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Safetybreaker

February 22, 2024

breaker

5 min read

February 22, 20245 min read

Optimize Decision Making

How artificial intelligence and machine learning dramatically improve organizational decision-making

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As artificial intelligence research and technology continue to rapidly advance, more and more businesses are turning to machine learning applications for support.

Optimizing operations, improving design, enhancing corporate strategy, screening and sorting applicants for opening positions and even reducing human error in professional sports officiating would further improve human productivity. Reducing human error risk at work would ideally boost the beneficial results of decision-making.

But how exactly can machine learning services strengthen human decision-making?

It’s important to understand that there are multiple categories of artificial intelligence or machine learning, however one may want to refer to the technology. For a quick explainer on some of the key terminology, check out our previous blog here.

One particular category of artificial intelligence, known as Reinforcement Learning, focuses on one (or several) objective functions it must obtain or maintain while providing recommendations to a human decision-maker. This type of AI enables computers to continually learn from the impacts decisions have in a particular action space and improve future recommendations from that experience without being explicitly reprogrammed.

The human brain is a learning machine from birth to approximately age 25 when it has formed enough neural connections to have, what it believes, is a pretty reliable model of the world around it. All experiences, lessons, mistakes, victories, loves, hopes, and fears are all captured. Then they're cataloged for safety and security. Usually, the human brain can draw from the mind and act upon experiences quickly with the catalogued knowledge.

The challenge with the human brain is that all experiences are unique to the individual. Not all information learned is accurate. This creates bias -- a tendency for human decision-making to remain unduly influenced by past paradigms. It leads to a “that’s the way we’ve always done things” type of thinking.

Machine learning systems can have some of these same difficulties, so the accuracy and richness of the provided training dataset and input information are supremely important for excellent output. When the data is clean, labeled, and accurate, a machine learning system can help human decision-makers challenge their own biases and create better outputs altogether.

There are likely to be more applications as the technology continues to mature, but here are just a few ways machine learning can be applied to assist human decision-making:

Better data analysis

Machine learning algorithms can quickly and accurately analyze large amounts of data to help business leaders make informed decisions more rapidly. By using machine learning to analyze data from past operations, decision-makers can identify patterns and trends that would be difficult or impossible for humans to detect on their own.

Of course, the data being analyzed must be incredibly precise otherwise the patterns and trends identified would not be valuable.

Improved accuracy

Humans are prone to error and bias, so machine learning algorithms can be trained to make decisions based solely on data, without any human intervention.

This means that decisions made using machine learning are often more accurate and objective than those made by humans. Relying on old paradigms can lead human decision-makers to see no other options.

Besides, a human with guidance from a machine learning algorithm may see a path forward that was never considered before.

Increased efficiency

Machine learning systems can automate many decision-making processes, which saves time and money.

By automating mundane, repetitive or routine decisions, businesses can free up humans to focus on more complex tasks that require creativity or hands-on support that a computer is not capable of providing.

Predictive response, today

Machine learning can be used to make predictions about what needs to happen today to meet future business or production expectations. By combining analysis of past data with real-time condition data and having a specific objective to achieve, machine learning can offer the human user recommendations on the best actions at the moment to meet or exceed future expectations.

This is particularly useful in industries where precise, accurate and quick decisions can impact results significantly, like manufacturing, finance or healthcare.

By using machine learning to predict outcomes, or rather create better outcomes, humans can make proactive decisions to help them outperform.

In mission-critical facilities--like cooling systems for pharmaceutical production, campus energy or data centers--the importance of accurate and timely decision-making cannot be overstated.

Machine learning can help facility managers optimize operations by analyzing data from all relevant sensors, identifying patterns and trends, and making recommendations or autonomous changes for optimal control strategy.

By leveraging machine learning in this way, facility managers can leverage artificial intelligence to revolutionize production by automating routine decisions, improving historical data analysis, and enhancing efficiencies by implementing better strategies today to create the desired future outcomes.

This in turn helps operators reduce risk, improve energy efficiency, minimize downtime or maintenance, and ultimately save time and money.

Featured Expert

Learn more about one of our subject matter experts interviewed for this post

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Frank Nerkowski

Director, Solutions Engineering

Frank is the Solutions Engineering Lead at Phaidra and is directly responsible for managing the team that builds virtual models and ensures data tagging is set up properly for our customers’ mission critical facilities. These representations of physical real-world systems are the foundation upon which AI control strategies are built. Prior to Phaidra, Frank spent 20+ years working in mission critical cooling facilities leading the design, commissioning and operation of large complex industrial cooling systems, including 15 years as a controls lead with Trane.

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