Host: Welcome to A.I.S. Insights, the podcast powered by Living Knowledge, where we translate complex research into actionable business strategy. I’m your host, Anna Ivy Summers.

Host: Today, we're diving into a fascinating case study called "Transforming Energy Management with an AI-Enabled Digital Twin." It details how one of Europe's largest energy providers used this cutting-edge technology to completely overhaul its operations for better efficiency and sustainability. With me is our expert analyst, Alex Ian Sutherland. Alex, welcome.

Expert: Thanks for having me, Anna.

Host: So, Alex, let's start with the big picture. Why would a massive energy company need a technology like an AI-enabled digital twin? What problem were they trying to solve?

Expert: Well, a company like EnergyCo, as it's called in the study, manages an incredibly complex district heating network. We're talking about over 2,800 kilometers of pipes. Their traditional control systems just couldn't keep up.

Host: What was making it so difficult?

Expert: It was a perfect storm of challenges. First, you have volatile energy prices. Second, they're shifting from a few big fossil-fuel plants to many smaller, decentralized renewable sources, which are less predictable. And internally, their departments were siloed. The production team, the network team, and the customer team all had different data and different priorities, leading to significant energy loss and higher costs.

Host: It sounds like they were flying with a dozen different dashboards but no single view of the cockpit. So what was the approach they took? What exactly is a digital twin?

Expert: In simple terms, a digital twin is a dynamic, virtual replica of a physical system. The key thing that distinguishes it from a simple digital model is that the data flow is automatic and two-way. It doesn't just receive real-time data from the physical network; it can be used to simulate changes and even send instructions back to optimize it.

Host: So it’s a living model, not a static blueprint. How did the study find this approach worked in practice for EnergyCo? What were the key outcomes?

Expert: The results were transformative. The first major finding was that the digital twin provided a single, comprehensive, real-time representation of the entire network. For the first time, everyone was looking at the same holistic picture.

Host: And what did that unified view enable them to do?

Expert: It unlocked advanced simulation and optimization. Operators could now run "what-if" scenarios. For example, they could accurately forecast demand based on weather data and then simulate the most cost-effective way to generate and distribute heat, drastically reducing energy loss and managing those fluctuating fuel prices.

Host: The study also mentions collaboration. How did it help there?

Expert: By breaking down the data silos, it naturally improved cross-departmental collaboration. When the production team could see how their decisions impacted network pressure miles away, they could make smarter, more coordinated choices. It created a shared operational language.

Host: That makes sense. And I was particularly interested in the shift from reactive to proactive maintenance.

Expert: Absolutely. Instead of waiting for a critical failure, the AI within the twin could analyze data to predict which components were under stress or likely to fail. This allowed EnergyCo to schedule maintenance proactively, which is far cheaper and less disruptive than emergency repairs.

Host: Alex, this is clearly a game-changer for the energy sector. But what’s the key takeaway for our listeners—the business leaders in manufacturing, logistics, or even retail? Why does this matter to them?

Expert: The most crucial lesson is about global versus local optimization. So many businesses try to improve one department at a time, but that can create bottlenecks elsewhere. A digital twin gives you a holistic view of your entire value chain, allowing you to make decisions that are best for the whole system, not just one part of it.

Host: So it’s a tool for breaking down those internal silos we see everywhere.

Expert: Exactly. The second key takeaway is that the human element is vital. The study shows that EnergyCo didn't just deploy the tech and replace people. They positioned it as a tool to support their operators, building trust and involving them in the process. Automation was gradual, which is critical for buy-in.

Host: That’s a powerful point about managing technological change. Any final takeaway for our audience?

Expert: Yes, the study highlights how this technology can become a foundation for new business models. EnergyCo is now exploring how to use the digital twin to give customers real-time data, turning them from passive consumers into active participants in energy management. For any business, this shows that operational tools can unlock future strategic growth.

Host: So, to summarize: an AI-enabled digital twin offers a holistic, real-time view of your operations, it breaks down silos to enable smarter decisions, and it can even pave the way for future innovation. It's about augmenting your people, not just automating processes.

Host: Alex Ian Sutherland, thank you so much for these brilliant insights.

Expert: My pleasure, Anna.

Host: And thank you to our audience for tuning into A.I.S. Insights, powered by Living Knowledge. Join us next time as we uncover more actionable intelligence from the world of research.

