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AI-Powered Clean Tech: Transforming Business Operations

Tom Spencer

In this blog post, we will explore how consulting firms can assist companies in adopting AI-powered clean technologies and highlight specific examples of how consulting firms can adapt their services to support organizations in this area. This assessment can help identify gaps and develop a roadmap for successful implementation.

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Main Risks of Aerospace Engineering Projects: How to Implement Wise Risk Management into Your Organization

Epicflow

developing a poor project plan), Executing (e.g., For example, welding can be dangerous for workers; assembling an air- or a spacecraft involves moving large-scale parts from one facility to another, working on the top of an air-/spacecraft, which increases chances for accidents and/or employees’ injuries. . Step 3: Analyzing risks.

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The Impact of Carbon Taxation on Supply Chains in China

Comatch

One famous example is Volvo announcing in 2021 that from 2030 on, they would only sell fully-electric cars (phasing out combustion engines, including hybrids). . Green finance is becoming increasingly dominant, as long-term investors see environmental risk management as a fundamental success factor in securing long-term returns.

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How AI Is Changing Contracts

Harvard Business

But recent technological developments like artificial intelligence (AI) are now helping companies overcome many of the challenges to contracting. AI contracting software can, for example, identify contract types (even in multiple languages) based on pattern recognition in how the document is drafted. Keeping Contracts Consistent.

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Managing Risks and Controlling Projects: How Data-Driven Project Management Come in Useful for Multi-Project Environments

Epicflow

Risk Analysis: Essential Components A risk is an event or circumstance with either a positive or negative effect on a project. Risk Analysis: Essential Components A risk is an event or circumstance with either a positive or negative effect on a project. So, what do you need for an efficient risk analysis?

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Make “Fairness by Design” Part of Machine Learning

Harvard Business

Unsurprisingly, given the speed of change and ever-increasing complexity, there have been several recent high-profile examples of “machine learning gone wrong.” The idea is that if a doctor could know that a patient was, for example, skeptical of the health profession, they could tailor their care to overcome that lack of trust.

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Hourly Workers Need Flexibility the Most, But Are Often the Least Likely to Get It

Harvard Business

Elder care demands, for example, are rising with the global aging of populations. In order to integrate and make sense of these definitions, we developed our own, which went through a blind review: employment scheduling practices that are designed to give employees greater control over when, where, how much, or how continuously work is done.