ECIS 2026 (2026) AI Processed
STRUCTURING READINESS FOR AI INNOVATION:THE ROLE OF THE DESIGN CHALLENGE BRIEF AS A COORDINATION AND GATEKEEPING ARTEFACT
This qualitative study investigates how organizations evaluate and establish readiness for artificial intelligence (AI) initiatives before formally launching them. The authors analyze the usage of a collaboratively authored document, the Design Challenge Brief (DCB), across five AI innovation projects to observe how it structures pre-project planning.
Problem
Many AI initiatives fail because standard project frameworks cannot adequately address the unique uncertainties of AI, such as fluid data availability and non-deterministic system outcomes. Currently, there is little research explaining how diverse team members successfully navigate these challenges to determine when a project is mature enough to begin.
Outcome
- The study outlines a preliminary three-phase qualitative model for establishing AI project readiness: divergent exploration, negotiation/translation, and convergent alignment, though these findings are descriptive and lack statistical testing.
- The Design Challenge Brief is shown to act as a gatekeeping artifact that structures readiness reviews and allows teams to selectively revisit earlier project assumptions without resetting the entire process.
- The study is limited by its small qualitative sample of five projects and its setting within a structured, university-led innovation lab, which may restrict generalizability to less-structured corporate settings.
- The Design Challenge Brief is shown to act as a gatekeeping artifact that structures readiness reviews and allows teams to selectively revisit earlier project assumptions without resetting the entire process.
- The study is limited by its small qualitative sample of five projects and its setting within a structured, university-led innovation lab, which may restrict generalizability to less-structured corporate settings.
What it means for you
- CIO / IT Executive: Draft and mandate a standard 'Design Challenge Brief' template that all AI project champions must complete and get approved before receiving formal project launch funding.
- IT Manager: Gather your AI project team for a 1-hour workshop to map your current pre-project planning stage to the three phases of divergent exploration, negotiation/translation, and convergent alignment, explicitly documenting how you will handle fluid data availability.
- Business Strategist: Audit your pipeline of proposed AI initiatives and flag any project that has not yet completed a collaborative 'Design Challenge Brief' to prevent launching projects with high uncertainty around non-deterministic outcomes.
- Researcher: Draft a research proposal and survey instrument to test the generalizability of the three-phase AI readiness model across a quantitative sample of 100+ corporate AI projects outside of academic lab settings.
- Policymaker: Integrate a 'Design Challenge Brief' gatekeeping requirement into your public sector AI procurement and funding frameworks to ensure public agencies evaluate data readiness and outcome uncertainties before awarding AI contracts.
Transcript
Host: Welcome to A.I.S. Insights - Turning IS Research into Business Action. I am your host, Anna Ivy Summers. In each episode, we look at academic research from the European Conference on Information Systems and discuss how business and technology professionals can apply these findings in their organizations. Today, we are discussing a study titled, "STRUCTURING READINESS FOR AI INNOVATION: THE ROLE OF THE DESIGN CHALLENGE BRIEF AS A COORDINATION AND GATEKEEPING ARTEFACT." 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: To start us off, Alex, could you briefly explain what this study is about?
Expert: This is a qualitative study that investigates how organizations evaluate and establish readiness for artificial intelligence initiatives before they are formally launched. The researchers analyzed how a collaboratively authored document, called a Design Challenge Brief, or DCB, was used across five AI innovation projects to observe how it structures pre-project planning.
Host: That sounds highly relevant, especially since so many organizations are trying to figure out how to manage AI initiatives. What is the real-world problem that this study addresses? Why do standard project frameworks fall short here?
Expert: Many AI initiatives fail to progress past the pilot stage. A major reason is that standard project frameworks, like traditional project charters, are not designed to handle the unique uncertainties of AI. With traditional software, you can usually specify requirements upfront. But with AI, feasibility depends heavily on fluid data availability, non-deterministic system outcomes, and emergent feasibility. In other words, you cannot fully know if the system will work as intended until you start working with the data. Currently, there is very little research explaining how diverse team members, such as data scientists, business managers, and designers, successfully navigate these early-stage uncertainties to determine when a project is mature enough to actually begin.
