Global companies are expecting to apply artificial intelligence (AI) within their organisations in the next few years, but are lagging behind when it comes to discussing the ethics of the technology, it has been revealed.
New research from CX and contact centre solutions firm Genesys has revealed that more than half of all employers questioned in a multi-country opinion survey say their companies do not currently have a written policy on the ethical use of AI or bots, although 21 percent expressed a definite concern that their companies could use AI in an unethical manner.
Genesys, which is sponsoring the upcoming 2019 UK Customer Experience Awards, questioned 1,103 employers and 4,207 employees regarding the current and future effects of AI on their workplaces. The 5,310 participants were drawn from six countries: the UK, Germany, the US, Japan, Australia, and New Zealand.
Almost two-thirds (64 percent) of the employers surveyed expect their companies to be using AI or advanced automation by 2022 to support efficiency in operations, staffing, budgeting, or performance, although only 25 percent are using it now.
However, in spite of the growing trend, 54 percent of employers questioned say they are not troubled that AI could be used unethically by their companies as a whole or by individual employees (52 percent). Employees appear more relaxed than their bosses, with only 17 percent expressing concern about their companies.
Twenty-eight percent of employers said they are apprehensive their companies could face future liability for an unforeseen use of AI, yet only 23 percent say there is currently a written corporate policy on the ethical use of AI/bots.
Meanwhile an additional 40 percent of employers without a written AI ethics policy believe their companies should have one – a stance supported by 54 percent of employees.
Meanwhile, just over half of employers (52 percent) believe companies should be required to maintain a minimum percentage of human employees versus AI-powered robots and machinery. Employees are more likely (57 percent) than employers (52 percent) to support a requirement by unions or other regulatory bodies.
The Genesys survey found that millennials (ages 18-38) are the age group most comfortable with technology, yet they also have the strongest opinions that guard rails are needed. Across the countries, the survey questions about AI ethics resonated more with millennials than with Gen X (ages 39-54), or Baby Boomers (ages 55-73).
Whether it’s anxiety over AI, desire for a corporate AI ethics policy, worry about liability related to AI misuse, or willingness to require a human employee-to-AI ratio – it’s the youngest group of employers who consistently voice the most apprehension. For example, 21 percent of millennial employers are concerned their companies could use AI unethically, compared to 12 percent of Gen X and only six percent of Baby Boomers.
Steve Leeson, VP UK & Ireland, Genesys, said: “As a company delivering numerous Customer Experience solutions enabled by AI, we understand this technology has great potential that also comes with tremendous responsibility. This research gives us important insight into how businesses and their employees are really thinking about the implications of AI – and where we as a technology community can help them steer an ethical path forward in its use.”
He continued: “Our research reveals both employers and employees welcome the increasingly important role AI-enabled technologies will play in the workplace and hold a surprisingly consistent view toward the ethical implications of this intelligent technology. We advise companies to develop and document their policies on AI sooner rather than later – making employees a part of the process to quell any apprehension and promote an environment of trust and transparency.”
Artificial intelligence (AI) has become engrained in our day-to-day lives without us even noticing.
From basic voice assistants that can play music by just saying one word, to self-driving cars – there’s no turning back from the world of AI. Today’s tech-savvy consumers have grown to love AI so much due to its ability to improve overall Customer Experience and resolve issues in a timely way. As a result, businesses are jumping on board the AI journey at an unprecedented pace. There is little doubt in AI’s ability to dramatically transform CX, so why isn’t the same attention being given to the Employee Experience?
Today’s workforce has changed dramatically compared to that of previous generations. More employees are working remotely than in traditional offices, and recent research shows that by the year 2020 more than 50 percent of employees will enjoy the benefits of working someplace other than a traditional office.
In addition to where we work, how we work is also changing. While millennials have had access to cell phones and the internet for virtually their entire lives, even generations that have not grown up with this technology are embracing well-designed, easy-to-use applications. Employees across industries expect technology to make jobs easier and more productive, however, the bar for what companies believe is user-friendly technology is often far too low.
Even companies that are forward-thinking and want to move beyond antiquated systems, are struggling to implement technology that is as easy to use as Alexa, but also seamlessly fits into the current processes and workflow – and it’s having an impact on retention and employee satisfaction. Research suggests that a majority of employees that are looking for new jobs are doing so because of broken company processes, including being able to connect with support departments like IT and HR.
A direct correlation
One wrong Customer Experience can create a lasting impression. Therefore, businesses are now so focused on providing exceptional CX that Employee Experience becomes an afterthought. Businesses know that if they want to compete with the Amazons of the world, they need to go above and beyond to ensure a superior CX.
They have done this by pulling out all the stops and implementing new technologies that allow consumers to do things like virtually design homes with furniture they’re considering buying or try on clothing in a virtual dressing room.These innovations have changed the game when it comes to Customer Experience.But behind the curtain, employees are under constant pressure to provide this experience and are not equipped with the same flashy technologies to help them do their jobs.
In fact, the technologies designed to support the modern workforce often times do the opposite – they hinder employees’ productivity, efficiency, and, as some would claim, even the ability to produce meaningful work.
In a business-driven world where time is money, no-one should struggle to figure out technologies that are supposed to ‘support’ them and make their lives easier. The reality is that many existing support solutions today are outdated and actually work against the employee, inhibiting the ability to help them and the business thrive.
The workplace of the future
In what ways can businesses improve Employee Experience whilst also giving their employees the freedom to do the best work? We already know that workplaces of the future are likely to be increasingly more remote, as more companies choose to run their businesses from co-working spaces or have no office space at all. With the workplace becoming more fluid and dynamic, and employees working out of home offices or coffee shops, in varying locations, businesses need to be prepared to support employees across state lines and time zones.
We also know that future of the workplace will be increasingly more digital, as the technical innovations that alter the way we live outside the office will become expected in the professional environment as well.
Businesses need to reimagine the workplace the way they’ve reimagined the customer journey.Emerging technologies like AI-powered chatbots, for example, are helping with everything from onboarding and training, to providing assistance during meetings, to helping solve common employee questions that often plague IT, HR, facilities and other support teams at organisations.AI is helping businesses save time and energy – while still ensuring employees have help every step of the way.
Inundations of Help Tickets
A great example of AI in the workplace is in IT, which isn’t surprising with IT being the backbone of technology exploration and vetting at organisations.These teams spend a good majority of their days working through cluttered support queues full of repetitive tickets – whether its password resets, email access or printer setups. These are questions that can often be found in knowledge management systems or intranets, but when employees have questions – especially if those issues are hindering them from getting work done – they would much rather ask their IT buddy than go searching through a sea of URLs and documents to find the answer.
This endless onslaught of requests cuts down on the amount of time the IT team can devote to higher-value problem solving or long-term strategic initiatives. Not to mention, it must be incredibly frustrating when ten people in one day ask you how to access a remote server – copy and paste at its finest. IT teams, which are already stretched thin,are drowning in these requests day in and day out, and it becomes a problem for the entire business operation.
And IT isn’t the only one affected by this cyclical support queue. While the help desk team is busy working its way through tickets or dealing with an unexpected ‘fire drills’, employees who are waiting for support grow frustrated with resolution time.
Sometimes they even turn to unauthorised solutions that bring their own security implications. Employing an AI-powered support partner to help answer these questions removes the pain of searching through outdated and hard-to-read knowledge articles, empowers employees to self-serve and opens up the IT team to work with the employees who need them the most. Thanks to Google, today’s workforce is programmed to take a DIY-approach to problem solving and often prefers self-service, so organisations need to embrace and capitalise on this – and AI is one of the ways to help bring it to the workplace.
Time is money
The famous saying, “time is money”, must be remembered. However, if businesses don’t focus on Employee Experience, they will be diminishing their success in the long-run, creating lasting inefficiencies for the bottom line. Now is the time to start removing friction from the day-to-day by using tools that will enable employees to do their best work. Ultimately, these efforts will allow businesses to thrive as employees will feel motivated to become more productive and simultaneously more satisfied.
At its best, science fiction taps into our contemporary anxieties to predict the fate of humanity.
An episode of Doctor Who, for example, featured robotised mega-corporations, human irrelevance, and despair. The Doctor may be sci-fi fantasy, but the issues are real.
Artificial intelligence (AI) technology is reshaping many sectors – for both good and ill. Gartner predicted that artificial intelligence would generate $1.2 trillion in business value in 2018 – an increase of 70 percent from 2017. But on the negative side, it creates much anxiety about the elimination of jobs, and prolonged focus on the cost and job-cutting aspects of AI has overshadowed how the technology can help human employees.
The CX example: how tools can hinder trade
In the customer service sector the rise in AI, decision-support, automation, and chatbots has exploded across the industry, driving multi-channel customer experience (CX). But adoption of these technologies for employee engagement has been slow. Contact centres have some of the highest employee turnover rates in the world, and there’s been troubling analysis suggesting new technology is inhibiting employee performance, engagement, and satisfaction.
Gartner analysis reveals service representatives use the mindboggling average of 8.2 different systems and tools during a customer interaction. Small wonder, then, that talk-time is up nearly 14 percent while call volume has remained the same.
