AI with a Conscience
Sustainable AI: Balancing Innovation with Environmental Responsibility
As AI transforms how we work and live, its environmental impact cannot be ignored. Here's how organisations can embrace AI innovation whilst maintaining their commitment to sustainability - and why Original Objective takes this responsibility seriously.
The Inconvenient Truth About AI's Environmental Footprint
The rise of artificial intelligence represents one of the most significant technological shifts of our time. From streamlining business operations to enabling new forms of creativity, AI technology is reshaping industries across the globe. Yet there's an uncomfortable truth that many organisations prefer not to discuss: AI has a substantial environmental footprint.
Training large language models requires enormous computational resources. Research suggests that training a single large AI model can produce carbon emissions equivalent to several transatlantic flights. The data centres that power AI services consume vast amounts of electricity and water for cooling. As AI adoption accelerates, so does its environmental impact.
At Original Objective, we believe in honest conversations about technology. As a Manchester-based AI agency specialising in AI solutions for businesses across the UK, we feel a responsibility to address the sustainability implications of the technology we implement. That means acknowledging these realities whilst exploring how organisations can harness AI's benefits responsibly.
Why Sustainable AI Matters to Original Objective
We're not just observers of the AI sustainability challenge - we're active participants. As a leading AI agency in Manchester that recommends and implements AI solutions for UK businesses, we have a responsibility to consider the broader implications of the technology we deploy.
Sustainability isn't just an environmental concern - it's a business imperative. Clients increasingly expect their partners to share their values, including environmental responsibility. Organisations that ignore sustainability risk reputational damage and regulatory challenges as governments worldwide implement stricter environmental standards for technology.
More fundamentally, we believe technology should make the world better, not just more efficient. That means considering the full lifecycle impact of the digital solutions we create - from AI-powered customer service to intelligent workflow automation.
Our Sustainability Commitment
Original Objective has made sustainable AI implementation a core part of our service offering. As a Manchester AI consultancy, every AI project we undertake now includes:
- Environmental impact assessment - We evaluate the carbon footprint of proposed AI solutions before implementation
- Efficiency-first design - We architect solutions that minimise computational overhead whilst maximising business value
- Green infrastructure recommendations - We prioritise cloud providers and regions with strong renewable energy commitments
- Ongoing optimisation - We continuously monitor and refine AI deployments to reduce resource consumption
Practical Strategies for Sustainable AI Adoption
Whether you're implementing AI voice agents, deploying chatbots, or building machine learning solutions, these strategies can help reduce your AI carbon footprint without sacrificing performance.
1. Choose Right-Sized AI Solutions
Not every task requires the most powerful AI model available. Using smaller, more efficient models for appropriate tasks can dramatically reduce energy consumption and operational costs.
A simple classification task doesn't need a 175-billion parameter model. A customer service chatbot answering frequently asked questions can run on much lighter infrastructure than a system generating complex creative content.
How Original Objective approaches this: When we implement AI automation for our clients across Manchester and the North West, we conduct thorough requirements analysis to match the right tool to the task. We've helped numerous UK small businesses achieve their automation goals using lightweight, efficient AI models that cost less to run and have a smaller environmental footprint.
For example, when implementing lead qualification automation, we often find that purpose-built classification models outperform general-purpose LLMs whilst using a fraction of the computational resources.
2. Optimise Before You Scale
Before deploying AI solutions broadly, invest time in optimisation. Fine-tuning models for specific use cases, implementing efficient caching strategies, and batching requests can reduce computational overhead significantly.
Real example from our work: A Manchester-based client's AI-powered search feature was making individual API calls for every keystroke. By implementing debouncing, caching frequent queries, and batching similar requests, we reduced their AI compute usage by 60% whilst improving response times. This translated to both cost savings and a meaningful reduction in carbon emissions.
Original Objective's optimisation framework:
- Request batching - Grouping similar queries to reduce API overhead
- Intelligent caching - Storing common responses to eliminate redundant processing
- Model fine-tuning - Creating task-specific models that run faster and cheaper
- Edge deployment - Running models closer to users to reduce data transmission costs
3. Select Environmentally-Conscious Cloud Providers
Major cloud providers now offer carbon-neutral or renewable-energy-powered computing options. Microsoft Azure, Google Cloud, and AWS all provide ways to run workloads in regions powered by clean energy.