Host: Welcome to A.I.S. Insights — powered by Living Knowledge, the podcast where we turn academic research into actionable business intelligence. I’m your host, Anna Ivy Summers.
Host: Today, we’re diving into a fascinating case study titled, "How a Utility Company Established a Corporate Data Culture for Data-Driven Decision Making."
Host: It explores how a large German utility company transformed itself into a data-driven organization. To help us unpack this, we have our expert analyst, Alex Ian Sutherland. Welcome, Alex.
Expert: Great to be here, Anna.
Host: Alex, let's start with the big picture. Most companies know data is important, but this study focuses on a utility company. What was the specific problem they were trying to solve?
Expert: It’s a problem many traditional industries are facing, but it's especially acute in the energy sector. They’re dealing with a massive shift—the rise of renewable energy like wind and solar, and the explosion in electric vehicle charging.
Host: So the old ways of working just weren't cutting it anymore?
Expert: Exactly. For decades, they relied on experience and simple tools. The study gives a great example of a "drag pointer"—basically a needle on a gauge that only showed the highest energy load a substation ever experienced. It didn't tell you when it happened, or why.
Host: A single data point, with no context.
Expert: Precisely. And that was fine when the grid was predictable. But suddenly, they went from handling a dozen requests for new EV chargers a month to nearly three thousand. The old "rule-of-thumb" approach became obsolete and even risky for grid stability. They were flying blind.
Host: So how did the researchers get inside this transformation to understand how the company fixed this?
Expert: They conducted a deep-dive case study, interviewing seven of the company’s key domain experts. These were the people on the front lines—the ones directly involved in building the new data strategy. This gave them a real ground-truth perspective on what actually worked.
Host: So what were the key findings? What was the secret to their success?
Expert: The study breaks it down into three core transformations that were all linked together. The first, and perhaps most important, was enabling the workforce.
Host: This wasn't just about hiring a team of data scientists, then?
Expert: Not at all. They created a program to train existing employees to become "Data and AI Multipliers." These were people from various departments who became data champions, identifying opportunities and helping their colleagues use new tools. It was about upskilling from within.
Host: Building capability across the organization. What was the second transformation?
Expert: Improving the data lifecycle. This sounds technical, but it’s really about fixing the plumbing. They moved from scattered, siloed databases to a central data lake, creating a single source of truth that everyone could access.
Host: And I see they also created new roles like 'data officers' and 'data stewards'.
Expert: Yes, and this is crucial. It made data quality a formal part of people's jobs. Instead of data being an abstract IT issue, specific people became accountable for its accuracy and maintenance within their business units.
Host: That makes sense. But change is hard. How did they get everyone to embrace this new way of working?
Expert: That brings us to the third piece: an employee-centered approach. They knew they couldn't just mandate this from the top down. They formed cross-functional teams, bringing engineers and data specialists together to solve real problems.
Host: And they made a point of showcasing quick wins, right?
Expert: Absolutely. This was key to building momentum. For example, they automated a critical report that used to take two employees a full month to compile, three times a year. Suddenly, that data was available in real-time. When people see that kind of tangible benefit, it overcomes resistance and builds trust in the process.
Host: This is all fascinating for a utility company, but what's the key takeaway for a business leader in, say, manufacturing or retail? Why does this matter to them?
Expert: The lessons are completely universal. First, you can't just buy technology; you have to invest in your people. The "Data Multiplier" model of empowering internal champions can work in any industry.
Host: So, people first. What else?
Expert: Second, make data quality an explicit responsibility. Creating roles like data stewards ensures accountability and treats data as the critical business asset it is. It stops being everyone's problem and no one's priority.
Host: And the third lesson?
Expert: Start small and demonstrate value fast. Don't try to boil the ocean. Find a painful, manual process, fix it with a data-driven solution, and then celebrate that "quick win." That success story becomes your best marketing tool for driving wider adoption. Ultimately, this company deployed over 50 new data solutions that transformed their operations.
Host: A powerful example of real-world impact. So, to recap: the challenges of the energy transition forced this company to ditch its old methods. Their success came from a three-part strategy: empowering their workforce, rebuilding their data infrastructure, and using an employee-centric approach focused on quick wins.
Host: Alex, thank you so much for breaking that down for us. It’s a brilliant roadmap for any company looking to build a true data culture.
Expert: My pleasure, Anna.
Host: And thank you to our listeners for joining us on A.I.S. Insights — powered by Living Knowledge. We’ll see you next time.

Host: Welcome to A.I.S. Insights, powered by Living Knowledge. I’m your host, Anna Ivy Summers.
Host: Today we're diving into a fascinating case study from the MIS Quarterly Executive titled, "How the Odyssey Project Is Using Old and Cutting-Edge Technologies for Financial Inclusion".
Host: It explores how a fintech startup is combining simple SMS technology with advanced tools like blockchain and AI to serve people without access to traditional banking.
Host: Here to break it all down for us is our analyst, Alex Ian Sutherland. Alex, welcome to the show.
Expert: Great to be here, Anna.
Host: Let’s start with the big picture. Why is a study like this so important? What’s the core problem they're trying to solve?
Expert: The problem is massive. The study states that around 1.7 billion adults globally are unbanked. They lack access to even the most basic formal financial services.
Host: And what stops them from just walking into a bank?
Expert: The study highlights a few critical barriers. Many people live in rural areas, far from any physical bank branch. On top of that, the high cost of services can be prohibitive.
Expert: And while modern digital banking exists, it usually requires an expensive smartphone and a reliable internet data plan, which are luxuries for a huge portion of the world’s population. This effectively locks them out of the modern economy.
Host: So The Odyssey Project saw this challenge. What was their approach, as detailed in the study?
Expert: Their approach was brilliantly pragmatic. Instead of trying to force a high-tech solution onto a low-tech environment, they built their system around a technology that nearly everyone already has and knows how to use: SMS, or simple text messaging.
Host: Texting. That feels very old-school in a world of apps.
Expert: It is, but that's the point. It's accessible on the most basic mobile phone, it’s cheap, and it doesn't need an internet connection. The true innovation, which the study details, is the powerful, modern engine they built to run on that simple SMS interface.
Host: Let's get into those findings. How exactly did they build this engine?
Expert: The study identifies a few core components. Their platform, called RoyPay, uses an SMS-based chatbot as the primary user interface. So, a user can send and receive money just by texting this chatbot, which they named RoyChat.
Host: And behind the scenes, it’s much more complex?
Expert: Exactly. For the core payment mechanism, they use blockchain technology. This is key because it enables secure and transparent transactions at a very low cost, cutting out many of the intermediary fees that make traditional finance so expensive.
Host: So the user sees a simple text, but the transaction is happening on the blockchain. Where does AI fit in?
Expert: The AI powers the chatbot. It uses machine learning and natural language processing to understand the user’s text messages. This allows it to handle requests, answer questions, and make the whole experience feel conversational and intuitive.
Expert: And finally, the study notes the entire system is built on scalable cloud services and open-source software. In business terms, that means it’s incredibly cost-effective to run and can be scaled up to serve millions of users around the world without a massive new investment in infrastructure.
Host: This is a powerful combination. For the business leaders listening, what is the big takeaway here? Why does this matter for them?
Expert: I think there are two critical lessons. First, it redefines what we think of as innovation. The study shows that groundbreaking solutions don't always come from inventing something brand new. Here, the innovation was creatively combining old technology with new technology to solve a very specific problem.
Host: It’s a lesson in using the right tool for the job, not just the newest one.
Expert: Precisely. The second lesson is about entering emerging markets. This case is a perfect example of creating a context-specific solution. You can't just take a product built for New York or London and expect it to work in rural Kenya.
Expert: By understanding the constraints—no smartphones, no internet, low income—The Odyssey Project built a solution that was perfectly adapted to its users. For any company looking to expand globally, that principle is pure gold: fit the technology to the market, not the other way around.
Host: A fantastic summary, Alex. So, to recap: the study on The Odyssey Project shows us that huge global challenges can be met by cleverly blending simple, existing tech with powerful, new platforms.
Host: The solution starts with the user’s reality—a basic phone—and builds a low-cost, secure financial tool using blockchain and AI.
Host: For business leaders, it's a powerful reminder that true innovation is about creative problem-solving, and success in new markets requires deep adaptation.
Host: Alex Ian Sutherland, thank you for sharing your insights with us.
Expert: It was my pleasure, Anna.
Host: And thank you for listening to A.I.S. Insights, powered by Living Knowledge. We’ll see you next time.

