Tag: ai

Art & Craft

Robot waiters, chatbots, and AI image generation

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I love technology. I work in tech and I am a big consumer of tech in its various forms. But I’m not blind about the shortcomings of a lot of technology, and part of what I find fun about using new technology is comparing how it actually performs with the marketing bluster about it.

Yesterday I went to a hotpot restaurant which had a robot waiter. Not so much the sleek, cool and futuristic robot that you might have imagined if you were asked a decade ago what the robots of today would look like. This robot had a cat face, played muzak-style elevator tunes, and needed rescuing when it got stuck on the edge of a wall. It was cute and novel, but only capable of bringing out dishes and returning to the kitchen. I’m sure if the human cooks and wait staff there were asked if it made any significant impact on their workload, the answer would be a no.

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Learning & Training, Technology, Work

Intelligent Assistants (+7 Insights on How Agent-Facing AI Can Accelerate Training and Onboarding)

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How does your contact center handle new starter training and onboarding?

Most businesses fall into three camps:

  1. Trial by fire: give agents a manual to read, then throw them on customer queries and hope for the best.
  2. Outsource to colleagues: make training another agent’s responsibility, ask the new starter to shadow them, and expect the new hire to be ready in a week or so.
  3. Actual training: devoting resources (not just e-learning!) to coaching each employee to success, measuring and providing feedback along the way.

Option three is by far the best to develop happy, engaged staff, giving them what they need to become successful and confident before they get anywhere near customers. This investment in employee experience (EX) can be found at the foundation of all great customer experiences.

But great training or onboarding comes at a cost. Research suggests that for centers who invest in training, onboarding a new employee can cost upward of $14,000, with a new hire’s break even point for ROI not kicking in until week 22.

That’s a significant amount of time, resource and expense, and smaller businesses especially will know how tricky it can be to secure buy-in for these efforts. It’s still vastly better than trial by fire, where savings on training cost are dashed by poor quality customer interactions leading to dives in customer satisfaction and peaks in churn.

However your center trains your staff, you’ll know that there’s been a lot of talk about how AI can be used to improve customer-facing interactions – but there’s more to AI than meets the eye. The development of new technology means that it is possible for HR and contact center managers to augment traditional training and onboarding processes to help employees learn and access information in better ways than before.

Where does AI fit in the training and onboarding process?

Put yourself in one of your new agent’s shoes for a moment. It’s your first day, you’ve been introduced to your team, signed into your computer for the first time and you’re ready to start learning how to answer customer queries. 

For complex queries, it’ll take you a decent amount of learning to figure out when you’re making the right judgment call – those queries that fall into the gray-area of your organization’s rulebook where a good answer starts with “Well, it depends on…”

But for a lot of other queries, answers are more black and white. When it comes to getting comfortable with basic FAQs and straightforward inquiries, you’re not so much learning them as remembering the right sequence of clicks to get to find information or memorizing answers by rote.

While AI isn’t meant to help employees make tricky judgment calls, it can lighten the load of those straightforward queries when integrated into the systems that agents use in their day-to-day work.

Intelligent Assistants are a form of AI that can do this by integrating customer communication channels with your existing knowledge resources to present answers to agents, at the point they need them.

Equipped with natural language processing (NLP) and machine learning (ML) capabilities, Intelligent Assistants work by scanning text-based customer conversations and providing answer suggestions based on your internal or external knowledge bases, chatbot responses, and other knowledge resources you already have stored in text form.

These systems can even learn from customer interactions within the system, eventually building a response model that’s more robust than your recorded knowledge resources alone.

Just as you have everything you need to drive your car while sitting in the driver’s seat, locating key resources in the agent console has huge benefits – allowing for new starters to start using internal resources confidently and with speed, right in the window where they work. 

What other benefits does AI bring to the onboarding and training process?

We often talk about the necessity of eliminating friction in the customer experience, but we rarely think about what the equivalent might mean for employees.

It’s a reality that for customer-facing employees, getting the right answer to even black-and-white questions might mean fruitlessly consulting a FAQ page, then paper-based manuals, then your online knowledge base, and finally other colleagues, all the while knowing your customer is getting more irate the longer they’re on hold.

The beauty of integrating Intelligent Assistant AI within your communication systems means that you can draw on the combined wisdom of all of these resources and let the AI present you the best answers, no waiting required.

While much of what has been discussed so far is especially relevant to onboarding, Intelligent Assistants can even be helpful to train veteran agents during a new update or product release. 

Many organizations struggle with operationalizing knowledge management and obtaining resources to manage KBs. A lack of solid knowledge resources is a major reason why some companies don’t feel ready to start automating.

But the beauty of internal-facing AI is that you can give it exactly the same resources as you would give any new employee, or what you already present to customers, and start from relatively rough and humble beginnings without that ever impacting on the customer.

