Balancing The Benefits and Risks of Artificial Intelligence: A C-Suite Guide

Given the rapid evolvement of hardware, computer processing power, and data availability, AI development has only accelerated, while the barrier to its adoption significantly lowered. AI is now a centerpiece of technological transformation as it continues to foster the emergence of new business models.

In essence, the human ability to analyze data falls short compared to that of AI. This advanced level of data analysis has opened up new horizons for personalizing customer experience, optimizing risk assessment, growth opportunities recognition, automating processes, and enhancing decision-making overall.

In this article, we explore the advantages and disadvantages of artificial intelligence, the common risks and challenges companies face when enabling AI transformation, and what can be done to address them.

This is one of the most overlooked steps in artificial intelligence adoption. Far too often, companies rush to implement this technology because, on the surface, there are numerous potentially viable use cases. If you think about it, almost anything can be automated, but it rarely means it should be. Some of the use cases may even seem to be economically advantageous, but the factors making AI feasible are always far more granular and require all-round consideration for the technology adoption to be successful in the long term.

Let’s take one of the most common AI applications — customer request processing automation. Given that many user requests are similar and thus are treated by human experts similarly, this appears as a perfect area for AI implementation. However, more often than not, a more detailed analysis reveals that each customer still has their own set of requirements and nuances that need to be individually considered. This would make such an AI chat robot extremely complex, which usually takes more time and money than any average company can afford. Moreover, especially at large enterprises, there can be multiple different input systems that would require substantially different AI model variations. As a result, such an AI initiative would create more problems instead of improving customer service efficiency.

AI adoption usually requires a complete redesign of particular tasks rather than the introduction of small changes. While we advocate a complete AI-driven transformation, sometimes you need to get back to the blackboard, recalculate the ROI, and reconsider if AI adoption is really the best option for your particular business case. For example, when it comes to AI for small businesses, it becomes increasingly popular to implement the technology for various automation purposes. While it largely depends on a specific business case, many automation issues can be solved with basic RPA programs with no need to go for full-on AI.

So before making any conclusions about AI feasibility for your business case, ask the following questions:

• How significant are the process changes we will need to introduce?
• Do we have enough data? Is this data of sufficient quality? Is it accessible?
• How much time will the team members need to get hold of the new AI-augmented process?

Despite AI’s rapid pace of adoption, its black box problem continuously poses ethical problems that are often hard to address. Companies need to do their best to anticipate how exactly their particular AI use case will process consumer and employee data. For example, unless thoroughly examined and tested, many applications of AI in recruiting have already proved to be biased in more cases than it would be comfortable to admit. To address this risk, top managers need to work closely with business-ethics experts and legal professionals to come up with testing methods that will expose ethical flaws in artificial intelligence models.

Unless business teams can understand the exact reasons behind certain AI decisions, further project development can get substantially risky. Mitigating this might require compromising some of the technology’s functional capabilities in order to address all the ethical and societal concerns.

As the saying goes, your AI model is as good as the information you feed into it. Far too often, business leaders tend to think that just cleaning all their available data is the way to go. While the intent is commendable, in the absolute majority of cases such initiatives result in economic losses and waste of resources. Not all datasets are useful, hence not all of them need to be scrubbed clean.

Your scope and depth of data cleaning will always depend on the use case you choose. Apart from this, ideally data governance practices should be embedded in your organization by default.

As you can see from the image above, data quality remains a significant barrier to successful AI adoption. Developing and implementing an effective data strategy can take years. However, it doesn’t mean that you need to postpone the realization of the AI initiative because your data management practices are not perfect yet. Ensure that the most important datasets are ready to be consumed by AI and adjust strategy as you go.

Given that artificial intelligence has already brought some advantages in numerous industries, its use cases are various. Risks involved in each AI initiative vary respectively. For instance, a healthcare institution might need to pay the most attention to the accuracy of their diagnostic AI model to eliminate the possibility of inadvertent harm caused by algorithms. Similarly, a healthcare app team might need to look closely at its privacy policies to avoid any legal risks. Again, this is why it’s important to get an expert opinion about potential risks and disadvantages in each area of the organization affected by AI deployment.