Kandy White, Vice President of Business Service Technology at ADP will be speaking at Keynotion’s upcoming Big Disruption Summit. She will deliver a keynote presentation on ‘Enabling Innovation at Scale through Constructive Technologies’. In the exclusive interview with Corporate Parity she shared her perspective on digital transformation, human-technology integration and prospects for businesses in the new environment.
On a broader scale, the emergence of 5G will change what’s possible for devices and technologies that need higher bandwidth to reach their potential. You can get connected just about anywhere via wired or wi-fi access to the internet, but our digital highways are increasingly heavily trafficked and congested. Research done by Business Insider suggests that there will be more than 24 Billion connected devices by 2020. Network clogging caused by the interaction of these devices with the data center can significantly slow processing and responsiveness. The arrival of 5G enables this processing to happen „on the edge“ at the level of the device itself and so removes boundaries for autonomous smart devices and other IOT. This opens the door to real time computational possibilities where robots, cameras, sensors, unmanned drones, and other IoT devices are able to make immediate judgments enabled by AI, without having to consult a server back at the data center. For example, a smart camera will be able to execute image recognition, identify you in real time, and unlock entry to your home or office.
According to ABI Research, the number of devices with the ability to execute edge AI will grow from 79 Million in 2017 to 1.2 Billion by 2023, and the tasks these devices complete on the edge will increase by 7X. So we can expect rapid advancement of edge AI in support of smart cities and buildings, autonomous vehicles on the ground and in the air, augmented reality, wearables – the possibilities are almost overwhelming.
In terms of truly life-changing technology, health and wellbeing will be greatly improved through the advancement of AI, edge computing, biometric scanning/monitoring, gene-editing, and 3D printing. These technologies will start to converge to make remote healthcare, earlier diagnosis — possibly even reversal of a permanent disability – a reality. Today, AI is already in use to help visually impaired people perceive the world. Gene editing is being used to eliminate susceptibility to certain illnesses and other aI has potential to reverse some forms of paralysis – that is– if you’re willing to have a chip implanted in your brain.
The bottom line is: Our healthcare will be higher quality and availability than ever before, and it’s exciting to imagine how things will change. Think about a smart city where a first responder can get to the scene faster because smart cameras and traffic lights clear the way. Once she gets to the scene, she has already been presented with details about what to expect so she’s better prepared to provide the right medical care and save lives.
There will be many hurdles to cross to ensure the solutions are ethical, safe, and reliable. What if there are numerous emergencies after a catastrophic event, and a machine chooses who gets treated first? These are tough questions. Even so, these technologies will make modern healthcare faster, smarter, and better for many who don’t have it today. Over time, these will reshape the healthcare industry entirely.
Forbes Insights recently surveyed over 300 executives and found that 95% believe that AI will play an important role in their future responsibilities. As these emerging technologies start to scale, there is potential to increase productivity, reduce costs, improve time to market, and deliver through new business models. The early adopters will be the industries that are under pressure and must change to survive. As with any new technology, tenured, regulated, and traditional companies will struggle to adapt their legacies and overcome talent deficits, technical debt, and outdated structures to take full advantage. Start ups and Upstarts will have an easier time adopting and embracing new tech, but may struggle with the investment and scale needed to be profitable.
I expect to see innovation and disruption in a variety of sectors including urban planning, transportation, retail, healthcare, telecommunications, travel, defense, security, finance, and many others.
It’s difficult to choose one industry, but I’d look to transportation and automotives as industries that are being disrupted already with ridesharing apps like Uber and Lyft. With the advent of autonomous vehicles and real time analytics of traffic patterns, this disruption will accelerate. Companies like Waymo are already piloting autonomous taxi service with very little oversight or regulation. It will be interesting to see how quickly these solutions take hold of the market and if they deliver on the promise of safer, lower volume traffic. I’m also interested to see what role governments will play in helping or hurting the rise of autonomous transportation.
There are a couple of immediate use cases. Let’s start with service transactions. From B2C perspective, I’m closely watching the intersection of AI driven Machine Learning (ML) and Natural Language Processing (NLP) to support Asychronous Messaging. To keep this simple, AI represents a broader spectrum of artificial machine intelligence. Machine Learning is a specific method of AI, and NLP is a specific method of Machine Learning that enables bots to communicate in a language that is natural to humans. Bots need training, but as they learn to process information faster, they deliver faster responses leading to greater adoption by the consumer. At ADP, we’re currently training our digital assistant, Rosie, to handle simpler payroll transactions and answer questions in real time without human assistance. She has handled tens of thousands of client interactions without a handoff, but we have humans there to jump in if Rosie gets stuck. Our long view for Rosie is to introduce Asynchronous Messaging wherein continuous conversations happen via a text-like experience that toggles between AI bot assistance and humans based on where the need is best served. This kind of support will break down barriers between businesses and their customers creating more personal engagement, but with digital solutions. It enables true real time connectivity for businesses to serve their customers any time on any device based on what the customer wants.
