7 Best Practices for Implementing AI

Artificial intelligence (AI) will play a critical role in the future of work and in retaining a competitive advantage. In an increasingly crowded and always-on marketplace, organizations need to evolve. Implementing AI is an essential step towards creating optimized operational efficiencies that increase longevity. 

It’s easy to comprehend the benefits of AI. Manual labor and surging costs of hiring human resources for global companies is an issue AI can mitigate. It can unlock unforeseen opportunities while driving revenue. However, it’s critical to understand how to do so, from forming teams to prepping the data to testing and more. 

For many companies, the typical approach is to use specific features within their existing platforms. As a result, it should come as no surprise 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Unfortunately, many organizations are burdened with outdated legacy systems and complicated tech stacks, making it challenging to implement AI. If your organization operates in this environment, it is critical to look at how you can create the proper foundation and how to do so realistically. You may realize you already have a few AI projects stalled in the pipeline as you do so. Before you deploy any cohesive AI strategy, you must resolve these types of issues across 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 enhance process efficiencies?
  • Will AI mitigate risk and compliance?

The questions above are pretty similar to what you may have asked for any new application development strategy. To successfully execute your artificial intelligence strategy takes discipline and usage of best practices listed here. In addition, your answers can drive implementation. Consider resource utilization in terms of time, costs, complexity, and skillsets needed to build your AI models and justify your business case.

2. Determine use cases

Search for relevant use cases for the optimized deployment of artificial intelligence 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 successfully deployed AI platforms. Look for vendors with a reliable track record to reduce risk. Consult with stakeholders on your use cases and the advantages of implementing AI. 

Also, leverage AI accelerators from prominent cloud service providers (CSPs) that may already be included within your LCAP, DMS, BPM, RPA, and iPaaS platforms. By working with your stakeholders and teaching them how to use your AI solution, the more likely they are to use it, driving organization-wide adoption.

3. Interpret the raw data

Insufficient data may lead to misrepresented results and AI implementation failure. If you can comprehend the raw data, garner your business experts’ assistance to access a detailed interpretation. Comb through the data to ensure there aren’t any typos, missing components, skewed labels, and other errors. Ensure your data samples contain every element you need to analyze.

Think about the relationship between your data and what you want to predict. Make sure the data isn’t biased. When you take the time to understand the raw data carefully, you may also notice limitations. These limitations can help you to set expectations for the scope of your predictions. If human intervention is needed, verify all trigger points, APIs, edge cases, exception handling, and system boundaries.

4. Train the models

You will need high-quality historical data to train your ML models. Use AutoML engines to build image, speech, video, and natural language, recognition models. With AutoML engines, any user can upload their images and automatically create an ML model using a drag-and-drop interface. Essentially, it imports data, tags the data, and trains the model. The best part is that an AutoML engine manages all the complicated work for you.

5. Measure and track your results

You should experiment with artificial intelligence, but you should also incorporate disciplined tracking, monitoring, and measurement at every step using a critical approach. Also, it’s essential to continually audit your deployment to ensure it consistently aligns with your business objectives. Changing your strategy is more effective than accepting failure. 

Continue testing your models and predictions to drive further improvements where necessary. Keep your data clean, and retain a master raw data set to use for every testing round. You can also use your master data set to test modified use cases. Monitor your model for potential risks and issues. Don’t forget to add time for managing any unexpected problems. 

6. Instruct your team and collaborate 

Artificial intelligence continues to get better, but it still requires the correct data. The issue is it’s difficult to find data science experts. Therefore, invest in continuing education for your stakeholders. 

Add to your training initiatives by creating an environment where collaboration is part of the culture. A crucial factor for AI implementation success is change management. Create short-term and long-term objectives of what you expect to achieve using predictive analytics and then machine learning and then natural language processing and on down the AI list. Map out how each deployment affects each business line and how it enhances your employee workflows.

7. Recognize the wins

Celebrate every win, and involve every executive and stakeholder. Try to complete your projects within or before 12 weeks to encourage continued engagement. As you learn from each successful project, you can scale AI across more business lines and company locations.

Use your goals as success benchmarks, and focus on your results. When focusing on the outcome, keep in mind that AI platforms can take structured and unstructured data sets. 

Finally, using best practices for implementing AI requires a long-term perspective. Remember that AI deployment is a marathon and not a spring. Understand what AI is currently capable of executing, and be realistic about your timelines and your expectations.

Final thought

Regardless of how you program your AI platform, it will only work as well as the raw data you input into it. Nonetheless, AI is the most important technological innovation of our era. It reduces the time needed to build essential business models and processes while reducing the rate of human error. ProcessMaker offers an award-winning intelligent business process management suite (iBPMS) that helps organizations implement AI by seamlessly integrating with a wide range of systems and automation technologies





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