The choice of infrastructure matters enormously. Running AI workloads in a data centre powered by renewable energy has a fundamentally different environmental impact than one running on fossil fuels.
Original Objective's infrastructure approach: When recommending infrastructure for AI deployments, we factor in the provider's sustainability commitments and help clients select greener options. We maintain partnerships with cloud providers who lead in environmental responsibility, ensuring our clients can access the most sustainable options available.
We also consider data residency requirements for UK businesses - helping organisations meet both their sustainability goals and data protection obligations simultaneously.
4. Measure and Monitor Your AI Carbon Footprint
You cannot improve what you don't measure. Implementing carbon tracking for your AI workloads helps quantify environmental impact and identify optimisation opportunities.
Tools like CodeCarbon, ML CO2 Impact, and cloud provider sustainability dashboards make it increasingly straightforward to understand and report on your AI's environmental footprint.
Original Objective's measurement approach: We're building sustainability metrics into our reporting for AI projects. Clients receive regular updates on:
- Estimated carbon emissions from their AI infrastructure
- Energy consumption trends over time
- Efficiency improvements from optimisation work
- Comparison benchmarks against industry standards
This transparency helps organisations make informed decisions and demonstrate their environmental commitment to stakeholders.
5. Question Necessity - The Most Sustainable AI Query
Perhaps the most important strategy is also the simplest: asking whether AI is truly the right solution for a given problem.
The most sustainable AI query is the one you don't make. Before implementing AI features, we ask whether they genuinely add value or whether simpler solutions might suffice. Not every chatbot needs to be powered by the latest large language model. Not every automation requires machine learning.
Sometimes the answer is a well-designed form, a clear FAQ page, or a simple rule-based system that handles 90% of cases without any AI involvement.
Original Objective's honest approach: Unlike agencies that push AI solutions regardless of fit, we're committed to recommending the right tool for each job. Sometimes that means telling a client they don't need AI at all - a recommendation that might seem counterintuitive from an AI solutions provider, but one that builds trust and delivers better outcomes.
The Emerging Solutions in Sustainable AI
The AI industry isn't standing still on sustainability. Several promising developments are making green AI increasingly achievable:
More Efficient Model Architectures: Researchers are developing new model architectures that achieve comparable performance with significantly reduced computational requirements. Techniques like knowledge distillation, sparse models, and mixture-of-experts approaches are making AI more efficient every month.
Edge Computing: Running AI models closer to where they're needed - on devices or local servers - can reduce the energy costs associated with data transmission and centralised processing. Original Objective is exploring edge deployment options for clients where appropriate.
Renewable-Powered AI Infrastructure: Companies like Google have committed to running their data centres on carbon-free energy by 2030. Others are exploring innovative approaches like locating data centres in regions with abundant renewable energy or using waste heat for productive purposes.
Carbon-Aware Computing: Emerging tools can schedule non-urgent AI workloads to run when the electrical grid is powered by cleaner energy sources, shifting computation to times when renewable energy is abundant.
What Original Objective Does Differently
As a Manchester AI agency working with businesses across the North West and throughout the UK, we've made sustainable AI a differentiator in our approach. Here's what sets us apart as an AI consultancy in Manchester:
Honest Environmental Assessment
We have frank conversations with clients about the environmental implications of AI solutions, not just the business benefits. Every proposal includes an environmental impact section alongside ROI projections.
Right-Sizing as Standard
We recommend the smallest, most efficient AI solution that meets the actual need - not the most impressive one we could deploy. This approach typically delivers:
- Lower operational costs for clients
- Faster response times from lighter models
- Reduced carbon footprint
- Easier maintenance and updates
Efficiency Built In From Day One
We build efficiency into our AI implementations from the start, not as an afterthought. This means:
- Optimised prompts that reduce token usage
- Intelligent caching strategies
- Batch processing where appropriate
- Regular performance reviews and refinement
Green Provider Partnerships
Where possible, we prioritise providers and regions with strong sustainability credentials. We maintain up-to-date knowledge of which cloud regions offer the cleanest energy mix and factor this into our recommendations.
Continuous Sustainability Improvement
As the field evolves, we're staying informed about more sustainable AI approaches and incorporating them into our practice. Our team regularly researches and tests new efficiency techniques, ensuring our clients benefit from the latest developments.