Intelligent Assistants only suggest answers that can be edited before sending, so if answers aren’t fully-formed or grammatically correct then they can be built upon by the agent. Agents can also suggest extra answers to the assistant for an administrator to add into the tool, improving its responses over time.

In this way, Intelligent Assistants can help to build stronger internal knowledge tools. They can reinforce a living knowledge management system, where agents interact regularly with a tool that can capture the best of their knowledge and expertise.

If you’ve ever tried to implement KCS or other knowledge management workflows within your centre, you’ll know that encouraging contact center employees to update knowledge resources alongside query handling can be incredibly difficult. There simply isn’t the time in their day to do so. But integrating those knowledge resources in the console where they work means that building robust knowledge resources suddenly becomes a lot easier.

7 insights for smarter onboarding and training

Like any AI investment, it pays to plan well from the inception of the project. The more time you invest in the initial set-up, the better the AI will work, and the more confident you can feel in your new employees with the software guiding them through customer interactions.

The three areas you need to consider the most when deploying Intelligent Assistants are the information it draws on, the deployment process, and a continuity plan. The insights below touch on each of these items, ensuring that quality of information is balanced with speed and cost benefits. 

1. Ensure your knowledge resources are up-to-date

Ask your team to check your existing resources to ensure they are up-to-date and don’t include any glaring errors.

Because Intelligent Assistants are able to draw on your cache of support tickets, previous chat transcripts, and they can learn from agent feedback, it’s not necessary to have a 100% robust knowledge library from the off – the system will become more robust over time.

You should, however, ensure that any information you feed your assistant isn’t outright wrong.

2. Plan the automation process

Be realistic about the types of questions your Intelligent Assistant will be able to handle.

AIs won’t be able to empathize authentically or grow real relationships with your customers – those are the things your agents shine at. Your agents are also best equipped to make the judgment calls on complex queries that really draw on their skills and expertise.

Select relevant queries for your AI to handle from your knowledge resources accordingly.

3. Communicate with your agents

In the same way, let your agents know the strategy and purpose for your Intelligent Assistant. Involve them from the earliest planning stage, secure internal champions, be open and transparent. Including agents from the design stage means that you’ll end up with a tool your team is brought into, and that won’t be perceived with fear or negativity.

You’ll also need to be clear about the types of questions that the tool is best equipped to handle by giving them some example questions so they can see where the boundaries lie. Introduce them to the feedback process within the tool, reward your best contributors, and consider whether you need to reinforce the new process with agent KPIs.

4. Add in knowledge resources and synonyms

Each question will need an answer, and you’ll need to add them into the tool accordingly.

One extra thing you’ll need to account for at this stage are synonyms or business-specific language that your customers and agents use. By adding in a number of alternate word definitions for the same term, for example: customers, clients, and members, your Intelligent Assistant will be able to better handle variations in language that your customers and agents use.

5. Test it, then test it again

Just like you would never want to throw a new employee into any task without making sure they know how to do it right; you never want to deploy any form of technology to your team without making sure it works. Is it fetching the right information? Are the workflows processing the correct information? Most importantly, is the AI helping your agents?

6. Create a maintenance plan

Just like keeping your resources up to date, making sure your AI is up to date is important. While the Intelligent Assistant will learn from customer conversations and agent feedback, any and all product updates, releases, and other new information or links still need to be programmed into the AI.

7. Tune and refine as you go

In the back end of your Intelligent Assistant, you’ll have access to a wealth of information to fine-tune your AI – agent suggestions, stats and statistics on usage, and suggestions from the platform itself. Use this information to keep refining the information your system provides.

The start of an automation journey

Intelligent Assistants are a low-risk way to get started with automation, strengthening your internal knowledge resources to build a customer knowledge model that understands your customers and the way your agents speak to them. Since strong knowledge resources are key to effective chatbots and more, the possibilities for further automation then unfold.

Even if the chatbot route isn’t for you, it’s not just in training and onboarding that Intelligent Assistants can provide benefits. Extended use cases include having the Assistant pull personalized information from a CRM, eliminating the need for agents to put the customer on hold and look up an answer in that system. 

Intelligent Assistants can also be used to automate entire workflows – such as the process of order tracking, password resets or taking payments. Any process which requires multiple, standard steps can be kicked off automatically by agents to gather details, and the agent can then take back control when the customer completes the workflow.

Better onboarding and training with no more trial by fire

Five years ago, nobody would have believed that this degree of automation within contact center training would be here today. Back then, we were barely getting to grips with the concept of omnichannel marketing, yet now it’s a part of standard contact center working.

Technological advancement is happening fast, and Intelligent Assistants are here right now. It’s amazing to be on the forefront of what promises to be change that disrupts our contact centers and training programs for the better.