Host: So how did the researchers approach this problem to understand how teams establish this readiness?
Expert: The researchers adopted an interpretive qualitative approach. They analyzed thirteen successive versions of Design Challenge Briefs produced over pre-project phases lasting two to three months across five different AI innovation projects in industries like telecom, banking, healthcare, logistics, and sports. Rather than just observing team meetings, they treated these document versions as material traces of how the teams' problem framing, data assumptions, and commitments evolved over time.
Host: And what did they find? What were the key outcomes of the study?
Expert: The study outlines a preliminary three-phase qualitative model for establishing AI project readiness. The first phase is divergent exploration, where teams use the brief to make uncertainty visible and articulate multiple alternative value propositions and data assumptions. The second phase is negotiation and translation, where different domain perspectives are reconciled, and specific constraints, particularly around data access and technical feasibility, are clarified. The third phase is convergent alignment, where teams consolidate their decisions and translate questions into concrete intentions.
Host: You mentioned the Design Challenge Brief itself. How does it help teams manage this process?
Expert: The study shows that the Design Challenge Brief acts as both a coordination and a gatekeeping artifact. It structures readiness reviews by establishing explicit thresholds, like data feasibility assessments, to indicate when a team is ready to move forward into formal design and modeling. A key finding is that it allows for what the authors call selective re-opening. If a new constraint emerges, like discovering that a required dataset is actually inaccessible, the team can use the brief to selectively revisit earlier assumptions and adjust the scope without having to reset the entire project planning process from scratch.
Host: It is important to note the boundaries of these findings. What are the limitations of the study?
Expert: Yes, there are some important boundary conditions. The study is based on a small qualitative sample of five projects. Additionally, it was conducted within a structured, university-led innovation lab. Because of this structured environment, the findings may not fully generalize to less-structured corporate settings. It is also a short paper, meaning these findings are descriptive and currently lack statistical testing.
Host: Understood. Let us pivot to the business value. How can technology and business professionals apply these findings to their own AI initiatives? What can practitioners actually do with this knowledge?
Expert: The key takeaway is that readiness in AI innovation does not happen organically; it requires structured coordination. First, practitioners should move away from rigid, traditional project charters for early-stage AI. Instead, they can implement a lightweight, repeatable mechanism like the Design Challenge Brief to explicitly document and track problem framing, data assumptions, and value hypotheses across iterations.
Host: How does that help with decision-making?
Expert: It standardizes the process by which problem definitions become "good enough" to proceed, rather than forcing teams to lock in premature solutions. Organizations can use these briefs to establish explicit readiness thresholds. Instead of waiting for absolute certainty, which is rare in AI, teams can proceed when the brief shows that uncertainties have been bracketed as acceptable and manageable. This makes it an excellent tool for project governance, portfolio steering, and ensuring responsible decision-making before committing significant budget to development.
Host: That provides a very practical way to manage the initial uncertainty of AI projects. Thank you, Alex, for sharing these insights with us today.
Expert: It was my pleasure, Anna.
Host: And thank you to our listeners for tuning in to A.I.S. Insights. We hope you can apply these research findings to improve your organization's AI project planning. Join us next time as we continue to bring academic research into business action.
Expert: Thank you, Anna. It is great to be here to discuss this study.
Host: To start us off, Alex, could you briefly explain what this study is about?
Expert: This is a qualitative study that investigates how organizations evaluate and establish readiness for artificial intelligence initiatives before they are formally launched. The researchers analyzed how a collaboratively authored document, called a Design Challenge Brief, or DCB, was used across five AI innovation projects to observe how it structures pre-project planning.
