2023 was a year of great activity in the field of artificial intelligence as well as customer experience, AI and CX. Generative AI became mainstream and made headlines over the last 12 months. In the meantime, expectations for customers increased as more and more businesses tried to push the limits of the kinds of experiences AI automation services could provide. The companies at the forefront were leaning towards the next generation of AI methods, which include generative AI as well as emotion AI as well as knowledge AI and many more. These technologies, which form the foundational elements for Enterprise AI, enabled companies to provide more effective, meaningful and, perhaps most importantly, more personal interaction with customers.
In the course of the year, we examined these issues and more with the top AI and CX thinkers, influential experts, industry experts and leading decision makers. Here are some of the most important misconceptions:
Myth #1: Generative AI could replace jobs
Can Generative AI replace humans?
Although experts are of the opinion that Gen AI will be able to take on some tasks however, most believe that the technology is creating rather than replacing. When we examine our contact centers, employees who utilize the generative AI tools are clearly more content and are getting a boost in their productivity. It’s easier to be kind and empathetic when you’re not working with complicated systems and have repetitive tasks.
In reality, AI strategies are created to enhance the capabilities of humans, and not replace jobs. The Workday survey found that 98% percent of CEOs support making use of Artificial Intelligence (AI) and Machine Learning (ML) in their businesses, increasing their team’s efficiency and creativity. They tend to be more inclined to embrace AI/ML technology to build an inclusive and creative workforce.
AI is likely to create new opportunities in the coming years instead of replacing humans. As per Multiverse’s research 73% of companies are planning to invest in existing employees’ training and upskilling in order to remain competitive.
Myth #2: LLMs or Gen AI is for Large Enterprises
The advent of Large Language Models (LLMs) and Generative AI might tempt some to believe that LLMs are for large enterprises. While large corporations can reap the benefits of deploying LLMs as can small-scale companies. Custom LLMs are a better alternative for small-scale businesses, allowing them full benefits for LLMs. As they’re seeing the advantages, 90% of small-sized companies in the US prefer this for optimizing operational costs. Organizations can choose SaaS-based AI tools and platforms like VoiceOwl to optimize their spending and budgets.
Myth #3 The implementation of AI requires a lot of technical know-how
While having technical knowledge can be helpful, many low-code and no-code platforms and tools like VoiceOwl that are user-friendly help non-technical users to set up and use AI solutions. Low-code and no-code platforms are growing rapidly which allows non-tech users to access and implement AI functions without writing complicated code. This helps to make AI use even more accessible to those who have no technical background.
You can, for instance, make use of tools such as VoiceOwl to simplify, and automate repetitive tasks and establish workflows that range from lead management to sales pipeline automation, and even custom marketing campaigns. These initiatives can drastically reduce the cost of lead acquisition and increase the efficiency of your team.
Don’t let misconceptions hold you back.
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VoiceOwl equips you with the tools to deliver personalized, meaningful interactions that build loyalty and drive growth.
The potential applications of Generative AI in the enterprise are vast and multifaceted. Let’s delve into some key areas where this VoiceOwls Gen AI powered automation solutions are redefining the game:
- Contact Center Optimization
- Lead Generation and Qualification
- Data Verification and Enrichment
- Security and Compliance
VoiceOwl stands at the forefront of this revolution, shattering AI myths and empowering enterprises to redefine their operations and achieve measurable business outcomes.
Conclusion:
The future of enterprise success lies in embracing transformative technologies like Generative AI and not getting lost in AI myths. By harnessing the power of natural language processing and voice-powered interactions, businesses can unlock a new era of efficiency, agility, and customer-centricity.
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