Contact centers are balancing rising customer expectations for speed, accuracy, and personalization against increasing complexity and cost pressure. AI is helping organizations meet those demands at scale. At the same time, the human elements of experience, including trust, understanding, and emotional connection, continue to shape how customers judge interactions and how brands earn loyalty. The result is a new CX equation: one where technology accelerates what’s possible, and empathy still defines what truly resonates.

Rather than viewing empathy and automation as opposing forces, many CX leaders are exploring how both can coexist within modern service ecosystems.

Empathy Has Always Been Part of CX

Customer experience has always involved an emotional dimension. Regardless of industry, customers typically arrive with some combination of urgency, uncertainty, frustration, or expectation. How those emotions are acknowledged often shapes whether an interaction is remembered as positive or negative, even when the outcome is identical.

Historically, this emotional work was carried out almost entirely by frontline teams. Agents listened, interpreted tone, adjusted their approach, and made decisions in real time, all without extensive automation support. Today, that dynamic is shifting. AI systems can now:

• Detect sentiment in voice and text interactions
• Adjust conversational responses dynamically
• Predict escalation risk
• Surface relevant history to agents in real time
• Automate post-interaction summaries and follow-ups

These capabilities create new possibilities for how empathy is delivered, supported, and measured within CX operations. What once depended solely on individual skill now often involves a partnership between human judgment and machine intelligence.

Artificial Empathy: Simulation, Support, or Substitute?

One of the most visible developments in this space is “artificial empathy,” AI systems designed to recognize emotional cues and respond with language that signals understanding, concern, or reassurance. In live chat and virtual assistance environments, empathetic phrasing has been shown to reduce customer friction, de-escalate tension, and improve conversational flow.

From an operational perspective, this capability can:

• Improve containment rates without sacrificing tone
• Create more consistent service language across interactions
• Reduce the emotional burden on agents for routine inquiries
• Enable 24/7 responsiveness at scale

At the same time, artificial empathy remains a simulation. AI does not experience emotion; it identifies patterns associated with emotional expression and generates responses based on training data and probabilities. For many routine or low-stakes interactions, this distinction may be irrelevant to outcomes. Customers often prioritize speed, clarity, and resolution above all else.

For other interactions like those involving financial hardship, health concerns, account recovery, or emotionally charged disputes, the presence of a human remains vital to many customers. In these moments, empathy is not simply linguistic; it’s expressed through judgment, discretion, and shared understanding of context that current systems cannot fully replicate.

As a result, organizations are increasingly designing hybrid models in which artificial and human empathy coexist.

Human-Centered CX in an Automated Environment

As automation handles an increasing share of routine interactions, the nature of human-agent work is changing. Contacts that reach live agents today tend to be:

• More complex
• More emotionally charged
• More policy-driven
• More likely to involve exceptions or escalations

This concentration effect has significant implications for workforce design, training, and support. Human-centered CX in an AI-enabled contact center often focuses on:

1. Agent Enablement

AI is increasingly used to support agents with real-time guidance, next-best-action prompts, compliance monitoring, and automated documentation. The goal is not to replace empathy, but to remove the friction that prevents agents from focusing on the customer.

2. Cognitive and Emotional Load Management

With higher-complexity interactions becoming the norm, agent well-being, recovery time, and emotional resilience become operational concerns.

3. Skills Evolution

The value of human agents is shifting from information recall to interpretation, judgment, negotiation, and relationship management. These are skills that remain difficult to automate at scale.

In this environment, empathy becomes less about repeated soft phrases and more about situational awareness, discretion, and trust-building in moments that truly matter.

Empathy as a Design and Operating Principle

Many CX leaders now approach empathy not only as a frontline behavior but as a system-level design principle. This shift influences how AI is implemented across the service ecosystem.

Organizations taking a human-centered design approach often focus on:

• Journey orchestration: Defining when automation is optimal and when human engagement should be prioritized
• Escalation design: Building intentional handoffs between virtual and live agents
• Context preservation: Ensuring customers do not repeat themselves across channels
• Transparency: Clearly signaling when customers are interacting with AI versus a human

From a governance standpoint, this also affects how success is measured. In addition to operational key performance indicators (KPIs), many organizations assess:

• Repeat contact rates
• Escalation patterns
• Sentiment trend shifts
• Customer trust indicators
• Brand perception following automation changes

Empathy, in this sense, becomes embedded in both experience architecture and performance strategy.

