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Illustration for: Why do similar small businesses adopting the same digital platform experience vastly different business model outcomes?

Key question

Why do similar small businesses adopting the same digital platform experience vastly different business model outcomes?

The impact of digital platform introduction on business models in SMEs—The interplay of efficiency and innovation orientation
Hönigsberg, Li, Korneeva, et al. · Journal of Strategic Information Systems · 2026

Operations & Supply Chain

What it means for you
  • CIO / IT Executive Review the platform's user adoption metrics from the past month, specifically looking at the depth of operational embedding (e.g., number of workflows integrated, data points shared) across different teams, and identify any teams demonstrating shallow engagement.
  • IT Manager Schedule a brief check-in with the team responsible for platform integration, asking them to identify one specific, recurring operational task that could be further streamlined through deeper platform usage, and outline the steps to achieve this.
  • Business Strategist Analyze the platform's current capabilities and identify one potential co-branding opportunity with another SME on the platform that leverages existing operational efficiencies but could lead to a novel offering.
  • Researcher Begin compiling a list of the specific operational metrics used by the four case study firms to measure 'efficiency-focused platform orientation' and note down any qualitative data collected on 'deep operational embedding'.
  • Policymaker Initiate a review of existing SME digital platform adoption support programs, focusing on how they encourage 'deep operational embedding' beyond initial efficiency gains and measure long-term transformative outcomes.
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Illustration for: How can organizations turn responsible artificial intelligence principles into practical governance practices?

Key question

How can organizations turn responsible artificial intelligence principles into practical governance practices?

Responsible artificial intelligence governance: A review and research framework
Papagiannidis, Mikalef, Conboy · Journal of Strategic Information Systems · 2025

Operations & Supply Chain

What it means for you
  • CIO / IT Executive Initiate a review of your organization's current AI project lifecycle documentation to identify where the seven core responsible AI principles (accountability, diversity & fairness, human agency & oversight, privacy & data governance, technical robustness & safety, transparency, and social & environmental well-being) are explicitly addressed and where gaps exist, preparing to assign ownership for these areas.
  • IT Manager Schedule a 30-minute meeting with your team responsible for AI development or deployment to collaboratively identify one specific AI project that could benefit from enhanced procedural practices (e.g., implementing a new risk assessment checklist based on transparency principles) and assign a team member to draft this enhancement.
  • Business Strategist Dedicate an hour to researching how responsible AI governance (structural, procedural, and relational practices) could directly contribute to your organization's stated business objectives and competitive advantage, identifying at least one concrete opportunity to explore further.
  • Researcher Select one of the seven core responsible AI principles that you find most underdeveloped in current practice and spend two hours identifying three specific research questions from the provided framework that directly address operationalizing this principle, preparing to draft a short proposal for investigation.
  • Policymaker Dedicate an hour to identifying which of the seven core responsible AI principles your current regulatory framework or internal policies address most weakly, and then draft a one-page summary of potential areas for future policy development or amendment, focusing on bridging the gap between abstract principles and actionable practices.
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