Artificial intelligence (AI) is causing a digital transformation that is changing businesses’ operations. It is likely to bring a sea change compared to the Industrial Revolution.

Many challenges with AI are technical, but most failures occur because of poor strategy and execution. Fortunately, there are some steps you can take to avoid some of the most common mistakes when implementing AI.

Overall, creating and deploying AI models should be a multi-step process. You have to identify the business problem you are trying to solve. You then have to consider how best to solve it and establish metrics for success. Finally, you need to build a trusting relationship between humans and AI.

Target AI where it will have the most impact

One of the most important things to consider when deploying AI for business purposes is the desired outcome. It is crucial to ensure that AI solutions fit the organisation’s strategic vision. Therefore, the first step in adopting AI is identifying business needs and goals.

Adopt a human-centric attitude

Another common mistake in AI implementation is focusing on the technology rather than the people. AI systems can improve human capabilities and intelligence, but they must be developed as functional extensions of teams and designed to fit the team’s needs. In other words, they must be human-centric. Many businesses fail to consider the importance of user-friendliness and human interpretation of AI models. Without these elements, AI systems won’t deliver the expected value. It would help if you also considered how you would manage stakeholders, especially internal ones. They need to see the value of the project, or the outputs are unlikely to be used.

Make sure your data is ready first

Common mistake companies make when implementing AI is assuming that the data is ready. Companies generally know that value is buried in their data, but they often neglect to check the quality of the data or validate the use cases with data. It is essential to define the use cases required and then develop the necessary data to support them.

Manage the risk of your AI

Establishing company-wide solid controls and policies for AI is essential in avoiding potential problems and maximizing opportunities. A critical element of the rules is having proper oversight of models. Vendors of intelligent features will often introduce changes and updates without fanfare, exposing their customers to unexpected risks and vulnerabilities. You must stay aware of model actions and ensure they continue operating as expected and required.

Measure the impact of your AI project

It is important to understand how you will measure the effectiveness of AI before deploying it. An objective baseline measurement of AI efforts will help you track and measure results. It would help if you also audited your algorithms to ensure that they are doing what was expected, that the ‘answers’ are credible, and that there are clear “why” messages. You also need to consider how you will measure your project's return on investment, including how you will balance your AI portfolio across the whole company.

Build trust between humans and AI

As AI and robotization continue to change our society, trust is essential for the interactions between humans and machines. Robots now contribute to tasks previously performed entirely by people, and we need to understand how to develop trust in this relationship. For instance, it is important to consider the process of both initial trust formation and continuous trust development. No system is perfect, and it is generally better to be skeptical. Perhaps the ideal scenario is the idea of “calibrated trust,” where users adjust their trust level depending on the system’s performance. Unfortunately, many companies overstate the abilities of their products, which does not lead to trust. In a high-risk situation, zero trust is better than unquestioning acceptance.

Have a control group for implementation

Finally, it is essential to monitor implementation against “business as usual.” You can do this by benchmarking the new process with the old one if you have a control group of users who are not using AI. If you are testing multiple applications, you should benchmark them against each other. Benchmarking will also help to build trust in the technology.

Taking precautions against failure

AI is rapidly becoming a mainstream technology, but many executives remain wary. They fear jumping on the bandwagon, but this may prove a costly mistake. These seven points should help companies to avoid the most obvious problems and make better decisions about AI implementation.

Read more about how to scale cost-effectively, increase productivity and innovate faster with AI in the cloud.


About Author

Dwijendra Dwivedi

Director, Pre-Sales Support

Dwijendra Dwivedi is a leading AI and IoT data science team for EMEA + AP at SAS. He is AI evangelist and Business Analytics subject matter expert. As a thought leader, he is bridging the gap between business needs and analytical enablers to drive successful business strategies. With over 19 years of experience in applying AI & analytics across different industries, he conducts business analytics seminars and workshops for the executive audience and also for power users. He has published more than 17 AI research papers in leading Journals and Books.

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