Transparency and Trust in AI-Driven CX

As virtual agents become more conversational and emotionally responsive, questions about transparency have become more critical. Customers increasingly encounter AI systems that resemble human interaction in tone and pacing. While this can improve usability, it also introduces new considerations for trust.

Many organizations are now establishing principles around:

• Clearly identifying AI-powered interactions
• Explaining handoff paths to human support
• Defining guardrails for emotionally sensitive use cases
• Ensuring customers retain access to live assistance when needed

These practices are not uniform across industries, but they reflect a broader recognition that transparency itself is a component of respectful, human-centered experience design.

Organizational Alignment Matters as Much as Technology

Technology alone does not determine whether a customer experience feels empathetic. Organizational incentives, policies, and performance metrics play a defining role.

In environments where speed, cost containment, and deflection dominate success measures, even well-designed AI systems may inadvertently deprioritize emotional quality. Conversely, organizations that align automation strategy with customer trust objectives often approach AI deployment differently, using automation to absorb transactional strain while reserving emotionally complex interactions for human teams.

This alignment extends across:

• Workforce planning
• Agent training
• Vendor selection
• Experience design
• Leadership priorities

Empathy in modern CX extends beyond how conversations sound to how decisions are structured behind the scenes.

Blended Experience Models

What’s emerging across many contact center environments is neither a fully automated future nor a return to human-only service models. Instead, CX leaders are building blended experience ecosystems in which:

• AI manages scale, speed, and consistency
• Humans manage judgment, nuance, and emotional complexity
• Data flows unify the experience across channels
• Design decisions shape where empathy is delivered artificially, humanly, or in combination

This blended model acknowledges both the practical advantages of automation and the enduring importance of human connection in moments of friction, vulnerability, and decision-making.

Empathy in the Next Chapter of CX

As AI becomes deeply woven into customer experience operations, empathy is not disappearing; it is being redistributed across digital and human touchpoints. The question for CX leaders is no longer whether empathy matters in an automated environment, but how it is defined, supported, and delivered at scale. The answers will differ by industry, customer base, and risk profile. Some organizations will lean heavily into virtual engagement. Others will preserve high-touch human service as a brand differentiator. Most will continue refining hybrid approaches as technologies and customer expectations evolve.

What remains consistent is this: trust, understanding, and emotional resonance still shape how customers evaluate their experiences. In an ecosystem increasingly powered by algorithms and automation, how those human elements are designed into the system will continue to influence loyalty, reputation, and long-term value.

Continue the Conversation at Customer Response Summit (CRS)

The questions surrounding AI, empathy, and the future of human-centered customer experience don’t have simple answers, and they’re best explored together.

Join CX leaders, practitioners, and technology innovators at CRS, February 25–27, 2025, at The Ritz-Carlton, Amelia Island. Through mainstage keynotes and panels, case studies, and peer-led breakout discussions, CRS offers a space to examine how empathy, automation, and performance intersect in today’s contact centers. We invite you to be part of the dialogue shaping what comes next.

Learn more and register today.

The post The New Architecture of Empathy in Customer Experience appeared first on Execs In The Know.

Fueled by the rise of artificial intelligence (AI), organizations are now integrating a new kind of workforce; one that isn’t human, but incredibly capable. Meet agentic AI, an intelligent digital teammate designed to make autonomous decisions, execute tasks, and collaborate alongside human agents.

This isn’t about replacing people. It’s about evolving how work gets done. And it’s happening faster than you might think.

Digital Teammates, Not Just Tools

Agentic AI is more than a support bot or knowledge assistant. It’s a system with agency, the power to act independently toward a goal. These AI agents can assist, co-pilot, or even fully complete customer service tasks, depending on the use case.

Whether surfacing real-time knowledge to a human agent or independently resolving password resets and refunds, agentic AI is designed to drive outcomes while integrating seamlessly into the contact center environment. It’s not just doing tasks, it’s reshaping roles.

Rethinking the Frontline: A New Operating Model

With this new teammate on the floor, it’s time to revisit the operating model. The future of contact centers will depend on how effectively AI and humans can work together. That means defining clear collaboration patterns:

• AI Assist: AI supports the agent with suggestions or workflows.

