7 Best Practices for AI Implementation
Posted: Mon Dec 23, 2024 7:58 am
Artificial intelligence (AI) will play a critical role in the future of work and maintaining a competitive advantage. In an increasingly crowded and ever-changing marketplace, organizations must evolve. Implementing AI is a critical step in creating optimized operational efficiencies that increase longevity.
It’s easy to understand the benefits of AI. Manual labor and rising human resource hiring costs for global businesses are problems that AI can alleviate. It can unlock unforeseen opportunities while increasing revenue. However, it’s essential to understand how to go about it, from team training to data preparation to testing and more.
For many companies, the typical approach is to use specific features within their existing platforms. Therefore, it is not surprising that many AI projects fail.
Here are 7 best practices to follow when implementing artificial intelligence:
1. Assess your IT infrastructure
Unfortunately, many organizations are burdened by outdated legacy systems and complicated tech stacks, making it difficult to implement AI. If your organization operates in this environment, it’s critical to examine how you can build the right foundation and how to do it realistically. As you do so, you may realize that you already have a few AI projects stuck in the pipeline. Before you can deploy a cohesive AI strategy, you need to address these types of issues within your leadership team and departments. Take the time to answer these fundamental questions:
Will AI help our organization create better products and services?
Will AI Improve Time to Market?
Will AI improve process efficiency?
Will AI Mitigate Risk and Compliance?
The questions above are quite similar to the ones you may have asked yourself for any new application development strategy. Successfully implementing your AI strategy requires discipline and the use of the best practices listed here. Additionally, your answers can guide implementation. Consider resource utilization in terms of time, cost, complexity, and skills required to build your AI models and justify your business case.
2. Determine use cases
Look for relevant use cases for optimized AI deployment in each of the following areas:
Machine Learning (ML)
Natural Language Processing (NLP)
Natural Language Understanding (NLU)
Optical Character Recognition (OCR)
Chatbots
Learn how your competitors and peers have oman telephone number search successfully deployed AI platforms. Look for vendors with trusted track records to reduce risk. Consult with stakeholders on your use cases and the benefits of implementing AI.
Also leverage AI accelerators from leading cloud service providers (CSPs) that may already be included in your LCAP, DMS, BPM, RPA, and iPaaS platforms . By working with your stakeholders and teaching them how to use your AI solution, they will be more likely to use it, which will drive adoption across the organization.
3. Interpret the raw data
Insufficient data can lead to inaccurate results and failure of AI implementation. If you can understand the raw data, seek help from your business experts to access detailed interpretation. Comb through the data to ensure there are no typos, missing components, skewed labels, and other errors. Make sure your data samples contain all the elements you need to analyze.
Think about the relationship between your data and what you want to predict. Make sure the data is not biased. When you take the time to understand the raw data, you may also notice limitations. These limitations can help you set expectations about the scope of your predictions. If human intervention is required, review all trigger points, APIs, edge cases, exception handling, and system limitations.
It’s easy to understand the benefits of AI. Manual labor and rising human resource hiring costs for global businesses are problems that AI can alleviate. It can unlock unforeseen opportunities while increasing revenue. However, it’s essential to understand how to go about it, from team training to data preparation to testing and more.
For many companies, the typical approach is to use specific features within their existing platforms. Therefore, it is not surprising that many AI projects fail.
Here are 7 best practices to follow when implementing artificial intelligence:
1. Assess your IT infrastructure
Unfortunately, many organizations are burdened by outdated legacy systems and complicated tech stacks, making it difficult to implement AI. If your organization operates in this environment, it’s critical to examine how you can build the right foundation and how to do it realistically. As you do so, you may realize that you already have a few AI projects stuck in the pipeline. Before you can deploy a cohesive AI strategy, you need to address these types of issues within your leadership team and departments. Take the time to answer these fundamental questions:
Will AI help our organization create better products and services?
Will AI Improve Time to Market?
Will AI improve process efficiency?
Will AI Mitigate Risk and Compliance?
The questions above are quite similar to the ones you may have asked yourself for any new application development strategy. Successfully implementing your AI strategy requires discipline and the use of the best practices listed here. Additionally, your answers can guide implementation. Consider resource utilization in terms of time, cost, complexity, and skills required to build your AI models and justify your business case.
2. Determine use cases
Look for relevant use cases for optimized AI deployment in each of the following areas:
Machine Learning (ML)
Natural Language Processing (NLP)
Natural Language Understanding (NLU)
Optical Character Recognition (OCR)
Chatbots
Learn how your competitors and peers have oman telephone number search successfully deployed AI platforms. Look for vendors with trusted track records to reduce risk. Consult with stakeholders on your use cases and the benefits of implementing AI.
Also leverage AI accelerators from leading cloud service providers (CSPs) that may already be included in your LCAP, DMS, BPM, RPA, and iPaaS platforms . By working with your stakeholders and teaching them how to use your AI solution, they will be more likely to use it, which will drive adoption across the organization.
3. Interpret the raw data
Insufficient data can lead to inaccurate results and failure of AI implementation. If you can understand the raw data, seek help from your business experts to access detailed interpretation. Comb through the data to ensure there are no typos, missing components, skewed labels, and other errors. Make sure your data samples contain all the elements you need to analyze.
Think about the relationship between your data and what you want to predict. Make sure the data is not biased. When you take the time to understand the raw data, you may also notice limitations. These limitations can help you set expectations about the scope of your predictions. If human intervention is required, review all trigger points, APIs, edge cases, exception handling, and system limitations.