What's Up with Tech?
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With over three decades in telecom and IT, I've mastered the art of transforming social media into a dynamic platform for audience engagement, community building, and establishing thought leadership. My approach isn't about personal brand promotion but about delivering educational and informative content to cultivate a sustainable, long-term business presence. I am the leading content creator in areas like Enterprise AI, UCaaS, CPaaS, CCaaS, Cloud, Telecom, 5G and more!
What's Up with Tech?
Unveiling the Future of Dynamic Work: QuickBase's Guide to AI Integration and Productivity Mastery
Interested in being a guest? Email us at admin@evankirstel.com
Join us as we venture into the transformative realm of dynamic work with Peter from QuickBase, where the fusion of tools and systems isn't just fantasy, it's the future. Embarking upon a quest to redefine productivity, our enlightening exchange with Peter reveals the secret sauce for AI implementation: a robust data foundation. We don't just skim the surface; we get into the nitty-gritty of creating bespoke business processes that not only streamline decision-making but also fortify against risks with predictive analytics. Get ready to absorb strategies that will help you structure your data and choose AI platforms wisely, ensuring your business remains at the cutting edge.
Ever wondered how to manage a multitude of business projects without breaking a sweat? Well, Peter from QuickBase is here to share the blueprint for success. Our discussion unfurls the '75/25 rule'—a master plan for blending central governance with end-user innovation. By pulling from real-world examples in manufacturing and construction, we illustrate how centralized dynamic work management can revolutionize supply chain and resource management. This episode is a treasure trove for anyone looking to enhance their toolkit with AI predictions that help steer clear of project pitfalls and drive efficiency to new heights.
As we gear up for a bustling year, we wrap up with a moment of gratitude for Peter's invaluable contributions and a tip of the hat to QuickBase for their arsenal of resources. Whether you're a seasoned pro or just dipping your toes into the world of dynamic work, this episode is your ticket to staying ahead of the curve. So, grab your headphones and let's tackle the year ahead, armed with the wisdom of dynamic work and AI integration from the minds at QuickBase!
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Hey everybody. Super interesting chat here on a Friday on the future of dynamic work. What is it? And you're with QuickBase, Peter, how are you?
Speaker 2:Good Thanks for having me today. I'm really excited.
Speaker 1:Well thanks for being here. Really casual conversation, but a topic I'm intrigued with. Before we dive in, maybe introduce yourself. More importantly, who's QuickBase for those who aren't familiar.
Speaker 2:Yeah, so I'm a fellow solutions consultant here at QuickBase. I've been at QuickBase for eight years and here at QuickBase for a software company really kind of leading the charge with dynamic work management. We are a platform for dynamic work You'll learn all about it today, I hope and our goal really is to help make teams, organizations, especially within really complex industries, more productive and more efficient.
Speaker 1:Oh, it's a great mission, especially these days and the era of efficiency and doing more with less. But maybe explain a little bit about the genesis of QuickBase. Ears is quite a great run. Tell me about the mission of QuickBase. So what is dynamic work? Maybe we can start at a very, very high level as we dive in deeper.
Speaker 2:Yeah, the way I think about it on a high level, it really comes from the challenges we've had with digitization over the last 10, 20, 30 years and if you can think about the onset of software and the internet, what you've seen a lot, especially within industries like construction and manufacturing, is kind of bandaid fixes for very specific problems. So you can go into an environment and see purchases for all different types of tools and systems and over 10, 20, 30 years.
Speaker 2:What you have now is a whole lot of potential for productivity and a way to work better, but a whole different a lot of systems that people are using, from ERPs all the way through spreadsheets. So not many companies have really stepped back to think, well, how can we look at all these things holistically and then how can we work with platforms and tools that are meant for kind of the way we need to work today, which is across these types of tools and systems? So that is really the genesis for why we're really defining this category and positioning our tool as very unique within this category, and it's resonating.
Speaker 1:Well it is, and your timing is amazing, given the importance of preparing for AI that most companies are looking at and integration of AI into their workflows and tech stacks. What are the key things that an enterprise, a business, needs to prepare for for these sort of step-by-step implementations of AI into every aspect of the business?
Speaker 2:Yeah, so AI is coming up a lot today. Ai isn't a new topic, as you know. I really think of it as the cherry on top of a data strategy pyramid, so you can't really get the benefits at the top without really having the foundation, which is the very bottom, is people, its systems, its processes and it's really making sure that your data is structured, that it's stored and then it's accessible. So the steps to be able to get the benefits of AI all the way at the top is really kind of focusing on how do we centralize the functions within an organization that deal with the bottom and we can get into the steps. But what I always recommend is, when you do centralize that kind of gets a little scary.
