Pascal Bornet is a recognized global expert, thought leader, and author in the field of Intelligent Automation. As machines become more intelligent and human-like, we don't need to fear that this will somehow diminish us as humans. Instead, it will augment us and potentially enable us to become even more human. Kristian Hammond, professor of computer science at Northwestern University, put it eloquently: "As we humanize machines, we stop mechanizing ourselves." In other words, the more effective machines become at doing repetitive tasks for us, the more we are empowered to spend our time and energy on interesting and creative tasks, leading to greater fulfillment and self-actualization.

The benefits of digital transformation derive from the combination of its two key building blocks, technology and people, though there's debate about which plays a more important role. Clearly, it's technological advances that make digital transformation possible – big leaps in cloud computing, data analytics, edge computing, and artificial intelligence. On the other hand, you'll often hear that "digital transformation is about people, not technology." However, at its most advanced, digital transformation is not precisely about people or technology, but about the relationship between people and technology. When these two powerful entities are merged with a digital transformation strategy, a business reaps major productivity benefits that neither element can deliver by itself.

The PACE – Performance Against Customer Expectations – survey has been measuring SMB customer perspectives on banking providers and the impact of technology since 2017. On considering the latest 2019 findings as a whole, four key takeaways emerge: managing customer churn, building out trust, equipping and enabling people alongside technology, and focusing on the user experience. Over the next 18 months to 2 years, I also anticipate a fifth takeaway – embedding social impact by design, as consumers demand a commitment to financial inclusion from their core banking provider, alongside transparent metrics to measure its progress and open dialogue channels to contribute to its evolution. Indeed, SMBs may be small to medium size businesses in terms of employee numbers, but they are increasingly big, innovative and sophisticated business for the banks which serve them and help them to grow. How can you best consider your customers and evaluate their needs alongside the banking, technology and social trends that matter most?

During Aragon Research's research community meeting this week, Jim and I were discussing what it will take to manage your workforce in the future; a hybrid mixture of humans and digital labor. The discussion got us thinking about what it means to manage technology versus what it takes to manage people, and how this will change as organizations introduce AI-enabled technologies. In this blog, we explore the differences and similarities between managing humans and technology to understand how management will change as we introduce technologies that can learn, recognize patterns, and change/respond. Digital labor is a term that applies to the automation of tasks that are performed by computer applications. Our future workforce will be a hybrid combination of humans and AI-enabled technologies (i.e., bots, assistants, robotics, etc.), supported by traditional non-AI technology.

People have more trust in robots than their managers, according to the second annual AI at Work study conducted by Oracle and Future Workplace. The study of 8 370 employees, managers and HR leaders across 10 countries, found that AI has changed the relationship between people and technology at work and is reshaping the role HR teams and managers need to play in attracting, retaining and developing talent. Contrary to common fears around how AI will impact jobs, employees, managers and HR leaders across the globe are reporting increased adoption of AI at work and many are welcoming AI with love and optimism. AI is becoming more prominent with 50% of workers currently using some form of AI at work compared to only 32% last year. Workers in China (77%) and India (78%) have adopted AI over two-times more than those in France (32%) and Japan (29%).

For more than 70 years, the study of artificial intelligence (AI) and the study of communication have proceeded along separate trajectories. Research regarding AI has focused on how to reproduce aspects of human intelligence, including the ability to communicate, within the machine (Frankish and Ramsey, 2014). In contrast, communication historically has been conceptualized as foremost a human process (e.g. Schramm, 1972), with research within the discipline as a whole focused on how people exchange messages with one another and the implications thereof (see Craig, 1999). Today, this gulf between AI and communication research is narrowing, bridged by AI technologies designed to function as communicators. Recent advances in AI have led to more powerful and consequential AI technologies being integrated across daily life (Campolo et al., 2017). Individuals routinely chat with Amazon's Alexa, Apple's Siri, and other digital assistants (Pew Research Center, 2017), with people's interactions with smart devices expected to grow along with the emerging Internet of Things (Rainie and Anderson, 2017). Within industry, media providers such as the Associated Press are using AI-enabled technologies in the production and distribution of news (Marconi et al., 2017). In response, some communication scholars are advocating for the discipline to devote greater attention to understanding increasingly life-like and communicative AI technologies, people's interactions with them, and their implications (e.g. However, communication researchers studying communicative AI face a substantial hurdle: AI and people's interactions with it do not fit neatly into paradigms of communication theory that for more than a century formed around how people communicate with other people (Gunkel, 2012a).

The time when we start with chatbots'because it can' is over. Instead, we look at data-driven chatbots that will really add something to our business processes. Chatbots will become a stable factor in our developments and with this we will confidently enter the fourth industrial revolution with artificial intelligence, robotization, Internet of Things, Cloud Computing and 3D printing. Or not…..? How has this hype developed in recent years? And how should we continue from here?

It shouldn't just fall on businesses to address the talent shortage issue – universities have a role to play too. It is encouraging to see that universities are adding more machine learning and data science courses every day, with some making these new disciplines part of core curricula for certain degrees. But it shouldn't stop there – while academia provides students with theoretical training, enterprises can provide insight and experience based on real-world business problems. Businesses should step in by working with universities to help students gain practical, on-the-job experience. One way of doing this is to make work experience a course requirement. Progressive universities make it compulsory for students taking these courses to spend a semester working for a company in a relevant field.

Machine learning is poised to pave the way for many exciting opportunities for businesses, but there are many hurdles to be crossed before getting to the finishing line. Many organisations are still struggling with legacy systems and are slow to invest in more advanced technologies. But the more pressing issue at hand, one that has been an ongoing problem for the technology sector, is the short supply of qualified talent to match what is a fast-moving and demanding industry. By design, machine learning is experimental and often unpredictable – a lot of exploration is required before organisations can even begin to make sense of the data and which machine learning algorithms will work best. While the unpredictable nature of machine learning is understandably daunting, many organisations have yet to fully grasp what is required to effectively deploy and manage it.

In many respects, the adoption of AI in corporate America seems to be facing greater resistance than in the domestic sphere, where devices like the Amazon's Alexa have gained traction. So, what is preventing the acceptance of AI at work? It is virtually impossible to go online or pick up a newspaper without seeing headlines about Artificial Intelligence (AI) and the imminent disruption of every industry on the planet. Distinguishing between the hype and reality can be challenging. There is also a distinction between how AI will be used by consumers - in our personal lives - and in the workplace.