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Illustration for: How do explanations with AI feedback affect people's learning and task performance based on their prior knowledge?

Key question

How do explanations with AI feedback affect people's learning and task performance based on their prior knowledge?

Tell me more, tell me more: the impact of explanations on learning from feedback provided by Artificial Intelligence
Förster, Broder, Fahr, et al. · European Journal of Information Systems · 2025

Operations & Supply Chain

What it means for you
  • CIO / IT Executive Initiate a cross-functional working group involving IT, product development, and user experience teams to prioritize the integration of AI-driven feedback systems that include clear, context-specific explanations, focusing initially on applications with less experienced user bases.
  • IT Manager Review the current AI feedback mechanisms within your team's systems and identify 1-2 pilot opportunities to implement or enhance explanations, specifically targeting areas where user error or confusion is frequently observed, and gather baseline performance data.
  • Business Strategist Task your team with identifying key business processes or customer-facing interactions where AI feedback is being used or considered, and assess the potential impact of explanation-enhanced AI on user adoption, efficiency, and overall customer satisfaction, particularly for novice users.
  • Researcher Design a pilot study protocol to specifically investigate how the *type* and *level of detail* of explanations provided by AI feedback influence perceived informativeness and learning outcomes for users with varying levels of domain expertise in a controlled environment.
  • Policymaker Begin drafting guidelines or a framework for the ethical deployment of AI systems that mandate, where feasible, the inclusion of explanations for AI-generated feedback, prioritizing sectors with high user impact and potential for knowledge gaps, such as education and public services.
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Illustration for: How can organizations effectively design and implement artificial intelligence to support business decision-making?

Key question

How can organizations effectively design and implement artificial intelligence to support business decision-making?

Design principles for artificial intelligence-augmented decision making: An action design research study
Pathirannehelage, Shrestha, von Krogh · European Journal of Information Systems · 2024

Operations & Supply Chain

What it means for you
  • CIO / IT Executive Schedule a 30-minute kickoff meeting with your heads of data science, infrastructure, and business intelligence to review the company's current AIADM initiatives and identify one high-priority business model requirement for AIADM to address within the next quarter.
  • IT Manager Initiate a preliminary audit of existing data pipelines and AI model repositories, specifically noting any inconsistencies or gaps in documentation that hinder synergy between input data, AI models, and visual outputs.
  • Business Strategist Draft a concise one-page document outlining at least two specific AIADM use cases that directly align with the company's current strategic objectives, identifying the key data inputs and desired decision outcomes for each.
  • Researcher Review the internal documentation for the AIADM system currently in use, and identify one specific instance where human domain expertise could have significantly improved the AI's output or decision recommendation.
  • Policymaker Begin drafting a preliminary checklist of essential components for an ethical AI governance framework, focusing on elements relevant to data privacy, bias detection, and clear lines of decision accountability for AI-augmented decisions.
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Illustration for: How can organizations successfully manage and sustain digital transformations over the long term?

Key question

How can organizations successfully manage and sustain digital transformations over the long term?

Transform or be transformed: the importance of research on managing and sustaining digital transformations
Carroll, Hassan, Junglas, et al. · European Journal of Information Systems · 2023

Operations & Supply Chain

What it means for you
  • CIO / IT Executive Schedule a 30-minute meeting with your direct reports to discuss the 'execution engine' concept and brainstorm one specific process within your IT department that needs fundamental adaptation for long-term digital transformation success. Assign one person to draft a brief proposal by end-of-week.
  • IT Manager Identify one digital initiative currently underway that is showing signs of slowing adoption or unexpected friction. Gather the project team for a 1-hour 'root cause analysis' session, focusing specifically on potential cultural or structural obstacles, not just technical issues, and document the top 2-3 hypothesized causes.
  • Business Strategist Review your organization's current digital transformation roadmap and flag any objectives that are solely focused on short-term adoption metrics. Draft a list of 3-5 questions to explore how these objectives will be sustained and normalized over the next 1-3 years, and schedule a preliminary discussion with your team.
  • Researcher Analyze your current research project's data collection plan. Identify if it allows for longitudinal observation of digital transformation normalization (beyond initial adoption) and, if not, sketch out one modification to incorporate this perspective in future data gathering or analysis.
  • Policymaker Review existing public funding guidelines or incentives for digital transformation. Identify one specific criterion or reporting requirement that might unintentionally discourage longitudinal sustainability efforts and draft a brief memo suggesting a minor amendment to encourage long-term impact.
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