Speaker 1:It's like why don't I want to?
Speaker 2:put all the decision-making into one single function. You centralize with flexibility in mind, and that's why I? Don't see a lot of organizations do is create standards, but also with the recognition that your teams and your businesses and your functions are always going to want to kind of customize that last mile and once you get everything together, once you get its structure, once you combine kind of the big rock systems with the little rock tools, then you can start to answer the questions that AI really wants to answer, which is well how do I get?
Speaker 2:insights out of all this data that maybe I can't believe just by looking at it right.
Speaker 1:Yeah, totally great insight. And you're on the front lines working with clients and real-world applications. What strategies are you seeing them adopt to kind of build the foundation for AI and get a return on that investment, even though it might be an incremental approach? What are you seeing and hearing from your customers, your clients, today?
Speaker 2:Yeah, to me, to really get a benefit from AI, you have to. It's an additional competency on top of your day-to-day work.
Speaker 1:And this is something that we're doing at QuickBase today.
Speaker 2:So it's step one is you have to have a working council, some group in an organization that can look across all the different pieces that will touch AI workflows. And that's going to be hey, how do we use AI technology to make ourselves more efficient internally? And that's just. We have a lot of data in our organization that we can use to serve our customers. So there's that piece of it. And then, oh well, how do we use tools, things like generative AI, and expose that out to our customers that want to be able to interact with us in a little more intelligent way?
Speaker 2:And then from there it typically gleam kind of like the processes and flows that dictate what technologically, you need. But really it starts with a core working group and then you could identify well, what are the vendors in this space we need to partner with? And then what I recommend that in solutions for years is you've got to kind of have your outcome of success in mind. We're going to be in that nirvana state when somebody could go into a window, ask this type of question and get this type of insight.
Speaker 2:And that's where I think a lot of solutioning starts is asking the right questions as to what you want to get on the other end of the tunnel. And then you work backwards to figure out well, what are the data, flows and pipelines and moving pieces we need in place to get all that information in one place, and then right.
Speaker 1:Yeah, you mentioned data blood as kind of data being the lifeblood of AI. I love that term of R, I'm going to steal that. But what is the significance of having a clear data strategy, knowing where your data is stored, how it's managed? That's a fair amount of work up front.
Speaker 2:Yeah, absolutely. It's not the easiest and most glamorous work because you really have to go back in time 10, 20, 30 years. Your data is in a lot of different systems. It takes a lot of different formats. So I think, as part of the strategy, you have to consider that data isn't just all the orders from our customers in the last 100 years. It's also the stories and anecdotes. This thing broke down because a certain person recognized a certain thing, that it's the tribal knowledge. There's data at tribal knowledge. There's data in events and sensor data. There's data in activities what somebody did to cause a certain result. So I think part of the strategy is identifying, well, what are all of the sources of information that could potentially be valuable in answering a business question. And then how do we instrument our processes, our environments, our tools to give us that data and then how do we structure it?
Speaker 2:And, more likely than not, that data isn't all in one place.
Speaker 2:You're going to have some data maybe in a data lake, a data warehouse, some of it in an ERP, mrp type system, maybe in legacy databases, and I think what we're seeing a lot of is organizations going source by source by source and answering the question is this the right place for this information?
Speaker 2:And, if not, can we go to a world where data is more accessible and can we consolidate and can we eliminate? And the trends that I'm seeing, at least for the kind of organizations that really value data, is they're investing in cloud-first, cloud-native technologies. So things like a cloud-native data lake, data warehouse, but then things like no code, low code, dynamic work management platforms that can almost put that data to work, because too often what I'm seeing is data is in a warehouse and you've got like an analytics team that can build out reports and really to put data to work requires a way to put people in the loop with data, which is, hey, here's an insight, I'm going to take an action, I'm going to see a result and hopefully whatever thing I'm trying to improve actually starts to improve, and then so what you want is that cycle where the trend line goes down in the sense where the performance goes up, and that's people in the loop with the information, making it change, improving your process and then feeding that back into a positive cycle.
Speaker 1:Brilliant and talk about that transition from a world of more structured, planned work to dynamic work and how an organization approaches this shift, this change in mindset and project planning and objectives. It's quite a big shift, is it not?
