ECIS 2026 (2026) AI Processed Human Approved
FROM BROWN TO GREEN:HOW AI-DRIVEN TRANSFORMATION ENABLES BUSINESS MODEL RECONFIGURATION IN THE TWIN TRANSITION
This paper examines how an AI-enabled platform supports sustainability-oriented business model reconfiguration through a qualitative case study of a Canadian engineering firm. Drawing on interviews, observations, and documentary data, the researchers analyze the transition from transactional consulting to recurring, outcome-oriented services enabled by the firm's AI platform.
Problem
While artificial intelligence is recognized as a cornerstone of digital transformation, its role in enabling sustainability-driven business model reconfiguration remains underexplored. Organizations frequently struggle to scale AI initiatives beyond pilot projects to achieve combined digital and sustainable ("twin") transition goals.
Outcome
- The AI-enabled platform qualitatively supported a business model shift from transactional, time-based billing to recurring, outcome-based services focused on asset durability and lifecycle performance.
- A strategic alignment gap was identified: executives framed the AI and sustainability transformation as radical, whereas operational managers experienced it as incremental.
- Successful scaling of the AI initiative qualitatively depended on adaptive governance (looser decision-making, rapid experimentation), pre-requisite capability building in data engineering, and strong leadership support.
- Key organizational trade-offs and barriers emerged, specifically related to skills gaps, legacy system integration, high energy consumption of AI models, and delayed financial return on investment.
- Limitation: The findings of this study are based on a single qualitative case study of a Canadian engineering firm, which may limit the direct generalizability of the results to other industries or regions.
- A strategic alignment gap was identified: executives framed the AI and sustainability transformation as radical, whereas operational managers experienced it as incremental.
- Successful scaling of the AI initiative qualitatively depended on adaptive governance (looser decision-making, rapid experimentation), pre-requisite capability building in data engineering, and strong leadership support.
- Key organizational trade-offs and barriers emerged, specifically related to skills gaps, legacy system integration, high energy consumption of AI models, and delayed financial return on investment.
- Limitation: The findings of this study are based on a single qualitative case study of a Canadian engineering firm, which may limit the direct generalizability of the results to other industries or regions.
What it means for you
- CIO / IT Executive: Initiate an audit of legacy data pipelines to identify bottlenecks for AI integration, and establish a 'green sandbox' environment with relaxed governance rules to allow developers to experiment with asset-durability algorithms while tracking model energy consumption.
- IT Manager: Hold a 30-minute team sync to map out existing data engineering skill gaps and assign one developer to clean and structure a single historical dataset of asset lifecycle performance to prepare it for future AI model training.
- Business Strategist: Draft a pilot proposal to transition one high-trust client from hourly billing to a recurring, outcome-based pricing model focused on extending their asset lifecycle, and schedule a meeting with operational managers to align on realistic, incremental milestones.
- Researcher: Design a survey instrument targeting business and IT leaders across multiple industries to quantitatively test whether the perceptual gap between executive (radical) and operational (incremental) views on AI-driven green transitions exists outside of the engineering sector.
- Policymaker: Draft a policy brief proposing a targeted grant program that subsidizes data-engineering upskilling and offsets energy-efficiency auditing costs for mid-sized firms adopting AI-enabled, sustainable business models.
Transcript
Host: Welcome to A.I.S. Insights - Turning IS Research into Business Action. I'm Aaron Ivan Sterling.
Expert: And I'm Ava Irene Solis.
Host: Today we are discussing a study presented at the European Conference on Information Systems, ECIS 2026. The study is titled "FROM BROWN TO GREEN: HOW AI-DRIVEN TRANSFORMATION ENABLES BUSINESS MODEL RECONFIGURATION IN THE TWIN TRANSITION". Ava, can you share the core focus of this study?
Expert: Yes, Aaron. This study examines how an artificial intelligence-enabled platform can support a sustainability-oriented business model reconfiguration. The researchers conducted a qualitative case study of a Canadian engineering firm, analyzing how they used their AI platform to shift from traditional, transactional consulting to recurring, outcome-oriented services focused on asset durability and lifecycle performance.