We have amazing systems driving less-than-amazing experiences for the people charged with using them. A primary source of the problem stems from something obvious. We’re measuring the wrong things.
Just exactly what should we measure?
In our rush to capitalise on AI technologies, we’re failing to evaluate the way they ultimately integrate into human workflows. In the customer service sector, technology is better at handling many discrete tasks but does not replace human representatives.
It’s becoming standard practice, for example, for companies to host automated, largely self-service interaction options for customers that are always available. Digital account portals supply constant access and handy personalisation capabilities, while well-designed chatbots and virtual assistants are excellent at taking orders, payment processing, status checks, or informational queries.
But for more complex requests that require human nuance and context, technology-enhanced services can complicate the situation. When dealing with the customer, human agents are at a loss without access to what transpired during those digital interactions. And even when human agents can access those systems, they shouldn’t be flipping back and forth between applications and databases while attempting to deliver proper support to a customer.
This problem is perfect for AI solutions. Analytics engines that deliver historical and/or relevant customer information to support agents automatically and in real time can speed rather than delay productive conversation. Natural Language Processing (NLP) systems recognise spoken keywords and supply agents with useful prompts or notes, sparing them from app fatigue and task-switching. Virtualised on-demand training systems can keep them stimulated and engaged.
This employee-centric AI deserves more study and development. Systems that aren’t generating a positive Employee Experience will negatively affect the Customer Experience they deliver. Exploring ways AI can better serve employees is the solution. And measuring how employees view these tools should be the first metric for success, not an afterthought.
Collaborate to work out what best to measure
Applying AI to better serve the employee is crucial, but should be measured and managed with caution, given the enormous amount of data available.
One of the greatest struggles from an AI development perspective is determining how often a system should prompt the employee and whether there should be a trigger. Can such a mechanism be ranked? Do we allow the employee to turn off certain notifications because they’re annoying?
There’s a risk of overdoing AI assistance for Employee Experience. It could get very frustrating, very quickly. The only way to arrive at balanced employee-centric AI application is through collaboration. The people using the technology should have representation at the development table, which is also an excellent way to increase job satisfaction.
The future will require us to adapt what we measure
As AI technology becomes integrated into the enterprise, we must adapt how we gauge human performance. In CX management, technological innovation dictates that businesses restructure how they view customer contacts and the human staff who perform those jobs.
Contact centre positions will no longer be entry-level or outsourceable roles. With automation handling all the basic contact tasks, human customer service becomes a more specialised profession. Savvy and emotionally intelligent customer service employees with thorough understanding of a business and its technology will be a necessity. They’ll be managing only the most important, complex, or delicate customer concerns.
Today’s metrics, such as talk-time or calls-per-hour, provide little quantification under such circumstances – but the quality of this work will largely determine a company’s reputation among human beings.
Explaining to a child how to cross the street in front of their school without being hit by a car only takes a few repetitions and their knowledge can then be generalised to most roads and vehicles.
It would instead require huge quantities of images for an AI to learn the same and it would make mistakes as soon as confronted with situations which are slightly different from what it had seen in his training data set.
The current breed of artificial intelligence – in its most advanced version – is built upon a metaphor of the human brain as a computer made of interwoven neurons. Through a ‘training’ process, the system can ‘learn’ to ‘recognise’ identical patterns without being programmed by a human and then apply this ‘knowledge’ to real world situations, more and more with a better accuracy than humans themselves.
The limit of this metaphor is that it takes a huge quantity of data to obtain this type of result and those hard-learned skills are confined to the very domain where the AI was trained.
The abstraction and generalisation capabilities of humans are still a mystery to AI researchers, but an element that may guide them in their quest is the emotional nature of human beings. We memorise much better when feeling strong emotions than in ‘boring’ situations. Children’s ability to quickly learn how to properly cross the street is certainly related to their feeling of danger and somehow fear of what could happen if they made the wrong decision.
A machine obviously doesn’t feel – we’ll leave to the sci-fi fans the debate of whether consciousness could emerge as a property of complex systems such as neural networks. AI is high on IQ and low on EQ some might say. But progress in mimicking the functioning of the human brain could require an acknowledgement and a modelling of the emotional nature of homo sapiens.
Current AI algorithms are not yet able to learn from less data and improve their abstraction and generalisation capabilities using emotions. But they are improving at recognising them within humans, exploring correlations between symbolic representations of emotions and human expressions, whatever their format.
Progress being made
Some research has already be done on the range of human emotions, thanks to the EU-Emotion Stimulus Set, and people like Houwei Cao, assistant professor in the Department of Computer Science at New York Institute of Technology, who is busy working on algorithms that can read emotions.
Initial efforts were called ‘sentiment analysis’, trying to guess an individual’s state of mind based on what they write or say. This has now taken a larger perspective by adding language patterns, voice tone, facial movements, sentence structures, and eye motions into the mix.
For instance, a mouth shaped in a particular way, plus voice with a specific pitch compared to its baseline, plus use of words tagged as being positive, equals happiness. Of course, to the average philosopher, that is a rather partial and limitative definition of happiness. But it only needs to be operational in the specific context where it is used.
Emotional AI applied to customer engagement
Indeed, those efforts are improving AI’s relevance to the business world and the fields of application are numerous.
Whether it’s customer engagement or support, a hiring process, or addressing disputes, emotional AI can play an important and useful role for humans. Employees can base their interactions on its insights, adapt their response to emotional changes in the customer and have a more effective communication with the person on the other side of the line or table.
For instance, the stakes are high for the call centre industry: born out of financial necessity so businesses can afford to serve and support large customer bases, it often turns out to be a source of frustration for users despite well-scripted conversation scenarios followed by the responding agent. When there’s pressure, good manners and empathy can be forgotten. Emotional AI can act as a reminder to employees, so it doesn’t happen.
It is also true of the sales forces whose likelihood to convert a prospect into a customer is directly linked to their ability to empathise with the individual(s) they want to strike a deal with. Indeed, approaching another human with an offering that is rational (adapted to its needs and budget for instance) but presented without taking into account their current state of mind is at best a waste of time and at worst a loss opportunity.
Emotional AI can help a business stand-out from its competitors for the quality of its customer engagement.But what will be the acceptance of emotion-driven algorithms by humans?
There will be challenges
In the age of GDPR and stringent privacy rules, considerations about voice, face, and writing being processed by emotional AI algorithms is something that businesses will need to explain to customers, since there is a very thin line between individual mood monitoring and intrusive Orwellian surveillance.
Will a customer value consideration for his or her feelings or mood by a computer as much as genuine empathy expressed by another human-being? If after asking how I am doing – something most people won’t need an AI to remind them to ask – the next question about my latest holiday is in fact an AI-scripted line, the whole introduction might sound a bit phony.
Eventually, could overly relying on AI to read other individuals state of mind turn us all into sociopaths unable to properly relate to other humans, like GPS has slowly but surely decreased our ability to use a map to navigate in the real world?
However, those questions might be irrelevant in the not-so-distant future. With the growing sophistication of virtual personal assistants – think Alexa, Siri or Google Home – we may soon delegate our buying decisions to those machines. This would imply that vendors’ own AI systems now have to pitch our AI agents instead of ourselves. And the billions spent annually by marketing departments on branding and ads designed to appeal to our emotions would fall flat.
A majority of UK adults are worried for the future of their jobs due to the growth of artificial intelligence (AI), a new report has revealed.
According to the findings of think tank Fountech.ai, 67 percent of 2,000 adults polled are worried AI will result in machines taking people’s jobs. Meanwhile, the survey also shows that 58 percent find the use of AI tools such as those used by Amazon and Netflix to recommend products to us “creepy”, and 59 percent are nervous about the way their personal data is collected and used since the rise of AI tech.
However, according to the poll, 62 percent believe AI will do more good than harm to the world, while 37 percent admit they do not fully understand what AI means.
Furthermore, only 30 percent claim to regularly use technologies powered by AI. This is despite the fact that popular tools such as Google’s search engine, Siri, most major email providers, and Facebook – as well as the aforementioned Amazon and Netflix platforms – all use AI.
One-in-three (31 percent) respondents said they do not think AI will ever be able to truly replicate the cognitive ability of humans. Nevertheless, three quarters (74 percent) want to see the UK government do more to govern the way AI technologies are developed and used.
Nikolas Kairinos, CEO and founder of Fountech, said: “People tend to fear what they don’t understand, and today’s research is an example of this. For decades, AI has been misrepresented in sci-fi movies and literary fiction, but we should not let this blinker our view of how this amazing technology can enhance the world around us.
“AI can solve problems and achieve tasks that we previously considered impossible – it will undoubtedly open doors to countless opportunities so we can make the world a better place. Importantly, as this study shows, the technology must be harnessed and used in the right way – the ethical questions surrounding the development of AI will rightly remain until both governments and businesses show they are applying it in responsible, safe ways.”
It used to be that when someone said “AI”, images of sci-fi films came to mind.