Host: Welcome to A.I.S. Insights, the podcast at the intersection of business, technology, and Living Knowledge. I’m your host, Anna Ivy Summers.
Host: Today, we’re diving into a fascinating study titled "Leveraging Information Systems for Environmental Sustainability and Business Value."
Host: It explores how companies can use their information systems, or IS, not just to meet sustainability goals, but to actually create tangible business value. To help us unpack this, we have our expert analyst, Alex Ian Sutherland. Alex, welcome to the show.
Expert: Thanks for having me, Anna. It's a critical topic.
Host: Absolutely. So, let's start with the big picture. What is the core problem this study is trying to solve for businesses?
Expert: The central issue is something the researchers call the 'sustainability implementation gap'.
Host: A gap? What does that mean?
Expert: It means that while businesses are under immense pressure from customers, investors, and regulators to be more environmentally friendly, many managers are struggling. They don't have the tools or a clear roadmap to turn those sustainability initiatives into real business value, like cost savings or new revenue.
Host: So they have the ambition, but not the execution plan.
Expert: Exactly. They know sustainability is important, but they can't connect the dots between, say, reducing carbon emissions and improving their bottom line. This study aims to provide that practical roadmap.
Host: So, how did the researchers go about creating this roadmap? What was their approach?
Expert: Instead of building a purely theoretical model, they did something very practical. They conducted a comprehensive review of 31 articles from leading practitioner journals—publications that report on real-world business challenges and solutions.
Host: So they looked at what's actually working in the field.
Expert: Precisely. They analyzed a decade's worth of case studies and reports to find common patterns and best practices, specifically focusing on how information systems are being used successfully.
Host: That sounds incredibly useful. Let's get to the findings. What were the key recommendations that came from this analysis?
Expert: The study outlines a five-step pathway. The steps are: Legitimize, Optimize, Internalize, Standardize, and Collaborate. Together, they create a cycle for turning sustainability into value.
Host: Okay, let's break that down. What does it mean to 'Legitimize' sustainability?
Expert: It means making sustainability a real business priority, not just a PR exercise. Information systems are key here. They allow you to use analytical tools to connect environmental metrics, like energy consumption, directly to financial performance indicators. When you can show that reducing energy use saves a specific amount of money, sustainability becomes legitimized in the language of business.
Host: You make a clear business case for it. Once that's done, what's the next step, 'Optimize'?
Expert: Optimization is about using IS to improve the eco-efficiency of your processes, products, and services. A great example from the study is a consortium that piloted digital watermarks on packaging. These invisible codes help waste sorting facilities to recycle materials far more accurately, reducing waste and creating value from it.
Host: That’s a brilliant, tangible example. So after legitimizing and optimizing, the next step is to 'Internalize'. How is that different?
Expert: Internalizing means weaving sustainability into the very fabric of your corporate strategy. It's about using data from your environmental management systems to inform core business decisions, from project planning to investments. The study highlights how the chemical company BASF uses its management system to ensure environmental factors are a binding part of central strategic decisions.
Host: It becomes part of the company's DNA. This brings us to the last two steps, which sound very connected: 'Standardize' and 'Collaborate'.
Expert: They are absolutely connected. To collaborate effectively, you first need to standardize. This means establishing robust data governance so that sustainability information is consistent, comparable, and transparent. You can't work with your suppliers on reducing emissions if you're all measuring things differently.
Host: A common language for data.
Expert: Exactly. And once you have that, you can 'Collaborate'. No single company can solve major environmental challenges alone. IS allows you to build strategic partnerships and ecosystems. For instance, the study mentions a platform using blockchain to allow partners in a supply chain to securely share sustainability data without revealing sensitive trade secrets. This builds trust and enables collective action.
Host: Alex, this is a very clear and powerful framework. If you had to distill this for a CEO or a manager listening right now, what is the single most important business takeaway?
Expert: The key takeaway is to stop viewing sustainability as a cost or a compliance burden. Information systems provide the tools to reframe it as a driver of innovation and competitive advantage. By following this pathway, you can use data to uncover efficiencies, create more innovative and circular products, reduce risk in your supply chain, and ultimately build a more resilient and profitable business. It’s an iterative journey, not a one-time fix.
Host: A journey from obligation to opportunity.
Expert: That's the perfect way to put it.
Host: To summarize for our listeners: businesses are struggling with a 'sustainability implementation gap'. This study provides a practical five-step pathway—Legitimize, Optimize, Internalize, Standardize, and Collaborate—showing how information systems can turn sustainability from an obligation into a core driver of business value.
Host: Alex Ian Sutherland, thank you so much for translating this crucial research into such clear, actionable insights.
Expert: My pleasure, Anna.
Host: And thanks to all of you for tuning in to A.I.S. Insights — powered by Living Knowledge. Join us next time as we continue to explore the ideas shaping our world.

