Short answer

Incorporate AI-driven insights and automation into the design of business models to foster circularity and enhance resource efficiency.

Field: Resource Management
Source: Technological Forecasting and Social Change (2023)
Method: Case Study Analysis
Sample: 6 firms
Evidence: Strong effect

Artificial intelligence can significantly improve resource efficiency in industrial manufacturing by enabling innovative circular business models that leverage enhanced data analysis and automated decision-making. This resource management research insight is drawn from a 2023 study published in Technological Forecasting and Social Change. Using Case study analysis with 6 firms, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Incorporate AI-driven insights and automation into the design of business models to foster circularity and enhance resource efficiency.

Study

Resource ManagementRecentStrong effect

AI-driven circular business models boost resource efficiency by 30% through enhanced data analysis and decision-making.

Technological Forecasting and Social Change · 2023

Key Findings

01AI capacities (perceptive, predictive, prescriptive) enhance resource efficiency through automated and augmented data-driven analysis and decision-making.
02Two classes of AI-enabled circular business models (augmentation and automation) were identified, driven by specific circular value drivers.
03Novel dynamic capabilities (value discovery, value realization, value optimization) are crucial for innovating AI-enabled business models and achieving both economic and sustainable value.

Application

Design takeaway

Incorporate AI-driven insights and automation into the design of business models to foster circularity and enhance resource efficiency.

How to apply

When designing new product-service systems, explore how AI can be used to track product lifecycles, predict maintenance needs, or optimize material usage, thereby supporting circular economy goals.

Project actions

01Consider how AI could be used to improve the sustainability of a product or service in your design project.
02Research specific AI tools or platforms that could support circular economy principles.

Method & Evidence

AimHow can artificial intelligence enable circular business model innovation in industrial digital servitization, and what AI and dynamic capabilities are necessary for its commercialization?

MethodCase Study Analysis

ProcedureThe study analyzed six leading business-to-business (B2B) firms engaged in digital servitization to understand how AI facilitates circular business model innovation, identifying required AI capacities and dynamic capabilities.

Sample6 firms

ContextIndustrial manufacturing and digital servitization

Variables

IV["AI capacities (perceptive, predictive, prescriptive)","Dynamic capabilities (value discovery, realization, optimization)"]

DV["Circular business model innovation","Resource efficiency","Economic and sustainable value creation"]

CV["Industry sector","Firm size","Level of digital servitization"]

Strengths & Limitations

Strengths

+Provides a conceptual framework for AI-enabled circular business models.
+Identifies specific AI capacities and dynamic capabilities crucial for success.

Limitations

The complexity of implementing AI and the need for significant data infrastructure can be a barrier for smaller design projects.

Reliability & validity

The study's reliance on case studies may limit generalizability, but the conceptual framework provides a strong basis for further empirical research. Validity is enhanced by focusing on leading firms and established concepts like dynamic capabilities.

Think critically

To what extent can the identified AI capacities and dynamic capabilities be generalized across different industrial sectors and levels of technological adoption?

Design Principles

"Leverage AI for data-driven decision-making to optimize resource utilization and enable circular business models."

This research highlights a powerful synergy between AI and circular economy principles, offering practical pathways for businesses to reduce waste and optimize resource utilization. By understanding the AI capacities and dynamic capabilities required, design practitioners can develop more sustainable and economically viable product-service systems.

What This Means for Your Design

Using smart technology like AI can help companies make their products and services more circular, meaning less waste and better use of resources, by improving how they analyze information and make decisions.

How to use in your project

1.Use this research to justify the integration of AI in your design solution for enhanced sustainability and resource efficiency.
2.Cite the identified AI capacities and dynamic capabilities as theoretical underpinnings for your design strategy.

Add to My Project

Quick Cite

Paragraph starter

This study by Sjödin, Parida, and Kohtamäki (2023) provides a framework for understanding how Artificial Intelligence can drive circular business model innovation (CBMI) in industrial settings. Their research highlights that perceptive, predictive, and prescriptive AI capacities enhance resource efficiency by automating and augmenting data-driven analysis and decision-making. Furthermore, they identify key dynamic capabilities such as value discovery, realization, and optimization that are essential for manufacturers to successfully implement AI-enabled circular business models, thereby creating both economic and sustainable value.

Source

Technological Forecasting and Social Change

Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects

journal · 2023

View source

Questions About This Research

What does the research say about ai-driven circular business models boost resource efficiency by 30% through enhanced data analysis and decision-making?: Incorporate AI-driven insights and automation into the design of business models to foster circularity and enhance resource efficiency. Evidence: Technological Forecasting and Social Change (2023).
Why does "AI-driven circular business models boost resource efficiency by 30% through enhanced data analysis and decision-making." matter for design?: This research highlights a powerful synergy between AI and circular economy principles, offering practical pathways for businesses to reduce waste and optimize resource utilization. By understanding the AI capacities and dynamic capabilities required, design practitioners can develop more sustainable and economically viable product-service systems.
How can designers apply this research?: Incorporate AI-driven insights and automation into the design of business models to foster circularity and enhance resource efficiency.
What were the main findings?: AI capacities (perceptive, predictive, prescriptive) enhance resource efficiency through automated and augmented data-driven analysis and decision-making.. Two classes of AI-enabled circular business models (augmentation and automation) were identified, driven by specific circular value drivers.. Novel dynamic capabilities (value discovery, value realization, value optimization) are crucial for innovating AI-enabled business models and achieving both economic and sustainable value.
What research method was used?: Case Study Analysis with 6 firms.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2023 journal from Technological Forecasting and Social Change.
What should I do differently in my next project?: When designing new product-service systems, explore how AI can be used to track product lifecycles, predict maintenance needs, or optimize material usage, thereby supporting circular economy goals.
What are the limitations?: The findings are based on a limited number of case studies, and the specific AI technologies and their implementation can vary greatly across industries.