Nowadays, the effect is more prosaic: it’s become de rigeur to use AI as shorthand for “business wonder cure”. When we hear “AI”, we know we’re supposed to see it as the answer to any and all business problems, whatever the industry.
At the same time, we’ve seen Customer Experience become the key differentiator in all sorts of sectors, but particularly retail. We all want to be treated as individuals and expect those we buy from to act as value-add service providers, not just vendors. That fact has fuelled the rise of AI as the perceived silver bullet for retailers – the more data you have at your disposal and the faster you can put it through effective analysis, the more likely it is that you’ll provide a beguiling, personalised Customer Experience and product offering.
However, AI is a term that’s used too freely. It has become confused with machine learning, losing its original meaning of a completely independent decision-making engine. True AI would make decisions based on so much information that you couldn’t know the decision it’s going to make before it makes it – much like a human being.
In contrast to that, current machine learning applications are very controlled – they put a set data flow through a known algorithm which auto-updates based on the results it receives, but they’re nowhere near the level of independence required for true AI.
There are good business reasons why AI remains in the realm of hype rather than frontline use, though. Financial services firms aren’t using AI, for example, because they can’t control its results – there’s too high a risk that mortgage applications could be affected by unconscious bias, for example.
The same risks apply to retailers. A good example of this played out a few years back when Amazon attempted to take the human element out of hiring. It took data about the type of people it had previously hired, processed it, and then set the resulting algorithm to hiring people. However, it ended up only hiring men – because that was what Amazon had historically done. It discounted women based on past experience, and therein lies the problem. AI based on past data doesn’t let you change for the better.
This kind of coded bias is also indicative of the people who’ve made the algorithm – if it’s a non-diverse group designing the algorithm, they’re more likely to almost accidentally code bias in. This is one of the main reasons why pure AI isn’t often used – the data you have might be biased, and you won’t know until it’s running. If it goes wrong, you can’t unpick it.
Think of it like a child growing up: once they’ve picked up negative behavioural traits, it’s very difficult to undo the hardwiring.
Despite all this, genuine AI is a possibility. The key is ensuring that applications have access to sufficient data – and the right kind of data. The more data you have, the more likely it is that you can build something that will provide accurate, non-biased results.
Unfortunately, we’re simultaneously seeing a sharp decrease in willingness to share data, as scandals like the Cambridge Analytica debacle damage confidence in the safety and privacy of large-scale data sharing. As a result, businesses have to navigate a very fine line between feeding enough data into their automated systems to achieve an optimal, non-biased Customer Experience and preserving customers’ privacy and trust.
Take the worked example of calling HMRC. As most of us will know, it can often take forever – it’s not a positive experience. To solve that problem, HMRC recently announced a desire to use voice biometrics for identification to speed up the process and remove the endless questioning. The system would be able to tell your mood, quickly identify why you’re calling, and put you through to exactly the right person to solve your problem.
The idea is a good one, but it depends entirely on people being comfortable sharing their voice data. Without that data, how do you optimise the system? As we move away from data-sharing as the default, how can companies still optimise Customer Experience with AI-driven technology?
Prioritising end users
In a nutshell, the answer is preparation. Lots of organisations come to my practice and say they want their customer journey to take advantage of AI and machine learning, but often they don’t have the tools in place to support it – no security, no data automation, and no clear understanding of the business need they’re trying to address.
They’re starting from the wrong point. Technology is not the end goal – Customer Experience is.
Companies need to start their AI CX journey with basic identity and access management. They need to inform their customers that they value their data and their security, and that they’re going to use their data with set controls in order to then keep them informed and give them proactive customer service, say.
Transparency is key – and you have to have the technology in place to back it up.
The bottom line is that an AI-enabled customer journey should always start with security. It’s essential to be able to protect your customer’s data as you use it. Not only that, but you need to make sure you’re providing a service that actually meets real needs, so find out what your customers want.
Start with their needs, not your technological goals. AI can take customer relationships to the next level, but it must be designed with the end user in mind.
There is certainly plenty to think about with the rising cost of salaries, managing schedules to meet customer demand, looking after staff wellbeing, PCI DDSS compliance, and now the added requirements of GDPR (General Data Protection Regulation).
Initial concerns about how the new GDPR regulations would affect contact centres, in terms of increasing costs and complexity of managing enquires, have to some extent dissipated. For those contact centres taking payments and already PCI DSS compliant, it was a relatively straightforward process to embrace GDPR regulations. They had typically invested in secure technologies, encryption, and working with third party compliant companies in terms of PCI DSS. On the whole they were able to extend their technology and processes to protect personal data and meet GDPR requirements.
However, other organisations are still evaluating how new ways of streamlining processes can help meet GDPR data governance and management regulation, but are uncertain how to choose the best solution. We have identified three ways that contact centres can apply technology to help them remain compliant:
1. Mobile automated identification & verification (ID &V)
Often a significant amount of time can be spent on identifying and verifying the caller. Having a person perform this task is expensive and means that customer data is at risk. A customer engagement platform is an alternative way to offer a cost-effective, secure solution to automate the screening and identification process.
It can take the customer through set identification questions using Artificial Intelligence (AI) to simulate agent conversations, or it can use SMS text messages to authenticate the device being used. On initial registration and once the two-factor authentication process has been successful, the platform will accept and authorise payment requests that are automatically debited from the card holder’s account.
The advantage of this approach is that all information is encrypted and the agent is not exposed to any personal data, thereby complying with GDPR and PCI DSS. The data is processed and stored securely elsewhere. In addition, having signed up to the service, the customer has agreed to a data handling agreement that sets out how their information can be shared with a third party, ensuring confidentiality.
2. Customer self-service screening using IVR
Accepting credit and debit cards via IVR has long proved to be an effective and secure way of taking payments. It allows customers to pay quickly, via their own unique identifiers – a PIN, date of birth, even voice recognition. Again, reducing or removing agent contact time is a more secure way for contact centres and their customers to comply with PCI DSS. Since everything is fully automated and confidential, the client information is stored centrally and securely within the system hosting the data, taking it out of scope for both PCI DSS and GDPR.
Capturing customer data via IVR also enables calls to be routed to the right agent with the correct skills, in the event of a request to speak to an advisor. The agent then has all of the relevant information available to manage the call successfully, but with key identification data screened, thereby ensuring GDPR compliance.
3. Cloud-based third party payment solutions
The third option to consider, and one that has gained significant traction over recent years, is to choose a cloud-based payment service provider. A trusted third party that complies with PCI DSS demonstrates proven adherence to a recognised security standard, which can also help contact centres to meet the GDPR legislation. Companies can apply a process of ‘de-scoping’ to reduce the number of requirements (tick-boxes) for GDPR, in the same way that they might do for PCI DSS compliance.
Of course, like PCI DSS compliance, the responsibility for GDPR cannot be entirely removed from the contact centre, however the effort required can be dramatically reduced by working in partnership with a payment solution provider.
Aligning GDPR and PCI DSS: the route to successful compliance
There is no doubt that GDPR has improved standards around privacy and data protection, but at what cost? Contact centres that have worked hard to blend people and technology to enhance data and payment processes in the last year, have typically done everything they can to comply with both GDPR and PCI DSS.
For the rest, the good news is that it’s not too late to review what’s in place and make the switch, to new technology and/or a third party solution provider, to enable a secure, multi-channel seamless route for customer payments. The choice is there for the taking.
Analysts predict that spending on Artificial Intelligence in the retail sector will reach $7.3 billion by 2022, a majority of which will be poured into customer-facing conversational AI solutions like voice assistants and chatbots.
That’s not surprising, given how the power of conversation is poised to fundamentally transform Customer Experience across industries.
The use of consumer-grade digital assistants has exploded in recent years. Consumers have quickly moved beyond ‘talking’ to digital systems for basic information (weather, traffic, trivia, etc.) and now use them to engage in commerce and other activities. For example, half of respondents to a PWC survey last year said that they had made a purchase via a voice assistant, with an additional 25 percent saying they would consider doing so in the future.
While the thought of increased sales through conversational AI is sure to bring a smile to any business decision maker, one shouldn’t lose sight of this technology’s other benefits – particularly its potential to optimise all points of the customer journey.
The new journey
When it comes to locating information about a product or service, consumers are becoming more interested in simply asking for it, rather than typing or tapping to search for it. Conversational UIs offer many advantages over other digital interfaces, in that they help users to find information quickly, allow them keep their hands free for other activities, and perhaps most importantly, play on the human brain’s natural inclinations for conversation and engagement.
A recent survey found that consumers preferred chatbots over apps when it came to receiving quick answers to both simple and complex questions. The bar for human-like conversational experiences is being raised constantly through platforms like IPsoft’s Amelia, which are helping tilt consumer behaviours even further toward these types of interactions. As this trend takes hold, it would be in a retailer’s best interest to automate and optimise these engagements through conversation – not just to provide the best consumer experience, but to gain substantial competitive advantage.
