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How to Avoid the Risk of Failure in Your Decision Making


With artificial intelligence (AI) gaining strong grounds, many organizations are tempted to implement it on a mass level. In the past year, we have seen big brands giving unnecessary authority to AI in decision making that had led to huge losses. One such example is a tech giant that implemented AI in the oncology department of a healthcare unit that led to the wrong diagnosis having severe implications.

IBM’s Watson gave unsafe recommendations for treating cancer (The Verge)

So we can say that AI is a double-edged sword that can do the same level of damage in comparison to good or even more.

But if you have hired an expert in artificial intelligence services, your work pressure is shared greatly. They will keep a close check on AI progress and manage activities with their expertise.

The steps to avoid the risk of failure in your decision making through AI are as follows:

Data Gathering

Failure in AI happens when you have incomplete data availability, as AI software learns to make decisions based on data available. So you must have all data available that represents the real picture of organizational processes.

Specified Area To Work

It might look tempting to have AI implemented throughout business processes. But remember the number of failures in AI history, where the technology can help you but it can share sensitive information with irrelevant people. Proper scrutiny is required for the purpose, but before that, you should have the vision to specify the area you want AI to work. Here provider of artificial intelligence services can help you pinpoint the potential areas that could be worked on, but it is you as a company that should take the decision.  

Be Clear On Target

AI can manage the decision-making process on multiple fronts, but it is you as an organization that needs to keep the check whether the target is achieved or not. So your objectives should be clear since the beginning. Like ordinary technology, you cannot give margin to trial and error learning, as it not only costs more but also wastes resources. Once the target is clear, artificial intelligence services should be implemented. Hence work on identifying how this will provide business value and take someone who has handled such project on-board.

Managing Authority between Machine and Human

Although the implementation process brings its own set of challenges, we believe that experts will guide you in the process. Once the AI is implemented it is crucial to keep a close check on its progress. As the unfiltered content mimicry without protective cushion has made another tech-giant pay the price. The AI Chat bot by Microsoft was launched on Twitter with the expectation to improve conversational understanding but ended up being a racist and misogynist (Forbes- Microsoft Troy On Twitter). Hence it is not the failure in your own decision making that you need to be careful of, but also that of AI that you implemented to support decision making. Here is the top of the mind ways you should confront the risk of decision making through artificial intelligence.

Be Clear of Critical Risks

A clear understanding of critical risks pre and post-AI services implementation is necessary. Identify the significant harm and damages that could happen when which part of the operation goes wrong. It means knowing the pain-points that will lead to customer loss or regulatory fines. It is already discussed that one needs to implement AI on operations that have low organizational risk and can work efficiently. But as no operation can have 0 risks, so one should find control strategies to further minimize possible hazards. The benefit of a structured risk-identification process is that one can easily prioritize risk control, by deploying time and resources accordingly.

Robust Control with Human Support

It is only the start when an organization identifies risks and allocates resources. The implementation part is tricky and needs special care. The management needs to identify where human is needed in the process for control and checking of result before it goes to the next stage. There are times when two-stage checking is required before the solution given by artificial intelligence services are made public.

Taking an example of a bank, workers identify if the customer under special circumstances should be recommended a solution or not. In the COVID-19 pandemic today, the government will fine the bank heavily if they penalize the customers for paying dues late, which the AI tool suggests. It can be an investment that the machine considers appropriate and recommend to the next stage; that can backfire.

Hence we can say that to avoid the risk of your decision-making failures while using artificial intelligence services a balance should be there. In the technology of today, AI and humans should work together to make decisions beneficial for the organization.


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