Host: Let's discuss the real-world problem this study addresses. Why is this transition so challenging for organizations today?
Expert: Many companies are trying to navigate what is called the "twin transition", which is the simultaneous pursuit of digital transformation and environmental sustainability. While AI is recognized as a cornerstone of digital change, its specific role in enabling sustainability-driven business model redesign remains underexplored. Organizations frequently struggle to scale their AI initiatives beyond pilot projects to achieve combined digital and sustainable goals.
Host: How did the researchers investigate this challenge in the study?
Expert: The study adopted a qualitative case study approach focused on a mature Canadian engineering firm, referred to under the pseudonym EnginiCo. The researchers gathered data through semi-structured interviews with executives and managers, direct observations of meetings and workshops, and internal strategic documents. They specifically analyzed the development and deployment of the firm's AI platform, called OptiMesh, which supports asset-lifecycle decisions.
Host: What were the primary findings from this qualitative analysis?
Expert: First, the study showed that the AI-enabled platform supported a transition in the firm's business model from transactional, time-based billing to recurring, outcome-based services focused on asset durability and lifecycle performance.
Host: That is a significant structural shift. Were there any alignment challenges during this process?
Expert: Yes, a strategic alignment gap was identified. Executives framed the AI and sustainability transformation as a radical and urgent change. However, operational managers experienced it as an incremental, gradual set of improvements. This perception gap is critical because when frontline employees experience change as merely incremental, organizations risk under-investing in the capabilities, governance shifts, and cultural work required to scale AI.
Host: And what did the study find was required to successfully scale the AI platform?
Expert: Successful scaling depended on three main factors. First, adaptive governance, which involved loosening decision-making structures to allow for rapid experimentation. Second, building prerequisite capabilities in data engineering and machine learning operations. Third, strong leadership support that communicated digital and green initiatives as inherently linked.
Host: Did the study highlight any barriers or trade-offs that managers should be aware of?
Expert: Yes, several trade-offs and barriers emerged. These included skills gaps, difficulties integrating legacy systems with new AI solutions, a delayed return on investment, and the high energy consumption of the AI models themselves, which can create tension with sustainability goals.
Host: It is important to note, though, that this study has certain limits, correct?
Expert: Yes, a key limitation is that the findings are based on a single qualitative case study of one Canadian engineering firm, which may limit the direct generalizability of the results to other industries or geographic regions.
Host: Let's focus on what business and technology professionals can do with these insights. How can practitioners apply this knowledge to their own organizations?
Expert: The study offers clear, practical guidance. First, organizations should sequence their capability building. Before attempting a large-scale rollout of AI for sustainability, they must invest in foundational data engineering, machine learning operations, business intelligence, and AI training.
Host: So, don't rush into a massive rollout without those data foundations in place. What about governance?
Expert: Practitioners should adopt adaptive governance models with faster decision cycles and eco-economic key performance indicators. By linking environmental and financial metrics directly in decision-making, managers can better balance short-term constraints with long-term sustainability goals.
Host: And how should organizations frame their AI projects from the start?
Expert: The study suggests that organizations seeking to align AI with sustainability should design for business model change from the outset, rather than treating sustainability as an add-on to technical deployment. Finally, fostering an intrapreneurial culture and co-creating solutions directly with clients can accelerate finding the right market fit and reduce implementation uncertainty.
Host: Thank you, Ava, for breaking down this study for us. To summarize, transitioning from transactional models to sustainability-oriented, recurring services requires aligning digital capabilities with business model design, building foundational data engineering skills, adapting our governance, and actively managing the economic and environmental trade-offs of AI itself.
Expert: Exactly, Aaron. It is about treating digital and sustainable goals as two sides of the same coin.
Host: That is all for this episode of A.I.S. Insights. Thank you to our listeners for joining us. We will see you next time.
Expert: And I'm Ava Irene Solis.
Host: Today we are discussing a study presented at the European Conference on Information Systems, ECIS 2026. The study is titled "FROM BROWN TO GREEN: HOW AI-DRIVEN TRANSFORMATION ENABLES BUSINESS MODEL RECONFIGURATION IN THE TWIN TRANSITION". Ava, can you share the core focus of this study?