Speaker 2:It is and it requires. You know, I think classically what you find is the ownership over solutions in a small group, and that means one single funnel for hey, I need a widget. That group takes that as an intake and then provides that widget to the group that needs it, and inevitably what you end up with is systems that are overly customized or not really flexible to what individuals need. And dynamic work is all about creating more systems and recognizing that a solution is a combination of tools, platforms and people, and so when you think systems rather than individual, you know one-off solutions. You have to almost take more time on the discovery and the upfront. You know identifying the processes and then you're designing for you know I think we talked a little bit about the 75-25 rule You're designing for standardization, for maybe 75%, and these are the core things that as an organization, we really want to capture horizontally across the board. But then you also need to design for that last mile of flexibility that teams in the business are going to need to work more quote dynamically which is hey, our processes out in a certain region are going to be region specific.
Speaker 2:We have additional approvals. The way we order materials is a little bit different. The way we request changes is different. So you have to kind of pick the things that are, you know, in the sand that you want to standardize on but then provide capability to the business to close the gap with that last 25%. And we see this a lot in manufacturing and construction, where tools can be defined only to a certain point, but then the way local teams operate, they need to build out the last piece. That also means you need to invest in training, enablement and providing regional resources that can take the standard rules, the standard operating procedures, how you govern data, but then also enable the business to use that as a platform to build custom solutions at the edge. So it can be a bit nuanced, but hopefully those principles kind of help guide how you think about putting in a strategy for dynamic work.
Speaker 1:Oh, great insights. And so let's say you've laid the foundation, you have a plan. How do you see clients going about identifying those first practical AI use cases? And maybe what have you seen in terms of the low hanging fruit out there that can be addressed?
Speaker 2:Yeah, well, I think the thing that people want to do the most with AI is make smarter decisions but, also do something that helps with forecasting and prediction, because what are we trying to do?
Speaker 1:We're trying to increase value for one, but really one way to increase value is to avoid risk.
Speaker 2:Well, how do risks come up In industries? It comes up by you're missing on a budget mark, you're spending more for something, or you're not hitting a certain delivery window or a parts missing, and there's going to be things that come up that are totally out of your control.
Speaker 2:But, what I think of is that data actually has the keys to. There are actually some things that are in your control, in your organization, and that insight is in your data. What you can do with AI is define an outcome which is an unhealthy result so a project is unhealthy, an asset is unhealthy and then define all the things that can affect that input. So, when you think about a project, what are the things that affect the success of a project? Maybe it's? What month did it start on? Who's the project manager? What was the budget cost, the actual cost?
Speaker 2:So most organizations today have enough information historically where they could have some understanding of the outcomes, the chances of the outcome of success, based off of that data. That's a pretty low hanging use case that organizations can put to work today. Then you can get a little bit more intricate with fine-tuning a model where you can actually look at the details within a project or within an asset and say, well, okay, based off of how we spend money, based off of how we repair these assets, you actually could start to predict. Well, it sounds like during a certain period of time, things tend to fail more Well, what are the reasons behind that? So all that to answer your question, is it's the predictive nature of AI based off of history is a huge area. That's what's practical, I think, for operations where you don't need these massive, complex models. You really just want to better use your information to assess an outcome beyond just what you can see. So I think along that's what comes to mind when you first asked that question.
Speaker 1:Yeah, fantastic. We know you're from quick base so you might be a little biased with this next question, but it's overwhelming the number of tools and platforms and approaches out there. How should an enterprise go about looking at defining a solution that meets their actual needs and putting that into production? It's overwhelming.
Speaker 2:Yeah Well, without being too biased, I do recommend pillars that you want to look for when thinking about platforms that address dynamic work. So the first and really the foremost, is access and connectivity. So data is a currency, and to get data requires a way to push it and pull it between systems. I can't tell you how many times I've had to look at tools for our customers and find that, well, we really can't get information out of this thing. It has not been built or instrumented.
Speaker 2:Or I'll talk to companies that are looking to migrate from tool A to tool B and I'll do a little research and I'll find that, hey, this thing you're migrating to. There's no way to get the information out without saving it to a spreadsheet or something manual and backward.
Speaker 2:So make sure that the tool has some sort of programmatic interface. The data that's most important and the common thing. There is some sort of restful API that structures it in a common way. Even better than that is some automation interface, so that not only can I get the data, I can set up routines where, once a day, I can push and pull information to your other tools and system.
Speaker 2:So it may not be the thing that comes to people's minds at the top, but it's someone that's worked on integrations and implementations. I absolutely value that one. Two is can you structure the data? I see so many companies that are going to kind of simple tools that are rows and columns and just basic sheet trackers, which is great for small projects, but what.
Speaker 2:I'm finding within complex industries is they really need a good relational structure database and a way to very easily map information to data forms and then to put rules and validation on those forms so that you can define different roles and you can define different access permissions for those roles. And so part of dynamic work is recognizing you have different roles that play a part in moving work forwards. Those roles are going to want to touch information differently, interact with it differently. So you really kind of need a way to combine the database with the interfaces, with the roles, the rules and permissions. Then that leads to the third thing I really say to look for is reporting an insights, analytics.
