Combining Low-Code/No-Code with Noncompliant Workarounds to Overcome a Corporate System's Limitations
Robert M. Davison, Louie H. M. Wong, Steven Alter
This study explores how employees at a warehouse in Hong Kong utilize low-code/no-code principles with everyday tools like Microsoft Excel to create unofficial solutions. It examines these noncompliant but essential workarounds that compensate for the shortcomings of their mandated corporate software system. The research is based on a qualitative case study involving interviews with warehouse staff.
Problem
A global company implemented a standardized, non-customizable corporate system (Microsoft Dynamics) that was ill-suited for the unique logistical needs of its Hong Kong operations. This created significant operational gaps, particularly in delivery scheduling, leaving employees unable to perform critical tasks using the official software.
Outcome
- Employees effectively use Microsoft Excel as a low-code tool to create essential, noncompliant workarounds that are vital for daily operations, such as delivery management. - These employee-driven solutions, developed without formal low-code platforms or IT approval, become institutionalized and crucial for business success, highlighting the value of 'shadow IT'. - The study argues that low-code/no-code development is not limited to formal platforms and that managers should recognize, support, and govern these informal solutions. - Businesses are advised to adopt a portfolio approach to low-code development, leveraging tools like Excel alongside formal platforms, to empower employees and solve real-world operational problems.
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.
Low-Code/No-Code, Workarounds, Shadow IT, Citizen Development, Enterprise Systems, Case Study, Microsoft Excel
AI & SOCIETY (2025)
What it takes to control Al by design: human learning
Dov Te'eni, Inbal Yahav, David Schwartz
This study proposes a robust framework, based on systems theory, for maintaining meaningful human control over complex human-AI systems. The framework emphasizes the importance of continual human learning to parallel advancements in machine learning, operating through two distinct modes: a stable mode for efficient operation and an adaptive mode for learning. The authors demonstrate this concept with a method called reciprocal human-machine learning applied to a critical text classification system.
Problem
Traditional methods for control and oversight are insufficient for the complexity of modern AI technologies, creating a gap in ensuring that critical AI systems remain aligned with human values and goals. As AI becomes more autonomous and operates in volatile environments, there is an urgent need for a new approach to design systems that allow humans to effectively stay in control and adapt to changing circumstances.
Outcome
- The study introduces a framework for human control over AI that operates at multiple levels and in two modes: stable and adaptive. - Effective control requires continual human learning to match the pace of machine learning, ensuring humans can stay 'in the loop' and 'in control'. - A method called 'reciprocal human-machine learning' is presented, where humans and AI learn from each other's feedback in an adaptive mode. - This approach results in high-performance AI systems that are unbiased and aligned with human values. - The framework provides a model for designing control in critical AI systems that operate in dynamic environments.
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.
Communications of the Association for Information Systems (2024)
Design Knowledge for Virtual Learning Companions from a Value-centered Perspective
Ricarda Schlimbach, Bijan Khosrawi-Rad, Tim C. Lange, Timo Strohmann, Susanne Robra-Bissantz
This study develops design principles for Virtual Learning Companions (VLCs), which are AI-powered chatbots designed to help students with motivation and time management. Using a design science research approach, the authors conducted interviews, workshops, and built and tested several prototypes with students. The research aims to create a framework for designing VLCs that not only provide functional support but also build a supportive, companion-like relationship with the learner.
Problem
Working students in higher education often struggle to balance their studies with their jobs, leading to challenges with motivation and time management. While conversational AI like ChatGPT is becoming common, these tools often lack the element of companionship and a holistic approach to learning support. This research addresses the gap in how to design AI learning tools that effectively integrate motivation, time management, and relationship-building from a user-value-centered perspective.
Outcome
- The study produced a comprehensive framework for designing Virtual Learning Companions (VLCs), resulting in 9 design principles, 28 meta-requirements, and 33 design features. - The findings are structured around a “value-in-interaction” model, which proposes that a VLC's value is created across three interconnected layers: the Relationship Layer, the Matching Layer, and the Service Layer. - Key design principles include creating a human-like and adaptive companion, enabling proactive and reactive behavior, building a trustworthy relationship, providing supportive content, and fostering a motivational and ethical learning environment. - Evaluation of a coded prototype revealed that different student groups have different preferences, emphasizing that VLCs must be adaptable to their specific educational context and user needs to be effective.
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.
Conversational Agent, Education, Virtual Learning Companion, Design Knowledge, Value
MIS Quarterly (2025)
REGULATING EMERGING TECHNOLOGIES: PROSPECTIVE SENSEMAKING THROUGH ABSTRACTION AND ELABORATION
Stefan Seidel, Christoph J. Frick, Jan vom Brocke
This study examines how various actors, including legal experts, government officials, and industry leaders, collaborated to create laws for new technologies like blockchain. Through a case study in Liechtenstein, it analyzes the process of developing a law on "trustworthy technology," focusing on how the participants collectively made sense of a complex and evolving subject to construct a new regulatory framework.
Problem
Governments face a significant challenge in regulating emerging digital technologies. They must create rules that prevent harmful effects and protect users without stifling innovation. This is particularly difficult when the full potential and risks of a new technology are not yet clear, creating regulatory gaps and uncertainty for businesses.