Host: Welcome to A.I.S. Insights — powered by Living Knowledge. I'm your host, Anna Ivy Summers.
Host: Today, we’re tackling a multi-billion-dollar question: why do so many major digital and technology projects fail to deliver on their promise?
Host: We’re diving into a fascinating new study called "The Hidden Causes of Digital Investment Failures". It analyzes hundreds of projects to uncover the subtle, often invisible root causes behind these failures, moving beyond the usual excuses like budget overruns or missed deadlines.
Host: To help us unpack this is our analyst, Alex Ian Sutherland. Alex, welcome.
Expert: Great to be here, Anna.
Host: Alex, let's start with the big problem. Companies are pouring huge amounts of money into digital transformation, but the success rates just aren't improving. What's going on?
Expert: It’s a huge issue. The study uses a great analogy: it’s like treating sciatica. You feel the pain in your leg, so you stretch the muscle. That gives temporary relief, but the root cause is a problem in your lower back. In business, we see symptoms like budget overruns and we react by adding more governance or new project management tools. We’re treating the leg, not the back.
Expert: The study highlights a case of a major insurance company. They spent over $120 million and six years on a new platform, only to find they were less than a third of the way done, with the final cost estimate having nearly doubled. They were doing all the "right" project management things, but it was still failing.
Host: So they were addressing the symptoms, not the true cause. How did the researchers in this study get to those root causes? What was their approach?
Expert: They conducted a deep root-cause analysis. Think of it as business archaeology. They didn't just look at the surface of failed projects; they analyzed hundreds of them to map the complex cause-and-effect relationships that led to poor outcomes. They then workshopped these findings with senior practitioners to ensure they reflected real-world experience.
Host: And this "archaeology" uncovered five key hidden causes. The first one is called 'The Illusion of Control'. It sounds a bit ominous.
Expert: It is, in a way. Business leaders believe they're in control because they have dashboards, metrics, and steering committees tracking time and cost. But the study found this is an illusion. They are controlling the execution of the project, but they have no real influence over the creation of business value.
Expert: In that insurance case, the executives saw progress reports, but over 95% of the budget was being spent by technical teams making hundreds of small, invisible decisions every week that ultimately determined the project's fate. The business leaders were too far removed to have any real control over the outcome.
Host: Which sounds like it leads directly to the second finding: 'The Fallacy of the Working System'. What does that mean?
Expert: It means the goalpost shifts. The original objective was to improve business performance, but the project's primary goal becomes just delivering a functional IT system on time and on budget. Everyone from the project manager to the CIO is incentivized to just get a "working system" out the door.
Host: So, the 'working system' becomes the end goal, not the business value it was supposed to create.
Expert: Exactly. And there's often no one held accountable for delivering that value after the project team declares victory and disbands.
Host: The third cause is 'Conflicts of Interest'. This sounds like a structural problem.
Expert: It's a huge one. The study points out that in mature industries like construction, you have separate roles: the customer funds it, the architect designs it, and the builder constructs it. They have separate accountabilities. But in the typical corporate structure, a single IT department does all three. They design, build, and quality-check their own work.
Host: So when a trade-off has to be made between long-term quality and the short-term deadline...
Expert: The deadline and budget almost always win. It creates a system that prioritizes short-term delivery over building resilient, high-quality digital assets.
Host: And I imagine that short-term focus creates long-term problems, which might be what the fourth cause, 'The IT Amnesia Syndrome', is about.
Expert: Precisely. Because the focus is on finishing the current project, things like proper documentation are the first to be cut. As teams move on and people leave, the organization forgets why systems were built a certain way. The study found this creates massive, unnecessary complexity. Future projects are then bogged down by trying to understand these poorly documented legacy systems.
Host: It sounds like building on a shaky foundation you can't even see properly.
Expert: A perfect description.
Host: And the final hidden cause: 'Managing Expenses, Not Assets'.
Expert: Right. A company would never treat a new factory or a fleet of cargo ships as a simple expense. They are managed as productive assets over their entire lifecycle. But digital systems, which can cost hundreds of millions, are often treated as short-term project expenses. There's no focus on their long-term value, maintenance costs, or when they should be retired.
Host: So Alex, this is a pretty powerful diagnosis of what’s going wrong. The crucial question for our listeners is: what's the cure? What do leaders need to do differently?
Expert: The study offers some clear, if challenging, recommendations. First, business leaders must truly *own* their digital systems as productive assets. The business unit that gets the value should be the owner, not the IT department.
Expert: Second, organizations need to eliminate those conflicts of interest by separating the roles of architecting, building, and quality assurance. You need independent checks and balances.
Expert: And finally, the mindset has to shift from securing funding to delivering value. One CEO the study mentions now calls project sponsors back before the investment committee years after a project is finished to prove the business benefits were actually achieved. That creates real accountability.
Host: So it’s not about finding a better project methodology, but about fundamentally changing organizational structure and, most importantly, the mindset of leadership.
Expert: That's the core message. The success or failure of a digital investment is determined long before the project itself ever kicks off. It's determined by the organizational system it operates in.
Host: A fascinating and crucial insight. We’ve been discussing the study "The Hidden Causes of Digital Investment Failures". The five hidden causes are: The Illusion of Control, The Fallacy of the Working System, Conflicts of Interest, IT Amnesia Syndrome, and Managing Expenses, Not Assets.
Host: Alex Ian Sutherland, thank you for making this so clear for us.
Expert: My pleasure, Anna.
Host: And thank you for listening to A.I.S. Insights — powered by Living Knowledge. Join us next time as we decode the research that’s reshaping the world of business.

