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Artificial Intelligence In Future Business

In the near future, artificial intelligence (AI) is expected to dramatically change both marketing strategies and customer behaviors. The authors propose a multidimensional framework for understanding the impact of AI that includes intelligence levels, task types, and whether AI is embedded in a robot, based not only on existing research but also from extensive interactions with practice.


Artificial Intelligence and Marketing Strategy


predictive ability


Since AI can help companies predict what customers will buy, the use of AI should lead to fundamental improvements in predictive power. Depending on levels of predictive accuracy, companies may dramatically change their business models, continuously providing goods and services to customers based on data and predictions about their needs. Thus, multiple research opportunities arise regarding different customer buying behaviors and marketing strategies.


Artificial Intelligence in Predicting Demand


A particularly important area of research may depend on how well AI-powered algorithms predict demand for new products. AI algorithms may have a good predictive power for the incremental new products; An open question is whether they have a good predictive ability of RNPsRibonucleoprotein Particles.


AI in Pricing and Pricing Research


AI is expected to play an important role in predicting not only what customers are willing to buy, but also what price to pay, and whether price promotions should be offered. Pricing and pricing are important drivers of sales, as well as an important area of research for marketing researchers. Thus, an important area for future research is how AI techniques can best be used to predict optimal prices and whether or not price promotions should be offered.


AI Help Grow Customer Awareness?


Another important research avenue concerns the allocation of advertising resources. Much of the advertisements focus on developing customer awareness and stimulating the search for customer information. Will these advertising dollars be needed in the future, as companies may be able to better anticipate customer preferences, so they won't need to advertise as often?


Sales and artificial intelligence


AI may change all stages of the selling process, from prospecting to pre-presentation approach to follow-up. Thus, a variety of research questions arise:


  • Can AI analyze customer communications and other customer information (for example, social media posts) in ways to devise more persuasive future communications or increase engagement?
  • Can AI technologies provide real-time feedback to sales reps to help them improve their sales presentations, based on evaluations of verbal and facial customer responses?
  • How can AI combine text and other communication inputs (for example, voice data), actual customer behavior, and other information (for example, the behaviors of similar customers) to predict buybacks?
  • How should companies effectively deploy AI sales bots?

Answering these questions can help companies design sales to get the most from AI. Additionally, companies need to think about how to (re)organize their selling and innovation processes.


Sales operations


With AI, how should sales be organized and what skills will salespeople need? First, the best way to structure the sales organization where the organizational components include both AI robots and human salespeople.

Second, how should a company manage the trade-off between AI focusing on stated customer needs versus salespeople's relatively better ability to manage issues such as customer supervision?. Finally, will the salespeople be able/trained to be able to manage customer concerns related to AI, specifically issues related to data privacy and ethics? It is clear that selling will require innovation-related not only to AI technologies but also to job design and skills.


The process of creating artificial intelligence


As the impact of AI is uncertain, companies need to figure out how best to (continuously) develop AI. Experts noted that the company encourages its data scientists to pursue projects themselves, constantly engaging in preliminary testing of new project ideas.


A data scientist (One Stitch Fix) has created a Tinder-like app called Style Shuffle, to allow users to indicate preferences for different clothing styles. This app not only informed the designers of customer preferences (expected benefit) but also helped match designers with specific customers (unexpected benefit).


Clothing suggestions from designers who similarly “swiped” the app for certain customers elicited more positive responses from customers (for example, both qualitative comments about the designer and increased sales of designer-sponsored clothing). 

When implementing AI, companies may achieve better results if they allow their data scientists to spend time on unauthorized “pet projects,” a research and development practice that already exists in companies. The search for the best way to apply artificial intelligence, to take advantage of expected and unexpected benefits, is a fruitful area of ​​research.


Modeling the development of artificial intelligence


Finally, companies need to develop realistic expectations, because, in the short term, AI will provide evolutionary benefits; In the long run, it is likely to be revolutionary. That is, the benefits of AI can be overestimated in the short term but underestimated in the long term, a point (sometimes called Amara's Law) according to Gartner's model of the hype cycle on how new technologies evolve.


Artificial intelligence and customer behavior


New technologies often change customer behavior and we expect AI to do so as well.


Adopting artificial intelligence


As a general point, due to a variety of factors, customers view AI negatively, which is a barrier to adoption. As noted, these negative opinions often stem from customers feeling that AI is unable to sense or that AI is relatively less able to determine what is unique to each customer. Customers see AI bots as less empathetic. Customers are also less likely to adopt AI in later domains and more prominent tasks in their identity.


Thus, an important area of future research, important from the point of view of both research and practice, is to examine how best to mitigate the impact of the above. Initial brainstorming with fellow researchers and practitioners suggests that placing AI as an educational (artificial) object or placing AI as an application that combines AI and human input (as in Stitch Fix), may help in part to mitigate the impact of the points above.


Artificial Intelligence In Future Business


The annoyance with AI grows if an AI app is embedded in a bot. As the robots become more human-like, due to the long ultraviolet (UVH) rays, customers find these robots worrisome. These factors may hinder the adoption of AI and are worth considering. An interesting mediator of this effect, worth investigating, maybe whether customers view the AI model as a server or a partner; The effects of UVH may be stronger if the AI achieves partner status.


Other ways to mitigate these effects are also worth considering. Early efforts in this direction include trying to show empathy, by convincing customers that robots have some ability to see things from a customer's point of view, and (also) have some ability to feel empathy for a customer if a customer is suffering. Other possible approaches could relate to AI embodiment, as this might convince customers that AI has somewhat more empathy (this point needs to be weighed against concerns about UVH).


Conclusion


Sociologists seem particularly interested in how robots with built-in artificial intelligence can make a breakthrough in society, noting that “complications arise when cultural preferences associated with humans are considered versus the provision of personalized services to a machine.