How To Avoid Common AI Implementation Mistakes 

Artificial intelligence has become increasingly enmeshed in a growing number of industries, and the insurance and employee benefits spaces are clearly no exceptions to the rule. 

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There are already many business functions across numerous fields that largely depend on AI and machine learning platforms, thus rendering these technologies as essentially critical infrastructure, even when little to no human oversight is involved. 

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Further, given the trends of how quickly these technologies are both evolving and being adopted, it’s reasonable to assume that barring some unforeseen intervention, business operations will become even more AI-dependent going forward into the future.

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With those considerations in mind when looking at the bigger picture, even though artificial intelligence is practically ubiquitous in many arenas already, we are likely still in the very early stages of the ultimate AI development timeline. As a result, there remain both many novel ways to put these technologies to use as they exist today that have not yet been explored, as well as many potential pitfalls that have yet to be experienced and for which there are no warnings. 

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In light of both the opportunity and the risks involved with staying on the cutting edge of artificial intelligence, and the importance of doing so in order to keep up with the competition, it may be wise to keep an eye on what’s happening with AI across a variety of industries in order to learn vicarious lessons and adopt/implement analogous uses for the technology from one industry or business process to another. 

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To those ends, this recent piece from author and data advisor Bernard Marr pulls together a collection of tips that’s worth taking a look at on how to avoid common AI-implementation mistakes that different types of companies are making for often very similar reasons:

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• Clearly define your objectives. In order to best address the problems that it is tasked with solving, AI must have clearly drawn parameters and guidelines for evaluating success, otherwise the bulk of the potential advantages that AI offers are largely wasted.
• Implement a change management strategy alongside AI adoption. Recognize in advance that incorporating AI platforms into your business process often creates concerns about learning curves and/or job security among employees that lead to pushback and low adoption rates. These issues can be countered with well-conceived and executed change management efforts that better frame the benefits of AI adoption in employee-centric terms.
• Manage AI capability expectations. Although artificial intelligence and machine learning are very powerful tools, their abilities are not without limitation and often depend on human input, both for the collection of data and execution of strategy, which provides additional opportunities for error.
• Test AI systems: Inaccuracies and system errors are the probable result of a failure to adequately test and validate artificial intelligence system operations for accuracy and reliability.
• Don’t overlook ethics and privacy issues. There’s a particularly sensitive lawsuit vulnerability when it comes to both privacy and ethical issues, which can be exacerbated when AI is involved. Further, the conclusions drawn from AI are only as good as the information on which those concussions are based, which means that bias in data and/or human-conceived process can be perpetuated through AI systems if not properly addressed. 
• Ensure adequate talent is in place to manage AI operations. Optimizing the implementation of artificial intelligence while maximizing results and minimizing disruption requires a specific skill set and knowledge base that are not easily learned on the fly. It is advisable to work with outside consultants that have experience in your field at least until internal staff with the necessary skills and experience have been trained and/or hired.
• Don’t ignore data strategy. Information is the lifeblood that drives artificial intelligence, so if that data isn’t clean, organized, and accessible, then the quality of the output generated by AI in return will be diminished. 
• Don’t underestimate costs. Properly implementing artificial intelligence requires investments not just in technology, but also in talent and in the underlying company infrastructure beyond just what’s required to host the new AI platform. Failing to properly budget sufficiently for all of these necessary expenditures often results in an underfunded effort that does not meet its goals. 
• Don’t Approach AI implementation like a one-off event. As these technologies continue to evolve, businesses that treat the launch and implementation of artificial intelligence as a singular occurrence will soon see their platforms become obsolete and fall behind those companies that understand the AI launch is just the first phase of an ongoing project. 
• Plan for scale. Even if your company’s initial ventures into the artificial intelligence space are relatively small in scope, it’s still a good idea to consider growth on the front end to ensure that no bottlenecks inhibiting platform expansion in the future are built into the system inadvertently. 

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You can read more about this topic here. 

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