Evan Leybourn, Christopher Morales
February 1, 2025
Executive Summary
Over the past two years, organizations have invested in generative, predictive, and other modern Artificial Intelligence (AI) innovations at an unprecedented scale. AI's transformative potential to streamline operations and amplify staff effectiveness is too great to ignore.
This report explores AI's potential as a force multiplier for business outcomes, enabling people and teams to achieve significantly greater business outcomes than they could alone. This impact is highest when AI works alongside people, enhancing decision-making and creativity (augmentation) rather than replacing human effort (automation). This cooperative relationship redefines the role of people as “composers” who provide the inputs, quality control, and creative vision that AI cannot generate on its own.
Principles for Effective Human-Centric AI
- Augmentation, Not Automation: Amplifying, not replacing, human-produced value.
- From Creator to Composer: Redefining the role of humans to direct AI's potential.
- AI as a Force Multiplier: Achieving greater business impact within existing capacity.
The findings reveal that even when organizations successfully complete their AI initiatives, they often fall short of achieving their anticipated business benefits. Despite its potential, AI's impact is often undermined by organizational constraints and bureaucracy, such as rigid budgeting cycles, employee skill gaps, lengthy decision-making, and poor data quality.
Unlocking these constraints and AI's potential requires a shift in traditional business practices. Leaders need to focus on new capabilities, such as cultivating a learning organization, funding work dynamically, streamlining workflows, and adopting governance systems that balance innovation with risk.
This human-centric approach provides a foundation for the ethical, responsible use of AI and maximizes its value as a transformative tool for innovation and growth. By addressing constraints and rethinking traditional business practices, leaders can ensure their organizations can compete at the speed of AI.
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Methodology
This study employed the Delphi method, a structured, iterative process designed to gather insights and achieve consensus from a panel of experts. The Delphi method was chosen for its ability to harness collective intelligence and facilitate the synthesis of diverse perspectives across multiple rounds of consultation.
Panel Selection
The expert panel consisted of 18 individuals selected based on their expertise in either Business Agility or AI development and strategy across various industries, including technology, aerospace, telecommunications, healthcare, and professional services. Panelists were identified through professional networks, industry affiliations, and peer recommendations, ensuring a diverse range of experiences and viewpoints.
Data Collection and Iterative Rounds
The study was conducted over four iterative rounds. In the first round, open-ended questions were used to capture a broad range of perspectives on AI adoption, benefits, and constraints alongside perspectives of business agility capabilities. Responses were analyzed to identify recurring themes and key areas of focus. In the second round, panelists were presented with a summary of the first-round findings and asked to provide feedback, prioritize issues, and elaborate on areas of disagreement. The third round refined the findings further, seeking consensus on critical factors and AI force multipliers. The fourth and final round was a series of group calls to clarify and finalize any open questions or insights from the panel.
Quantitative ranking and qualitative commentary were incorporated in each round to balance measurable insights with nuanced expert input.
Data Analysis
Responses from each round were analyzed using thematic analysis to identify patterns and consensus areas. Quantitative data, such as rankings and ratings, were statistically summarized to highlight priority issues, while qualitative feedback provided contextual depth and illustrative examples. Discrepancies between panelists’ views were explored to ensure a comprehensive understanding of divergent perspectives.
Ethical Considerations
Participation in the study was voluntary, and informed consent was obtained from all panelists. Responses were anonymized to encourage openness and mitigate biases. The iterative design ensured that panelists could review and refine their inputs based on collective feedback, enhancing the reliability and credibility of the findings.
By leveraging the Delphi method, the study achieved a robust and iterative examination of the role of AI in business, providing actionable insights grounded in expert consensus.
A Note on AI Technology
Different AI technologies were being adopted by participating organizations. Generative AI, Predictive AI, Machine Learning, and NLP systems were most common, although other technologies such as Neural Networks, Computer Vision, and Digital Twins were also explored. Throughout this report, unless explicitly called out, we will refer to all of these as AI.
Acknowledgments and Thanks
This report is only possible through the gift of time, knowledge, and leadership from the research team. Special thanks to the expert panel and report authors for all their effort and insights.
All data collected through surveys is anonymized, securely stored, and made accessible only to those on the research team. Except where explicitly agreed, names of individuals, companies, and other potential identifiers have been removed or anonymized.
Expert Panel (alphabetical)
- Pete Behrens, USA,
Founder and CEO, Agile Leadership Journey - Dr Ferenc Birloni, Australia,
Partner, PHI Institute for Augmented Intelligence - Debbie Brey, USA,
Technical Fellow, Aerospace Company - Bill Dominguez, USA,
Founder and CEO, Ecocentric Strategies - Dr. Marwane El Kharbili, Germany,
AI Product Engineer, WOMAT - Giles Lindsay, UK,
Chief Information and Technology Officer (CITO), Lif3away - Stuart Munton, UK,
CIO | Chief for Group Operations & Technology, AND Digital - Andrew Park, USA,
Founder, Edensoft Labs - Paula Riano,
New Zealand, Gen AI Change Program Lead, Spark - Tony Ta, Australia,
Co-Founder, olla.ai - John Tanner, USA,
Chief Strategist, C4G Enterprises - Dr. Jess Tayel, Australia,
Founder, Transformation Leadership Institute
Four expert panel members requested to remain anonymous in the final report.
Primary Authors and Reviewers
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