For example, when a customer is making a purchase or asking a question, modern conversational systems – integrated and automated end-to-end to back-office systems – can tap into individual purchase histories and other data sources to organically up- or cross-sell additional items (e.g. “Hello Mr. James, thank you for purchasing your new smartphone, would you also like to purchase a screen protector as you did with your previous device?”).
Similarly, more advanced systems allow brands to scale and target marketing/messaging campaigns to very specific segments within the confines of a conversation. For example, an AI system fronted by a conversational UI could alert a 25-year-old consumer who has purchased more than $200 worth of goods in the last six months (indicating she likes the retailer’s products] about an upcoming weekend sale.
As these marketing strategies can be modified instantaneously at scale through automation, companies are free to experiment and A/B test different approaches, determining the best response – such as making the same offer to “men aged 18 to 25 in London” versus “any consumers who spent more than £100 in the last year”.
Conversational AI can also be used to enhance the conventional brick-and-mortar experience, using voice-enabled kiosks or mobile apps. These channels can provide in-store customers with instant access to helpful information such as in-store locations of various items, or enhanced services such as scheduling deliveries. When implemented on site, this new functionality benefits customers through access up-to-date information and services, and it also frees employees on the floor from answering routine customer FAQs to work in other areas.
Humans are designed to experience the world through conversation. Up until recently, this inclination was somewhat incompatible with modern consumers’ expectations for 24/7 access to goods and services, given the lack of tested and effective conversational AI interfaces. However, now that AI allows companies to automate and scale conversational engagements, they can completely reinvent the customer journey to engender consumer loyalty and generate new revenue.
Brands are working harder than ever to enhance Customer Experience as people increasingly demand a streamlined and immediate service from the companies they interact with.
Research from Dimension Data shows that 58 percent of consumers would be willing to spend more money with those businesses that provide excellent customer service.
But while companies currently offer an average of 11 channels through which customers can make contact –from webchat and apps to email and phone communication – the challenge is that these systems, and the processes behind them, are rarely connected. Insights gathered from each interaction are often fed into individual silos for each channel. The Dimension Data research found that only eight per cent of companies believe that all of their points of contact with the customer are integrated, with a third unable to track customer journeys at all.
To put it simply, these businesses lack a ‘single view of the customer’ that can help overcome the barrier of disjointed CX by providing agents with all the information they need about a customer on one platform. By adopting this approach, brands can avoid the common problem of consumers quickly becoming frustrated if they must explain their problem multiple times to more than one representative, or discuss historical issues and interactions each time they make contact.
A 360° view customer platform
The wealth of data companies have available to them can help them transform how they engage with their customers. Retailers, for example, can harness information relating to buying history, delivery, and returns, and even personal details such as product preferences and customer tastes, to provide a joined-up and bespoke, tailored experience.
The solution is to create one automated platform, underpinned by artificial intelligence (AI), that collates data for each customer at every possible touch point. By providing this information to customer service agents in a dashboard format, they have all of the up-to-date, relevant information they need to answer a query. For automated systems, such as chatbots, the data can be continually fed into the system’s software.
By using AI, the platform can also generate recommendations for agents to follow, based on data covering customers’ past purchases, browsing behaviour and previous interactions with the brand. Along with helping representatives to have positive and productive dialogue to improve loyalty, it can also be applied to outbound campaigns to boost sales by identifying the right time and right channel to contact a customer.
Integration in practice
Before implementing a 360-degree platform, companies must first audit all of the existing channels available to their customers. The process allows brands to identify any areas that are not currently integrated, and pinpoint areas for improvement and optimisation.
Once the audit is complete, data can then be automatically consolidated and integrated into one easily accessible platform. Automation plays a key role here, continually collating and feeding information from multiple databases into one customer relationship management (CRM) system that can be updated in real time.
But technology is only half the story. New digital tools can enhance the role of advisors by empowering them to interact with customers in the most efficient and knowledgeable way. They also provide an opportunity for businesses to upskill their employees, equipping them with top quality communications skills to enable them to field the most complex queries and handle high-value requests, such as those requiring negotiation or emotional sensitivity.
Investment in staff is an effective way of boosting service quality, helping them to build and maintain relationships that cannot be handled by technology alone, ultimately serving to boost the bottom line.
Data presents an opportunity for businesses to create a holistic view of each of their customers, which they must capitalise on in order to deliver the outstanding service expected of them. This approach will allow them to benefit from the highest levels of customer satisfaction, helping to boost customer loyalty for the long-term and – crucially – drive sales.
I rarely use and never want to pick up my phone anymore.
That is, the phone part of the phone. I happily use my mobile phone all of the time – to communicate, read, and for entertainment. But using the call functionality and dialling a human? No, thank you.
It’s partly because I get dozens of unwanted robocalls every week, and partlybecause I’ve wasted a lot of time on hold. It’s also because one of the few things that I can control in life is my time – and when I’m on the phone, the person on the other line has effectively hijacked my time.
This is especially true when it comes to getting customer support via the phone. If I need help, I’m probably not feeling particularly sociable. The last thing I want to do is pick up the phone, talk to an agent and hope they can solve my problem – or worse, risk bouncing around a poorly implemented interactive voice-response system (IVR). I’d much rather search for and find an answer online. Better yet, I’d like to type a question and let a well-trained chatbot instantly find the answer for me.
I’m not alone
It’s human nature that we don’t want to rely on other people – and the phone – to accomplish certain tasks or gather information. That’s part of what’s driven the internet explosion.
Take Ticketmaster. The event ticketing company launched a self-service website in the early 90s, where event-goers could, for the first time, purchase tickets online rather than going to in-person kiosks – or making phone calls to human ticketing agents. This is illustrated perfectly by the following excerpt from Paul Allen’s memoir, The Idea Man, about Ticketmaster’s very first online sale:
“When customer number one had completed the first transaction, our Web people called him and said, ‘Congratulations, you just bought the first concert ticket in the history of the Internet! Can you tell us why you decided to buy online?’ The man said, ‘Because I don’t like talking to people, and I don’t like talking to you.’ And he hung up.”
More than 25 years after Ticketmaster’s first online sale, there’s proof that people are relying less on phone calls than ever before – and it’s having repercussions across various industries.
A few stats to consider:
The number of landlines in use is down dramatically. A report from the Centers of Disease Control and Prevention (via Statista) showed that, in 2004, 92 percent of U.S. households had a working landline. By 2018, that number dropped to 42 percent because of the growth of mobile phones.
British telecom service provider Ofcom released a study in 2018 revealing that the number of monthly mobile voice call minutes was on the decline among its customers, from an average of 159 minutes per month in 2016 to 157 minutes in 2017. But while phone calls were down, data consumption skyrocketed, from an average of 1.3 GB in 2016 to 1.9 GB in 2017.
Pew Research reported that response rates for phone surveys plummeted to six percent in 2018. The steady and sharp decline has continued since 1997, when response rates were as high as 36 percent.
Nearly 60 percent of contact centre leaders believe inbound call volumes will decrease over the next five years, according to a 2018 McKinsey survey, while 40 percent said the number of calls will fall dramatically, perhaps to zero, in the next decade.
Automation takes over self-service
In this era of internet-enabled instant gratification, we as consumers expect to get fast answers to virtually any question – without making any calls.
This includes the realm of customer support. The phone call is no longer the primary medium for support – instead, phone calls are the last resort, and this isn’t just because consumers (like me) prefer it. Businesses do too, as companies are implementing AI-powered support automation technology to both improve the customer experience and better manage operational costs. Here’s some data behind that shift:
A recent report by call centre industry analyst firm ContactBabel found that only 25 percent of customer support agents believe that customers prefer human support.
41 percent of consumers would choose live chat as their preferred support channel, according to a study from Kayako, while 32 percent prefer phone calls, followed by email and social media (note: the survey did not include chatbots or virtual assistants as an option).
This doesn’t mean that businesses can totally dismiss phone support. However, it does point to the fact that most consumers would prefer not to dial company support unless they absolutely have to.
As Forrester analyst Kate Leggett wrote: “Today, customers have more choice: more products to buy, more information to influence purchasing decisions, and more devices and channels over which to seek customer service. What they don’t have is more time. It’s no wonder that self-service interactions have overtaken all other channels.”
It’s worth restating Leggett’s words: “What they don’t have is more time.”
That’s why we often turn to Google or a company’s online forums for answers. But a traditional search online or in managed forums can leave you with an endless list of links to sift through. This is where AI comes in. It might take us several minutes or hours to find an answer amidst a library of online information, but applied machine learning (ML) technology can surface the information we need in an instant.
AI also allows companies to provide a uniform quality of service, 24 hours a day, with little to no downtime. Effectively trained chatbots (a.k.a. virtual agents), with brains powered by AI, are becoming the new face of customer support.
The ContactBabel report found that 16 percent of all companies plan to implement artificial intelligence solutions for customer support within the next year, more than doubling the current installed base. Additionally, 27 percent of large contact centres (with 200-plus agents) expect to implement AI/ML within one-year, which means more than 50 percent will have AI/ML in place by 2020.
I know I speak on behalf of consumers everywhere when I say that the era of AI-led support can’t come soon enough. To paraphrase the great R.E.M., it’s the end of phone support as we know it…and I feel fine.
