ECIS 2026 (2026) AI Processed
THE ANATOMY OF A DESIGN CHALLENGE FOR INITIATING AI INNOVATION
This qualitative study conceptualizes the 'Design Challenge' as a boundary object designed to initiate and guide AI innovation in interdisciplinary teams. The researchers performed a qualitative cross-case analysis of 42 corporate AI innovation projects using a theory-guided comparative artifact analysis method.
Problem
Organizations frequently struggle to launch AI projects due to the technological complexity, friction between interdisciplinary teams, and the emergent nature of AI capabilities. Without a structured starting framework, teams often experience misalignment, set unrealistic expectations, or prematurely commit to rigid technical architectures that fail.
Outcome
- The study empirically validated a structural anatomy for the Design Challenge consisting of four robust elements (verb, object of innovation, target group, context) and three plastic elements (internal data, technology, potential), which was supported across all 42 cases analyzed.
- The robust elements were validated to successfully create a stable, shared identity and common direction among diverse team members, acting as a center of authority.
- The plastic elements were shown to successfully absorb technological uncertainty, providing a flexible framework for teams to negotiate and adapt to AI's emergent capabilities over time.
- The qualitative analysis identified three distinct nuances in shaping the 'object of innovation' (modular, abstract, and space) that guide teams toward either incremental or more radical innovation pathways.
- The robust elements were validated to successfully create a stable, shared identity and common direction among diverse team members, acting as a center of authority.
- The plastic elements were shown to successfully absorb technological uncertainty, providing a flexible framework for teams to negotiate and adapt to AI's emergent capabilities over time.
- The qualitative analysis identified three distinct nuances in shaping the 'object of innovation' (modular, abstract, and space) that guide teams toward either incremental or more radical innovation pathways.
What it means for you
- CIO / IT Executive: Mandate that all upcoming AI project proposals use a standardized 'Design Challenge' charter template structured around the 4 robust elements (verb, object of innovation, target group, context) and 3 plastic elements (internal data, technology, potential), explicitly forbidding teams from locking in a rigid technical architecture in their initial pitch.
- IT Manager: Gather your interdisciplinary project team for a 60-minute workshop on Monday morning to co-create a 'Design Challenge' statement, explicitly defining the stable 'robust' elements to align the business and tech members, while leaving the 'plastic' elements (like specific data sources and AI models) open as flexible targets to be negotiated as the project progresses.
- Business Strategist: Review your company's AI project portfolio on Monday morning and intentionally rewrite the 'object of innovation' for each project: frame them as 'modular' if you need predictable, incremental improvements, or rewrite them as 'space-based' (e.g., focused on broad capabilities) if you want to push the team toward radical, disruptive business model innovation.
- Researcher: Update your qualitative coding scheme for ongoing field studies of AI adoption to track how frequently and at what project stages teams modify the 'plastic' elements (internal data, technology, potential) versus the 'robust' elements of their project charters to measure the empirical threshold where technological uncertainty triggers project misalignment.
- Policymaker: Revise the application guidelines for the upcoming public AI innovation grant cycle to replace rigid 'technical specifications' requirements with a mandatory 'Design Challenge' framework, scoring applicants on the clarity of their robust elements (target group, context) and the adaptive flexibility of their plastic elements (data and technology integration plans).
Transcript
Host: Welcome to A.I.S. Insights - Turning IS Research into Business Action. I am your host, Anna Ivy Summers, and today we are looking at how organizations can successfully launch artificial intelligence initiatives. We are discussing a study presented at the European Conference on Information Systems, or ECIS 2026, titled "THE ANATOMY OF A DESIGN CHALLENGE FOR INITIATING AI INNOVATION". Joining me to break down this research is our expert analyst, Alex Ian Sutherland. Welcome, Alex.
Expert: Thank you, Anna. It is great to be here to discuss this study.
Host: Let's start with the big problem this research addresses. Many organizations want to leverage artificial intelligence, but actually starting these projects is notoriously difficult. Why is that?
