Today, artificial intelligence (AI) stands at the forefront of innovation, transforming visionary concepts into tangible realities. AI’s potential impact spans all stages of tech development, from initial ideation to final implementation, accelerating the transformation of creative ideas into practical applications. This integration can result in more efficient processes, smarter products and enhanced user experiences. Organizations that place customer experience and operational efficiency at the heart of their AI adoption strategy are poised to reap the most benefit from this transformative technology.
AI Today
In the last several years, increased support and resources have driven the advancement of AI, making the technology more integrated and efficient. Companies’ daily operations are the epicenter of this development, mainly because of AI’s promising predictive ability, which can provide meaningful business and customer insights. The Association for Financial Professionals (AFP) notes that one benefit of AI is the technology’s ability to “process information from diverse sources that identify hidden trends and generate predictions above human capabilities.” The APF anticipates significant benefits from AI in demand forecasting, supply chain, inventory management and even energy consumption predictions.
Organizations are also increasingly using AI to improve customer interactions and targeting. In Invoca’s “The State of AI in Digital Marketing” report, more than 75% of respondents said AI worked well or very well in a variety of use cases. AI can provide 24/7 service to customers, whether through chatbots on a company’s website or built-in tools like conversational user interfaces (CUIs). Even fast food restaurants are getting on board; White Castle and Wendy’s both rolled out voice-based AI ordering at the drive within the last year.
AI is increasingly used to offer increased personalization to customers. The most well-known example is Netflix, which has provided personalized suggestions to viewers for years. The company’s AI strategy isn’t solely to offer recommendations to individual users; it also predicts larger trends and preferences for different demographic groups, using AI-driven tools to deliver content in various languages more quickly and accurately. Whether the technology is used for business forecasting or to improve customer experience, the method is the same: Acquire the necessary data, create a model and continuously tweak the model with new data.
Data is Key
The volume of data available to businesses is immense, and the insights locked within that data are incredibly valuable if they can be extracted. AI thrives in this new era of data. With smaller amounts of data, manual analysis is relatively straightforward, but with modern data volumes, AI is invaluable. The data type still matters because a diversity of high-quality data is essential for training AI models.
The quality and potential benefit of AI and machine learning (ML) outputs rely heavily on the data fed to the model. Both volume and quality are necessary for accurate and useful outputs. Whether a dataset is balanced or imbalanced is a critical consideration. In an imbalanced dataset, one category of data dominates over the others, which can lead an AI model to focus more on the so-called majority class and potentially result in biased results. A balanced dataset is derived from various sources. If a business collects customer feedback, a balance of gender, age, occupation, and other factors is essential. These diverse, clean, and balanced datasets generate better predictions and help reduce bias.
Customer Experience
Across industries, AI is booming, and companies are increasingly looking to invest in the technology lest they be left behind. In a recent McKinsey survey, 80% of organizations predicted growth in their AI investments for internal use this year. There is less agreement, however, on how businesses will implement these tools. In another survey conducted earlier this year, organizations reported using AI in several functions, including marketing and sales, product or service development, IT and software engineering.
For companies adopting AI and ML, an effective strategy is to align their use of AI with their business goals and to ensure a focus on customer care and ease. By centering on customer experience, they identify and eliminate customer pain points and improve satisfaction. Increased ease for customers will naturally lead to more automation and the implementation of AI tools like round-the-clock customer service, personalized predictions and improved anticipation of customers’ needs.
For companies, success correlates directly to the customer experience. Organizations with happy customers see increased revenue, engagement and customer retention. To track the success of AI implementation, organizations can use key performance indicators (KPIs) focused on customer satisfaction and retention. Building and maintaining customer engagement are vital, and a simple, straightforward experience is foundational to achieving these goals.
AI offers endless opportunities for innovation and transformation, making its future hard to predict. What is certain, however, is that change is inevitable and AI is here to stay. Organizations across industries are already implementing AI strategies, and those that are either unprepared or hesitant risk being left behind. AI is a new technology with constantly evolving best practices and plenty to learn. Enterprises willing to embrace AI’s future and fail fast will reap the benefits of the innovative technological landscape.