We’re flush with new ways to engage with customers, but businesses should be more data-driven, rather than simply throwing more manpower on the frontlines.
In the era of new contact centre touchpoints, the touchpoints themselves matter less and less because they should be managed in a unified way. That’s not to say we should disregard the touchpoints – in fact the opposite is true. We should be able to add them and monitor the data from customer interactions to create contact centres that offer better service and embrace innovation when it comes to engaging with customers.
In real terms, that means putting an end to seeing telephone, web chat, or mobile app communications as an island in their own right. Each channel will have its own considerations and technological challenges to take on board – that much is true. Yet as agent desktop interfaces better integrate the new channels that emerge, we should start to think of how we can solve new business challenges and get smarter, as well as becoming more efficient.
Hearing the voice of the customer
For many contact centres, voice has been their bread and butter for years. The difference now is that voice is used less – at least in its traditional sense. Meanwhile, phones are being used in different ways, particularly with the growing use of smartphones. Voice now has a closer relationship with other digital channels, and as a result, firms should prepare all channels to account for customers flowing between each.
Although customers are generally using phones less for voice calls than they used to, we’re now seeing an increase in phones being used as a digital backstop. If a customer doesn’t get the response they expect from digital channels, they will probably pick up the phone to speak to an agent. This brings to the surface the importance of managing the two types of contact centre interactions – those driven by bots and those driven by humans. Human agents will want to deal with the queries where they feel like they can add value. The simple issues such as the loss of a password can be dealt with automatically.
Agent time is both precious and costly and so should be used for issues where it is necessary. It’s important then, for businesses to find the right match between interactions handled by chat bot, and interactions that require a human touch. The best approach is to use a mix of both, where bots escalate to an agent when needed, without customers feeling like they are being passed between non-connected entities.
We also have to prepare for a new era of voice interaction. There were 9.5 million active smart speaker users in the UK last year, which is an increase of 98.6 percent against 2017, according to eMarketer. Consumers are getting more comfortable in asking these devices to perform basic tasks and provide them with information. The next step is for them to be the conduit to getting in touch with the outside world. That doesn’t just mean communicating with close friends and family as is the case now but increasingly, with brands. In fact, voice assistants are just one part of a larger move towards a more integrated IoT service, which also includes connected cars.
We’re using bots to answer more customer questions with speed and accuracy. Doing the same thing with voice-activated devices will cut out the middle-man where needed, while still basing the approach on the voice model that has operated in contact centres for years. But as with any channel, it’s vital that voice plugs into a bigger picture view of customer interaction. Omnichannel rules the roost and provides a great deal of insights that are valuable for businesses.
Data insights enhancing Customer Experience
On the whole, companies have to get better at proactively engaging with customers and artificial intelligence (AI) will help to do this. For example, with the right data coming from previous customer interactions and insights it is able to obtain from initial contact, AI can be used to provide a more targeted response, and through a combination of virtual assistants, machine learning and customer data analytics, businesses are able to predict customer needs.
Not only that, they can proactively address these needs to prevent repeat contacts for similar issues, deliver superior experiences to retain existing customers and improve offers or interactions in a way that attracts new customers.
There’s also the intelligence that businesses can uncover to shape their products better – all from the way they monitor customer interaction. When firms automatically capture and analyse interactions, they can make sure they never miss the vital signs that should be spotted immediately. They are able to identify gaps in products, processes, and interactions – and make sure agents meet the needs of demanding customers.
One of our customers is a coffee company who was looking to carry out a strategic launch of a premium product. They automatically analysed all their calls and as a result, they were able to better train underperforming agents with targeted coaching. By analysing interactions at the contact centre, it enabled them to better understand how agents were pitching the product and it also helped them to see how well the new product was being perceived. Using these measures, the company increased sales penetration using best practice, and increased basket size by pushing promotions at the right time.
I’m excited by the prospect of new touchpoints and technologies coming together to offer a better service to customers, better performance for agents and better efficiency for businesses. And with voice assistants, IoT and other connected ways for businesses to interact with people, the whole area of customer services has been blown wide open. There’s so much potential for innovation.
But with all these touchpoints, it’s vital that businesses can connect the dots across the different channels they use. It’s an approach that includes not just the communications channels but the knowledge captured from CRM systems and contact centre insights. We know that the channels will probably change in the future as consumers find new ways to interact with brands but in the grand scheme of things, that shouldn’t matter. What is important is a technology agnostic approach through providers that incorporates the channels, and provides a single dashboard that enables businesses decisions to be made based on insights, rather than just intuition.
The thing with data is that the findings are hard to dispute, so long as you are confident in the original sources, sensors and algorithms. The future won’t necessarily be dictated by the latest flashy communications channel. Instead it will be led by smart approaches, and increasingly, that means taking steps to focus on automation, analytics and innovation of Customer Experience in a meaningful way.
It found nearly two-thirds of employees value new technological tools such as AI in the workplace. In fact, 64 percent of UK employees say it makes them more effective and allows them to focus on other tasks.
The findings reveal an overwhelmingly positive outlook from employees, despite the negative headlines anticipating such technologies would replace humans in the workplace. More than two-thirds of employees say they don’t feel threatened by technology at work. They don’t expect the technology to become a threat anytime soon either, given that 59 percent don’t believe AI or bots will take their jobs within the next ten years.
In fact, employees see AI as pivotal to business success with more than a fifth saying they believe AI or bots will be crucial to their companies ability to stay competitive in the future. While the survey shows that people are more excited about AI technology than fearful, it also found that in the long-term they want assurances from their employers in the form of training. The research showed an overwhelming majority (86 percent) of employees expect their employers to provide training that helps them prepare for an AI-enabled workplace.
Meanwhile, a fifth of employees say they are already working with AI, while just 16 percent report a negative experience of new technological tools in the workplace.
Other findings include 64 percent of employees believing there should be a requirement that companies maintain a minimum percentage of human employees versus AI-powered robots and machinery, and 41 percent of millennials saying they spend 50 percent or more of their time interacting with machines and computers rather than humans.
Steve Leeson, Vice President for UK and Ireland for Genesys, said: “It’s encouraging that the UK’s workers recognise the potential new technologies such as AI have to make their jobs more fulfilling and the value it can bring to businesses.
Some jobs will evolve as human work combines with the capabilities of AI. It’s increasingly important for companies to assess the need for training programs to help employees further skills like creativity, leadership and empathy, which AI just can’t replace.
“Businesses that adopt a blended approach to artificial intelligence, where AI-technologies work in unison with employees, will get the best out of their technology investment and their skilled workforce.”
There is ever increasing interest in the role emotions play when managing Customer Experience in the contact centre.
At the same time, there is a drive to introduce technology such as chatbots to make customer service teams more efficient; removing repetitive tasks and providing ‘always on’ customer service. These potentially conflicting trends are happening at a time when the demand for customer service is growing, and organisations are fighting to differentiate themselves through their customer service offering.
A recently commissioned study by Forrester Consulting suggested that 90 percent of customer service leaders agree personalisation is core to the future of automation, and existing chatbot technology is stalling their efforts. The key challenge is to build simple yet personalised experiences for customers.
As Maya Angelou famously said: “People don’t always remember what you say or even what you do, but they always remember how you made them feel.” If your chatbot or AI solution leaves the customer feeling frustrated or angry because they have to put in more effort to get the answer to what they perceive is a routine query or task, all that is being achieved is an increased chance that the customer will look for an alternative supplier who can make this task easier.
In addition, quite often humans want to talk to humans. A study by PwC found that an average 74 percent of non-US consumers want more human interaction in the future and that 59 percent of all consumers feel companies have lost touch with the human element of Customer Experience.
Certainly, there have been strategies employed whereby chatbots are being disguised as humans which can only lead to frustration on behalf of the customer when they find they are being deceived and the bot cannot fulfil their needs for a more emotional or complex issue response. While customer views are constantly evolving, I still think Userlike got it right with their view on avoiding the ‘uncanny valley’.
Organisations need to be up front when a customer engages with them by disclosing that they are talking with a bot, and take advantage of the benefits that can be gained when effectively deploying it for more routine and simple tasks. In addition, they need to give the customer the opportunity to seamlessly switch to a human agent, without the need for the customer to repeat themselves. In short, make it easy, make it simple and, when the customer is speaking to an agent, make it personal.
No one can deny that AI is getting better and better, and chatbots will certainly have their place in our future. A well-designed customer-centric journey will allow the bots to tackle low level tasks, but companies also have to be cautious in blindly launching bots into the contact centre eco-system. When poorly executed the effect upon customers can be detrimental to their overall experience. It’s all too easy to deploy a chatbot that can get stuck in a loop, resulting not only in an increased cost to serve but also a decrease in overall customer satisfaction.
Hockenbury & Hockenbury in Describing Psychology (1997) described emotion as “a complex psychological state that involves three distinct components: a subjective experience, a physiological response, and a behavioural and expressive response”. Delivering customer service for an organisation dealing with often highly emotive subject of money, we have yet to find an AI solution that can effectively replicate the human touch our industry-leading customer service team can deliver. They can handle the simple routine tasks well, but then so can a well-designed FAQ or Help Centre. Until such a time as when chatbots can manage all three psychological states, there will always be a need for humans.