Why Manchester Businesses Choose Original Objective for AI
As a Manchester-based digital agency with deep roots in the local business community, we understand the unique challenges and opportunities facing organisations in our region. The North West has a proud history of innovation - from the Industrial Revolution to today's thriving tech sector - and we're committed to ensuring that innovation continues responsibly.
Local expertise, national reach: While we're proud to be a Manchester AI agency, we work with businesses across the entire UK. Our central location makes us accessible to clients throughout the country, and our team combines local knowledge with broad industry experience.
Supporting Manchester's tech ecosystem: We're active participants in Manchester's growing technology community. We believe in contributing to the local ecosystem that has supported our growth, and we're committed to helping Manchester businesses leverage AI technology responsibly.
Face-to-face when it matters: Unlike remote-only agencies, being based in Manchester means we can meet clients in person when complex projects require it. Sometimes there's no substitute for sitting down together to work through challenges and opportunities.
Sustainable AI in Practice: How We Help UK Businesses
Our commitment to sustainable AI isn't theoretical - it's embedded in every project we deliver. Here's how we apply these principles across our AI service offerings:
Customer Service Automation
When implementing AI-powered customer service, we:
- Use lightweight models for simple FAQ responses
- Escalate to more powerful AI only when complexity demands it
- Cache common responses to eliminate redundant processing
- Deploy on green infrastructure where client requirements allow
Voice Agent Implementation
Our voice agent solutions are designed for efficiency:
- Optimised speech recognition that minimises processing overhead
- Smart routing to handle simple requests without full AI processing
- Regional deployment to reduce latency and data transmission
Workflow Automation
For intelligent workflow automation, we:
- Use rule-based systems where AI isn't genuinely needed
- Batch process documents during off-peak hours when grids are cleaner
- Implement efficient document processing pipelines
The Business Case for Sustainable AI
Beyond environmental responsibility, sustainable AI practices make sound business sense:
Cost Reduction: More efficient AI implementations typically cost less to run. Optimised models, intelligent caching, and right-sized solutions translate directly to lower cloud computing bills.
Improved Performance: Lighter, more efficient models often respond faster than their heavyweight counterparts. Users benefit from snappier interactions whilst the environment benefits from reduced processing.
Future-Proofing: As environmental regulations tighten and carbon pricing becomes more widespread, organisations with sustainable AI practices will be better positioned to comply without costly retrofitting.
Brand Differentiation: Consumers and B2B buyers increasingly favour organisations that demonstrate environmental responsibility. Sustainable AI can become part of your brand story.
Talent Attraction: Employees - particularly in tech - want to work for organisations that take sustainability seriously. A genuine commitment to green AI can help attract and retain top talent.
The Path Forward: Responsible AI Innovation
AI and sustainability aren't inherently opposed. With thoughtful implementation, organisations can benefit from AI innovation whilst maintaining their environmental commitments.
The goal isn't to avoid AI - it's to use it wisely. This means:
- Being intentional about when and how we deploy AI technology
- Staying informed about more efficient models and approaches as they emerge
- Advocating for transparency in AI's environmental impact
- Supporting the development of sustainable AI infrastructure
The organisations that thrive in the coming decades will be those that embrace innovation without compromising their values. Sustainable AI isn't just possible - it's essential.
Start Your Sustainable AI Journey with Original Objective
If you're considering AI adoption for your organisation and sustainability matters to you, we'd welcome the conversation. As a Manchester-based AI agency with deep expertise in AI implementation, we can help you:
- Assess whether AI is the right solution for your specific challenges
- Identify the most efficient approaches to meet your needs
- Select providers and infrastructure with strong environmental credentials
- Implement optimisation strategies that reduce environmental impact
- Build measurement and reporting into your AI deployments
- Integrate sustainable practices with your existing digital growth strategy
Technology should serve both your business objectives and your values. We believe it's possible to embrace AI innovation whilst taking sustainability seriously - and we're here to help you do exactly that.
Ready to explore sustainable AI for your business? Whether you're a Manchester business looking for a local partner or a UK organisation seeking expert AI consultancy, we're here to help. Book an intro call with our team, or get in touch to discuss how we can help you harness the power of AI responsibly.