• AI Lead, Human Review: AI does the heavy lifting, but agents approve key decisions.

• AI Complete: AI independently resolves simple, repetitive issues.

Mapping these models across your contact landscape builds trust, clarifies responsibilities, and helps both AI and humans shine in their strengths.

Redefining Human Roles

As AI takes on more routine work, the human role becomes more critical, not less.

Agents are freed up to focus on emotionally complex or high-stakes interactions. They become the empathy engines and strategic problem-solvers of the contact center. But to thrive in this new model, their roles must evolve.

Upskilling is essential. Think training in:

• AI literacy

• Emotional intelligence

• Data fluency

• Critical thinking

• Collaboration with AI tools

New roles will also emerge: AI operations manager, conversation designer, governance lead. Investing in these areas isn’t optional. It’s table stakes for brands that want to stay competitive.

Cultural Shift Required

Let’s be honest: AI can feel threatening to frontline employees. And when fear enters the conversation, adoption stalls.

That’s why a substantial change leadership plan is essential. Integrating agentic AI is not a tech upgrade; it’s a cultural transformation. Transparency, inclusion, and communication must be front and center.

Leaders should actively:

• Involve employees early

• Clarify role changes

• Offer reskilling pathways

• Create space for feedback

• Celebrate AI-human wins

When employees are part of the process, they become advocates instead of skeptics.

Governance Matters

With power comes responsibility. Autonomous AI systems must be governed carefully, with ethical guidelines, escalation protocols, and human-in-the-loop controls in place.

Ask yourself:

• What are our guardrails for AI autonomy?

• Who’s accountable if something goes wrong?

• How are we protecting data privacy and compliance?

• What metrics will we use to track trust and performance?

Don’t just launch AI, monitor it. Improve it. Keep it aligned with your values.

Pilot. Learn. Scale.

Before transforming your entire contact center, start small.

Run pilot programs with measurable use cases. Monitor performance, gather feedback from agents and customers, and adjust your approach. Use this as a test-and-learn period to refine your AI-human model before scaling.

Start by evaluating:

• ROI potential

• Workflow complexity

• Employee readiness

• Customer impact

According to Gartner, agentic AI could autonomously resolve 80 percent of customer service issues by 2029, reducing operational costs by 30 percent. That’s not a futuristic dream, it’s a near-term opportunity.

Is Your CX Team Ready?

Agentic AI doesn’t just automate, it redefines how we deliver care at scale.

Done right, it protects the customer experience while unlocking new levels of efficiency, consistency, and growth. But to capture its full potential, you’ll need more than a shiny new tool. You’ll need a vision, a roadmap, and a team ready to lead change.

Because CX is won or lost on the frontline, and the frontline is being transformed.

Want to dive deeper into the strategy behind agentic AI? Read the full article in July’s issue of CX Insight magazine for expert insights, practical frameworks, and real-world examples of brands embracing this emerging technology.

The post How Agentic AI Is Rewriting the Rules of the Contact Center appeared first on Execs In The Know.

Here are four examples of problems that occur when automation goes wrong and how to avoid them.

1. Automation goes wrong when you lack human involvement.
   Sometimes, customers need to speak to a real person who can empathize with their situation, provide personalized solutions, or escalate their issue to a higher authority. You risk losing their trust if your automation system does not allow customers to opt for human assistance or transfer to an agent when needed.
2. Automation goes wrong when your AI solution can’t determine intent.
   Customers may have complex or ambiguous queries that require more than a simple yes/no answer. Automation systems that force customers to repeat themselves multiple times to understand their intent can lead to frustration and lower CSAT and NPS scores.
3. Automation goes wrong when a human can’t listen in on a call.
   Automation systems may encounter technical glitches, errors, or unexpected situations that require human intervention. If your automation system does not have a monitoring mechanism that allows human agents to listen in and intervene, when necessary, you may miss opportunities to resolve issues quickly and effectively.
4. Automation goes wrong when used in place of a conversation with a human agent.
   A 2020 NICE inContact survey found that 91% of consumers want to speak with a live agent when they have a complex issue. Disrespecting their preference can alienate customers and damage your brand reputation.

