As artificial intelligence (AI) rapidly reshapes industries, questions about its practical applications, challenges, and future potential are more prevalent than ever.
From enhancing workplace safety and efficiency to ethical considerations and strategic innovations, AI is transforming industries and it’s crucial for businesses to understand where it’s headed next. This blog aims to answer the following questions on the future of intelligent technology:
- What is AI?
- How are organizations approaching the question of AI in EHS?
- What do users look for from AI?
- What are some common use cases for AI within EHS?
- What is the biggest obstacle organizations face when looking to bring AI into their systems?
- For organizations looking to enter the AI space, what is their first step?
What is AI?
AI can be broken down into three broad categories, two of which have been around for a while: analytical AI and predictive AI. The third, generative AI, is what has caught so much attention recently.
Analytical AI
Analytical AI is often confused with data analytics, which is the ability to analyze datasets and provide specific insight. For example, if there is a list of addresses in a dataset, analytical AI can hypothetically be used to create a subset of data for all addresses that are within a 50-mile radius of a given point. Many of us use analytical AI frequently.
Predictive AI
Predictive AI uses trends in a dataset to estimate future values. For example, if someone tracks their running distance over several weeks, predictive AI could estimate how far they will run in the future based on that dataset.
Generative AI
Generative AI uses large language models (LLMs), trained on extensive datasets, to detect and track patterns within data and generate new content. This technology can also apply to images and other media. For example, it can be trained on millions of cat images and their descriptions to generate new images of cats based on user input.
Regarding generative AI, this technology is still in its infancy. Cority’s interpretation and goal for any AI research and development is to create technologies that augment human capabilities and improve overall productivity, efficiency, and quality of life.
AI technologies are designed to complement human skills by automating routine tasks, providing insights from large amounts of data, assisting in decision-making, and enabling new forms of interaction and communication.
How are organizations approaching AI in EHS?
Organizations and users generally approach AI with optimism but caution. Expectations often vary between “it will change everything” and “there’s risk to consider.”
Currently, organizations are looking to automate highly manual and repeatable tasks, such as data collection, quality assurance, notification of escalation workflows, and aggregation and analysis tasks.
What do users look for from AI?
Users seek actionable insights from AI-driven data analyses that incorporate past performance and trends. As AI technology matures, it is essential for use cases to start simple, so users can gain confidence in the tools. Clear value must be demonstrated at the individual level to encourage adoption.
How do goals and expectations vary from front-line users?
Organizations are typically seeking measurable value, while individuals look for use cases that provide personal benefits. A successful AI strategy requires co-creation between the organization and its employees. Organizations need to understand the use cases that are most relevant to their employees and the level of AI maturity among their people, ensuring proper education and training.
What are some common use cases for AI within EHS?
Recent research from Verdantix has identified 5 distinct use cases within EHS/ESG that are most desired by users:
- AI Assistants: Providing data analysis and insights or performing summary generation
- Computer Vision: Identifying unsafe behaviors and non-conformances, as well as 3D motion capture (often within Ergonomic spaces)
- SDS Automation: Automating Safety Data Sheet indexing
- Permit Deconstruction: Extracting information from free-text permits of varying formats
- SIF Prediction: Identifying events and allocating resources for reductions in said events
What is the biggest obstacle organizations face when looking to bring AI into their systems?
One of the biggest issues is the quality or quantity of the data in their systems. AI systems rely on accurate data to function effectively. For generative AI, many organizations cannot or do not want to use public learning models due to concerns about context and security.
Finally, organizations often expect technology to be flawless, which is an unfair expectation. Significant time and effort go into training AI models, and patience is required when implementing new technologies.
For organizations looking to enter the AI space, what is their first step?
The first step is to identify the problem statement and ensure that everyone on the team is aligned with the goals. Once clarity is achieved, organizations can develop the framework and governance for the responsible use of AI and the data architecture that will accelerate AI implementation and scaling.
In Cority’s on-demand webinar, Rethinking AI: Dive into the Future of Intelligent Tech, they discuss the EHS market’s current perception of AI and how technology is being leveraged in existing EHS & ESG ecosystems.