Humans will always be essential to the customer experience, but we need to better support and develop those humans that serve our customers. AI offers us the opportunity to do that.

The beauty of Intelligent Assistants

When your fledgling agents finally start taking their first queries, even if they’re not 100% confident (and even with months of training, many rarely are), they’ve got an extra safety net to help them out.

While Intelligent Assistants will never be able to coach and mentor, dispense deep wisdom or grow authentic human relationships, it’s possible for them now to take enough of the strain so our teams can have more time to focus on those things.

That’s ultimately the goal of AI adoption. To allow us humans to better exercise our uniquely human skills, and to free us from basic, transactional work – allowing our agents and ourselves more time to focus on the things that truly matter.

Originally published at G2Crowd.

Customer Experience, Technology, Work

The Challenge of AI Voice Assistants in Customer Service

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During May, Google’s I/O 2018 conference was held to show the latest in Google’s offerings to developers around the globe. While Google demonstrated a lot of different new tech at the conference, it was their keynote demonstration of its latest “Duplex” technology which has lit up the internet.

Duplex uses Google Assistant to call companies on a user’s behalf to perform simple, structured tasks, such as booking a haircut or scheduling a restaurant reservation. While voice synthesis isn’t exactly new, it was the humanlike inflections and natural conversational flow in these calls that many found to be jaw-dropping (or, alternately, terrifying).

If you haven’t yet seen Google’s demo, click through to watch it now, and prepare for your mind to be blown. (Skip to 43 seconds to get straight into the demo.)

Although this technology isn’t yet consumer-grade, Google says it will start to test Duplex within Google Assistant as early as this summer. How then should our customer service operations handle this upcoming customer-side automation in voice calls?

Identify Verification & Trust

Part of the reason why Duplex has caused so many ripples is because it gives a glimpse into a rather dystopian future – one where humans can’t tell whether they’re talking to an AI (causing many to wonder if the Turing testhas been passed by this new tech).

Before now, voice assistants haven’t been capable of holding natural-sounding conversations. But the calls demoed by Google, complete with inflections such as “Mm-hmm” or “Ah, gotcha”, sounded so lifelike that it’s clear the human operators on the other end had no idea they were speaking to an AI.

That in itself has caused outrage – with commentators pointing out that ethical problems occur when service workers and call center staff are unsuspectingly experimented on by Google’s human-sounding AIs. Google reacted to this outcry by asserting that working versions of Duplex should have the ability to identify itself built in.

But whether AIs self-identify or not, the cat’s already out of the bag for anyone considering whether their identity verification processes will need to change as a result of this technology – the answer is undoubtedly, yes. The key to how exactly processes will need to change lies in whether AIs are required to self-identify or not – whether by Google themselves, governments or any other regulatory body.

If AIs are required to self-identify themselves as such and state that they’re acting on behalf of a human, should agents be responding to their wishes as if they were that human? I can easily envisage scenarios where AIs can eventually make payments, change data or perform any other process that has impacts on customer or company – only for the customer to respond that the AI’s actions were a mistake and not authorized by them. How then can we determine human intent behind the actions of an AI?

If AIs are not required to self-identify, issues emerge around trust and standards. As it stands, technology like Duplex is only effective in a limited range of scenarios, making it easy to ask a question that sits outside of the AI’s programming to test whether it’s a robot or not (for example, “Who is the president of the United States?”)

Having agents ask these types of questions to try to weed out the “robots” from the humans is reasonably straightforward. But how will those questions evolve as AIs get smarter? Will they constitute a new, more intrusive layer of data protection processes that we have to subject unsuspecting customers to? What happens then when we speak to human customers who cannot answer these questions – through health issues, a lack of shared cultural understanding, or anything else? Could we be dooming them to be treated like little more than unfeeling robots?

Emotion & Empathy

Speaking of feelings, Duplex brings big questions as to what will constitute effective customer service in the future. Our current, human-focused model of optimal customer experience runs on the premise that if you focus on solving problems quickly, accurately and in a friendly manner, you’re likely to achieve good customer outcomes.

But AIs don’t feel. All the niceties and small talk in the world don’t matter to them. Considering that humans and AIs have different needs and priorities during issue resolution, we could see two distinct sets of standards emerge.

The first relates to service standards for humans – and as beings who have thought and felt in much the same way for thousands of years, I can’t see these undergoing any huge revolution in the future.

But a second set of service standards relates to how we can provide optimal service to AIs. I can see these standards relating to focusing on clear language, accurately clarifying intent, and decreasing emotionality in speech which could cause confusion to an AI – quite the opposite of the emotion-centered training we’ve been giving to front-line agents for decades.

Taking Humans Out of Interactions

Thinking about the role of our front-line customer service agents in the potential applications of this technology, we must consider the messages that Google is implicitly sending about the service employees customers speak to every day to get things done.