To avoid these pitfalls, design your automation system with the customer in mind. That means playing by the rules of conversation and building trust. It also means balancing your automation system with human agents who can provide customers the personal touch and support they expect and deserve.

The post What to Do When Contact Center Automation Goes Wrong appeared first on Execs In The Know.

The potential lies in pairing the natural language capabilities of a generative AI model like ChatGPT with an AI solution that can also learn and perform actions. Compared to chatbots, which simply present information available elsewhere before transferring more complex cases to an agent, a generative AI solution could facilitate an entire interaction with a customer—even a highly complex problem or an issue that is not well defined by the consumer.

Why GenAI Alone Isn’t Enough

Many businesses rushed into adding chatbots on their sites with the goal of cutting costs. They assumed bots would deflect calls from coming into the contact center, thereby decreasing labor expenses. But chatbots can’t actually do anything other than present basic information a customer could likely find elsewhere, so these companies ended up paying for chat responses that didn’t fully solve customer inquiries—the bots still had to pass customers to the agents to complete any necessary actions.

ChatGPT now offers powerful natural language processing and can comprehend even highly detailed plain-language questions, but even those well written responses are likely offering information that can be found elsewhere by customers—and it may even be completely wrong. ChatGPT is designed to write very compelling text, but accuracy is not guaranteed or even a priority of the model. Brands need to ensure that any information coming directly from their representatives is a source of truth. This is why you can’t just open up a ChatGPT window on a contact center agent’s desktop and leave them to it.

Without properly training the AI, putting in guardrails to prevent incorrect information and off-brand or inappropriate responses, and adding the ability to take action, ChatGPT doesn’t add much more value that the traditional chatbot—and can, in fact, present significant risk. Still, it’s an incredible breakthrough and companies are very quickly learning how to use it for its advantages, adding technology and process guardrails to harness the power of its natural language processing.

Creating the Contact Center Digital Worker with GenAI

When contact centers implemented chatbots, many imagined deflecting a significant amount of live interactions—say, 20% fewer calls answered by agents. However, even if they achieved that level of deflection, chatbot results would inevitably plateau. Now imagine, instead of deflection, making each live agent 20% more efficient. And imagine that efficiency improves over time, reaching 25% more efficient, 30%, 50%, and beyond. That’s what generative AI has the potential to do in the contact center.

The biggest limitation in machine learning and AI is data. GPT represents such a major breakthrough because of the sheer volume of annotated data used to train it. When you transfer that technology to the contact center space, where it can observe and learn from human agents, and pair it with intelligent automation, you get a digital worker that can deliver higher concurrency, shorter handle times, and a higher level of precision.

It’s not tomorrow’s technology. It is here today.

Guest blog post written by Laivly. To learn more about this topic and others, visit the events page to check out all of our upcoming events.

The post How Generative AI Succeeds Where Chatbots Failed appeared first on Execs In The Know.

Personalization has moved from hype to hyper over the years and will take on another remarkable resurgence. Crunching customer data is at the crux of it all, and applying data insights to decisions is the most challenging barrier to the success of a hyper-personalization strategy for 53% majority of marketing influencers, according to a report by Ascend2. In a marketplace where anticipating and surpassing customer expectations is the norm, brands need to connect meaningfully and engage in authentic conversations. Here are some pointers to consider.

Embracing scalable and flexible APIs

Service providers need to craft hyper-personal messages to their customers in real-time. Digital-first enterprises should move away from disintegrated and modular technology stacks that are proving to be a disservice to the end customers today. Considering how customer needs and digitalization have evolved, enterprises need to have a holistic API strategy that is scalable and cloud-driven. Regardless of what channel a customer uses, AI-infused personalized APIs are highly likely to deliver seamless support. Through programmable communications, enterprises can leverage API frameworks that support omnichannel customization across the customer journey at any channel in real-time.

Voice and messaging APIs are at the core of creating programmable communications that drive interactions. Prime examples include those in domains such as retail and banking, where online merchants communicate with shoppers right from their identification to purchase and delight them with a discount for their next purchase through messaging or other channels. Banks leverage APIs for their messaging, authentication, account details, payment, and loyalty programs.

According to Forbes, enterprises already down the road of API development for digital transformation need to relook at how their ecosystems operate and squeeze real strategic value from them. CX practitioners need to get strategic about their APIs, considering the current omnichannel environment, proliferation of connected devices, need for deep insights, and the importance of heightened data security.