Human agents have a big advantage. They understand compassion, they can demonstrate empathy and they have their own shared experiences of everyday life which continues to become busier and more stressful for us all. In having this unique skill set, the human agent is here to stay and will own the complex matters where a human touch is needed.
The recent Genesys Xperience19 conference in Denver, Colorado, saw some of the most exciting developments in Customer Experience technology showcased to an eager global audience, and in case anyone was under any illusion about the future of CX – it involves AI.
The tech itself is dispassionate, and can appear benevolent to users as it cheerfully helps them along their customer journey. However, decades of pulp sci-fi dystopia has left AI with an image problem – no matter how helpful it may seem, some simply cannot shake the idea that bots might someday pull a Hal 9000 and see humans as inferior and deserving of subjugation…or worse!
Such fears ought to be dispersed when one discusses the details of AI technology with the real intelligence behind it – someone like Olivier Jouve, Executive Vice President of Genesys Purecloud, perhaps the planet’s most popular contact centre platform.
Olivier took on the role of PureCloud EVP in 2017, having spent over three decades honing his craft in pioneering customer sentiment technology development, including through senior positions at IBM.
His impressive resume also reveals a stint as an associate professor in computer science at Leonardo da Vinci University in Paris, and today, with a 150-strong AI team under him, Olivier is one, if not the world’s foremost authority on AI and its ability to make our journeys as customers easier.
He knows, in detail, how much his tech helps us in our lives – often without us realising it – but still the idea that AI will have a negative impact on humanity can cloud the vision and judgement of some sceptics who see it as an evil overlord-in-waiting.
Fresh from a timely Xperience19 breakout session on AI Ethics, Olivier took time out to chat with Customer Experience Magazine about his work, its reputation, and just how much AI is used for the betterment of our lives as consumers.
Speaking of the “creepiness factor” that some associate with AI, its access to personal data, and how it could be used in the wrong hands, he describes why being open and honest with customers, and letting them see the advantages with their own eyes, is the best way to go.
“We want the customer to know that we respect their data, and we need them to see what data we are using, so they are able to opt out if they so wish,” he explains.
“In the way we build our products, we do a lot of design thinking with customers to understand where the limit is. You know, what type of data they are comfortable with.
“And of course, you are being careful not to introduce any bias, which is something that’s very complex – not using any gender, or lifestyle, race…whatever, that could turn your model into something that is going to be targeting a specific minority.
“This wasn’t on the table 15 years ago when we were already scoring contact centres for next best action, cross-selling, up-selling, and so on, and using that data. Now there is much more sensitivity about how you use the data, and I think that’s actually a good thing, as it forces us to be clear from the get-go.”
Olivier highlights that those who are creeped out by an AI’s use of data, to the point where they will walk away from it, are a small minority compared to those who see the benefits and remain loyal to brands brandishing the tech.
“Companies which use AI the right way will enjoy great benefits, by being fair and respecting privacy,” he adds.
A common cause of ‘creepiness’ is the notion that a customer is unaware if they are interacting with a human or a bot on their journey with a brand, but as Olivier sagely states, that uncanny valley effect is being superseded by good old fashioned customer satisfaction when the AI does its job – and does it well.
“Me, personally, I don’t care if it’s a bot, as long as I get what I want, quickly, and with a great experience,” he continues.
“I don’t think customers care as much about the technology they use as much as the experience they have. I do think we should disclose that it’s not a human though – that should be part of the disclaimer. But at the same time, I don’t see that as something that should be discouraging people, who may think ‘oh no I’m not talking to a bot as I won’t get anything from it’.
“That’s also a danger of going to market too quickly with AI tech – some chatbots don’t provide the right experience. There are, however, things chatbots and voicebots can do very well, and I think we should double down on those.”
As Olivier points out, it’s not as if customers aren’t already used to interacting with bots on a less ‘intelligent’ level already.
“When someone asks for the balance of their bank account, they don’t care if it’s a human giving it to them, so people are already used to this sort of automation. We just have to be careful that if we go deeper with more complex things that users don’t get the feeling we are not responsive.
“People like empowerment, and chatbots can be great for that. But there are still some limitations, so we are not yet at the stage where AI is going to replace humans. We have chatbots that are very specialised and do things very well, but we need to find the right moment where you have to hand over to a human.”
Yet will there be a day when there is no human to hand over to? Will we fleshies be redundant in a future where all the work is being done by bots?
“I don’t think it will replace humans, as we are putting humans into something they are really good at, and so I see that more as a collaboration between AI and human – something we call blended AI. We can do sentiment analysis automatically, but it has limitations,” Olivier replies.
So what are these human skills that we can still feel superior to the bots on, and that customers still desire on their journeys? What’s the key difference that currently keeps humans in contact centre customer-facing roles?
“Empathy – we aren’t there yet,” says Olivier.
“Humans are great at it, and we need to think of the overall CX, the CX we want to provide. AI does things a human cannot do because you could not integrate all the different insights you have about a customer, but AI is really good at that.
“However, to take the conversation to the next step, at some point currently you have to hand over to a human. Of course, even humans need to be taught empathy in some cases!
“Perhaps in 10 to 15 years we will be able to train AI better in applying empathy, but for now that’s why we need this combination of human and technology.”
Ok, enough of what humans can do better than bots! It’s time to let Genesys genius shine, as Olivier outlines exactly why today – not years from now – AI is simply superior in most non-emotional ways to humans when it comes to steering customer journeys and earning the desired end result – superior Customer Experience.
“Our products work on finding the best agent for an incoming call or interaction, something we do very very well thanks to machine learning. The AI is understanding what the topic of the interaction is and uses historical data and a sophisticated decision tree to move the interaction forward.
“We want to optimise the customer journey, so we have a solution called predictive engagement. We can look at what a user is going to do on a website, view their navigation, and see at which moment he or she might need some help, and decide what the best outcome is for this customer.
“Once you have this interaction you can develop additional models which could be for retention or selling – more things we do very very well that a human couldn’t match.
“When you do next-best action and you have a customer calling who is ready to leave, our tech knows if you go for a specific action, there’s an 85 percent chance that this person is going to remain as a customer, for example.
“That’s from crunching a lot of data, gained through similar situations, and handling so much data – well that’s not something a human can do.”
Other than the aforementioned empathy, does Olivier feel his AI is lacking in any other areas that might be beneficial to overall CX?
“I think what AI does not do very well yet is go deeper into the conversation,” he tells me.
“We see breakthroughs when we look at things like Google Duplex, where, you can find yourself questioning if you are interacting with a real human or not, but an AI able to handle 100 percent of complex interactions? I don’t think we are there yet, though we are making a lot of progress.”
Despite my impatience as a customer to know when exactly this will be possible, Olivier sensibly refuses to give a date.
“I don’t like giving predictions, but I see how fast we are moving forward. I think Duplex was really a breakthrough – suddenly you see something and you think ‘wow’ – the voice, the type of interactions…it’s all very human.
“I don’t think it’s about developing the technology now – it’s about the right data and making it accessible. All of that is moving at an exponential speed. What’s really accelerating AI is that everything is in the cloud. Every single interaction from the employee or customer’s side – all that is feeding our AI platform. The more data we have, the more we are going to be able to power the customer journey.”
In the midst of such dazzling tech capabilities, it can easy to forget any regulation necessities to protect data and ensure AI is used for the good of humanity.
“It’s our responsibility to propose how we want to be regulated. It’s the right time to do the right thing,” adds Olivier
“Over 30 years I’ve been through a few AI ‘winters’, where AI had been at peak hype, but then died. I don’t want this one to die because some people are not responsible, so I will do whatever I can to make sure we are doing the right thing.
“There remains a fragility to this whole sphere, caused by the actions of Cambridge Analytica for example, which rocked people’s confidence in AI and data use, but I believe what we are developing at Genesys is promising, and beautiful, in a way that will not kill the hype this time around.”
Artificial Intelligence (AI) plays an important role in Customer Experience, marketing, and personalisation; It has the power to generate predictions about what goods and services customers are likely to want, when the demand will arise or propensity to switch will occur, and which platforms they are most likely to purchase on.
As a result, AI has become critical for brands wanting to improve their capabilities to offer personalised experiences, offers and recommendations. What’s more, marketers should not overlook the importance and the business case for investing in AI technology to deliver high-quality personalisation at scale.
Personalisation is key to customer loyalty, customer experience and increasing sales, with Accenture recently finding that 91 percent of European and American consumers are more likely to shop with brands which provide relevant offers and recommendations. Clearly, enhancing a brands ability to engage with customers on an individual basis is critical for brand success.
However, investment alone is not enough – in order for AI to provide effective personalisation, it must be implemented and used in the right way.
Organisations need to develop more sophisticated AI capabilities which can collect and analyse data in real timeto offer tailored services and products. This capability must be advanced enough to work for customers as they move through an omnichannel journey which often spans across websites, apps, email and high-street stores, for example.