Speaker 2:You can't forget about that. Some of our customers will have central BI teams that have a Power BI, but you lose something when you have to push it to an analytics tool. Find a way to integrate the reporting, the KPIs, and make sure there's a lot of flexibility to customize those interfaces by role.
Speaker 2:That way, a production leader versus a project manager versus a controller versus a field technician can all have different entry points to the data. We've already defined roles and permissions such that, down to the field level, you can control whether they can edit it, view it or have no access to it. The reason that's important is data integrity. We've talked about AI is only as good as the quality of the data within it. One of the problems we see is the quality is bad because there's really no governance around how it is used and handed off from part A to part B to part C. That ultimately leads to the access to the data should be both browser-based but as well as mobile. We're seeing almost you need to have a mobile-first approach. It should be relatively easy to take something you build and you need adoption of your tool.
Speaker 2:Adoption comes to having interfaces that folks in the fields are comfortable accessing and providing the data input to. Because, again, quality of data. If you don't have dynamic work, all the participants adding to your system, adding to your ecosystem, then you're missing out on a huge source of valuable insights for AI. We provide all those pillars, from the integration to the automation, to the forms, the data structure, the rules, the governance. That's why we're really passionate about this space, because we think there's a lot of opportunity to disrupt some of the challenges in implementing dynamic work. But those principles I mentioned are universally applicable and things that I suggest everyone looks for, whether it's a no-code platform, a low-code platform. Don't miss out on one of those key components.
Speaker 1:No, I love that. That's a really great insight. Talking about that dance of flexibility versus standardization, what's your impression of adoption of low-code and no-code? How are your clients and others adopting those in real life? Is it working to give them that flexibility they are after?
Speaker 2:It works. I've seen both ends of the spectrum. Really, if you go 100 percent of flexibility, it's almost like you get the chaos, because what you don't want to do is provide one platform without rules, without governance, without standardization. You don't get any of the benefits of using dynamic work management platforms. You just get Excel in the cloud. That is one avenue that it's easy to take if you don't take the time up front to ask the question what does 75-25 look like for us? I typically recommend the 75 percent piece is owned by teams in the center that can look across the business and the need and help to deploy applications in a way that they scale. The 25 piece is saying hey, when we do deploy a dynamic work platform, we want to enable the last mile of end users to customize the tool. It's on us in the center to give them guidelines, to give them access to data, to give them a process or a set of processes for learning how to use this tool and also for creating new applications. Because what you don't want to do is we've seen this a lot where they'll create the same work order tracking system in 10 different flavors across 10 different units. So, especially in industries where you have standardization.
Speaker 2:From a company standpoint, you have the opportunity to standardize your applications. You have to ask the question what is our process for sharing across region A to region B, to make sure we're not reinventing the wheel? Then you have a nice hub and spoke model where you can centralize best practices, centralize common patterns and applications, and then, when somebody wants to build something new, we say, hey, we want you to build something new, but let's first check to see if this already exists in the library, as it were. Then only once we say it hasn't been solved yet, can you deploy it out. So you got to ask the question first, do we value, say 5.25, and then from there you go on to okay, well, how do we make that work for the way our organization operates?
Speaker 1:Well, brilliant insight there. Let's dive into some real-world applications examples, industries that you're involved with. Any favorites come to mind. I know you don't want to pick a favorite child, but you're going to.
Speaker 2:Well, I've got some children like more than others. I'll say that I want to say that if I like kids. But we see this problem that we've been talking about dynamic work management and the struggle to manage data and people and processes where there's more complexity and there's more volume of work and moving parts and data. It's probably not a surprise that that comes up in the manufacturing world and in the construction world. So the examples that come to mind are some of the largest construction companies are using us for strategic supply chain. That's an example of a space where you don't just have to track the widgets and the moving parts. You need to track information that goes down the supply chain and back. That's an area of opportunity where dynamic work cuts across company boundaries.
Speaker 2:As a Tier 1 supplier of a good or a service in the pandemic obviously accelerated this I need to have visibility to what are the lead times on the most critical parts in my business. Well, instead of calling up each vendor and asking what are the lead times for certain part numbers, you build an application and you include the vendor directly. You give them siloed, segregated access so they can't see what's happening to the vendor next to them, but you give them a window to push and pull lead times from their manufacturing process into a single app. So as the provider of the end solution, I now have a real-time view into the risks of delivering on promises and a way to quickly extend more parameters that I need captured to my strategic suppliers. That work started years before the pandemic started. It was just they really capitalized it when they already had that collaboration set up. So supply chain, I think in manufacturing, I think it's a huge area to really look at. For this we also see individual release management, like manufacturers prefab, where you really want to look at your system for what has been ordered and how many parts and when does it need to be shipped by to what's on the line built and are we going to be able to meet that demand on an hourly basis.