Personal versus personalized

The already-tensed line between privacy and personalized experiences is getting tenser. With a robust data-driven path to tailoring individualized experiences, enterprises do a world of good to customers. Ill-informed data-driven approaches risk violating customers’ trust. According to Gartner, customers desire contextualized and personalized experiences but expect their data to remain secure and private.

The 2022 Gartner Customer Service and Support Survey reveals that 71% of B2C and 86% of B2B customers expect companies to be well-informed about their personal information. Privacy concerns highlight issues around data breaches and ethics. CX practitioners must keep an eye on local regulations (such as the GDPR or CCPA) that keep redefining standards around how data is collected, stored, and used.

Humanizing the experience

Customers seek a personal touch. In most support scenarios, the bot tries to resolve the issue. Despite cases where the issues are successfully resolved by a bot, returning customers would always desire to interact with a human agent. The clarion call is to bring in the spirit of empathy to CX, and it will take time and effort for the “empathy factor” to permeate the mainstream. Great relationships surpass outstanding transactions.

Customers love resolving issues quickly by themselves. With the help of conversational commerce (AI bots, messaging, voice assistants) infused with contextual and personalized recommendations and human-like interactions, service providers can handle multiple cases with minimum human interaction and boost brand loyalty. In a website, for example, effective chatbots zone in on what the customer is searching for and provide relevant help with convenience and speed. We see novel NLP models crossing new frontiers today, and this could chart a new path that organizations are likely to follow in the future. The demand for “interactions on the fly” will increase, and smart conversational commerce is the way to drive these conversations.

A long-term strategy would be a flexible and scalable gig support model. Consider the case of Movate’s OnDemand network of global gig experts who are specialists or product practitioners, and they in turn support customers with relevant advice. Explore a blended model that complements full-time agents with a gig support model that flexes based on demand fluctuations. Personalized human connections are possible with an on-demand, gig-based support model that provides relevant solutions to struggling customers. The result is empathetic support that leads to tangible cost savings.

Tips for a hyper-personalized strategy

Understanding customers individually is critical, and a hyper-personalization strategy lends a competitive edge to a brand. For example, proactively knowing if a customer is undergoing financial duress and is likely to cancel a subscription helps brands to offer customized plans at a competitive price to that customer. Top tips include streamlining the process of aggregating relevant and precise data via an automated engagement program. Based on reliable data, segment customers into sub-sets such as average spending, interaction history, CSAT levels, and other sub-sets. Next, target customers with hyper-personal interactions across omni-channels or a customer’s specific channel of choice. Gauging the success of targeted campaigns is vital for monitoring metrics like retention rates and revenues. With the increasing number of data breaches, it’s worth reiterating the value of privacy protection to guard against negative brand perceptions. Brands must ensure customers know how their data is being collected and utilized with the option to have complete transparency in their profiles. Helping customers view and manage their data boosts trust and reputation.

Brands have never been more reliant on digital channels than in today’s era of digital acceleration. In a 2023 economy witnessing recessionary headwinds, brands need to connect meaningfully with customers and employees. Hyper-personalization’s finest hour is here, and brands need to get “hyper” about personalization to maximize revenue, reduce cost and churn, and elevate CX. Now is the time to craft moments that matter.

Guest blog post written by Sid Victor, Head of Support Services at Movate. Movate is a digital customer experience services company disrupting the industry with agile human-centered innovation and an outcome-focused approach.

To learn more about this topic and others, visit the events page to check out all of our upcoming events.

The post Personally Yours: Getting All “Hyper” About Personalization appeared first on Execs In The Know.

Because the use of bots across digital channels is skyrocketing, there’s a pretty good chance consumers have had multiple bot experiences. Good, bad, or indifferent. Why settle for anything less than a great bot experience for your customer though. In this post, we’ll show you what the interaction looks like for your customers, but first, let’s take a closer look at why it’s important to create a great bot interaction.

Why Develop A Great Bot Interaction

Even before COVID, bots were growing in popularity. Salesforce predicts a 136% growth rate in the use of AI chatbots and according to Gartner, by 2020, around 85% of our interactions will be handled by bots instead of humans. This is not because they have replaced humans entirely, but because they have augmented humans by taking care of the tasks that don’t require a human to complete.