The Inconvenient Truth About AI's Environmental Footprint
The rise of artificial intelligence represents one of the most significant technological shifts of our time. From streamlining business operations to enabling new forms of creativity, AI technology is reshaping industries across the globe. Yet there's an uncomfortable truth that many organisations prefer not to discuss: AI has a substantial environmental footprint.
Training large language models requires enormous computational resources. Research suggests that training a single large AI model can produce carbon emissions equivalent to several transatlantic flights. The data centres that power AI services consume vast amounts of electricity and water for cooling. As AI adoption accelerates, so does its environmental impact.
At Original Objective, we believe in honest conversations about technology. As a Manchester-based AI agency specialising in AI solutions for businesses across the UK, we feel a responsibility to address the sustainability implications of the technology we implement. That means acknowledging these realities whilst exploring how organisations can harness AI's benefits responsibly.
Why Sustainable AI Matters to Original Objective
We're not just observers of the AI sustainability challenge - we're active participants. As a leading AI agency in Manchester that recommends and implements AI solutions for UK businesses, we have a responsibility to consider the broader implications of the technology we deploy.
Sustainability isn't just an environmental concern - it's a business imperative. Clients increasingly expect their partners to share their values, including environmental responsibility. Organisations that ignore sustainability risk reputational damage and regulatory challenges as governments worldwide implement stricter environmental standards for technology.
More fundamentally, we believe technology should make the world better, not just more efficient. That means considering the full lifecycle impact of the digital solutions we create - from AI-powered customer service to intelligent workflow automation.
Our Sustainability Commitment
Original Objective has made sustainable AI implementation a core part of our service offering. As a Manchester AI consultancy, every AI project we undertake now includes:
- Environmental impact assessment - We evaluate the carbon footprint of proposed AI solutions before implementation
- Efficiency-first design - We architect solutions that minimise computational overhead whilst maximising business value
- Green infrastructure recommendations - We prioritise cloud providers and regions with strong renewable energy commitments
- Ongoing optimisation - We continuously monitor and refine AI deployments to reduce resource consumption
Practical Strategies for Sustainable AI Adoption
Whether you're implementing AI voice agents, deploying chatbots, or building machine learning solutions, these strategies can help reduce your AI carbon footprint without sacrificing performance.
1. Choose Right-Sized AI Solutions
Not every task requires the most powerful AI model available. Using smaller, more efficient models for appropriate tasks can dramatically reduce energy consumption and operational costs.
A simple classification task doesn't need a 175-billion parameter model. A customer service chatbot answering frequently asked questions can run on much lighter infrastructure than a system generating complex creative content.
How Original Objective approaches this: When we implement AI automation for our clients across Manchester and the North West, we conduct thorough requirements analysis to match the right tool to the task. We've helped numerous UK small businesses achieve their automation goals using lightweight, efficient AI models that cost less to run and have a smaller environmental footprint.
For example, when implementing lead qualification automation, we often find that purpose-built classification models outperform general-purpose LLMs whilst using a fraction of the computational resources.
2. Optimise Before You Scale
Before deploying AI solutions broadly, invest time in optimisation. Fine-tuning models for specific use cases, implementing efficient caching strategies, and batching requests can reduce computational overhead significantly.
Real example from our work: A Manchester-based client's AI-powered search feature was making individual API calls for every keystroke. By implementing debouncing, caching frequent queries, and batching similar requests, we reduced their AI compute usage by 60% whilst improving response times. This translated to both cost savings and a meaningful reduction in carbon emissions.
Original Objective's optimisation framework:
- Request batching - Grouping similar queries to reduce API overhead
- Intelligent caching - Storing common responses to eliminate redundant processing
- Model fine-tuning - Creating task-specific models that run faster and cheaper
- Edge deployment - Running models closer to users to reduce data transmission costs
3. Select Environmentally-Conscious Cloud Providers
Major cloud providers now offer carbon-neutral or renewable-energy-powered computing options. Microsoft Azure, Google Cloud, and AWS all provide ways to run workloads in regions powered by clean energy.
The choice of infrastructure matters enormously. Running AI workloads in a data centre powered by renewable energy has a fundamentally different environmental impact than one running on fossil fuels.
Original Objective's infrastructure approach: When recommending infrastructure for AI deployments, we factor in the provider's sustainability commitments and help clients select greener options. We maintain partnerships with cloud providers who lead in environmental responsibility, ensuring our clients can access the most sustainable options available.