Expert: The study highlights that the initiation phase of AI innovation is highly complex. AI is not a static technology; it has an emergent, evolving nature with high levels of uncertainty. When organizations try to initiate these projects, they rely on interdisciplinary teams—such as business managers, IT staff, and data scientists. These groups often have different vocabularies, methods, and mindsets, which creates friction.
Host: And without a structured starting point, those differences can cause real issues, right?
Expert: Precisely. Without a clear starting framework, teams often experience misalignment, set unrealistic expectations, or make a premature commitment to a rigid technical architecture. When they commit to a solution too early, they focus on showing off technological capabilities rather than solving the actual business problem, which often leads to project failure.
Host: So how did the researchers investigate a solution to this starting problem?
Expert: They utilized the concept of a boundary object. In organizational theory, a boundary object is an artifact that is stable enough to maintain a common identity across different groups, yet adaptable enough to meet local needs. The researchers performed a qualitative cross-case analysis of 42 corporate AI innovation projects conducted between 2020 and 2024. These projects spanned various industries, including finance, manufacturing, and healthcare. Through a theory-guided comparative analysis, they studied the "Design Challenge" statements used to launch these projects.
Host: And what did the study find regarding the structure of these design challenges?
Expert: The analysis empirically validated a structural anatomy consisting of seven elements. These are divided into four robust elements and three plastic elements. The four robust elements are the verb, the object of innovation, the target group, and the context.
Host: What makes these four elements "robust"?
Expert: They are considered robust because they were present across all 42 analyzed cases and remained stable over time. They clarify *what* the innovation is about without prescribing *how* it should be done. This creates a stable, shared identity and a common direction for the interdisciplinary team, acting as a center of authority.
Host: And what about the three plastic elements?
Expert: The three plastic elements are internal data, technology, and potential. These are flexible. They are designed to absorb technological uncertainty, providing a framework for the team to negotiate, interpret, and adapt to AI’s capabilities as the project progresses.
Host: The study also identified different nuances in how teams define the "object of innovation" element. Can you explain those?
Expert: Yes, the qualitative analysis identified three distinct nuances. First, modular objects, like a "sales process," represent something divisible into smaller steps. This tends to guide teams toward incremental innovation pathways. Second, abstract objects, like "career development," are not yet fully clear, leaving room for the team to make them concrete. Third, space objects, like a "Green IT initiative," establish open-ended boundaries, which often guide teams toward more radical innovation pathways.
Host: This seems highly practical for managers. How can a business professional use this anatomy to write a better design challenge for an AI project?
Expert: The study provides a clear schema. A manager can structure their challenge as a question: "How might we [verb] our [object of innovation] using our [internal data] leveraging [technology] for our [target group] considering [context] conditioned by [potential]?"
Host: Could you give us an example of how that sounds in a business scenario?
Expert: Sure. In a banking context, it might look like this: "How might we *rethink* our *customer experience* using our *CRM data* leveraging *AI* for our *corporate customers* considering *future banking products* conditioned by *regulatory risks*?"
Host: That makes a lot of sense. The robust elements—rethinking the experience for corporate customers—keep the team aligned on the business goal. Meanwhile, the plastic elements—the CRM data, the AI technology, and the regulatory risks—give the technical team a flexible sandbox to explore solutions.
Expert: Exactly. It prevents the team from committing to a specific AI model on day one, allowing them to collaboratively learn and adapt as they discover what the data and technology can actually do. However, as a note of context, we should mention that the study's data came from projects supported by coaching from senior researchers. The researchers note that projects running in corporate environments without this external scientific support might face different dynamics.
Host: That is an important qualification to keep in mind. To wrap up, Alex, what is the main takeaway for our listeners?
Expert: The takeaway is that the way you frame an AI project at the very beginning matters. By systematically balancing stable, robust business goals with flexible, plastic technical elements, organizations can guide interdisciplinary teams toward successful collaboration and avoid premature technical lock-in.
Host: Thank you, Alex, for sharing these insights. And thank you to our listeners for tuning in to A.I.S. Insights - Turning IS Research into Business Action. We will see you next time.
Expert: Thank you, Anna. It is great to be here to discuss this study.
Host: Let's start with the big problem this research addresses. Many organizations want to leverage artificial intelligence, but actually starting these projects is notoriously difficult. Why is that?