More companies are turning to bots to facilitate a variety of common or repetitive tasks, to free up agents to handle more complex interactions. Bots can be integrated into a spectrum of workflows across an organization, like customer referral programs or promotional offers. Whether it’s a bot that greets your customers and gathers data to route them to the best suited agent or queue, or a bot that presents offers based on past purchases, bots can do a lot of heavy lifting.

In short, as bots become more integrated into your processes and workflows, interactions with your bots become intertwined with your brand. Just like training any new employee, you’ll want your bot to deliver against your brand values and voice. Every company is different, but there are a few key characteristics that are consistent with a great bot experience.

Characteristics Of A Great Bot Experience: Make It Purpose-Built

It’s important to remember that your bot will only be as good as your configuration of it. Thoughtfully planning out your bot flow before you jump into any bot designer is critical. Here are a few considerations you should keep top of mind when designing your bot.

• Conversational
  While the customer should know they are chatting with a bot, it shouldn’t feel like they are interacting with a machine. If your agents would normally say “Hey! Glad you’re here. How can I help you?” configure your bot to do the same. Ensure the bot mirrors your brand voice. Keep questions clear and simple with short suggested answers to make it easy for your customer to understand and respond.
• Efficient
  A great bot experience will usher the customer through a frictionless workflow with the shortest path possible to the answer they’re looking for. Design a bot that can transfer conversations to other queues, agents, or even other bots if necessary.
• Intelligent
  Just because it’s artificial intelligence doesn’t mean you can’t make your bot genuinely smart. Teach your bot industry terms, synonyms for common words, and alternate ways a customer might ask a question. A bot can deliver a great experience by being emotionally intelligent too. Enable sentiment analysis to understand your customers’ tone and general attitude during an interaction to deliver a personalized experience, or to automatically escalate to a live agent when necessary.
• Integrated
  Integrate your bot with other backend systems and/or your CRM. Make it easy for your bot to retrieve information that your customer needs to resolve their inquiry. Bots shine when it comes to retrieving information quickly. Finding an order number, sending a link to track the status of a shipment, or helping a customer reset a password are the easy, low effort requests that can be frustrating for customers to call or email about.
• Omni-capable
  Your customers aren’t using just one digital platform. Design a bot that can converse across multiple digital platforms. Keep it consistent by building one bot and enabling across multiple digital channels such as web chat, SMS/text, and social media.

Set The Chatbot Bar

Each experience your customer has with a chatbot creates their expectations of what the next experience should look like. Designing with the features above in mind can help you set the bar high for chatbot experiences.

If you’re ready to implement chatbots, or would like to learn more about this topic, register for the REPLAY of the June 9, 2020 webinar, “Embrace and Activate Digital Customer Engagement for Future Success.”

­­­­­­­­­­­­­­­­­­­­­­­­Blog post, written by Quiq
Quiq helps brands present their customers with consistently jaw-dropping customer experiences across SMS/text messaging, rich messaging, web chat, and social channels.

The post 5 Key Traits Of A Great Bot Design appeared first on Execs In The Know.

Lacking Necessary Resources

There is no out-of-the-box solution to implement intelligent automation in your organization. Companies seldom account for the IT infrastructure automation demands and are rarely prepared to produce data sets that are accurate and large enough for effective machine learning. Without these key ingredients, AI projects will fail.

The Fix: Before you invest in a vendor solution, ask the service providers you are scouting what you would need to use their tool. If data is a problem in your organization, you may be able to find a solution that leverages a different resource—one your company can provide.

Organizational Silos

Meeting resistance from other departments can stop your automation initiative before it gets out of the gate. Because customer experience touches so many areas of an organization, it’s vital to think through the consequences to each business unit before implementing the solution.

The Fix: Beyond involving the leadership in the departments who will be affected, training can be a great opportunity to get buy-in from the ground-level employees who will be using the AI solution. Instead of merely training workers on the tool, consider sharing the reasoning behind the automation and what benefits the company expects to get from it.

Choosing the Wrong Problem to Fix

Because intelligent automation is new to many companies, leadership is often leery of sinking too many resources into an AI project. If the initiative doesn’t live up to expectations, automation may suffer a setback at your organization. Too wide a scope, and your project will take years to reach completion. Too narrow, and it doesn’t make an impact.

