Wikipedia defines collaboration as the process of two or more people working together to complete a task or achieve a goal. Management papers profess greater results when teams collaborate than when acting in silos. With the emergence of AI, collaboration is being redefined. Collaboration is no longer is between people but includes machines and people.
MIT Sloan in their article 'Creating the Symbiotic AI Workforce of the Future' explores how work can be improved by having humans train AI systems to do low cognitive work on their behalf. Automating repetitive tasks (e.g. robotic process automation) is just the beginning and has been proven for many years in a wide range of use cases in marketing, supply chain, logistics and recruiting. Trust is the bedrock of collaboration in all forms and it's especially critical in partnering with machines. If people don't trust the actions taken by AI, they won't embrace the change and will find many ways to subvert their potential and progress.
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The collaboration possibilities between humans and machines are endless, but success requires new skills. To sustain collaboration, behavioral scientist and HBR author, Francesca Gino, believes you need to teach people to listen, not talk. I might add that we need to teach people to not only listen to other people but listen to data. My good friend, Catherine Kendall, former CIO of the California Department of Conservation said, "let the data do the talking and amazing things will happen."
AI provides a bigger megaphone for data than ever before and courageous leaders need to listen carefully to what it has to say even when it doesn't fit their narrative or intuition. Listening is always two-sided and with human to machine collaboration that fact remains the same. Machines forget less and can process more facts in shorter periods of times than humans and they listen to the training data. Feel free to read my earlier post on the ethical implications of AI.
I like to think of human to machine collaboration as having a hip, smart Data Friend in your pocket. You could confide in this Data Friend and share tough questions that keep you up at night. And while you slept, your Data Friend scoured data on your behalf, looked for patterns and outliers and brought scientific expertise to your questions. Oh, and your new Data Friend shows up when the time is right like a week before your quarterly business review or when you arrive at a customer, oil well or retail shop. This type of collaboration exists today in Oracle Analytics as part of our strategy to embed AI into every step of your analytic process. Our augmented analytics capabilities put this Data Friend in your hand with super cool capabilities in our Oracle Analytics Mobile features. Check them out here.
In my view, there are three dimensions that are needed to build confidence in the human to machine collaboration journey. Taken together, Reach, Repetition, and Risk are core for building trust between humans and machines.
Reach. This factor looks at the scale and scope of AI. If the algorithm makes recommendations that impact 10% of your customers, employees, partners or suppliers, you're probably more likely to accept and execute its actions. For recommendations that impact mission-critical processes for every product or customer, we're probably less likely to embrace until we build confidence in the new partnership with our Data Friend.
Repetition. For actions that are low cognitive and low impact, we are more likely to entrust the algorithm to act on our behalf. Airlines have for decades entrusted marketing offers to AI to make recommendations on which offer to make to travelers as they embark on their journeys.
Risk. The third and final factor involved in building trust between humans and machines is risk management as perceived by the individual responsible for the action to be taken. The appetite for risk varies as the consequences grow. Risk/reward is always a factor in maturing new technologies and AI is no different in this regard.
In my view, collaboration isn't just about improving decisions. Collaboration is essential to innovate faster, improve the customer experiences, optimize processes, simplify work and build deeper engagement among our people. Great analytic collaborators exchange ideas, data, and perspectives on many topics including:
· Business Goals & Targets
· Risk Indicators (KRI)
· Predictive Models
· Forecasts & Planning Assumptions
· Data Catalog
Collaboration is a core facet of the Oracle Analytics product strategy and our new product, Oracle Analytics for Applications takes collaboration to the next level. Executives from across the company can easily discuss, interact and collaborate on KPI's, Performance Targets to optimize performance.
Click here to learn more about how you can take your collaboration to the next level with our exciting new solution. To learn how you can benefit from the Oracle Analytics Cloud, visit Oracle.com/analytics.