Advances in artificial intelligence are transforming industry after industry. The pace of change makes it challenging to keep up, let alone get ahead. It is imperative to consider how these technologies are playing a transformative role in the enterprise. In order to understand this role, it is important to answer what is meant by “intelligence” in the context of digital products and platforms.

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Intelligence has many definitions depending on whom you ask and the context. Our definition of intelligent products and platforms are those that automate the ordinary, adapt to change, and discover actionable insights. This can be achieved by incorporating both established technologies as well as emerging technologies like artificial intelligence and machine learning.

Depending on where the enterprise is on its transformative journey, pursuit of intelligent products and platforms can add significant value. Our vision is a holistic, user-driven approach to building platforms and products that augment the workforce via intelligence to help our clients work better. To illustrate the intelligence aspect of our vision, we will briefly discuss the three core features that we use to define it: automating the ordinary, adapting to change, and discovering actionable insights.

 

Automating the Ordinary

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Knowledge workers spend an average of 10 hours per week on repetitive tasks that can be automated.

This is a highly variable figure by job, but on average equates to 65 days of lost time per employee per year. “What AI and automation can do is help reduce our workload of repetitive, mundane tasks, allowing us to focus on the human portion of work that requires our brains to work more effectively and creatively.” “[W]orkers of the future will spend more time on activities that machines are less capable of, such as managing people, applying expertise, and communicating with others.” Narratives like these are what led to our position that automation of the ordinary is central to intelligent products and platforms. 


A good everyday example of automation of the ordinary is the email spam filter. Email could still be manually moved to the trash from the inbox, but it is not. Rather, spam is automatically detected and moved to a separate folder that can managed by exception. Not having to manually identify spam frees time to focus on the much more uniquely human task of consuming and responding to relevant emails.

Recently we worked with a client with large packaging plants to automate product identification at certain points in the facility based on image classification. This process had previously been an entirely manual operation. Automating the ordinary helped us reduce the cognitive burden of a repetitive manual task, both reducing the recognition error rate and creating an environment where workers can apply themselves in more meaningful ways.

Adapting to Change

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A common quote that resonates with our definition of intelligent platforms and products is that “[i]ntelligence means getting better over time.  Whereas automation of the ordinary speaks to the relationship between humans and work, adapting to change speaks more directly to artificial intelligence. AI researcher R. R. Gudwin said it well: “Functioning of intelligent systems cannot be considered separately from the environment and the concrete situation including the goal. Simply put, adapting to change is a fundamental aspect of intelligent products and platforms given the appropriate context.

Spam is a good illustration of adapting to change. The spam filter is seeded with common patterns identified as spam. As you contribute to the knowledge of the system by indicating that new emails are spam or not by moving them to or from the spam folder, the spam filter adapts to these examples and to your personalized requirements for spam filtering.

Not long ago we created a recommendation system that generates relevant content for users based on a collaborative filter relating the users and content. This system includes the ability for a user to give feedback about the content, which dynamically updates the system to provide more tailored content the next time it is used.  Like the spam filter, this product exemplifies adaptability because every time it is used it adjusts to new information to make it perform better.

 

Discovering Actionable Insights

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The enterprise is most effectively run by applying business decisions to actionable insights.  Historically, these insights were discovered by analysts, but as the scale of data increases, the mechanism of discovery is shifting. In our view, a fundamental aspect of intelligent platforms and products is the ability to leverage large volumes of data to facilitate discovery of patterns and trends, and the ability to create actionable insights from these patterns and trends.

An example of discovering actionable insights is the Google Maps application. Specifically, the ability to suggest different routes depending on traffic and congestion. Depending on data regarding current traffic conditions, routes can be dynamically optimized taking this information into account. The maps application transforms raw data into insights about traffic patterns that are presented in an actionable way. It then goes one step further by providing the option of automatically taking action on the insight.

In the last few months we have worked on a financial reporting product that was created with actionable insight discovery as a primary consideration using data from a data lake. The product features automatic contextual identification of outliers and correlation of trends with exogenous factors. We also built feedback into the product so that the actionable insights that are discovered are able to adapt to user preference.

 

Summary

To recap, intelligent platforms and products are transforming the enterprise by automating the ordinary, adapting to change, and discovering actionable insights.  Our vision is to create platforms and products that augment the workforce to meet enterprise needs using a holistic, user-driven approach.  Our focus on integrating intelligence in these platforms and products is no different, taking these three key features into account on every platform and product we build.

 

Author
Michael Paladino

Michael Paladino

Co-Founder at RevUnit, Michael leads the company's efforts to stay on the cutting edge of emerging technologies. His current areas of focus include AI, machine learning and conversational interfaces which can all be used to help RevUnit clients work better.