To embed artificial intelligence (AI) sustainably into business operations and environmental practices, the OECD/BCG/INSEAD 2025 report urges companies to adopt a comprehensive strategy. This strategy calls for companies to transform their systems through integrated digital infrastructure, empower their workforce, uphold ethical principles, and coordinate with supportive policy frameworks. These efforts support the global movement toward sustainable AI adoption and directly advance several Sustainable Development Goals (SDGs):
- SDG 4 (Quality Education) by accelerating upskilling and digital literacy,
- SDG 8 (Decent Work and Economic Growth) by driving innovation and enabling inclusive job transitions,
- SDG 9 (Industry, Innovation and Infrastructure) by modernizing industrial practices and promoting smart manufacturing,
- SDG 10 (Reduced Inequalities) by expanding equitable access to AI tools and applications,
- SDG 12 (Responsible Consumption and Production) by boosting resource efficiency and reducing waste,
- SDG 13 (Climate Action) by cutting emissions and supporting climate modeling,
- SDG 16 (Peace, Justice and Strong Institutions) by ensuring data accountability and transparency,
- SDG 17 (Partnerships for the Goals) through public-private innovation ecosystems.
We present you seven pillars that drive this transformative approach to sustainable artificial intelligence in business transformation, each time illustrated with real-world case studies and actionable recommendations.
- 1 7 Business Pillars for sustainable AI in Business Transformation
- 1.1 1. Build Digital Maturity and Target Strategic Use Cases
- 1.2 2. Implement Long-Term Roadmaps for Sustainable AI Adoption
- 1.3 3. Cultivate Internal AI Competence Through Upskilling
- 1.4 4. Enforce Ethical, Legal, and Environmental Standards
- 1.5 5. Activate Public Sector Levers to Accelerate AI in Business
- 1.6 6. Foster Collaboration and Shared Innovation Ecosystems
- 1.7 7. Evaluate Impact Beyond Financial ROI in AI Initiatives
- 2 Transform AI from a Technical Add-On into a Strategic Enabler
7 Business Pillars for sustainable AI in Business Transformation
1. Build Digital Maturity and Target Strategic Use Cases
Companies must first strengthen their digital infrastructure and identify high-value use cases where AI can enhance operations. Avoid scattershot deployments; instead, focus AI where it complements current digital workflows and delivers measurable outcomes. Sustainable AI adoption begins with clarity and infrastructure readiness.
Examples:
- BMW integrated AI-driven robotics into its assembly lines while preserving human oversight, boosting productivity by 85% through hybrid systems that enhanced flexibility and control.
- A Canadian logistics company upgraded its ERP system, then layered AI for routing optimization – reducing fuel use and delivery times while cutting emissions.
2. Implement Long-Term Roadmaps for Sustainable AI Adoption
Companies must embed AI through phased, adaptive planning. Transformation requires coordinated rollouts, ongoing model evolution, and robust employee engagement.
Key actions:
- Align departmental strategies across HR, logistics, and operations
- Continuously retrain algorithms and refresh datasets
- Engage employees early to secure support and insights
Examples:
- The Made Smarter Technology Accelerator (UK) connects manufacturers with AI startups, driving long-term adoption through staged deployments and co-designed solutions.
- A French manufacturer rolled out predictive maintenance using a roadmap-based approach, followed by supply chain optimization supported by regular staff training.
3. Cultivate Internal AI Competence Through Upskilling
Empowering staff with AI fluency ensures the technology integrates meaningfully into day-to-day work. Companies driving artificial intelligence in business transformation must prioritize learning alongside deployment.
Key strategies:
- Launch company-wide AI literacy and digital upskilling initiatives
- Deliver tailored training through programs like Skills for AI (Ireland)
- Involve teams in live, collaborative AI projects
Examples:
- AI Singapore‘s “100 Experiments” initiative embeds AI projects within SMEs, upskilling teams through real-world collaboration.
- A Dublin SME improved demand forecasting accuracy by 25% after training its procurement team in applied AI techniques.
4. Enforce Ethical, Legal, and Environmental Standards
Firms must govern AI responsibly to mitigate risks around bias, energy consumption, and data misuse. Accountability must be built into every phase of deployment.
Best practices:
- Track and reduce energy usage, particularly for large-scale models
- Enforce data protection and ensure transparency in decision-making
- Establish and follow internal AI ethics charters aligned with GDPR
Examples:
- Over 60% of ICT firms in France appointed ethics officers to oversee AI systems operating under tight emissions and privacy controls.
- A German healthtech firm slashed energy use by 40% by deploying diagnostic AI on low-power local servers instead of energy-intensive cloud platforms.
5. Activate Public Sector Levers to Accelerate AI in Business
Governments play a catalytic role in de-risking innovation and democratizing AI access. Public support mechanisms amplify the impact of artificial intelligence in business transformation.
Support mechanisms:
- Co-investment grants and public-private projects
- R&D tax credits that foster university collaboration
- Infrastructure like AI sandboxes and open data platforms
- Public catalogues of AI use cases
Examples:
- Scale AI (Canada) funds up to 50% of AI project costs, enabling SME-led supply chain innovation.
- AI Singapore‘s AI Readiness Index guides firms in assessing maturity and securing tailored public support.
AI adoption accelerates when businesses, academia, and government share insights and co-develop standards. Firms should actively participate in knowledge-sharing networks.
Tactics:
- Join digital hubs, peer-learning platforms, and industry alliances
- Co-develop ethics frameworks and benchmark standards
- Share lessons on vendor performance and use case results
Examples:
- The NHS AI Skunkworks (UK) program unites clinicians and developers to co-prototype ethical, usable AI tools.
- The Netherlands AI Coalition facilitates algorithmic fairness benchmarking across sectors.
7. Evaluate Impact Beyond Financial ROI in AI Initiatives
True success in sustainable AI adoption comes when companies measure impact in terms of efficiency, equity, and environmental stewardship.
Alternative KPIs:
- Operational efficiency gains and quality of outputs
- Employee satisfaction, trust, and system engagement
- CO₂ savings, resource optimization, and digital footprint reduction
Examples:
- Mercedes-Benz prioritized adaptability and innovation over raw production volume in AI-human collaboration evaluations.
- A UK insurer used AI to shorten claims processing by 20%, improve customer satisfaction, and eliminate unnecessary paper use.
Transform AI from a Technical Add-On into a Strategic Enabler
The below visual SDG–Pillar matrix shows how each of the seven sustainable AI pillars supports specific SDGs.
By activating this seven-pillar framework, companies can transform AI from a technical add-on into a strategic enabler of sustainability, resilience, and trust. Each pillar drives progress toward global development priorities, including:
- Education and workforce readiness (SDG 4),
- Inclusive growth and decent work (SDG 8),
- Green industrial innovation (SDGs 9, 12, 13),
- Equitable access and transparent institutions (SDGs 10, 16), and
- Cross-sectoral partnerships (SDG 17).
When companies commit to sustainable AI adoption and lead with purpose, invest in people, and collaborate across ecosystems, artificial intelligence in business transformation becomes a powerful driver of shared and lasting value.
