Understanding the Challenge of Cleaner Mobile Energy
Many companies, especially in industries like film production, construction, and event management, rely heavily on mobile energy to power operations in the field. This often includes diesel generators, small gas generators (affectionately known as "putt-putts"), or electric battery systems.
I've spoken with over a dozen sustainability teams who oversee these operations, and they all face a common set of challenges:
Difficulty Changing Behaviors or Shifting the Status Quo: It’s tough to alter ingrained habits or challenge conventional practices, especially when dealing with multiple stakeholders.
Uncertainty About Equipment Needs and Alternatives: Teams often struggle to determine the right equipment for the job, especially when looking for cleaner alternatives to traditional energy sources.
Complicated Scenario Analysis: Understanding how different decisions will impact the company's emissions and costs requires complex analysis that is often beyond the team's capacity.
To address these challenges, sustainability teams need access to accurate and timely data. However, they often lack the tools and resources to collect, analyze, and leverage this data effectively, making it hard to lower emissions, reduce costs, and influence their organization positively.
This post explores how sustainability teams can use AI tools to tackle these problems, ensuring more efficient and cleaner mobile energy use across their operations.
Why Solving This Problem Matters
Solving the challenge of cleaner mobile energy is not just about meeting sustainability goals—it's about protecting the company’s triple bottom line: people, planet, and profit.
Reducing Emissions: Many industries, especially entertainment, are significant contributors to carbon emissions. The Producers Guild of America says that “productions emit on average between 391 and 3,370 metric tons of CO2, often surpassing the emissions of aerospace, clothing, hotel, and semiconductor industries.” Reducing these emissions is critical for the planet and can improve a company’s public image.
Cutting Costs: Cleaner energy alternatives, like electric battery systems or more efficient fuel types, can be more cost-effective in the long run, especially as fuel prices fluctuate. However, making the case for these investments requires a thorough understanding of both the immediate and future financial impacts.
Saving Time and Resources: By streamlining data collection and analysis, sustainability teams can focus on strategic initiatives rather than being bogged down in manual workflows. They can invest more time in developing programs that genuinely move the needle on sustainability.
The Current Approach: Inefficiencies and Roadblocks
Most sustainability teams currently address these challenges through several traditional methods, but these are often inefficient and ineffective:
Top-Down Mandates: Teams often rely on directives from executives to enforce behavior changes. While top-down leadership is essential, this approach can feel rigid and lacks the flexibility and creativity needed to adapt to on-the-ground realities.
Over-Reliance on Experts: Determining the appropriate mobile energy equipment often involves consulting experts or specialists. While expertise is valuable, waiting for their input can delay decision-making. Additionally, sustainability teams don’t have to be overly technical to understand or make informed choices about energy use.
Manual Data Workflows: Many teams still collect data manually—using spreadsheets, waiting for partner teams to supply data, or struggling to access internal databases. This results in outdated, non-standardized datasets and tedious processes that lead to burnout and mistakes. This manual approach makes it nearly impossible to conduct timely scenario analysis and assess the impact of different decisions on emissions and costs.
A Better Way: Leveraging AI for Cleaner Mobile Energy Use
The most successful sustainability teams leverage data effectively, operate autonomously, and foster a proactive, data-driven culture. To achieve this, consider adopting an AI-based approach to promote cleaner mobile energy use.
Here are three ways AI can help:
1. Automating Data Collection and Analysis
AI can significantly reduce the time and effort required to gather and analyze data. For example:
Using AI to Gather Real-Time Information: Tools like Perplexity.ai can be used to collect the latest emissions factors, fuel prices, and energy costs. AI can parse through vast amounts of publicly available data quickly, providing up-to-date information that is crucial for making informed decisions.
Enhancing Scenario Analysis: AI tools such as Claude.ai can compare multiple scenarios to help sustainability teams assess the costs and emissions associated with different energy options. This allows for quicker, more accurate decision-making.
2. Providing Creative Approaches to Influence Decision-Making
Influencing stakeholders within an organization can be challenging, especially when promoting cleaner energy alternatives that may require upfront investments or changes in behavior. AI tools can assist by:
Generating Compelling Narratives and Strategies: Tools like ChatGPT can help sustainability teams craft persuasive messages, presentations, and reports. They can explore strategies to engage different stakeholders, anticipate objections, and frame arguments in a way that aligns with the company's goals.
Supporting Communication Efforts: AI can analyze the success of previous communications and suggest new approaches to reach different audiences, ensuring that messages resonate and lead to the desired outcomes.
3. Assisting with Scenario Analysis and Strategic Planning
AI can help sustainability teams better understand how different energy choices will impact the company from both an emissions and cost perspective:
Scenario Simulation and Optimization: AI tools can run complex simulations to predict the outcomes of various energy decisions, considering variables such as fuel prices, equipment efficiency, and location-specific factors. This helps teams identify the most sustainable and cost-effective options.
Predictive Analytics: AI can analyze past data and predict future trends, allowing teams to make proactive decisions. For instance, if AI predicts a rise in diesel prices, the team can plan a switch to electric battery systems in advance.
Practical Steps to Get Started with AI Tools
Ready to integrate AI into your sustainability strategy? Here are some steps to help you get started:
Experiment with AI Tools: Begin by exploring AI tools like Perplexity.ai, Claude.ai, and ChatGPT. Use these tools to collect data, perform scenario analysis, and brainstorm creative strategies for engaging stakeholders.
Integrate AI into Existing Workflows: Start small by using AI for specific tasks, such as analyzing data or drafting communications. Gradually expand AI use as you see positive results.
Build a Data-Driven Culture: Encourage your team to embrace AI and data analytics by providing training and promoting a culture of continuous learning. The more comfortable your team becomes with these tools, the more effectively they can use them to drive sustainability efforts.
Measure and Adjust: Track the impact of AI-driven decisions on emissions, costs, and stakeholder engagement. Use this data to fine-tune your approach and maximize the benefits of AI.
Take the First Step Toward Cleaner Mobile Energy Use
AI offers a powerful new way for sustainability teams to promote cleaner mobile energy use. By automating data collection, providing creative strategies, and enhancing scenario analysis, AI empowers teams to make more informed decisions, reduce emissions, cut costs, and save time.
Ready to start your journey toward a more sustainable future? Need more personalized advice or want to dive deeper into specific strategies? Feel free to contact me—I’m here to help.