There are solutions already in market today for retail, travel, finance, and entertainment. More on that later. As consumers gravitate to these channels, bots get smarter from these continuous interactions. They will get better and better at providing the right experience and answers, and over time, I don’t think people will be able to tell if they are talking to a machine or a person.
A second, less interactive use case is the case for more secure access through biometric authentication. Biometrics like face, voice, or fingerprint authentication will make it more difficult to break down security protocols and gain access to private customer information. Most financial firms are already leveraging these kinds of solutions, and we can expect the same for other industries as consumers come to expect easy, highly secure access methods for their products and services. People don’t want to have to remember PINs or Passwords, and these access methods frustrate clients. We can and should do better.
While the immediate use case is for security, biometric solutions can also be used for assessing customer and employee sentiment or mood and recommending actions for a sales associate or manager to improve their interactions. So there is potential here to increase overall customer and employee satisfaction and retention, and that’s a pretty compelling opportunity.
Consumer expectations are always evolving based on their last great consumer experience, and most consumers are almost certainly using AI and ML today – even if they don’t know it. If the experience is compelling, people will expect that experience from every interaction across every facet of their lives. Most consumers are technology agnostic, they just want it to be simple and accessible. This is why many people gravitate to Google, Apple, Amazon, or Facebook first: because they know what to expect and they trust the experience will be reliable and good. These companies leverage AI to make life easier in simple ways: predicting better commutes, turning lights on and off, labeling and categorizing photos and emails, serving up relevant social media or other personal content, knowing what you want to buy before you know it, and providing day to day guidance or assistance— even ordering pizza. As the technology improves, adoption accelerates. Over time, large behavioral data sets underlying these transactions will inform and change how we design customer experiences and offers. Machine learning coupled with edge computing will identify patterns and deviations in consumer behavior that a human might miss, and then reconfigure experiences to remove friction and improve the flow of customers through a store or an online experience.
There are many notable examples of machine learning in market today, so I’ll highlight a few that stand out recently. Most of the big finance firms have deployed some combination of bot, human, customer interaction. A great example of this is Bank of America’s chatbot „Erica“ or Royal Bank of Scotland’s „Cora“. Both of these have been in market for a couple of years and are reaching maturity with thousands of conversational responses. These AI assistants help consumers view and schedule payments, transfer money, lock and unlock debit cards, and manage other banking transactions.
In media and content, Verizon has launched the Fios chatbot on Facebook Messenger to assist their Fios customers to find, watch and manage the video content that matters most to them. The same kind of experience is served up by Netflix, Youtube, Amazon’s Prime Video, and other content providers.
On the smarthome front, Nest’s learning thermostat is voice enabled and will actively anticipate and adjust the temperature throughout your home based on your behavior. Smart appliances like refrigerators, washers, dryers, vacuums, ovens, and microwaves are overtaking the traditional appliance market as the tech gets cheaper and more reliable.
In Retail, Japan’s SoftBank and French Aldebaran have developed Pepper, a humanoid robot that greets and interacts with customers live in retail stores, whose presence has increased foot traffic dramatically. Another retailer, North Face has leveraged AI to help customers select the right jacket or other clothing options based on information collected from the customer about activities, location, and gender.
In food service, Domino’s pizza has experimented with aerial drones delivering pizza and more recently rolled out DRU (Domino’s Robotic Unit) which keeps food and drinks at an optimal temperature while also finding the best route for the delivery. What’s really fascinating here is that pizza delivery is being influenced by technology that was initially developed for combat training. There will be more of this crossover and repurposing of technologies as consumer expectations and adoption accelerate. Consumers will just expect their experiences to get better, simpler, and more relevant than their last great consumer experience.
The acceptance and adoption of AI and other technologies changes across different generations and roles. A dwindling segment of consumers still feel a need to interact with a human to cultivate trust with a brand or protect their privacy and security. But, that sentiment is changing as the digital natives –who don’t know a world without digital engagement – enter the prime consumer markets and become entrepreneurs. These consumers are impatient and often won’t consider providers and applications that don’t have the latest technology driving the experience. Digital Natives don’t want to have to read instructions or talk to a person. They want their experiences customized and served up to them in a way that is only made possible through AI and ML. According to a study done by User Intelligence, Digital Natives consider speed to be the most important characteristic of digital products. The device has to work quickly (24%), it has to work for a long time (20%), and has to be fun to use (17%). So we’re left with the difficult challenge of delivering an experience that drives adoption for both digital natives and digital immigrants, not to mention defining what „fun to use“ means.