We also consider data residency requirements for UK businesses - helping organisations meet both their sustainability goals and data protection obligations simultaneously.
4. Measure and Monitor Your AI Carbon Footprint
You cannot improve what you don't measure. Implementing carbon tracking for your AI workloads helps quantify environmental impact and identify optimisation opportunities.
Tools like CodeCarbon, ML CO2 Impact, and cloud provider sustainability dashboards make it increasingly straightforward to understand and report on your AI's environmental footprint.
Original Objective's measurement approach: We're building sustainability metrics into our reporting for AI projects. Clients receive regular updates on:
- Estimated carbon emissions from their AI infrastructure
- Energy consumption trends over time
- Efficiency improvements from optimisation work
- Comparison benchmarks against industry standards
This transparency helps organisations make informed decisions and demonstrate their environmental commitment to stakeholders.
5. Question Necessity - The Most Sustainable AI Query
Perhaps the most important strategy is also the simplest: asking whether AI is truly the right solution for a given problem.
The most sustainable AI query is the one you don't make. Before implementing AI features, we ask whether they genuinely add value or whether simpler solutions might suffice. Not every chatbot needs to be powered by the latest large language model. Not every automation requires machine learning.
Sometimes the answer is a well-designed form, a clear FAQ page, or a simple rule-based system that handles 90% of cases without any AI involvement.
Original Objective's honest approach: Unlike agencies that push AI solutions regardless of fit, we're committed to recommending the right tool for each job. Sometimes that means telling a client they don't need AI at all - a recommendation that might seem counterintuitive from an AI solutions provider, but one that builds trust and delivers better outcomes.
The Emerging Solutions in Sustainable AI
The AI industry isn't standing still on sustainability. Several promising developments are making green AI increasingly achievable:
More Efficient Model Architectures: Researchers are developing new model architectures that achieve comparable performance with significantly reduced computational requirements. Techniques like knowledge distillation, sparse models, and mixture-of-experts approaches are making AI more efficient every month.
Edge Computing: Running AI models closer to where they're needed - on devices or local servers - can reduce the energy costs associated with data transmission and centralised processing. Original Objective is exploring edge deployment options for clients where appropriate.
Renewable-Powered AI Infrastructure: Companies like Google have committed to running their data centres on carbon-free energy by 2030. Others are exploring innovative approaches like locating data centres in regions with abundant renewable energy or using waste heat for productive purposes.
Carbon-Aware Computing: Emerging tools can schedule non-urgent AI workloads to run when the electrical grid is powered by cleaner energy sources, shifting computation to times when renewable energy is abundant.
What Original Objective Does Differently
As a Manchester AI agency working with businesses across the North West and throughout the UK, we've made sustainable AI a differentiator in our approach. Here's what sets us apart as an AI consultancy in Manchester:
Honest Environmental Assessment
We have frank conversations with clients about the environmental implications of AI solutions, not just the business benefits. Every proposal includes an environmental impact section alongside ROI projections.
Right-Sizing as Standard
We recommend the smallest, most efficient AI solution that meets the actual need - not the most impressive one we could deploy. This approach typically delivers:
- Lower operational costs for clients
- Faster response times from lighter models
- Reduced carbon footprint
- Easier maintenance and updates
Efficiency Built In From Day One
We build efficiency into our AI implementations from the start, not as an afterthought. This means:
- Optimised prompts that reduce token usage
- Intelligent caching strategies
- Batch processing where appropriate
- Regular performance reviews and refinement
Green Provider Partnerships
Where possible, we prioritise providers and regions with strong sustainability credentials. We maintain up-to-date knowledge of which cloud regions offer the cleanest energy mix and factor this into our recommendations.
Continuous Sustainability Improvement
As the field evolves, we're staying informed about more sustainable AI approaches and incorporating them into our practice. Our team regularly researches and tests new efficiency techniques, ensuring our clients benefit from the latest developments.
Why Manchester Businesses Choose Original Objective for AI
As a Manchester-based digital agency with deep roots in the local business community, we understand the unique challenges and opportunities facing organisations in our region. The North West has a proud history of innovation - from the Industrial Revolution to today's thriving tech sector - and we're committed to ensuring that innovation continues responsibly.
