By: Jay Lauer, Senior Innovation Strategist
In his 2004 book “The Future of Work,” Thomas Malone, renowned organizational theorist and founding director of the MIT Center for Collective Intelligence, noted that new information technologies and inexpensive communications make it possible to distribute decision making more widely in organizations – supporting a shift from traditional centralized organizational chains-of-command to loose workplace hierarchies, democracies and markets. Malone uses Wikipedia as an example of how a group of loosely connected individuals, each with specialized knowledge, can collaboratively create an output that is exponentially more valuable than their individual contributions. This concept of shared or group intelligence that emerges from collaboration is an example of collective intelligence.
Fast forward nearly two decades and we’ve witnessed expanded examples of collective intelligence, whether it is the continued development of open-source software, the crowd-powered features of the Waze navigation app, or platforms like Innocentive that extend innovation challenges beyond the walls of a traditional organization. Each of these illustrate how technology and communications have created or augmented value from the diverse thoughts and inputs from diverse individuals. The organization of people, centered not around a direct-reporting chain but around a process, project, problem or opportunity, has delivered significant business value – increasing scalability, filling knowledge gaps, accelerating processes and reducing operational costs.
Enter Artificial Intelligence
Though Artificial Intelligence (AI) is more than 60 years old, it is only over the last decade or so that it has finally outlived its hype and began delivering on its promises. Today, big data is supported by the affordability of massive, scalable data storage solutions – economics that have spawned incredible growth in advanced analytics and machine learning capabilities. Accompanied by technological advancements that drastically increased computing power, AI use cases are now prevalent across nearly every financial services business function and across a growing number of industries.
This rapid change has sparked concerns – fears of uncontrollable or rogue AI, the use of AI for unethical intent, privacy and security considerations, and, perhaps most notably, the potential impact to today’s workforce. While the proliferation of AI will certainly impact the tasks performed by humans across multiple business sectors, the impact shouldn’t necessarily be seen as adverse. The reason for this becomes apparent when we really understand the current state of AI.
Today’s AI is classified as narrow or specialized intelligence. That is, AI capabilities are limited to specific functions, with specific goals, in specific environments. While IBM can train Watson to compete with and beat even the strongest human Jeopardy contestant, that same instance of AI would fail miserably when tasked with playing tic-tac-toe or chess. We are decades away from AI progressing to the point where it could be considered general artificial intelligence, capable of performing a much broader range of things – to move closer to the human ability to synthesize and act on inputs from a constantly changing environment and across a myriad of scenarios.
Humans have a general intelligence that lets them perform a much broader range of things. Malone emphasizes the difference: “AI systems can be helpful in doing tasks such as interpreting X-rays, evaluating the risk of fraud in a credit card charge, or generating unusual new product designs. And humans can use their social skills, common sense, and other kinds of general intelligence to do things computers can’t do well. For instance, people can provide emotional support to patients diagnosed with cancer. They can decide when to believe customer explanations for unusual credit card transactions, and they can reject new product designs that customers would probably never want.”
Given that, AI will do more to refocus today’s workforce than replace it. Organizations, including financial institutions, should not be thinking about choosing between humans or computers, but focus on developing tight collaboration between humans and computers, especially as the shift to digital in our industry continues to accelerate.
Human-Computer Interaction
The realization that businesses will always have the need for both humans and computers has given birth to a new academic discipline, Human-Computer Interaction (HCI), which focuses on computer design and user experience, bringing together expertise from computer science, psychology, behavioral science and design to understand and facilitate better interactions between human users and machines. Just as digital businesses continually seek to reduce customer friction, all businesses should be striving toward a frictionless relationship between its human and digital workers.
Educating the organization on how and where AI or other technologies can best be applied is step one. Organizations always want to choose the best available resource for the task at hand. If the best resource is a digital worker, then a digital worker should be devised, designed and deployed. Subsequently, the development of digital workers must be done in such a way that human interaction and human interoperability is a fundamental requirement.
Organizational Implications
Executives and leaders wishing to maintain their organization’s collaboration mindset and the known benefits of collaborative solutioning must now consider the organizational, cultural and technical implications of their entire workforce, including their digital workers. They must clearly communicate their vision for the use of AI in the organization and ensure that digital workers are created under standards of ethical development and deployment. It may also mean a shift in the organization’s hiring practices, with prior human-computer interaction experience from applicants garnering greater weight.
Business line managers must be able to dissect a business process to lower-level or lowest-level tasks, assign those tasks to the most appropriate worker (human or digital), then knit the process back together in such a way that ensures continuity and completeness. This ability likely requires a shift from people managers to process managers and a heightened understanding of the practical uses of advanced technologies.
Employees must be able to learn, know and trust their digital co-workers. This relationship is likely the most critical to alleviate the fears that exist in the workforce today. Just as our human-to-human partnerships are rooted in mutual understanding and reliance, the human-to-computer partnership must also be understandable and reliable. As computers are incapable of understanding at the general intelligence level we humans possess, the onus is on the human worker to provide both the insight and oversight needed to ensure that trust.
And those responsible for the development of AI solutions? They shoulder the responsibility of developing solutions that are not only accurate, timely and trustworthy, but are also able to contribute to the organization’s collective intelligence. For financial institutions, these are important – but not small – endeavors.
As Senior Innovation Strategist, Jay Lauer is leading efforts to enable a broader and deeper adoption of AI-empowered solutions. Lauer’s focus on enterprise governance frameworks and operational readiness is supported by his experience in strategic planning and execution, program and product management, and risk and compliance roles in financial institutions. He has held leadership roles in banking and payment processing.