The Fix: Create an initiative that will move the needle in the area your company is most focused on at the moment. If your organization is in cost-savings mode and you can use automation to reduce call volumes or the time spent on service tickets, it gives you the ammunition you need to use AI for other goals, like increasing customer satisfaction.

Not Planning for Governance

Who will be responsible for the care and feeding of your AI? You might make it past the early stages of an implementation only to see a project stall because necessary staff were pulled away. If this is your company’s first foray into intelligent automation, you can’t know for certain if it will be successful. But if it is, there will be maintenance needed.

The Fix: Forecasting the downstream effects—a different organizational structure, reduced employees, etc.—is critical to the continued success of your initiative. Communicating these effects to the leaders at your company and having an idea of what governance will look like will save you some heartache down the road.

Join us in Chicago on August 16 for our one-day workshop, Intelligent Automation for Customer Experience, and learn what other senior customer experience leaders are doing with their intelligent automation implementations, ask questions of top service providers, and see their solutions in action in our Innovations Lab.

The post Where Intelligent Automation Implementations go Wrong appeared first on Execs In The Know.

Click here to catch up on Part 1 – Customer Expectations, Part 2 – Channels, and Part 3 – Operations.

The fourth installment of this series focuses on thoughts on technology.

• AI will begin to automate tasks for the agent and the customer – however AI will take longer to mature than previously thought, due to data infrastructure not mature enough to fully leverage.
• Companies will begin to shift from handling transaction to personalizing service.
• Digital will continue to experience strong growth, powered by mobile usage.
• Messaging channels will continue to grow social volumes.
• More conversations around the “social ethics” of AI deployment.
• Continued push on technology – moving infrastructure to the cloud.
• Deep/machine learning will play a role in 2018 for brands to meet customers where they are. I think brands are starting to get on board with the concept of machine learning and using data to predict behavior. But deep learning can extend beyond things like chatbots. If a brand can predict what a customer might be looking for, it can tailor content to the customer that allows the customer to self-service, send push notifications, etc.
• Thinking about AI secondarily to the types of use cases and outcomes desired that require it.
• Companies will have to address AI and ML in their strategies for customer acquisition and engagement. 2017 may be seen as a year of hype as lots of new vendors emerge on the scene. Expect consolidation and actual AI/ML deployments to increase in 2018.
• Interest in – and deployment of – customer journey analytics tools will increase, as companies try to identify and solve problems upstream before they occur.
• Strategic enterprise automation (front to back office) will help businesses begin to improve the customer experience, while optimizing back office operations, leading to stronger sales and a better bottom line. Examples include automated machine learning/AI, self help, chatbots/avatars, to RPA and beyond.
• People continue to overestimate innovation in the short term and underestimate it in the long term.
• We will see the first practical examples of AI in the areas of contact center routing, compliance and quality, with practical use cases.
• Some brands are testing AI for knowledge management (ie. agent support tool powered by AI vs. a traditional knowledge management tool). A lower risk approach to testing AI vs. directly with clients.
• Over the next 3 to 5 years, I believe that the majority of inquiries that come into the contact center will no longer be voice or email, but some form of messaging. SMS is only one form of messaging: it encompasses a broad swath of methods including iMessage, Facebook Messenger, Kik, WhatsApp, and more. Many people are following WeChat, which is now one of the largest standalone messaging apps with over 963 million monthly active users. Part of the reason it has been so successful is that you can do more than just send messages. Apple is moving down the path with the announcement of its Business Chat messaging solution which is an extension of iMessage and will include some ApplePay capabilities. The convergence of these technologies will be what ultimately tips the scale to shift consumers and businesses away from phone and email to messaging.
• Maturity issue with emerging virtual assistance technologies (chatbots, AI capabilities, etc.), continues to lag – requirements for specific user case studies at an all-time high.
• Buzz and hype are high, but adoption is low. Brands realize they need more data in order to leverage these technologies to be accurate.
• I’m not convinced that brands have the capacity/talent/full understanding of what precisely goes into machine learning and true AI (beyond chatbots). I think we’re going to see a wave of failed adoption on that front.
• Technology – Proliferate of technology out there – from measuring CX, to ‘omnichannel’, to speech analytics.