Another factor is the credibility of the solution. If digital transformation is not thoughtfully implemented with an understanding of where and how it is best suited to deliver results, the effort will fail and potentially damage trust. Despite fears about the rise of the machines, technology is still an enabler to what humans want to do, so it’s only as good as the process and people that support it. Since a large population of stakeholders are Gen Xers or Digital Immigrants, the challenge is showing those stakeholders that they have a duty and obligation to embrace emerging technologies in order to stay relevant for the next generation. Some of these stakeholders have been hearing about virtual reality and artificial intelligence for decades without seeing the promise or potential become reality, so convincing them can be harder than it might seem on the surface.
The psychologist in me believes that holdbacks ultimately come down to the question of „How does this impact or help me?“ Many of the business implementations today are structured to improve productivity and agility for hidden or back office activities, so there is some natural apprehension across all populations about job and asset security. The reality is that jobs and tasks will change and the profile of the worker will change. Humans are wired to fear and resist change, so they will fear and resist digital transformation if they are not convinced that something better is waiting for them on the other side of it. Digital transformations and constructive technologies will shift to be more about creating a personalized, intuitive experience for both customers and employees. Rather than taking humans out of the equation, AI will augment human decision making and performance. The challenge is helping people understand and believe it.
Once you get everyone on board, you’re next challenge is getting stakeholders to let go of the legacy business models and processes. These folks have to accept the elimination of the systems and methods that made them successful, in some cases, things they might have personally championed and developed. It’s hard for people to let go and acknowledge that their once successful solutions won’t work for the future.
Simply put, it’s having the right talent from the start. I’ve talked to a lot of leaders and colleagues who are driving innovation in their industries, and there are some common themes that we’ve all experienced in our transformation journeys. The biggest challenge is never the technology, but the people.
As I mentioned before, technology is an enabler. To deliver real transformation, an organization has to tackle some pretty tough stuff. You have to have the right talent to identify and weed out technical debt, upgrade or replace legacy business models and processes, and set a clear vision and roadmap for the organization. This is all new to most leaders, and they have to rethink and really challenge the way they operate. This leads to realigning organizations and teams, and in many cases, the top of the org isn’t up to the challenge and will need to get on board or be replaced.
Once the right leaders and talent are in place, culture is probably the single most important – and hardest – thing to change. An innovation culture is critical to ensuring that the right social cues and priorities are ubiquitous in the environment and that everyone at every level is focused on the same goals. Too often, organizations underestimate the importance of strong change management in driving the right outcomes. In the organizations that need to transform the most, there are legacy systems and processes that people find comforting and familiar. There has to be a recognition of what is best for the company, and a willingness to do the hard things. Everyone has to be well informed and engaged in driving the transformation. Leaders must be self aware and honest enough to admit when they are not the right person to lead an org through big change, but that’s often not the case.
Technical maturity and talent have to come together to understand and best utilize digital solutions. For most transformations, a good portion of the talent will be external, and when new people come into the organization, it’s disruptive to the status quo. The assimilation of new talent can represent its own challenge because the incumbent employees will put pressure on new folks to adopt existing norms. Without the leaders communicating early and often to set the right tone, the employees that actively support and influence change will get discouraged, and the culture change will grind very slowly, possibly not happening at all.
Culture change is disruptive and causes anxiety. Leaders have to be resilient and help their teams be resilient, even in the face of failure. This brings me to my last point: most digital transformation efforts take 2 or 3 times longer than expected, miss objectives, and often fail completely. There are reams of research to support this. It’s critical to push through those failures, fail fast, learn, and try again. I would almost say you should expect to fail on your first try, but it will get better if you keep trying.
Focus outward, not inward. Stay connected to what’s happening in technology, within and beyond your industry. Look for opportunities to model best of breed consumer and employee experiences.
It’s easy to get consumed by day to day meetings and responsibilities, but make sure you set aside time to think and ideate every week and make sure your people do the same. If you know you’re not good at innovation, make sure you surround yourself with people that you can learn from.
Never be satisfied. Take action when you’re successful to evaluate where you want your business to be 3 to 5 years from now, but stay focused on executing what you need today. My resting state is unsatisfied, and that keeps me focused on delivering something even better.
Start transforming talent now. As organizations transform, the profile of your employees will change, perhaps dramatically. That means you have to understand and predict what skills and experience you’ll need in the future and start actively seeking those profiles to join your team at the right time.
Always remember that technology is an enabler. It cannot be successful without innovation culture, change management, and talent behind it. I often tell my team that good technology is invisible – if it works and provides a great experience, people focus on the experience and don’t think about the technology. That’s how it should be.
Also check our upcoming events page.