The caveat is that its adoption must be conscientiously monitored at every phase of implementation by responsible, enlightened experts. How?
In a digital age where we have so much information at our fingertips, it has become harder to cut through the noise and accurately identify credible and relevant sources of information. This lack of actionable data can hinder the way we make business decisions; what risks we can take, and what we must steer clear of.
While evaluating all this information ourselves is not impossible, it is a time-consuming and sometimes tedious process. Hence, we must look to investing in support systems that can stand the test of time, and put the human experience at the center of operational optimization.
Here is where AI can come in to make a marked difference. We are now seeing ways in which a pseudo-symbiotic relationship between AI and humans can be achieved to tackle some of the challenges and barriers we face when it comes to business decision-making.
At the same time, the technology and tools present in the market today generate exponential value — helping optimize performance, enrich customer and employee experiences, and unlock new sources of sustainable growth.
Addressing imperfect communication
In our current world, where geographical barriers rarely hinder collaboration, what affects the performance of cross-market collaboration is likely to be imperfect communication.
This can arise in a multilingual and multi-cultural workforce where different language and cultural nuances have not been accounted for. The practical examples of AI tools such as natural language processing being used to translate documents from one language to another are well-known. Can AI bring additional values, and if so, how?
To explore the values brought by AI, let us assume we are a multi-national consumer product company headquartered in Asia. With a multi-cultural workforce comes different levels of language awareness across teams. Inevitable, one of the challenges will be getting a good read across various languages in all the markets we operate in, in real time.
It will be crucial for our business to ensure that we understand what is being said by local experts, influencers and consumers about the products we offer. Relying on local setup in each individual market to listen to, distill, and translate consumer sentiment and feedback can have significant lead time. To help address this issue, we can implement a global technology provider’s approach where texts in, say, a European language like French can be translated into English first before being analyzed and summarized in any other language, allowing for a better understanding of market specific consumption trends across our diverse teams.
This use case of AI brings not only operational efficiency, but also leads to enriched customer and employee experiences. In a bank for instance, there are ways to utilize AI to filter inbound calls and match up customer complaints or queries with the most relevant pre-recorded answers. The need for extended on-hold times and navigating through multiple menu options is reduced, thereby enhancing the customer experience. At the same time, the automation frees up staff from menial tasks so they can work on human problems that are more complex and sensitive but more challenging and rewarding.
Undoubtedly, this whole operation can fall through if the software experiences even the tiniest flaws, which is why experts must be involved in the AI adoption and implementation process from the ground up.
AI without fear and bias
Any type of technology brought in to help a business should do just that: help the business and the people running it.
Not every company employee needs to be a technology expert to use AI, but everyone will need to know how to use it in a way that speeds up their work processes and produces good quality content. It is an art form that we are getting to know more about, and with any journey of exploration, we must face it head on and without fear or bias.
Once AI objectivity can be leveraged via access to significantly broader data sets, humans will be able to add further insights to ultimately drive business value with. More specifically, when combined with AI and ML tools, human forecasting can be much stronger to inform vital decision-making and even inform new sources of long-term, sustainable growth.
To this end, a chemical firm is now saving millions each year in raw material procurement as a result of using human-machine collaboration to fine-tune market forecasting. Under some circumstances the AI component yielded dispensable information that could be ignored, but the firm had EY experts guiding them to fine-tune the AI/ML algorithms.
In order for any AI adoptions to be efficient and viable in the long run, we must look to leveraging insights from past major technological adoptions. The dot-com boom at the turn of the millennium has already provided something of a benchmark as to what to expect. That, coupled with cross-industry insight sharing on AI adoption, can help businesses around the world work around any barriers or challenges currently on our radar.
