We are at the tipping point where artificial intelligence (AI) is not just an add-on but is poised to be a technology that underpins how business is done more broadly moving forward. The implementation of AI is helping businesses become more customer-centric, in both local and international markets, resulting in efficiency, productivity and competence.
According to Bain’s Technology Report 2022, venture capital invested more than $170 billion in AI hardware and software platforms, as well as algorithms, over the past five years. Moreover, worldwide spending on artificial intelligence is expected to hit half a billion dollars this year.
More than 80% of tech providers say AI is important for gaining market share and building customer loyalty, and a growing wave of investment is accelerating the path to taking customer-centricity to the next level and building intelligent experience engines.
In the customer journey of the digital era, personalized customer experiences have become the basis for competitive advantage, and personalization moves us forward by having complete data at the ready when someone calls customer service or accesses a web landing page with customer-relevant offers.
Increasingly powered by AI, it is now an essential part of the design for every physical and virtual customer touchpoint.
Managed Services
Deploying a well-performing AI model is a complicated, resource-intensive task, especially if it is crafted creatively and insightfully, using the best possible data and expertise. As a result, companies are turning to managed services for assistance.
A company can partner with a managed service provider (MSP) for all or part of a given operation, and this concept is now being frequently applied to AI. As this technology moves to be a core component of modern business, particularly in improving customer experience, we expect to see more diversified managed service offerings across the entire AI value chain.
There are two typical types: narrow AI and general AI. Narrow AI, seen within computers and smartphones like speech and voice recognition systems, powers intelligent systems that perform specific tasks without necessarily being programmed. General AI, on the other hand, is the kind of AI that can learn on its own to do whatever tasks humans can do.
With MSPs, they assist in designing end-to-end solutions that can proactively lead customers toward achieving their goals. This is made possible by combining data and technology for rapid self-learning and optimization.
AI in Customer Service
It is pretty obvious that the way we interact with customers today is very different from years ago. The manual processes are being kept to a minimum, with AI transforming customer service to be much faster, simpler, and more efficient.
Customers expect a seamless experience when it comes to their needs, which can be satisfied through AI. Tools such as chatbots and NLP solutions can reduce waiting times, answer common inquiries and questions in real time, recommend relevant products and handle complaints.
The potential for AI in customer service success is promising, but every company needs an explicit strategy for building an intelligent experience engine. This should be aligned with the organization’s core business and enhanced using AI, personalization and agile processes to build deeper, more enduring brand loyalty.
In the most successful digital transformations of the past decade, we have observed the 70/20/10 rule: 70% of the effort of changing an organization involves people; 20% entails getting the data right; and the remaining 10% is about the technology foundation.
With AI, the 70% could be reduced significantly, with human oversight only needed to ensure the technology is orchestrated properly and that the many customer touchpoints are met. Analytics can be maximized with customer data to prevent inconsistent, stagnant experiences across channels.
Chatbot Innovation
At present, the most commonly used AI tool for customer service is a chatbot. This computer software mimics human conversations over chats to facilitate customer support, is intended to be available 24/7 and can speak numerous languages.
The rise of ChatGPT has given chatbots the spotlight for many use cases, and in its latest GPT-4 base model, the training process involves using publicly available data (such as internet data) as well as licensed data. To align it with the user’s intent, the developers of OpenAI have finetuned the model’s behavior using reinforcement learning with human feedback (RLHF).
A similar model could be proven to be beneficial in the customer service arena, especially if it considers the historical data of an existing customer or addresses a new customer’s query based on the previously stored and filtered data.
AI for Telcos
Zooming in on the ICT landscape, for telcos, AI could greatly enhance customer service. With a complete view of the customer journey — from first onboarding, ongoing service and monitoring, to replacement and renewal — AI systems can provide sharp insights and specific recommendations to build closer relationships with customers and introduce value-added services.
AI can improve and automate functions such as determining customer segmentation, reducing churn, upselling, tailoring features, suggesting the next-best actions and targeting services. In the next two to four years, CIOs and CTOs will consider the use of AI for customer success among their top priorities.
On the enterprise side, AI use cases become related to improving end-to-end operations and network automation, including the launch of various network operations centers (NOCs).
One example of this is a suite of digital engagement capabilities integrated within the Verizon Virtual Contact Center (VCC), which aims to improve the digital customer journey. Through a smart knowledge management tool, AI-generated insights are based on customer service conversations. The smart conversational AI tool fixes customer and employee issues “in a more natural way.” Omantel has also launched an advanced International Network Operations Centre (INOC), a state-of-the-art 24/7 facility that is specifically tailored to meet the needs of the cloud and content-centric market, while Huawei’s cloud-based NOC solution supports global network O&M and offers various complementary digital solutions to support carrier business development.
The idea of using the data produced to best predict what is going to happen is crucial for both the front-end and back-end of telcos. On the front side, AI will result in better understanding the customer and forecasting the business properly, while on the back side, it will mean understanding where the point of failure is attributable to specific elements of processes and trying to act preemptively in order to gain efficiency.