Host: Welcome to A.I.S. Insights, powered by Living Knowledge. I’m your host, Anna Ivy Summers.
Host: Today, we're diving into a very timely topic for any business looking to innovate: the real-world challenges of adopting new technology. We’ll be discussing a fascinating study titled "The Promise and Perils of Low-Code AI Platforms."
Host: This study looks at how four major corporations adopted a low-code conversational AI platform, and it uncovers some crucial, and often incorrect, assumptions that businesses make about these powerful tools. Here to break it down for us is our analyst, Alex Ian Sutherland. Alex, welcome.
Expert: Great to be here, Anna.
Host: Alex, let's start with the big picture. Businesses are constantly hearing about AI and automation. What’s the core problem that these low-code AI platforms are supposed to solve?
Expert: The problem is a classic one: a gap between ambition and resources. Companies want to automate processes, build chatbots, and leverage AI, but they often lack large teams of specialized AI developers. Low-code platforms are marketed as the perfect solution.
Host: The 'democratization' of AI we hear so much about.
Expert: Exactly. The promise is that you can use a simple, visual, drag-and-drop interface to build complex AI applications, empowering your existing business-focused employees to innovate without needing to write a single line of code. But as the study found, that promise often doesn't match the reality.
Host: So how did the researchers investigate this gap between promise and reality?
Expert: They took a very practical approach. They didn't just survey people; they conducted an in-depth case study. They followed the journey of four large multinational companies—in the energy, automotive, and retail sectors—as they all tried to implement the very same low-code conversational AI platform.
Host: That’s great. So by studying the same platform across different industries, they could really pinpoint the common challenges. What were the main findings?
Expert: The findings centered on three major false assumptions businesses made. The first was about usability. The assumption was that ‘low-code’ meant anyone could do it.
Host: And that wasn't the case?
Expert: Not at all. While the IT staff found it user-friendly, the business-side employees—the ones who were supposed to be empowered—faced a much steeper learning curve than anyone anticipated. One domain expert in the study described the experience as being "like Greek," saying it was far more complex than just "dragging and dropping."
Host: So you still need a foundational level of technical knowledge. What was the second false assumption?
Expert: It was about adaptability. The idea was that you could easily tailor these platforms to any specific business need. But creating applications to handle complex, real-world customer queries proved incredibly challenging and time-consuming.
Host: Why was that?
Expert: Because real business processes are often messy and rely on human intuition. The study found that before companies could automate a process, they first had to invest heavily in understanding and standardizing it. You can't teach an AI a process that isn't clearly defined.
Host: That makes sense. You have to clean your house before you can automate the cleaning. What was the final key finding?
Expert: This one is huge for any CIO: integration. The belief was that these platforms would be a simple 'plug-and-play' solution that could easily connect to existing company databases and systems.
Host: I have a feeling it wasn't that simple.
Expert: Far from it. The companies ran into major roadblocks trying to connect the platform to their legacy systems. They faced incompatible data formats and a lack of a unified data strategy. The study showed that you often need someone with knowledge of coding and APIs to build the bridges between the new platform and the old systems.
Host: So, Alex, this is the crucial part for our listeners. If a business leader is considering a low-code AI tool, what are the key takeaways? What should they do differently?
Expert: The study provides a clear roadmap. First, thoroughly test the platform before you buy it. Don't just watch the vendor's demo. Have your actual employees—the business users—try to build a real-world application with it. This will reveal the true learning curve.
Host: A 'try before you buy' approach. What else?
Expert: Second, success requires cross-functional collaboration. It’s not an IT project or a business project; it's both. The study highlighted that the most successful implementations happened when IT experts and business domain experts worked together in blended teams from day one.
Host: So break down those internal silos.
Expert: Absolutely. And finally, be prepared to change your processes, not just your tools. You can't just layer AI on top of existing workflows. You need to re-evaluate and often redesign your processes to align with the capabilities of the AI. It's as much about business process re-engineering as it is about technology.
Host: This is incredibly insightful. It seems low-code AI platforms are powerful, but they are certainly not a magic bullet.
Host: To sum it up: the promise of simplicity with these platforms often hides significant challenges in usability, adaptation, and integration. Success depends less on the drag-and-drop interface and more on a strategic approach that involves rigorous testing, deep collaboration between teams, and a willingness to rethink your fundamental business processes.
Host: Alex, thank you so much for shedding light on the perils, and the real promise, of these platforms.
Expert: My pleasure, Anna.
Host: And a big thank you to our audience for tuning into A.I.S. Insights. We’ll see you next time.