Host: That sounds highly relevant, especially since so many organizations are trying to figure out how to manage AI initiatives. What is the real-world problem that this study addresses? Why do standard project frameworks fall short here?
Expert: Many AI initiatives fail to progress past the pilot stage. A major reason is that standard project frameworks, like traditional project charters, are not designed to handle the unique uncertainties of AI. With traditional software, you can usually specify requirements upfront. But with AI, feasibility depends heavily on fluid data availability, non-deterministic system outcomes, and emergent feasibility. In other words, you cannot fully know if the system will work as intended until you start working with the data. Currently, there is very little research explaining how diverse team members, such as data scientists, business managers, and designers, successfully navigate these early-stage uncertainties to determine when a project is mature enough to actually begin.
Host: So how did the researchers approach this problem to understand how teams establish this readiness?
Expert: The researchers adopted an interpretive qualitative approach. They analyzed thirteen successive versions of Design Challenge Briefs produced over pre-project phases lasting two to three months across five different AI innovation projects in industries like telecom, banking, healthcare, logistics, and sports. Rather than just observing team meetings, they treated these document versions as material traces of how the teams' problem framing, data assumptions, and commitments evolved over time.
Host: And what did they find? What were the key outcomes of the study?
Expert: The study outlines a preliminary three-phase qualitative model for establishing AI project readiness. The first phase is divergent exploration, where teams use the brief to make uncertainty visible and articulate multiple alternative value propositions and data assumptions. The second phase is negotiation and translation, where different domain perspectives are reconciled, and specific constraints, particularly around data access and technical feasibility, are clarified. The third phase is convergent alignment, where teams consolidate their decisions and translate questions into concrete intentions.
Host: You mentioned the Design Challenge Brief itself. How does it help teams manage this process?
Expert: The study shows that the Design Challenge Brief acts as both a coordination and a gatekeeping artifact. It structures readiness reviews by establishing explicit thresholds, like data feasibility assessments, to indicate when a team is ready to move forward into formal design and modeling. A key finding is that it allows for what the authors call selective re-opening. If a new constraint emerges, like discovering that a required dataset is actually inaccessible, the team can use the brief to selectively revisit earlier assumptions and adjust the scope without having to reset the entire project planning process from scratch.
Host: It is important to note the boundaries of these findings. What are the limitations of the study?
Expert: Yes, there are some important boundary conditions. The study is based on a small qualitative sample of five projects. Additionally, it was conducted within a structured, university-led innovation lab. Because of this structured environment, the findings may not fully generalize to less-structured corporate settings. It is also a short paper, meaning these findings are descriptive and currently lack statistical testing.
Host: Understood. Let us pivot to the business value. How can technology and business professionals apply these findings to their own AI initiatives? What can practitioners actually do with this knowledge?
Expert: The key takeaway is that readiness in AI innovation does not happen organically; it requires structured coordination. First, practitioners should move away from rigid, traditional project charters for early-stage AI. Instead, they can implement a lightweight, repeatable mechanism like the Design Challenge Brief to explicitly document and track problem framing, data assumptions, and value hypotheses across iterations.
Host: How does that help with decision-making?
Expert: It standardizes the process by which problem definitions become "good enough" to proceed, rather than forcing teams to lock in premature solutions. Organizations can use these briefs to establish explicit readiness thresholds. Instead of waiting for absolute certainty, which is rare in AI, teams can proceed when the brief shows that uncertainties have been bracketed as acceptable and manageable. This makes it an excellent tool for project governance, portfolio steering, and ensuring responsible decision-making before committing significant budget to development.
Host: That provides a very practical way to manage the initial uncertainty of AI projects. Thank you, Alex, for sharing these insights with us today.
Expert: It was my pleasure, Anna.
Host: And thank you to our listeners for tuning in to A.I.S. Insights. We hope you can apply these research findings to improve your organization's AI project planning. Join us next time as we continue to bring academic research into business action.