There’s a growing belief that AI’s next “killer app” lies in customer service. This area benefits both agents and customers, highlighting AI’s potential to enhance experiences and streamline operations on both sides of the service equation.

Why is customer service AI’s next focal point? If you ask ChatGPT this is what it says:

1. Scalability and Efficiency: AI handles vast customer interactions simultaneously, outperforming human agents.

2. 24/7 Availability: AI systems provide constant global support.

3. Personalization: AI algorithms tailor responses to customer data.

4. Cost Reduction: AI automates routine inquiries, reducing labor costs.

5. Data-Driven Insights: AI analyzes interactions for customer behavior insights.

When you look at the impact in totality, it looks and sounds more and more like a service reinvention. A lot of the initial impetus was capital hungry startups using chatbots to automate online support. Enterprises quickly saw the value. Over the past decade, chatbots that providers like ServiceNow and Conversica (a supplier of chatbot solutions to ServiceNow among others) offer to enterprise customers and technology companies, for both customer and revenue support, have become ubiquitous. It’s become clear as time has gone on and the technology has evolved that people want to talk to a chatbot as much as they want to deal with voicemail trees: they don’t. They want the fastest path to resolution of the issue they are contacting you about. That was an impasse.

Thus, it’s the combination of chatbot technology (really automation right now, not AI) with generative AI that is transforming the industry. Imagine a chatbot with human response capabilities, trained on internal customer data.

That is essentially what ServiceNow has integrated into their customer support function of its platform. It’s early days, but the demo we saw at their customer conference was impressive.

AI’s Revolution in Customer Service

At the customer conference, we watched a customer service rep go into customer history, access information from different databases to resolve an issue, and actually be able to change the way they looked at and interacted with that data. Just watching in the audience, as a longtime user of enterprise technologies, was exciting. Enterprise tech is not known for its flexibility or UX.

We were shown the example of a customer getting a refund for a coffee, almost instantly, and watched as a Chat GPT-like generative AI session enabled a service rep to access multiple databases, widen a data view out from a couple of datapoints over a few days to several over a longer term, and get a refund processed almost immediately. Multiple databases at the same time, not twelve different reports stitched together from four systems? Expanded data views … instantly? Refund processed … instantly? If you’ve been in enterprise tech for as long as I have, that looks nothing short of miraculous.

This was one of the first examples I have ever seen of a user being able to not just access data, but adjust the way they were seeing and accessing that data. Normally any change for one user would mean the same change would have to occur for all users. Generative AI is changing that experience and it may be a more significant change than the customer experience, and it is moving automation past first level support. Chatbots can potentially go beyond level one and provide problem solving and information. These AI-driven innovations include:

1. Chatbots and Virtual Assistants: AI tools that handle FAQs and guide customers, and take initial input about reasons for service and support calls.

2. Predictive Service: AI can anticipate necessary responses to customer issues and deliver them from a chatbot for proactive action.

3. IoT Integration: AI receives real-time data for accurate support for hardware and monitors.

4. Advanced NLP (natural language processing): AI systems communicate more accurately and naturally.

5. Sentiment Analysis: AI adjusts strategies based on customer tone and sentiment, or agent tone and stress level.

The implications of these technologies integrated into a better offering for customers are enormous. The implications for workers are even bigger. Ironically, the customer service revolution had often come at the expense of front line employees, who are often subjected to anger, frustration and abuse that goes far beyond any actual support issues. And they are much more than transactional.This can take an enormous emotional toll. Now there is also evidence that call center workers are abusing customers. Technology can help address this, with deescalation techniques, the ability to access and use data with much more control, and techniques to mask emotion.

AI will likely dramatically influence the following aspects of customer service:

1. Enhanced Customer Experience: Customers can expect faster, more accurate, and personalized service.
2. Workforce Transformation: The role of human customer service agents will evolve, focusing on more complex, nuanced customer issues and interactions.
3. Data Privacy and Security: As AI systems handle more customer data, ensuring privacy and security becomes crucial.
4. Ethical Considerations: The use of AI in customer service both helps address some bias and also raises questions about transparency, bias, and the ethical use of customer data.
5. Economic Impact: While AI can reduce costs, it also impacts labor markets. There could be a shift in job roles, requiring new skills and training.
6. Customer Trust and Reliability: Building trust in AI systems is essential, as is ensuring their reliability and accuracy.
7. Regulatory Compliance: Companies will need to navigate evolving regulations regarding AI, customer data, and privacy.