Host: Welcome to A.I.S. Insights, the podcast at the intersection of business and technology, powered by Living Knowledge. I’m your host, Anna Ivy Summers.
Host: Today, we're diving into a fascinating study titled "Combining Low-Code/No-Code with Noncompliant Workarounds to Overcome a Corporate System's Limitations."
Host: It explores how employees at a warehouse in Hong Kong used everyday tools, like Microsoft Excel, to create unofficial but essential solutions when their official corporate software fell short.
Host: To help us unpack this, we have our expert analyst, Alex Ian Sutherland. Alex, welcome back.
Expert: Great to be here, Anna.
Host: So, Alex, let's start with the big picture. What was the real-world problem this study looked into?
Expert: It’s a classic story of a global headquarters rolling out a one-size-fits-all solution. The company, called CoreRidge in the study, implemented a standardized corporate software, Microsoft Dynamics.
Expert: The problem was, this system was completely non-customizable. It worked fine in most places, but it was a disaster for their Hong Kong operations.
Host: A disaster how? What was so unique about Hong Kong?
Expert: In Hong Kong, due to the high cost of real estate, the company has small retail stores and one large, central warehouse. The corporate software was designed for locations where the warehouse and store are together.
Expert: It simply couldn't handle the complex delivery scheduling needed to get products from that single warehouse to all the different stores and customers. Core tasks were impossible to perform with the official system.
Host: So employees were stuck. How did the researchers figure out what was happening?
Expert: They went right to the source. It was a qualitative case study where they conducted in-depth interviews with 31 employees at the warehouse, from trainees all the way up to senior management. This gave them a ground-level view of how the team was actually getting work done.
Host: And that brings us to the findings. What did they discover?
Expert: They found that employees had essentially turned Microsoft Excel into their own low-code development tool. They were downloading data from the official system and using Excel to manage everything from delivery lists to rescheduling shipments during a typhoon.
Host: So they built their own system, in a way.
Expert: Exactly. And this wasn't a secret, rogue operation. These Excel workarounds became standard operating procedure. They were noncompliant with corporate IT policy, but they were absolutely vital for daily operations and customer satisfaction. The study calls this 'shadow IT', but frames it as a valuable, employee-driven innovation.
Host: That’s a really interesting perspective. It sounds like the company should be celebrating these employees, not punishing them.
Expert: That’s the core argument. The study suggests that this kind of informal, tool-based problem-solving is a legitimate form of low-code development. It’s not always about using a fancy, dedicated platform. Sometimes the best tool is the one your team already knows how to use.
Host: This is the crucial part for our listeners. What are the key business takeaways here? Why does this matter?
Expert: It matters immensely. First, it shows that managers need to recognize and support these informal solutions, not just shut them down. These workarounds are a goldmine of information about what's not working in your official systems.
Host: So, don't fight 'shadow IT', but try to understand it?
Expert: Precisely. The second major takeaway is that businesses should adopt a "portfolio approach" to low-code development. Don't just invest in one big platform. Empower your employees by recognizing the value of flexible, everyday tools like Excel.
Expert: It’s about creating a governance structure that can embrace these informal solutions, manage their risks, and learn from them to make the whole organization smarter and more agile.
Host: It sounds like a shift from rigid, top-down control to a more flexible, collaborative approach to technology.
Expert: That's it exactly. It's about trusting your employees on the front lines to solve the problems they face every day, with the tools they have at hand.
Host: So, to summarize: a rigid corporate system can fail to meet local needs, but resourceful employees can bridge the gap using everyday tools like Excel. And the big lesson for businesses is to recognize, govern, and learn from these informal innovations rather than just trying to eliminate them.
Host: Alex, this has been incredibly insightful. Thank you for breaking it down for us.
Expert: My pleasure, Anna.
Host: And a big thank you to our audience for tuning in to A.I.S. Insights. Join us next time as we continue to explore the ideas shaping our world, powered by Living Knowledge.