PC Magazine sums this up deftly: the implicit message embedded within Duplex is that there’s no need for customers to ‘suffer’ through speaking to service employees to get things done. In one of Duplex’s demonstrations, the lady taking the call has a thick accent that is a little difficult to understand. The AI handles this with little awkwardness, making it clear that even in service situations that can be tricky for customers, machines can handle this instead, removing all of the ‘bother’.

I still believe that human interaction and emotion is what humanizes our brands, and makes them friendly and accessible. And putting myself in the shoes of my agents, there’s something that stings about the implicit message within AI-driven voice calls – that other people see talking to them as a waste of their time.

But I do believe that the best kind of customer service is invisible, that is, mediated through access to a range of easy self-service and digital options available to prevent customers from needing to make inconvenient phone calls. Maybe then we need to focus less on the perceived value of individual interactions, and think instead about downsides of the phone as a communication channel that have caused Duplex to become a customer need.

Phone Calls as Inconvenience

The development of Duplex points to an issue innate in customer service operations – and that is, while phone calls are often the best way for a customer to accomplish a goal, they aren’t always convenient. The rise of live chat, self-service and social messaging channel options has happened as a result of this issue. These channels allow more customers to connect with organizations in ways that don’t take up all of their time or attention, require them to take time out of their day, or prevent them from multitasking while they solve problems with organizations.

The necessity of Duplex (and its positive reception by many) shows that while many organizations see cost or effort barriers to providing service over non-voice channels, clearly for some customers that isn’t good enough. Given that organizations such as Deloitte predict volume of voice interactions to businesses to fall from 64% of all channel communications in 2017 to 47% in 2019, organizations need to consider better ways to connect with their customers than by relying on voice-centric service models.

Automation promises to hold the key to dismantling these cost and effort barriers to multichannel service, as we’re now seeing within Chatbot uptake by firms big and small all over the world. While we’ve been exploring Duplex as a tool for customers to take advantage of automation in their own lives, let’s look at what the impacts are when the tables are turned and organizations can use tools like Duplex to evolve and improve their service offerings in a multichannel climate.

What if Duplex Could Help Organizations?

In the spirit of Moore’s law, it’s feasible to consider that given the current pace of technological advancement, and as a privately-owned company, Google will be looking for other ways to apply this technology, helping them to profit from it and secure its future development.

Because of this, I predict that it won’t be long at all until AIs like Duplex are pitched as a replacement for customer service agents on voice channels.

We can already see the evolution from human-led to AI-led service within other channels. Chatbot services are now handling a good percentage of everyday organizational queries over live chat. Considering that studies show that it’s realistic to aim to deflect between 40% – 80% of common customer service inquiries to chatbots, the same deflection principles could be used to help technology like Duplex to drive the same change for voice.

For voice as a channel, the closest thing we have to this right now is the dreaded IVR. The difference between IVR and AIs, however, is in the promise of service that truly helps, rather than hinders. While IVR is almost universally viewed as an unwelcome hurdle to jump on the way to service from a human agent, chatbots are proving that for certain service scenarios, AI can be as efficient as humans – if not more so, due to their speed, constant availability and scalability.

Projecting the development of this technology for voice interactions within the contact center, we’re faced with some questions. What types of voice queries are ripe for automation, and how can we channel these to AIs in a way that doesn’t add more options to a traditional IVR? What happens when customers can’t tell whether a voice agent is human or an AI? Whether that AI self-identifies or not, how does that reflect on our companies? Could we even be ushered into an age of universal mistrust in customer service where our human agents are treated badly by customers, as if they were robots, because our customers just can’t tell the difference?

Perhaps exploring automation within live chat can throw some light on these questions. I’ve seen many organizations who are meeting these issues head-on within chat – and many are digging deep into customer needs and preferences to harness this technology in ways that are both comfortable for their customers, and effective for their businesses.

A Values-Centered Approach to Automation in Customer Service

Now is the time to reflect on how our businesses will handle customer-side automation coming this year, and how more organizations can handle automation-related issues generally as technology develops.

We can take the lead from design ethicists such as Joe Edelman to consider how best to work with this technology in a way that doesn’t result in negative outcomes for our businesses, our agents or our customers.

Edelman proposes a values-centered approach to the design of social spaces online, and by using this same philosophy, we can consider how AI voice assistants detract from or complements the values of customers and other stakeholders interacting with it. Whether it’s us or the customer who’s automating, great service design will come from a consideration of not only what each party aims to achieve but also how their service preferences are denied or accommodated.

When we can consider the values of our customers and our employees, and how those interface with the needs of our businesses, we can start to use this technology in ways that are helpful and useful to them, morally sound, and which deliver the time and resource benefits that both businesses and customers want.

Originally published here.