ServiceNow’s AI deployment includes proprietary LLMs (Large Language Models) tailored to internal workflows, including both general-purpose and domain-specific models. They also offer access to Microsoft Azure OpenAI Service and OpenAI API.

Implications of Cisco’s AI Tone Autosensing and Overlay:

Cisco, meanwhile, is introducing AI innovations to improve the conferencing-in and work from home experience. Their most advanced AI deployment, not seen live, is an autosensing function that adjusts speakers’ tones in real-time, compensating for fatigue or unintentional non-verbal cues which can significantly impact the effectiveness of communication.

How does it do this? They would have had to have built up an enormous LLM trained on tonal libraries of billions of clips of voices at various stress levels (TV? Film? interesting to think about the training data needed here) and various emotions, then created a voice filter that overlays real time the same way a Zoom or TikTok appearance filter works real time, overlaying on someone’s voice when it deviates from an established set of tonal stress parameters, which you can probably set.

Pretty wild.

There are both promising and concerning possibilities here:

1. Enhanced Communication: For professionals, especially executives, maintaining a certain level of engagement and energy in communication is crucial. This AI can ensure that their tone remains consistent and effective, even when they are tired or not fully attentive.
2. Authenticity Concerns: There will be discussions about the authenticity of communication when AI alters a person’s tone. It raises questions about whether the listener is getting a true representation of the speaker’s emotions or intentions.
3. Impact on Professional Interactions: In high-stakes environments like strategy sessions, where miscommunication can have significant consequences, this technology can be invaluable. It ensures that the intended message is conveyed effectively, reducing the risk of misunderstandings.
4. Emotional Intelligence Enhancement: This technology could be seen as an extension of emotional intelligence, where AI assists in maintaining the appropriate emotional tone, possibly leading to better team dynamics and understanding.

Meanwhile at small business email marketing provider Constant Contact, one AI deployment has been in place for years, nearly a decade, while other new applications are just gaining steam.

Email Marketing: AI Pioneers with Multiple Deployments

The digital advertising and email marketing industries have been pioneers in machine learning, using data to personalize and customize ad targeting and email campaigns since the 2000s. The “spaminator” as CEO Frank Vella calls it, has helped the company’s customers score messages’ likelihood of hitting inboxes for years. “It’s the subject line, the message, your list … if we see your list grew from 1000 to 10,000 subscriptions in a week you might be getting a call to make sure that is all valid growth … the industry has been using A.I. to identify spam and personalize messages for a long time.” The company is now also embedding a publicly available generative A.I. for message and copy creation into customers’ message development workflow. But otherwise, AI at constant contact is an internal deployment trained on internal data and that is a source of pride and differentiation for the company, according to Vella.

Conversica’s AI-Powered Conversational Tools:

Conversica provides human-like conversational software and Intelligent Virtual Assistants to engage customers. They use a multi-model Conversational AI platform for human-like interactions, enhancing lead engagement and qualification. Their Revenue Digital Assistants work alongside human teams, boosting workforce potential.

Conversica’s journey from AutoFerret.com to a leader in Conversational AI reflects AI’s increasing importance in business. They’ve expanded their customer base and raised significant funding, indicating AI’s growth in modern business practices.

The State of AI: Generating Real Results and Revenue

These stories show AI generating real results and revenue, being deployed in creative, strategic ways. Companies use multiple AI types simultaneously, from generative AI to personalization, autonomous agents, and chatbots. We’re seeing growing sophistication in AI adoption, starting to replace traditional roles. The first automation wave was about direct function replacement. With AI, it’s finding more intelligent functioning ways. The ServiceNow data liberation story is transformative not just for customer service but for employees too. 

The Big Picture Future

Imagine these instances managed by a central intelligence, possibly replacing human roles in the equation. This future is close, with unquantified social implications. The immediate risks are AI-driven warfare and employment losses, potentially leading to social destabilization. Basic income might be the long-term answer, hopefully realized before significant turmoil in the job market.

Written with support from chatGPT

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