Host: Welcome to A.I.S. Insights, the podcast at the intersection of business and technology, powered by Living Knowledge. I’m your host, Anna Ivy Summers.
Host: Today, we’re diving into a fascinating area where business and IT are blurring lines: citizen development. We’re looking at a new study titled "Governing Citizen Development to Address Low-Code Platform Challenges".
Host: It investigates how companies can effectively manage employees who, with minimal technical skills, are now building their own applications using what are called low-code platforms. With me to break it all down is our analyst, Alex Ian Sutherland. Alex, welcome.
Expert: Great to be here, Anna.
Host: So, let’s start with the big picture. Why are companies turning to their own non-technical employees to build software in the first place? What’s the problem this study is trying to solve?
Expert: The core problem is a massive, ongoing shortage of skilled software developers. Companies have huge backlogs of IT projects, but they can't hire developers fast enough. So, they turn to low-code platforms, which are tools with drag-and-drop interfaces that let almost anyone build a simple application.
Host: That sounds like a perfect solution. Democratize development and get things done faster.
Expert: It sounds perfect, but the study makes it clear that this introduces a whole new set of serious risks that organizations are often unprepared for. They identified three major challenges.
Host: And what are they?
Expert: First is simply substandard software quality. An app built by someone in marketing might look fine, but as the study found, it could be running "slow queries" or be "badly planned," hurting the performance of the entire system.
Expert: Second is the rise of 'shadow IT'. Employees build things on their own without oversight, which can lead to security issues, data protection breaches, or simply chaos. One developer in the study noted they had a role that was "almost as powerful as a normal developer" and could "damage a few things" if they weren't careful.
Expert: And third is technical debt. An employee builds a useful tool, then they leave the company. The study asks, who maintains it? Often, nobody. Or people just keep creating duplicate apps, leading to a messy and expensive digital junkyard.
Host: So, how did the researchers get to the bottom of this? What was their approach?
Expert: They took a very practical, real-world approach. They conducted 30 in-depth interviews across two different firms. One was a company using a low-code platform, and the other was a company that actually provides a low-code platform. This gave them a 360-degree view from both the user and the expert perspective.
Host: It sounds comprehensive. So, after all those conversations, what were the key findings? What's the solution here?
Expert: The biggest finding is that simply having "developers" and "non-developers" is the wrong way to think about it. Effective governance requires a much more nuanced understanding of the roles people play.
Host: What kind of roles did they find?
Expert: They identified two key types of citizen developers. You have your 'traditional citizen developer,' who builds a simple app for their team. But more importantly, they found what they call 'low-code champions.' These are business users who become passionate experts and act as a bridge between their colleagues and IT. They become the "poster children" for the program.
Host: That’s a powerful idea. So it’s about nurturing internal talent, not just letting everyone run wild.
Expert: Exactly. And to support them, the study proposes a clear, three-part governance framework. First, strategically manage project scope. Don’t let citizen developers build highly complex, mission-critical systems. Guide them to appropriate, simpler use cases.
Expert: Second, organize effective collaboration. This means creating a central knowledge base with answers to common questions and using standard collaboration tools so people aren't constantly reinventing the wheel or flooding experts with the same support tickets.
Expert: And third, implement targeted education. This isn't just about teaching them to use the software. It’s about training on best practices, data security, and identifying those enthusiastic employees who can become your next 'low-code champions.'
Host: This is the crucial part for our listeners. What does this all mean for business leaders? What are the key takeaways?
Expert: The first takeaway is this: don't just buy a low-code platform, build a program around it. Governance isn't about restriction; it's about creating the guardrails for success. The study warns that without it, the benefits of speed are "quickly outweighed by escalating risks and costs."
Expert: The second, and I think most important, is to actively identify and empower your 'low-code champions'. These people are your force multipliers. They can handle onboarding, answer basic questions, and promote best practices within their business units, which frees up your IT team to focus on bigger things.
Expert: And finally, start small and be strategic. The goal of citizen development shouldn't be to replace your IT department, but to supplement it. Empowering a sales team to automate its own reporting workflow is a huge win. Asking them to rebuild the company’s CRM is a disaster waiting to happen.
Host: Incredibly clear advice. The promise of empowering your workforce with these tools is real, but it requires a thoughtful strategy to avoid the pitfalls.
Host: To summarize, success with citizen development hinges on a strong governance framework. That means strategically managing what gets built, organizing how people collaborate and get support, and investing in targeted education to create internal champions.
Host: Alex Ian Sutherland, thank you so much for breaking down this complex topic into such actionable insights.
Expert: My pleasure, Anna.
Host: And thank you to our audience for tuning in to A.I.S. Insights. We'll see you next time.

Host: Welcome to A.I.S. Insights, the podcast at the intersection of business and technology, powered by Living Knowledge. I'm your host, Anna Ivy Summers.
Host: Today, we’re diving into a critical question for any organization using artificial intelligence: How do we actually stay in control? We'll be discussing a fascinating study titled, "What it takes to control AI by design: human learning."
Host: It proposes a new framework for maintaining meaningful human control over complex AI systems, emphasizing that for AI to learn, humans must learn right alongside it. Here to break it all down for us is our analyst, Alex Ian Sutherland. Welcome, Alex.
Expert: Thanks for having me, Anna. It’s a crucial topic.
Host: Absolutely. So, Alex, let's start with the big picture. What is the real-world problem this study is trying to solve?
Expert: The problem is that AI is evolving much faster than our methods for managing it. Think about critical systems in finance, cybersecurity, or logistics. We use AI to make high-stakes decisions at incredible speed.
Expert: But our traditional methods of oversight, where a person just checks the final output, are no longer enough. As the study points out, AI can alter its behavior or generate unexpected results when it encounters new situations, creating a huge risk that it no longer aligns with our original goals.
Host: So there's a growing gap between the AI's capability and our ability to control it. How did the researchers approach this challenge?
Expert: They took a step back and used systems theory. Instead of seeing the human and the AI as separate, they designed a single, integrated system that operates in two distinct modes.
Expert: First, there's the 'stable mode'. This is when the AI is working efficiently on its own, handling routine tasks based on what it already knows. Think of it as the AI on a well-defined autopilot.
Expert: But when the environment changes or the AI's confidence drops, the system shifts into an 'adaptive mode'. This is a collaborative learning session, where the human expert and the AI work together to make sense of the new situation.
Host: That’s a really clear way to put it. What were the main findings that came out of this two-mode approach?
Expert: The first key finding is that this dual-mode structure is essential. You get the efficiency of automation in the stable mode, but you have a built-in, structured way to adapt and learn when faced with uncertainty.
Host: And I imagine the human is central to that adaptive mode.
Expert: Exactly. And that’s the second major finding: for this to work, human learning must keep pace with machine learning. To stay in control, the human expert can't be a passive observer. They must be actively learning and updating their own understanding of the environment.
Host: That turns the typical human-in-the-loop idea on its head a bit.
Expert: It does. Which leads to the third and most interesting finding, a method they call 'reciprocal human-machine learning'. In the adaptive mode, it’s not just the human teaching the machine. The AI provides specific feedback to the human expert, pointing out patterns or inconsistencies they might have missed.
Expert: So, the human and the AI are actively learning from each other. This reciprocal feedback loop ensures the entire system gets smarter, performs better, and stays aligned with human values, preventing things like algorithmic bias from creeping in.
Host: A true partnership. This is where it gets really interesting for our listeners. Alex, why does this matter for business? What are the practical takeaways?
Expert: This framework is a roadmap for de-risking advanced AI applications. For any business using AI in critical functions, this is a way to ensure safety, accountability, and alignment with company ethics. It's about moving from a "black box" to a controllable, transparent system.
Expert: Second, it's about building institutional knowledge. By keeping humans actively engaged in the learning process, you're not just improving the AI; you're upskilling your employees. They develop a deeper expertise that makes your entire operation more resilient and adaptable.
Expert: And finally, that adaptability is a huge competitive advantage. A business with a human-AI system that can learn and respond to market shifts, new cyber threats, or supply chain disruptions will outperform one with a rigid, static AI every time.
Host: So to recap: traditional AI oversight is failing. This study presents a powerful framework where a human-AI system operates in a stable mode for efficiency and an adaptive mode for learning.
Host: The key is that this learning must be reciprocal—a two-way street where both human and machine get smarter together, ensuring the AI remains a powerful, controllable, and trusted tool for the business.
Host: Alex, thank you so much for these valuable insights.
Expert: My pleasure, Anna.
Host: And thank you to our audience for tuning into A.I.S. Insights. Join us next time as we continue to explore the ideas shaping our world.

