes, and then build strategy based on those data points and the scope you have defined.
So once you set the parameter, it becomes a little bit more manageable, and then assessing the problem, solving the problem, and continuously improving on it.
Jim Whittaker: Perfect. Decades ago, we didn't have enough data. Now we've got too much data. So a, a great [00:20:00] strategy. And with all that data, there's a lot of new technology.
So Mahnaz, with so many new tools out there, what's your advice for FM professionals trying to figure out where to even start?
Mahnaz Ensafi: Yeah, I wanna emphasize on what Dipin said. Again, don't start with the tool, start with the problem, start with the outcome you are looking for. And don't start with multiple problems or outcomes at the f- from the beginning.
Focus on one and start with a pilot project. So s- start small, don't just get a tool and apply it to your entire organization. Start with a pilot and measure the success, and learn from that implementation. Make any adjustments that is needed with the pilot, and be ready then to apply it to a bigger portion of your organizations.
The other item I really like to highlight is the training and the user feedback. These are not things that you wanna look into after implementation. They should be part of your process from the beginning. You need to understand how your users are gonna interact, and they know how to [00:21:00] use it before you put it in place and ask them to start using it.
Jim Whittaker: Great. Really enlightening. So y- you heard it here from the experts. So what, what I heard, harnessing the full potential of AI and digital FM requires a foundation of high-quality data. This is key to FM's pioneering business models, quantifying value, and achieving FM vision. And all the digital FM tools continue to change, but FM's responsibility remains the same: define the outcome, build trustworthy data, sound processes, and adopt technologies deliberately.
I wanna thank you both, Mahnaz, Dipin, really a wealth of experience, really fun discussion. Thank you very much.
Mahnaz Ensafi: Thank you for having us.
Dipin Kasana: Thank you, Jim
Host: Thanks for tuning into the Connected FM podcast. If you enjoyed today's episode, please take a moment to rate and review the show because it really helps us [00:22:00] reach more listeners just like you. And don't forget to hit the subscribe button so you never miss an episode. See you next time.