Stay tuned next week for the final installment in the series Part 5 – Use Case Studies/Data and Security/Risk. 

The post 2018 Predictions for the Service Leader: Part 4 – Technology appeared first on Execs In The Know.

We surveyed a number of customer service/experience leaders, from many of today’s leading brands, in our community to get their predictions for 2018. Over the coming weeks we will be releasing their thoughts on specific CX topics including customer expectations, channels, operations, technology, use case studies/data, and security/risk.

Click here to catch up on Part 1 – Customer Expectations and Part 2 – Channels.

The third installment of this series focuses on thoughts on operations.

•Further advancements in the area of personalization of the experience. The ability to leverage “big data” sources to create a more personalized experience. Driving demand for analytic platforms, as well as analytic resources (both offshore and onshore).
• Expansion of the work at home (work anywhere) models for care support for cost savings. Advancements of tools and technology to support this.
• ‘Employee engagement’ truly changing culture.
• Customer service must be able to show ROI to adopt messaging channels and chatbots for customer interactions.
• Companies will begin to focus more on the entire customer lifecycle from acquisition, to sales, to service/support. The lines will blur between organizational siloes that focus on acquisition and those that focus on customer care, as businesses embrace a more holistic approach.
• Business leaders will get better at overcoming common, but large, digital transformation challenges (e.g., data silos, governance, systems integration, executive buy-in, acquisitions, technologies, security, etc). This will enable them to develop better customer insights that are actionable, to improve the customer experience, as well as optimize business operations.
• Businesses will drastically adopt and improve how they apply predictive analytics across the enterprise.
• Businesses will further embrace outsource service providers as strategic partners vs. traditional labor arbitrage.
• The usage of behavior profiles will go mainstream in marketing and customer service.
• Companies realizing that they need to center operations, technologies and practices around people and customers, not case numbers and ticketing systems. Leveraging modern technology to replace legacy systems will take time, but brands realize they need to start now to execute on their 3-5 year vision.

Stay tuned next week for Part 4 – Technology.

The post 2018 Predictions for the Service Leader: Part 3 – Operations appeared first on Execs In The Know.

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The following is a guest blog by Vijai Shankar – Senior Director of Industry Marketing at [24]7.ai.

Digital transformation. It’s disrupting every industry and impacting how you deliver a great customer experience. Companies that ignore it risk their very survival; but those that embrace it can dramatically improve customer satisfaction and drive greater value – without driving additional costs.

In the world of customer service and support, there is significant change happening from both a technology and consumer standpoint. Customers are more sophisticated and have increasingly higher expectations of experience from the companies or brands they interact with, while new and innovative technologies are enabling companies to transform how they provide support. From artificial intelligence-based chatbots to messaging applications, it is very important to clearly understand how different technologies can be used to transform the customer experience (CX).

Businesses in all sectors are beginning to realize the importance of digital transformation and are raising the bar for each other. At the recent Customer Response Summit in Chicago, organized by Execs In The Know, I observed and interacted with executives from brands and we all agreed on one thing: customer experience is the forefront of digital transformation, and as each brand improves their service and experience they set the bar for the rest, even across industries. The growth of mobile has put today’s customers in the “Experience Era” and businesses need to support customers across multiple, interconnected devices and channels, with the optimal customer experience. Natural language processing, artificial intelligence and machine learning are the foundation of digital transformation and exponentially drive the potential and value of virtual agents or chatbots now and into the future.

However, digital transformation isn’t a single fix to improving the customer experience today, and the transformation will need to happen with a practical, iterative approach. It starts first with businesses defining what truly is digital transformation and its fit in customer experience. Businesses also need to know how to separate facts from fiction and learn from the best practices and proven strategies, from previous successful transformations.

Sound impossible? Not at all. Lend us your ear for 60 minutes on Nov 14th, 2017 and you will learn all about digital transformation and its impact on customer experience. Register for a free webinar sponsored by [24]7.ai and hosted by Execs In The Know, and learn:

• Why digital transformation needs to be a focus in customer service.
• What digital technologies are being used by top companies to maximize customer satisfaction and drive revenues.
• How to take an innovative approach to transforming the customer experience.

Register now: http://www2.execsintheknow.com/digitaltransformationwebinar 

The post How to Transform Your Digital Customer Experience appeared first on Execs In The Know.