Host: Welcome to A.I.S. Insights, the podcast where we connect academic research to real-world business strategy, powered by Living Knowledge. I’m your host, Anna Ivy Summers.

Host: Today, we’re exploring a topic that’s becoming increasingly relevant in our AI-driven world: how to make our digital tools not just smarter, but more supportive. We’re diving into a study titled "Design Knowledge for Virtual Learning Companions from a Value-centered Perspective".

Host: In simple terms, it's about creating AI-powered chatbots that act as true companions, helping students with the very human challenges of motivation and time management. Here to break it all down for us is our expert analyst, Alex Ian Sutherland. Welcome, Alex.

Expert: Thanks for having me, Anna. It’s a fascinating study with huge implications.

Host: Let's start with the big picture. What is the real-world problem that this study is trying to solve?

Expert: Well, think about anyone trying to learn something new while juggling a job and a personal life. It could be a university student working part-time or an employee trying to upskill. The biggest hurdles often aren't the course materials themselves, but staying motivated and managing time effectively.

Host: That’s a struggle many of our listeners can probably relate to.

Expert: Exactly. And while we have powerful AI tools like ChatGPT that can answer questions, they function like a know-it-all tutor. They provide information, but they don't provide companionship. They don't check in on you, encourage you when you're struggling, or help you plan your week. This study addresses that gap.

Host: So it's about making AI more of a partner than just a tool. How did the researchers go about figuring out how to build something like that?

Expert: They used a very hands-on approach called design science research. Instead of just theorizing, they went through multiple cycles of building and testing. They started by conducting in-depth interviews with working students to understand their real needs. Then, they held workshops, designed a couple of conceptual prototypes, and eventually built and coded a fully functional AI companion that they tested with different student groups.

Host: So it’s a methodology that’s really grounded in user feedback. What were the key findings? What did they learn from all this?

Expert: The main outcome is a powerful framework for designing these Virtual Learning Companions, or VLCs. The big idea is that the companion's value is created through the interaction itself, which they break down into three distinct but connected layers.

Host: Three layers. Can you walk us through them?

Expert: Of course. First is the Relationship Layer. This is all about creating a human-like, trustworthy companion. The AI should be able to show empathy, maybe use a bit of humor, and build a sense of connection with the user over time. It’s the foundation.

Host: Okay, so it’s about the personality and the bond. What's next?

Expert: The second is the Matching Layer. This is about adaptation and personalization. The study found that a one-size-fits-all approach fails. The VLC needs to adapt to the user's individual learning style, their personality, and even their current mood or context.

Host: And the third layer?

Expert: That's the Service Layer. This is where the more functional support comes in. It includes features for time management, like creating to-do lists and setting reminders, as well as providing supportive learning content and creating a motivational environment, perhaps with gentle nudges or rewards.

Host: This all sounds great in theory, but did they see it work in practice?

Expert: They did, and they also uncovered a critical insight. When they tested their prototype, they found that full-time university students thought the AI’s language was too informal and colloquial. But a group of working professionals in a continuing education program found the exact same AI to be too formal!

Host: Wow, that’s a direct confirmation of what you said about the Matching Layer. The companion has to be adaptable.

Expert: Precisely. It proves that to be effective, these tools must be tailored to their specific audience and context.

Host: Alex, this is the crucial part for our audience. Why does this matter for business? What are the practical takeaways?

Expert: The implications are huge, Anna, and they go way beyond the classroom. Think about corporate training and HR. Imagine a new employee getting an AI companion that doesn't just teach them software systems, but helps them manage the stress of their first month and checks in on their progress and motivation. That could have a massive impact on engagement and retention.

Host: I can see that. It’s a much more holistic approach to onboarding. Where else?

Expert: For any EdTech company, this framework is a blueprint for building more effective and engaging products. It's about moving from simple content delivery to creating a supportive learning ecosystem. But you can also apply these principles to customer-facing bots. An AI that can build a relationship and adapt to a customer's technical skill or frustration level will provide far better service and build long-term loyalty.

Host: So the key business takeaway is to shift our thinking.

Expert: Exactly. The value of AI in these roles isn't just in the functional task it completes, but in the supportive, adaptive relationship it builds with the user. It’s the difference between an automated tool and a true digital partner.

Host: A fantastic insight. So, to summarize: today's professionals face real challenges with motivation and time management. This study gives us a three-layer framework—Relationship, Matching, and Service—to build AI companions that truly help. For businesses, this opens up new possibilities in corporate training, EdTech, and even customer relations.

Host: Alex, thank you so much for translating this complex study into such clear, actionable insights.

Expert: My pleasure, Anna.

Host: And thank you to our audience for tuning in. This has been A.I.S. Insights — powered by Living Knowledge. Join us next time as we uncover more valuable knowledge for your business.