This article is by Featured Blogger Muddu Sudhakar from his Blog Page. Republished with the author’s permission.
Among the technology trends shaping 2019 and beyond, the adaptation of artificial intelligence (AI) to shape and propel the Customer Experience (CX) is among the most momentous. AI technologies, including machine learning (ML), natural-language understanding, and natural-language processing are proving transformative in acquiring, analyzing, and responding to customer feelings and feedback. In my experience, the scalability, speed and accuracy of AI is outperforming expectations and illuminating a forward path to even greater progress.
Companies are implementing AI to cut service costs -- resolving inbound requests via self-service and quickly answering customer questions through conversational AI and intelligent process automation. Not surprisingly, global spending on cognitive and AI systems is expected to reach $57.6 billion in 2021.
Companies lacking customer self-service face growing challenges. Complex tasks go un-automated. Support costs rise, and many routine chores remain manual when they could be automated. Response and resolution times expand and lengthen. All of that adds up to low customer satisfaction amid a high volume of support requests. Factor in budget limitations and falling productivity, and it’s obvious how urgent is the need for an Rx for CX.
Because AI is becoming instrumental to customer satisfaction, adopting AI has become an urgent priority for many organizations. AI enables a business to understand its customers better and do so more quickly. This capability feeds into an improved overall customer experience.
AI For The Service Desk And Help Desk
AI benefits the service desk and help desk at most levels -- and the benefits are quantifiable in terms of lower cost and operational gains. AI can trim mean time to resolution (MTTR) and can lower overall costs across the board for customer support and services. By automating, companies can resolve customer requests autonomously; in one survey, 63% of respondents said they prefer messaging an online bot to communicate with a business or brand.
Using AI, a business can create and personalize messages in such a way that “mere” humans cannot approach. Employing AI to engineer intelligent virtual customer assistants, a company can boost customer experience levels. Service agents can automate support and operations tasks -- and they may welcome the relief from repetitive, manual chores.
Where To Start?
It’s smart to begin by developing a customer experience vision to guide you through the details and the weeds. Put together a team and choose a specified CX leader. Also, an effective adoption strategy demands an understanding of the customer journey. That means your business must gain a deeper understanding of the entire relationship -- identifying and studying touch points from pre-sales to post-sales for customer success. This insight provides a foundation for using AI to improve that customer journey.
Can AI use simple English and not a “command-oriented” interaction? How can AI be conversational and dialog-driven -- and make for a more natural interaction with humans? These arising questions make one fact clear: AI-enabled customer service should go beyond a mere question-and-answer exercise. Instead, AI should be able to engage with humans in multi-step conversations while comprehending broader intents, sentiments and key messages.
An AI-driven customer service solution should encompass, drive and encourage user engagement. It must understand what users do not know and communicate useful, relevant information to these users. It should be able to help users acquire insights even beyond the direct question they are asking.
What’s The Ideal Fit?
Companies should seek a cloud-native solution that can leverage user and service behavioral intelligence using intelligent process automation. This solution should autonomously resolve repetitive customer service requests, tasks and complex workflows. The solution should be omnichannel and integrated through conversational AI, natural language understanding search and unsupervised natural language processing to speed the remediation of requests and ticket resolution. The ability to send automated responses to customers will allow those people to succeed in serving themselves, which can boost customer satisfaction.
Overcoming Hesitation, Roadblocks And Barriers
It’s only natural that challenges and barriers arise as a company embarks on a profound evolution. Implementing AI solutions raises many complex challenges that deal with security, regulations, enforcement, ethics, liability and so forth. Sometimes there are solid reasons for using caution and even slowing down AI adoption while working out these stumbling blocks.
Be sure to take note of a few points here. In the event of an error by an AI solution, does a chain of accountability exist and provide a path to resolution? Another concern is the need for valid use cases to generate sufficient underlying data to establish the “ground truth” that can support AI scaling. Also, are there reliable ways to measure results, such as a feedback loop capable of dealing with potential algorithm bias, for example? Results may depend having access to high volume, unbiased and diverse data on which to base decision-making.
Three barriers can potentially slow enterprise deployment of AI:
1) Many intelligent automation solutions are professional-services-driven, and many issues can arise because of this.
2) You will need extensive upfront training to create algorithms and models (which may require manual labeling data).
3) Hiring data science talent is difficult.
Keep in mind, though, that most enterprises face these barriers when deploying intelligent automation technologies for the sake of digital transformation. In addressing many of these and similar concerns, companies should use out-of-the-box AI solutions when possible. Software as a service and cloud services minimize upfront training and require no human intervention. You can also use self-learning algorithms, reinforcement learning and unsupervised AI/machine learning solutions so that training won't have to take place in your environment.
How Well Is It Working?
Measure accurately to get the truth about your results. That means finding the right metrics and key performance indicators to get a clear picture of your AI customer service initiative. Look for those projects that enable easier tracking and present a clear picture of success (or failure). These can help you blaze a trail for broader initiatives.