Best Practices for AI Adoption

Data preparation

Before data can be used by AI algorithms, it needs to be prepared to ensure that the results from the algorithm being used are correct—in other words, unprepared data can lead to GIGO (garbage in, garbage out). Additionally, data preparation isn't a one-time task as it should be done regularly to keep up with changing datasets to ensure the results you get from the algorithms you use remain accurate. There are a variety of techniques used to prepare data, including:

Data Integration: The process of combining data from multiple sources into a single, unified view.

Data Cleansing: The process of fixing/removing data that is incorrect, corrupted, improperly formatted, duplicated, or incomplete.

Feature Engineering: The process of creating, transforming, extracting, and selecting features (variables) that are conducive to creating an accurate machine learning (ML) algorithm.

Data Normalization: The process of transforming data into a more helpful form by making changes to the range, distribution, and/or structure of data to minimize errors, improve consistency across datasets, and enable better analysis.

Outlier Detection: The process of detecting data points that are substantially different from the rest of the dataset caused by errors during data entry, erroneous observations, or naturally occurring based on the data itself.

Data Splitting: The process of splitting data into a training set, a validation set, and a testing set which is done to avoid overfitting the machine learning model leading it to fail to reliably fit additional data.

Data preparation is an iterative process that is critical to ensuring the results you receive from your AI models is accurate. Without data preparation, the algorithms you feed data to will be unable to make the correct decisions or complete a task correctly. Erroneous decisions can have a negative impact on an organization's customers and its bottom line.

Choosing the Right AI Technologies and Tools

Organizations considering integrating AI into their operations should spend time researching the AI tools available to them and which tools best match its needs and goals. A major consideration for organizations should be the budget and associated costs of the AI technologies being considered. Organizations need to determine if the AI technologies they are interested in can integrate with existing systems or if there will be a cost to customize systems to ensure proper integration.

There are many other considerations organizations need to keep in mind:

• Are the right personnel in place to implement and maintain the AI system, or will there be a need to hire someone or train existing staff?
• Are there any ethical considerations with the AI technology being implemented, such as any known biases or potential privacy concerns?
• Are there any legal or governmental regulations that impact the use of AI?

If organizations have the right resources and have done due diligence before making their final decision, they will be able to reap the rewards of increased productivity, operational efficiency, and improved customer service.

Building and Training AI Models

To operate effectively, AI models need to be taught to think like humans and perform tasks at speeds far greater than humans can. Additionally, AI models can detect patterns or trends in large datasets that a human looking at the same data might miss or take far longer to find. When taught, AI models can predict outcomes as well as come to conclusions to help us make better, data-driven decisions. In a previous blog post, we showcased several machine learning models used to train AI, including:

• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Reinforcement learning

Once an AI model has been trained, it must be tested and evaluated to ensure that is performing as expected. If the model is not performing correctly, the dataset should be reanalyzed to determine the probable cause of the output errors:

• Re-verify that the dataset is comprehensive and is large enough to accurately train the AI model.
• Ensure the data contains a good mix of both labeled and unlabeled data so the AI model can adequately learn and detect patterns.
• Determine if the dataset was properly prepared before being used to train the AI model.
• Once data is re-verified, determine if the model architecture is appropriate for the task you are training it to perform.

As discussed earlier, data preparation is an ongoing, not a once-and-done, process to ensure that AI models are working correctly and are producing correct insights to be used for decision-making.

Integrating AI into Existing Systems and Processes

Sometimes, even with due diligence before selecting an appropriate AI solution, organizations can encounter issues integrating the AI tool(s) they selected with their existing systems and processes. Issues with AI integration can be due to a variety of reasons, such as:

• Difficulty in Identifying Data Sources: Organizations may struggle to identify data sources that can be used for AI-based solutions.
• Poor Understanding of AI Algorithms and Technologies: Organizations may not have enough knowledge about the various AI algorithms and technologies available to make informed decisions about implementation.
• Limited Access to Expertise: There is a lack of experienced personnel who can use, deploy, and maintain machine learning models or other AI capabilities.
• Resistance from Existing Employees: Staff members often resist changes driven by artificial intelligence, fearing that it will replace their jobs or cause job insecurity.
• Risk Concerns: Security, privacy, legal, ethical, and compliance risks must be addressed when deploying any type of automation technology.

To prevent major issues that can grind processes to a halt, organizations should have AI solutions and traditional approaches running in tandem. Once any issues with AI integration have been remedied, organizations can then apply AI to processes, allowing employees to focus on more strategic areas of concern or handle more complex tasks AI cannot perform. For example, organizations that wish to employ AI to handle chats with customers should first ensure the AI is thoroughly trained before allowing it to handle them. Until AI is fully trained, human customer service agents will handle customer chats. Once the AI has learned how to handle customer chats, it can take over simple issue chats while employees handle more complex and nuanced issues.

Continuous Monitoring, Evaluation, and Improvement

Once AI has been successfully integrated with systems and processes, organizations need to make sure there is a plan in place for continuous monitoring, evaluation, and improvement. Just as with ongoing monitoring of data, AI solutions should be monitored to ensure that it is performing tasks as it is intended to do. Through continuous monitoring, evaluation, and improvement, AI will be better able to perform tasks faster and more accurately, allowing organizations to make more informed decisions. Additionally, continuous improvement will allow organizations to ensure they remain competitive as technology changes to remain competitive.

Ultimately, continuous monitoring, evaluation, and improvement allow organizations to ensure that AI is helping further their goals, and if not, corrective actions can be taken to ensure that it does. It also allows organizations to make sure that any data being used is secure, not being misused or accessed by individuals not authorized to access it.

Conclusion

AI has become an essential part of how organizations do business, boosting efficiency and leading to overall growth. By removing mundane tasks from employees, AI is an avenue to allow employees to focus on more strategic tasks and provide them with opportunities for upskilling. Because of AI's ability to unleash an organization's potential, organizations should employ best practices to ensure that it provides expected results. Constant monitoring and improvement of AI is necessary to allow it to learn as technology changes, leading to greater accuracy, better decision-making, and an increased bottom line.