Balancing The Benefits and Risks of Artificial Intelligence: A C-Suite Guide
In this article, we explore the benefits and common risks companies face during AI transformation, and what can be done to address them.
Given the rapid evolvement of hardware, 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, identifying growth opportunities, automating processes, and enhancing decision-making overall.
In this article, we explore the common risks and challenges companies face when enabling AI transformation, and what can be done to address them.
Get a Bird’s Eye View of Your Company
As AI is still considered a relatively new business driver, few leaders have been able to fully understand this technology’s impact on their organizations. In a nutshell, one of the most common reasons for failed AI initiatives is treating AI implementation as a solely technology-led effort. This results in the inappropriate distribution of responsibilities between people and departments.
Companies that succeed with AI adoption always start with a complete reevaluation of their technological maturity, policies, talent, etc. It’s not uncommon to get a second opinion by hiring AI consultants, who review organizational processes from end to end in order to understand if AI can potentially fit into the company’s architecture.
This usually involves meeting key people from every department to prioritize areas of implementation and evaluate risks. Once leaders see the bigger picture, they can better understand where and how these technologies can create value and what resources are missing. Most importantly, the teams involved in AI projects will have a much clearer understanding of their accountability and scope of work.
Contrary to popular opinion, AI has far more chances to get adopted successfully if it makes an impact on all parts of the organization. In other words, AI initiatives are more likely to be doomed if they are executed in isolation.
Choose Use Cases Wisely
This is one of the most overlooked steps in AI 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 potent, 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 model 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 than it would solve.
AI adoption usually requires a complete redesign of a particular process 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?
Make Change Management a Priority
Well, even the smallest changes to business processes can cause employees’ resistance. As the impact of AI-enabled transformation on the organizational culture can be significant, change management becomes a priority. Here are the steps you should take to decrease employees’ reluctance to change:
- Redesign processes with users’ skills in mind. While we previously emphasized that AI adoption implies a complete revamp of existing processes, companies need to do their best to make these new processes as familiar to the old ones as possible.
- Consider employees’ technical maturity. Great business leaders are aware of how well their employees can learn and adapt. The level of this adaptability usually varies from department to department, and this is why it’s important to be selective about the area of implementation.
- Start preparing your workforce to the changes as early as possible. Providing live demos of the new software and organizing workshops that clearly explain technology limitations and benefits is crucial for stress-free workforce adaptation. Moreover, forward-thinking organizations involve employees in AI program development and design from the very beginning. This is of utmost importance both from technological and psychological perspectives.
Consider Ethical and Social Implications
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 AI 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.
Address Regulatory Implications
With the proliferation of AI, the increasing emphasis on big data privacy risks shouldn’t be surprising. In the past few years, compliance with various privacy-oriented regulations has become a fundamental part of every business that uses AI.
Although various regulatory frameworks including the GPDR and the OECD Principles on Artificial Intelligence have been continuously updated in the past few years, there is still certain regulatory ambiguity regarding some AI application areas. Consequentially, we can only expect additional regulations to complement the already strict frameworks.
Moreover, it’s not uncommon for companies that use AI to operate in multiple jurisdictions, which complicates compliance even further. Experts argue that the policies of different countries may misalign and conflict with each other. That’s why companies need to actively monitor constantly updating regulations and be prepared that compliance with these regulations may result in additional budget and resource spending.
Invest in Data Strategy
As the saying goes, your AI model is as good as the data 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.
Choose Your AI Vendor Wisely
The startup landscape exploded once software developers figured out the inner workings of AI. However, selecting an AI vendor based solely on technological expertise is a risky choice, to say the least. When it comes to AI adoption, continuity is crucial. Will your strategic partner be able to support the project once the AI system is deployed? Will they be there in five years? These are the important questions you need to ask before making a choice.
Assess Risks Continuously
Given that AI has already made a difference 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 the algorithm. 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 in each area of the organization affected by AI deployment.
There is no doubt that AI is here to stay. The lavish amount of investments into AI by the world’s largest companies and rapid sprouting of respective R&D centers imply that the competitive advantage is now largely defined by the level of AI adoption. Here at Iflexion, we believe that AI is a definite game-changer. New emerging companies are now formulating their business objectives around big data and AI, which, if done right, will make them stand out from the rest.
When it comes to adopting AI, the ‘follow the leader’ approach will most likely keep your business afloat but definitely won’t ever put you in the leader group. If your organization hasn’t done anything yet to bring about AI-led changes, become the advocate now. Whether it’s your in-house center of excellence or a small group of AI enthusiasts, any ideas that encourage AI-driven transformation will eventually pay off in the long term.
Until recently, focusing on narrow business challenges that could be solved with AI was the name of the game. Today, AI adoption is a matter of staying competitive in an ever-changing business environment.
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