Local expertise, national reach: While we're proud to be a Manchester AI agency, we work with businesses across the entire UK. Our central location makes us accessible to clients throughout the country, and our team combines local knowledge with broad industry experience.
Supporting Manchester's tech ecosystem: We're active participants in Manchester's growing technology community. We believe in contributing to the local ecosystem that has supported our growth, and we're committed to helping Manchester businesses leverage AI technology responsibly.
Face-to-face when it matters: Unlike remote-only agencies, being based in Manchester means we can meet clients in person when complex projects require it. Sometimes there's no substitute for sitting down together to work through challenges and opportunities.
Sustainable AI in Practice: How We Help UK Businesses
Our commitment to sustainable AI isn't theoretical - it's embedded in every project we deliver. Here's how we apply these principles across our AI service offerings:
Customer Service Automation
When implementing AI-powered customer service, we:
- Use lightweight models for simple FAQ responses
- Escalate to more powerful AI only when complexity demands it
- Cache common responses to eliminate redundant processing
- Deploy on green infrastructure where client requirements allow
Voice Agent Implementation
Our voice agent solutions are designed for efficiency:
- Optimised speech recognition that minimises processing overhead
- Smart routing to handle simple requests without full AI processing
- Regional deployment to reduce latency and data transmission
Workflow Automation
For intelligent workflow automation, we:
- Use rule-based systems where AI isn't genuinely needed
- Batch process documents during off-peak hours when grids are cleaner
- Implement efficient document processing pipelines
The Business Case for Sustainable AI
Beyond environmental responsibility, sustainable AI practices make sound business sense:
Cost Reduction: More efficient AI implementations typically cost less to run. Optimised models, intelligent caching, and right-sized solutions translate directly to lower cloud computing bills.
Improved Performance: Lighter, more efficient models often respond faster than their heavyweight counterparts. Users benefit from snappier interactions whilst the environment benefits from reduced processing.
Future-Proofing: As environmental regulations tighten and carbon pricing becomes more widespread, organisations with sustainable AI practices will be better positioned to comply without costly retrofitting.
Brand Differentiation: Consumers and B2B buyers increasingly favour organisations that demonstrate environmental responsibility. Sustainable AI can become part of your brand story.
Talent Attraction: Employees - particularly in tech - want to work for organisations that take sustainability seriously. A genuine commitment to green AI can help attract and retain top talent.
The Path Forward: Responsible AI Innovation
AI and sustainability aren't inherently opposed. With thoughtful implementation, organisations can benefit from AI innovation whilst maintaining their environmental commitments.
The goal isn't to avoid AI - it's to use it wisely. This means:
- Being intentional about when and how we deploy AI technology
- Staying informed about more efficient models and approaches as they emerge
- Advocating for transparency in AI's environmental impact
- Supporting the development of sustainable AI infrastructure
The organisations that thrive in the coming decades will be those that embrace innovation without compromising their values. Sustainable AI isn't just possible - it's essential.
Start Your Sustainable AI Journey with Original Objective
If you're considering AI adoption for your organisation and sustainability matters to you, we'd welcome the conversation. As a Manchester-based AI agency with deep expertise in AI implementation, we can help you:
- Assess whether AI is the right solution for your specific challenges
- Identify the most efficient approaches to meet your needs
- Select providers and infrastructure with strong environmental credentials
- Implement optimisation strategies that reduce environmental impact
- Build measurement and reporting into your AI deployments
- Integrate sustainable practices with your existing digital growth strategy
Technology should serve both your business objectives and your values. We believe it's possible to embrace AI innovation whilst taking sustainability seriously - and we're here to help you do exactly that.
Ready to explore sustainable AI for your business? Whether you're a Manchester business looking for a local partner or a UK organisation seeking expert AI consultancy, we're here to help. Book an intro call with our team, or get in touch to discuss how we can help you harness the power of AI responsibly.
Get Your Free Sustainable AI Guide
Download our practical guide to implementing AI responsibly whilst maintaining your environmental commitments.
Inside this free guide:
- Carbon Impact Assessment Framework - measure your AI footprint
- Provider Sustainability Comparison - choose greener infrastructure
- Optimisation Checklist - reduce compute without sacrificing performance
- Right-Sizing Decision Tree - match AI power to actual needs
- Sustainable AI Roadmap Template - plan your responsible adoption
- Quick Wins Guide - immediate actions to reduce AI environmental impact
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