While it is true that AI has the potential to bring about this ‘new era’ of personalisation, the technology alone cannot guarantee quality personalisation. For that, business leaders must appreciate the value of AI, develop a clear strategy for implementation and ensure that it is monitored and improved by experts who truly understand it.
Make the business case for investment
One common barrier to the adoption of sophisticated AI and machine learning technology is business leaders being deterred by the cost of the initial adoption process. While it’s true that AI can remove the manual, time consuming, and often overwhelming challenge of managing and understanding vast amounts of data, just like any new technology, the initial set-up process can be expensive and labour intensive.
Also, the skills required to implement this level of technology mean that organisations may face the added time and expense of employing dedicated data scientists, and adjusting towards a culture where marketers and technologists must work closely together As a result, organisations may feel that there is not a viable business case to invest.
To counter this point of view, take a step back and consider the long-term benefits of the initial investment. If AI is implemented strategically, the future pay-off will be enhanced capabilities for personalisation, improved Customer Experience, and increased sales.
Even as far back as 2014, McKinsey found that maximising customer satisfaction, which today largely comes from offering personalised experiences, would result in a 15 percent increase in a brands revenue. More recently, research from Econsultancyfound that 93 percent of companies see an uplift in conversion rates from personalisation. Together with a well implemented AI strategy, brands can personalise even more effectively and potentially see even greater conversion rates.
Develop a clear strategy first
It is important to understand that simply having AI and machine learning capabilities does not guarantee that a brand can offer a higher quality of Customer Experience. In order to see significant improvements, organisations must first decide what CX problem they are aiming to solve, which data sets they need to collect and monitor, and how they are going to use the data to remove the particular pain-points that customers face.
Whether a brand wants to convert more website views to purchases, increase the number of customers returning to the site, offer a smoother transition across different touchpoints, or improve online self-service, these priorities must be decided from the outset. Then, the right data can be collected and harnessed to address the issue.
With an overwhelming amount of data being generated and collected by companies today, this is an effective way to streamline efforts and ensure the most important issues are dealt with first.
Shoe retailer Footasylum provides a great example of the benefits of strategic AI implementation. It focused first on the specific pain-point of friction in the customer journey between stores and the web by using AI to link in-store purchases with online systems such as loyalty schemes, to create a single customer view. The brand can now predict which customers are most likely to purchase particular products and when. As a result, it has seen an 8,400 percent return on ad spend. Footasylum’s next mission is to breathe life back into the high-street by using AI to enable the web to automatically share valuable customer information with brick and mortar stores.
Lay the foundations for advanced AI
In order to get a good understanding of which CX problems to address first, brands should undertake background research with marketers identifying which personalisation processes are currently creating the most conversions online, and which are less successful.
Another important foundation is to ensure that all data sets are integrated and consolidated. In order to offer recommendations in real time, brands must be able to predict consumer needs and use data to meet them at the right moment, on the right platform. AI can be used to accurately forecast where the customer will be in their decision making. Without access to all of the data about any given customer, there will always be a limit to how successful these predictions can be.
In conclusion, it is key to remember that although AI has great potential to offer tailored experiences for customers in real time, the initial investment does not automatically guarantee quality personalisation and a return on investment. For that, a solid foundation must be put in place by understanding where customer experience can be improved, deciding on a clear strategy for implementation and removing data from silos.
Then, AI has the power to offer personalised experiences which offer true value to customers and meet, or even exceed, their expectations.
Genesys hasintroduced new orchestration capabilities powered by AI that connect native and third-party technologies to enable the most comprehensive customer journey management available today.
Currently, businesses are adopting an increasing number of artificial intelligence (AI) point solutions to solve specific challenges. However, businesses are failing to realise AI’s full potential to improve customer and employee journeys because data remains fragmented across the end-to-end experience. As a result, AI’s ability to impact business outcomes remains limited.
New orchestration capabilities from UK Customer Experience Awards sponsor Genesys make it possible for multiple AI applications to work together harmoniously in real-time from marketing to sales to service. By leveraging all relevant data throughout the customer’s entire journey, Genesys AI can orchestrate, measure and optimise processes at every touchpoint. This enables businesses to tailor automation, communication channels and marketing and sales offers for individual customers, introducing new levels of personalisation.
AI innovation at your fingertips
Genesys makes it easier for businesses to flawlessly connect and manage native and third-party AI across voice and digital channels. With its simple centralised orchestration, Genesys AI enables customers to map complex business logic, perform various back-end system integrations and swap AI providers.Businesses can move their AI technologies into production quicker by building once and deploying across all channels, leveraging microapps to reduce development time by 90 percent and improving analytics, resulting in 40-60 percent faster time to value. This enables businesses to leverage existing AI investments and buy a future-proof solution.
Both on-premises and cloud customers around the world are realising additional advantages. An example is Entel, one of the largest telecommunications companies in Chile. In just six months, Entel has increased revenue by five percent, decreased costs and improved customer satisfaction by using Genesys AI to orchestrate all customer interactions with technology from Google Cloud and IBM Watson.
Other Genesys customers, such as DNB, are achieving additional benefits including improved accuracy leading to better predictions and faster responses to customer inquiries. In speaking about the benefits of this advanced orchestration capability, DNB Head of Technical Operations and Customer Solutions, Anders Braten said: “Genesys sews everything together to make the perfect customer journey.”
Breaking down AI silos to realise value
“In customer service alone, on average, nine out of 10 enterprises deploy AI for six distinct uses, such as automated self-service, chatbots in instant messaging and IVR support,” said Peter Graf, Genesys Chief Product Officer.
“Genesys AI is an elegant solution that masterfully links underlying technologies and synchronizes data and event streams as needed. These AI capabilities are delivered by Genesys Cloud, the company’s high-velocity innovation platform that provides new ways to optimise customer and employee journeys.”
With hundreds of technology applications integrated with its Customer Experience platform today, Genesys is the only company in the industry able to orchestrate any AI for self and assisted service. This includes Kate, the customer and employee virtual assistant powered by Genesys AI, as well as third-party AI solutions such as Amazon Lex, Google Cloud Contact Center AI, Nuance and IBM Watson.
Dan Miller, lead analyst at Opus Research, said: “Genesys has stepped up to provide a framework for enterprises to support conversational engagements that helps businesses leverage existing investments in AI resources more fully. Genesys AI enables them to integrate natively developed elements of AI along with offerings from recognized, leading third parties.”
How Genesys AI orchestrates the cest Customer and Employee Experience
Genesys AI provides the common data framework for all AI integrations so systems are not working in silos. It captures, processes and analyzes third-party data in the same way as its own AI applications, such as Genesys Predictive Routing, Altocloud Predictive Engagement, and Automated Forecasting and Scheduling. In addition to delivering advanced orchestration, Genesys AI enables real-time predictions, speech and text analytics, self-service automation and more.
An example is the coordination between Genesys AI and chat and voice bots. When a customer begins an engagement with a bot, Genesys AI can detect if escalation is needed. It can then use Predictive Routing to identify the employee deemed the best match and pass the inquiry to that individual with full context for resolution.
Advanced AI orchestration kicks off summer innovations
The company announced its new orchestration capabilities at Xperience19, its signature event taking place this week in Denver. Genesys is also introducing a new analytics dashboard, enabling businesses to better understand customer intent, visualize containment rates and optimize bot usage in a single view.
The new dashboard and Genesys AI’s advanced orchestration capabilities are available now among a broader collection of the company’s Summer Innovations.The Innovations are comprised of multiple feature enhancements across the GenesysPureCloud®, PureConnect™ and PureEngage™ solutions and delivered via Genesys Cloud.
The ability of artificial intelligence (AI) to grasp morality and empathy are among concerns expressed by customers when it comes to interacting digitally with brands.
The lack of trust in AI has been revealed by Pegasystems Inc. and research firm Savanta, who surveyed 5,000 consumers across the globe. They found that many don’t understand the extent to which AI can make their interactions with businesses better and more efficient, while one-in-ten said they believed AI cannot tell the difference between good and evil.
The suspicions on morality seeped into customers’ overall opinions on brands, with 68 percent believing organisations have an obligation to do what is morally right for the customer, beyond what is legally required.
Sixty-five percent don’t trust that companies have their best interests at heart, raising significant questions about how much trust they have in the technology businesses use to interact with them. Less than half (40 percent) of respondents agreed that AI has the potential to improve the customer service of businesses they interact with, while less than one third (30 percent) felt comfortable with businesses using AI to interact with them.
Just nine percent said they were “very comfortable” with the idea. At the same time, one-third of all respondents said they were concerned about machines taking their jobs, with more than one quarter (27 percent) also citing the “rise of the robots and enslavement of humanity” as a concern.
Over half (53 percent) said it’s possible for AI to show bias in the way it makes decisions, and 53 percent also felt that AI will always make decisions based on the biases of the person who created its initial instructions, regardless of how much time has passed.