Expert: Yes, Aaron. This study examines how an artificial intelligence-enabled platform can support a sustainability-oriented business model reconfiguration. The researchers conducted a qualitative case study of a Canadian engineering firm, analyzing how they used their AI platform to shift from traditional, transactional consulting to recurring, outcome-oriented services focused on asset durability and lifecycle performance.
Host: Let's discuss the real-world problem this study addresses. Why is this transition so challenging for organizations today?
Expert: Many companies are trying to navigate what is called the "twin transition", which is the simultaneous pursuit of digital transformation and environmental sustainability. While AI is recognized as a cornerstone of digital change, its specific role in enabling sustainability-driven business model redesign remains underexplored. Organizations frequently struggle to scale their AI initiatives beyond pilot projects to achieve combined digital and sustainable goals.
Host: How did the researchers investigate this challenge in the study?
Expert: The study adopted a qualitative case study approach focused on a mature Canadian engineering firm, referred to under the pseudonym EnginiCo. The researchers gathered data through semi-structured interviews with executives and managers, direct observations of meetings and workshops, and internal strategic documents. They specifically analyzed the development and deployment of the firm's AI platform, called OptiMesh, which supports asset-lifecycle decisions.
Host: What were the primary findings from this qualitative analysis?
Expert: First, the study showed that the AI-enabled platform supported a transition in the firm's business model from transactional, time-based billing to recurring, outcome-based services focused on asset durability and lifecycle performance.
Host: That is a significant structural shift. Were there any alignment challenges during this process?
Expert: Yes, a strategic alignment gap was identified. Executives framed the AI and sustainability transformation as a radical and urgent change. However, operational managers experienced it as an incremental, gradual set of improvements. This perception gap is critical because when frontline employees experience change as merely incremental, organizations risk under-investing in the capabilities, governance shifts, and cultural work required to scale AI.
Host: And what did the study find was required to successfully scale the AI platform?
Expert: Successful scaling depended on three main factors. First, adaptive governance, which involved loosening decision-making structures to allow for rapid experimentation. Second, building prerequisite capabilities in data engineering and machine learning operations. Third, strong leadership support that communicated digital and green initiatives as inherently linked.
Host: Did the study highlight any barriers or trade-offs that managers should be aware of?
Expert: Yes, several trade-offs and barriers emerged. These included skills gaps, difficulties integrating legacy systems with new AI solutions, a delayed return on investment, and the high energy consumption of the AI models themselves, which can create tension with sustainability goals.
Host: It is important to note, though, that this study has certain limits, correct?
Expert: Yes, a key limitation is that the findings are based on a single qualitative case study of one Canadian engineering firm, which may limit the direct generalizability of the results to other industries or geographic regions.
Host: Let's focus on what business and technology professionals can do with these insights. How can practitioners apply this knowledge to their own organizations?
Expert: The study offers clear, practical guidance. First, organizations should sequence their capability building. Before attempting a large-scale rollout of AI for sustainability, they must invest in foundational data engineering, machine learning operations, business intelligence, and AI training.
Host: So, don't rush into a massive rollout without those data foundations in place. What about governance?
Expert: Practitioners should adopt adaptive governance models with faster decision cycles and eco-economic key performance indicators. By linking environmental and financial metrics directly in decision-making, managers can better balance short-term constraints with long-term sustainability goals.
Host: And how should organizations frame their AI projects from the start?
Expert: The study suggests that organizations seeking to align AI with sustainability should design for business model change from the outset, rather than treating sustainability as an add-on to technical deployment. Finally, fostering an intrapreneurial culture and co-creating solutions directly with clients can accelerate finding the right market fit and reduce implementation uncertainty.
Host: Thank you, Ava, for breaking down this study for us. To summarize, transitioning from transactional models to sustainability-oriented, recurring services requires aligning digital capabilities with business model design, building foundational data engineering skills, adapting our governance, and actively managing the economic and environmental trade-offs of AI itself.
Expert: Exactly, Aaron. It is about treating digital and sustainable goals as two sides of the same coin.
Host: That is all for this episode of A.I.S. Insights. Thank you to our listeners for joining us. We will see you next time.