Outcome
- Creating effective regulation for new technologies is a process of 'collective prospective sensemaking,' where diverse stakeholders build a shared understanding over time. - This process relies on two interrelated activities: 'abstraction' and 'elaboration'. Abstraction involves generalizing the essential properties of a technology to create flexible, technology-neutral rules that encourage innovation. - Elaboration involves specifying details and requirements to provide legal certainty and protect users. - Through this process, the regulatory target can evolve significantly, as seen in the case study's shift from regulating 'blockchain/cryptocurrency' to a broader, more durable law for the 'token economy' and 'trustworthy technology'.
Host: Welcome to A.I.S. Insights — powered by Living Knowledge. I’m your host, Anna Ivy Summers. Host: On today’s episode, we're diving into the complex world of regulation for new technologies. We’re looking at a study titled "REGULATING EMERGING TECHNOLOGIES: PROSPECTIVE SENSEMAKING THROUGH ABSTRACTION AND ELABORATION". Host: The study examines how a diverse group of people—legal experts, government officials, and industry leaders—came together to create laws for a new technology, using blockchain in Liechtenstein as a case study. Here to help us unpack this is our analyst, Alex Ian Sutherland. Welcome, Alex. Expert: Great to be here, Anna. Host: So Alex, let’s start with the big picture. What is the fundamental problem that governments and businesses face when a new technology like blockchain or A.I. emerges? Expert: It’s a classic case of trying to build the plane while you're flying it. Governments need to create rules to protect users and prevent harm, but they also want to avoid crushing innovation before it even gets off the ground. Host: The dreaded innovation killer. Expert: Exactly. The study highlights that this is incredibly difficult when no one fully understands the technology's potential or its risks. This creates what the authors call a "regulatory gap"—a gray area of uncertainty that can paralyze businesses. They don't know if their new business model is legal, so they hesitate to invest. Host: And how did the researchers in this study go about understanding this process? What was their approach? Expert: They conducted an in-depth case study in the European state of Liechtenstein. They essentially got a front-row seat to the entire law-making process for blockchain technology. Expert: They interviewed everyone involved—from the Prime Minister to tech startup CEOs to the financial regulators. They also analyzed hundreds of documents, including early strategy papers and evolving drafts of the law, to see how the thinking changed over time. Host: It sounds like they had incredible access. So, after all that observation, what were the key findings? What did they discover about how to create good regulation? Expert: The biggest finding is that it's a process of what they call 'collective prospective sensemaking'. That’s a fancy term for getting a diverse group of people in a room to build a shared vision of the future. It’s not about one person having the answer; it’s about creating it together. Host: And the study found this process hinges on two specific activities: 'abstraction' and 'elaboration'. Can you break those down for us? Expert: Of course. Think of 'abstraction' as zooming out. Initially, the group in Liechtenstein was focused on regulating "blockchain" and "cryptocurrency." But they realized that was too specific and would be outdated quickly. Expert: So, they abstracted. They asked, "What is the essential quality of this technology?" They landed on the idea of "trust." This allowed them to create a flexible, technology-neutral rule for any "trustworthy technology," not just blockchain. It future-proofed the law. Host: That’s a brilliant shift. So what about 'elaboration'? Expert: If abstraction is zooming out, 'elaboration' is zooming in. Once they had the big, abstract concept—trustworthy technology—they had to add the specific details. Expert: This meant defining roles, specifying requirements for service providers, and creating rules that would give businesses legal certainty and actually protect users. It's the process of giving the abstract idea real-world teeth. Host: So the target itself evolved dramatically through this process. Expert: It really did. They went from a narrow law about cryptocurrency to a broad, durable framework for what they called the "token economy." This was only possible because of that constant dance between the big-picture abstraction and the fine-detail elaboration. Host: This is fascinating, Alex, but let's get to the bottom line. Why does this study matter for business leaders listening right now, even if they aren't in the crypto space? Expert: This is the most crucial part. The study offers a powerful blueprint for how businesses should approach regulation for any emerging technology, whether it's A.I., quantum computing, or synthetic biology. Expert: The first takeaway is proactive engagement. Don't wait for regulation to happen *to* you. The industry leaders in this study who participated in the process helped shape a more innovation-friendly law. By being at the table, you can influence the outcome. Host: So get involved early and often. What else? Expert: Second, understand the power of language. The breakthrough in Liechtenstein happened when they shifted the conversation from a specific technology, blockchain, to a desired outcome, which was trust. For businesses, this is a key strategy: frame the conversation with regulators around the value you create, not just the tech you use. Host: It’s a narrative strategy, really. Expert: Precisely. And finally, this model provides predictability. The process of abstraction and elaboration creates a stable yet flexible framework. For businesses, that kind of regulatory environment is gold. It reduces uncertainty and gives you the confidence to invest and innovate for the long term. This is the path to avoiding that "gray space" we talked about earlier. Host: So to sum up, regulating new technology isn’t a top-down mandate; it's a collaborative journey. The key is to balance flexible, high-level principles with clear, specific rules. For businesses, the lesson is clear: get a seat at the table and help shape a predictable environment where innovation can thrive. Host: Alex Ian Sutherland, thank you for making such a complex topic so clear. Expert: My pleasure, Anna. Host: And thank you for tuning into A.I.S. Insights — powered by Living Knowledge. Join us next time as we continue to explore the ideas shaping business and technology.