Speaker 2:So a lot of times that information needs to come together into a single world where you can see where are we, where are we right, green, yellow and what action can we take to rejigger things. Then just a lot with resource and material management I mean job sites is just so many moving parts when it comes to making sure that trained people are pipelines to support key projects coming up, then people as an asset is almost up there with equipment as an asset. In addition, you need to make sure you got the right people on the site. You need to make sure you've got the right equipment that can perform the right duties on site.
Speaker 2:Dynamic work management platforms are great for looking at a project through a single pane of glass and all the things that are needed to make sure that project has the highest chance of successful outcomes. Then you got the AI piece that telling you hey, based off of how your project is tracking, we think you're on track to list XYZ risk. That's really where the full benefit of AI comes in is, as you move through a project, you can look at your past misses and have them surface at key junctures At least. You can't predict the 100% accuracy, but you can at least provide some flags to the project manager to look into something. You're not caught by a surprise and there's always going to be surprises To the extent that you can control them. That's the promise of AI. That is a win.
Speaker 1:You have an interesting event coming up. I see a call for speakers here and clearly construction, one of the most complex industries out there. But things like solar imagine large scale solar EV charger deployments, manufacturing, state, local governments. Will you be speaking at Empower 2024?
Speaker 2:Yeah, I just came off the solar conference. Believe it or not, I'm very excited to see our manufacturing solar construction customers at Empower. I'm heavily involved in making sure that event successful. I've spoken within the past, so I definitely will be playing a part this summer as well.
Speaker 1:Who are the folks that are planning these sort of implementations of quick based these days? That are the traditional Roles or they're kind of new roles. You're hearing about chief AI Officers and all new work description happening.
Speaker 2:Well I say quick base tends to force a company to In non to non standard processes. So the typical roles that implement software tend to not be the typical roles that implement quick based. The folks that find quick based are the ones that want the dynamic work, want the flexibility, and they're the ones that are typically Not being served entirely by more the central organizations Just because of the backlog and and the time it takes to deliver those solutions. So we're often finding operations leaders are our finding quick base and Implementing it initially, but over time. And where we recommend is it's got to be a close partnership your business leaders.
Speaker 2:If you're not figuring out ways to partner with the central shared services and IT as quickly as you can, it comes to haunt you at some point down the line. Because the Strategy, the execution of data strategy, needs to be a partnership with all the teams that have access to the data, as well as the Teams that are going to be creating the data and using the data. It can't be one or the other so, but in our world it starts with folks that aren't quite usually familiar with how to implement technology, but they have the problem, but then they realize they totally have the tools to do it themselves. That's the promise of dynamic work, and so they have to kind of figure out what are the best practices to take from. You know that the teams that have been doing this for 10, 20, 30 years and so it is a hybrid where you have to learn from each other.
Speaker 2:The teams that are in the central Solving capability need to learn about business requirements and use cases, and then the business needs to learn. But what are the threats, practices for deploying these types of things into the field and Producing the risk and really getting the value out of the tool in a way that that can scale and grow over time?
Speaker 1:Fantastic insight. Well, it's all very exciting to watch and I can't wait for and power, any other trips, travel events coming up. I know you've been on the road already. What's on your mind? What are you looking forward? To the next few weeks and months.
Speaker 2:Well, we're gonna be at the um Nate conference. A acronym is alluding me over National.
Speaker 1:Association of Tower Rectors.
Speaker 2:Yeah, that's what I'm talking about.
Speaker 1:They're amazing organization.
Speaker 2:Yeah, so we'll be. We'll be there soon and then, I know, later in the year and I typically Participate at ground break which pro course conference Autodesk has a conference for builders and manufacturers and Looking for a lot of opportunities just to the network locally too with with other large technology companies, because we think every large technology company is looking for ways to provide flexibility and dynamic work for their customers. So you know, between those three or four there's gonna be a bunch more than a little more at hot. We've got a busy year ahead.
Speaker 1:You do indeed, and thank you so much for taking time away from it just to share your vision and mission. Amazing insights. I learned a lot and Everyone thanks for watching. Reach out to quick base. They have some great content out there and really educational stuff, so I always like visiting their profiles. Thanks everyone. Happy Friday, have a good weekend.
Speaker 2:Thanks for having me on. I was gonna talk to you.
Speaker 1:Take care.