Expert: The study highlights that the initiation phase of AI innovation is highly complex. AI is not a static technology; it has an emergent, evolving nature with high levels of uncertainty. When organizations try to initiate these projects, they rely on interdisciplinary teams—such as business managers, IT staff, and data scientists. These groups often have different vocabularies, methods, and mindsets, which creates friction.
Host: And without a structured starting point, those differences can cause real issues, right?
Expert: Precisely. Without a clear starting framework, teams often experience misalignment, set unrealistic expectations, or make a premature commitment to a rigid technical architecture. When they commit to a solution too early, they focus on showing off technological capabilities rather than solving the actual business problem, which often leads to project failure.
Host: So how did the researchers investigate a solution to this starting problem?
Expert: They utilized the concept of a boundary object. In organizational theory, a boundary object is an artifact that is stable enough to maintain a common identity across different groups, yet adaptable enough to meet local needs. The researchers performed a qualitative cross-case analysis of 42 corporate AI innovation projects conducted between 2020 and 2024. These projects spanned various industries, including finance, manufacturing, and healthcare. Through a theory-guided comparative analysis, they studied the "Design Challenge" statements used to launch these projects.
Host: And what did the study find regarding the structure of these design challenges?
Expert: The analysis empirically validated a structural anatomy consisting of seven elements. These are divided into four robust elements and three plastic elements. The four robust elements are the verb, the object of innovation, the target group, and the context.
Host: What makes these four elements "robust"?
Expert: They are considered robust because they were present across all 42 analyzed cases and remained stable over time. They clarify *what* the innovation is about without prescribing *how* it should be done. This creates a stable, shared identity and a common direction for the interdisciplinary team, acting as a center of authority.
Host: And what about the three plastic elements?
Expert: The three plastic elements are internal data, technology, and potential. These are flexible. They are designed to absorb technological uncertainty, providing a framework for the team to negotiate, interpret, and adapt to AI’s capabilities as the project progresses.
Host: The study also identified different nuances in how teams define the "object of innovation" element. Can you explain those?
Expert: Yes, the qualitative analysis identified three distinct nuances. First, modular objects, like a "sales process," represent something divisible into smaller steps. This tends to guide teams toward incremental innovation pathways. Second, abstract objects, like "career development," are not yet fully clear, leaving room for the team to make them concrete. Third, space objects, like a "Green IT initiative," establish open-ended boundaries, which often guide teams toward more radical innovation pathways.
Host: This seems highly practical for managers. How can a business professional use this anatomy to write a better design challenge for an AI project?
Expert: The study provides a clear schema. A manager can structure their challenge as a question: "How might we [verb] our [object of innovation] using our [internal data] leveraging [technology] for our [target group] considering [context] conditioned by [potential]?"
Host: Could you give us an example of how that sounds in a business scenario?
Expert: Sure. In a banking context, it might look like this: "How might we *rethink* our *customer experience* using our *CRM data* leveraging *AI* for our *corporate customers* considering *future banking products* conditioned by *regulatory risks*?"
Host: That makes a lot of sense. The robust elements—rethinking the experience for corporate customers—keep the team aligned on the business goal. Meanwhile, the plastic elements—the CRM data, the AI technology, and the regulatory risks—give the technical team a flexible sandbox to explore solutions.
Expert: Exactly. It prevents the team from committing to a specific AI model on day one, allowing them to collaboratively learn and adapt as they discover what the data and technology can actually do. However, as a note of context, we should mention that the study's data came from projects supported by coaching from senior researchers. The researchers note that projects running in corporate environments without this external scientific support might face different dynamics.
Host: That is an important qualification to keep in mind. To wrap up, Alex, what is the main takeaway for our listeners?
Expert: The takeaway is that the way you frame an AI project at the very beginning matters. By systematically balancing stable, robust business goals with flexible, plastic technical elements, organizations can guide interdisciplinary teams toward successful collaboration and avoid premature technical lock-in.
Host: Thank you, Alex, for sharing these insights. And thank you to our listeners for tuning in to A.I.S. Insights - Turning IS Research into Business Action. We will see you next time.