Meanwhile, just 12 percent of consumers agreed that AI can tell the difference between good and evil, while over half (56 percent) of customers don’t believe it is possible to develop machines that behave morally. Just 12 percent believe they have ever interacted with a machine that has shown empathy.
The results of the survey coincide with plans by Pega to “improve empathy in AI systems”, and speaking of the poll results, the firm’s VP of Decisioning and Analytics, Dr Rob Walker, said: “Our study found that only 25 percent of consumers would trust a decision made by an AI system over that of a person regarding their qualification for a bank loan. Consumers likely prefer speaking to people because they have a greater degree of trust in them and believe it’s possible to influence the decision, when that’s far from the case.
“What’s needed is the ability for AI systems to help companies make ethical decisions. To use the same example, in addition to a bank following regulatory processes before making an offer of a loan to an individual, it should also be able to determine whether or not it’s the right thing to do ethically.”
He continued: “An important part of the evolution of artificial intelligence will be the addition of guidelines that put ethical considerations on top of machine learning. This will allow decisions to be made by AI systems within the context of customer engagement that would be seen as empathetic if made by a person. AI shouldn’t be the sole arbiter of empathy in any organisation and it’s not going to help customers to trust organisations overnight. However, by building a culture of empathy within a business, AI can be used as a powerful tool to help differentiate companies from their competition.”
The Barbican’s newest exhibition, AI: More than Human, is an artistic exploration of the possibilities thatmodern technology presents, examining the diverse potential of artificial intelligence (AI).
A particularly striking installation is MakrShakr, a robotic bartender which can mix cocktails for customers via an online pre-order system.While undoubtedly a fun gimmick, the introduction of AI into a traditional service role raises important questions about the future of our restaurants, cafés, and bars.
The food and drink industry is no stranger to new technologies, and the latest developments are an evolution of sector staples like the sushi belt and fast food self-service machines.However, the gradual move towards AI presents unique new challenges.Principally, to what extent can automation really reflect Customer Experience value generated by humans in what is an intrinsically personal sector?While few would argue that real employees can ever fully be replaced, increased automation should come with a few health warnings.
Choosing the right persona
Finding the right persona for an AI system is the first step to ensuring customers actually enjoy using it.It’s important to have a welcoming interface, but this can be quickly undermined if the technology doesn’t work as it should.Successful AI personalities like Alexa and Siri are approachable and lighthearted when the situation dictates, but they’re primarily programmed to be as helpful as possible so people can find what they want quickly.
In the service sector, making the interface fun and playful is especially important, but there also needs to be a level of emotional intelligence present for when things go wrong.Investment in self and situational awareness so that customers feel their needs (and frustrations) are understood goes a long way.For voice services this means ensuring bots recognise emotion and intonation when customers speak.And where the technology isn’t voice based, a simple on screen message – for example an apology in the case of slow service – makes technology feel as attentive as humans would be in that situation.
This isn’t to suggest AI can ever replicate the value ofexisting employees, who will always be the major drivers of high quality CX.Instead, AI should complement staff, freeing them up from administrative or procedural tasks and allowing them more time to engage qualitatively with customers and build brand loyalty and retention.
Upselling is a major part of successful service businesses – everything from ‘do you want fries with that?’ to making sure diners have dessert and coffee at the end of a meal.For AI, this represents both a challenge and an opportunity.Making these transactions appear conversational and informed is key; just think of the persuasiveness of a genuine recommendation from a well-read employee at Waterstones compared with the ‘frequently bought with…’ pop-ups seen online.
Like finding the right persona, successful upselling relies on engaging customers, showing awareness, and demonstrating genuine knowledge.Recommendations should be presented as being bespoke to specific customers, not just based on the habits of other people.
Keep it fresh
Multiple conversations with the same person do not feel like the same experience over and over again – and interactions with automated services should be just as refreshing.Where an AI uses voice, this might mean mixing up the repertoire and programming varied responses to common questions.For others, different aspects can be kept fresh.In the case of our robot barman, making sure the menu is regularly updated to encourage people to come back for more will engender regular customers.
At the time of writing, the Barbican’s robot barman has already temporarily closed because of technical issues – proving that the museum exhibition is a long way from the reality of frontline customer service. It is inevitable that automation will become more widespread, we just need to make sure that the consumer, not the technology, remains king.
Ask the leadership of any reasonably-sized company what technology they’re looking to implement and they’ll almost invariably mention artificial intelligence (AI).
In theory, that’s great, because AI has the potential to fundamentally change the way a businesses operates and creates a great Customer Experience. The longer the business uses an AI application, the better the experience should get. Given enough time, the system can collect enough data on each individual customer to provide meaningful, hyper-personalised experiences.
Implemented badly, however, AI can be a total disaster. Rather than feeling like the business they’re dealing with cares about them, they’re left with the impression that customer service has been handed over to a bunch of dimwitted machines.
Let’s talk about chatbots
The easiest way to illustrate how varied the AI experience can be is to look at chatbots. They’re the kind of front-facing AI that more companies are using and which an increasingly large body of customers are familiar with. Trouble is, most companies are terrible at implementing chatbots.
Apart from a few forward-thinking exceptions, companies tend to put a chatbot on their website in the hope that that it will learn from each interaction it has with a customer and that its answers will become more nuanced over time. They also operate in the belief that customers will tell the chatbot when it’s wrong, helping to train it further (hands up if you’ve ever done this willingly).
That would be great…if the chatbot was actually equipped to do so. However, for the most part, chatbots are simply going through the company’s existing knowledge bases and serving you with a document (or, in the worst cases, multiple documents) to try and help. It’s essentially a slightly smarter form of search.
As anyone who’s tried to use the search function on a corporate website will tell you – that’s not particularly helpful, especially when you’ve got a specific query. Let’s say that I want to know if I can insure my sunglasses. I don’t want to have to scour through insurance agency documents to try and figure out the answer. I just want the answer.
Contextual, hyper-personalised, relevant
As long as chatbots rely on a flawed architecture that depends on the existence of relevant documents containing the needed information, they won’t be able to provide that answer.
If you’re going to use AI to improve CX, you need to take a different approach. If you want to operate in the digital era and want to drive logic through data then you need to start it in data. That means looking beyond your existing documentation and CX architecture and integrating insight into customer behaviour across digital and offline channels.
This approach will, ultimately, allow you to offer customer support that is hyper-personalised, relevant, and compliant.
A chatbot built on this kind of framework understands what you’re asking and can answer specific questions according to what you actually need. While that’s just one small part of CX, anyone who’s cursed a company for failing to provide useful information, will know how important it is.
The aim of AI
That said, this approach shouldn’t be limited to chatbots. Consistency – in style, tone, and content – is one of the most important factors in successful CX.
It’s therefore imperative that any organisation turning to AI to improve CX apply a data-first architecture across every customer-facing channel. So, whether I make a query using a chatbot, the search function on a website, or a call centre, I should get the same – relevant – answer.
However, if this is going to happen, businesses need to stop trying to bolt AI onto their existing architectures and take an approach that allows it to reach its full potential.
Large majorities of British consumers prefer dealing with humans over automated services for everything from querying a bill (85 percent) and changing account details (62 percent), to making a complaint (84 percent), buying a product or service for the first time (77 percent), chasing an order (73 percent), or dealing with a fault (78 percent).
These are the findings of an online YouGov survey of more than 2,000 British adults commissioned by CX firm Webhelp.
Nearly half of respondents (45 percent) said they had never used any type of AI, but amongst those who had there was widespread dissatisfaction around its efficacy and perceived value. Nearly half (44 percent) also believe that AI will not positively impact their lives in any way over the next five years.
Just over a quarter of respondents said they had used a customer service chatbot (27 percent), interactive voice response or IVR (27 percent), or smart home speaker such as Amazon Alexa or Google Home (26 percent).
Whilst over a third of those who had used these types of AI were dissatisfied with the chatbots (35 percent) and IVR (38 percent), smart home speakers proved more popular – possibly because these are chosen rather than encountered by chance. Fewer than half (45 percent) of those who had experienced a customer service chatbot were satisfied with it and 38 percent were either fairly or very dissatisfied with IVR. In contrast, 77 percent claimed to be satisfied with smart home speakers.
Looking ahead five years, over a quarter of respondents (26percent) felt that increased use of AI-driven Customer Experience tools would make interacting with companies “much worse” compared to only 19 percent who felt the impact would be positive. Other negative perceptions include fear that AI will make dealing with brands/companies more impersonal (52 percent), increased threats to privacy and security (46 percent), and detrimental impact to human-to-human interactions (43 percent).
Webhelp CEO David Turner said: “We know from anecdotal evidence that human-to human contact is important, but this study goes even further, highlighting the degree to which people favour it over AI-powered customer service tools and are negative about AI’s potential future impact.
“As exposure to AI increases in day-to-day life, people are likely to become more receptive, but this research confirms the importance of striking the right balance between the advanced technology services we offer and the incredible human talent of our local teams of agents, advisors and planners. Our approach will always be customer experience driven, so this window into consumer perception is extremely valuable for helping our clients implement AI solutions which offer clear end-user value.”