Why you should shift your organizational focus from artificial intelligence to augmented intelligence

By adjusting your approach, you can set yourself up to adopt AI incrementally and create a foundation for successful human-machine collaboration

If you’ve done any work that touches technology, there’s no doubt you’ve spent a good portion of your career being bombarded by jargon. We’ve heard of the virtues of “working in a lean fashion with an agile methodology.” We’re constantly hearing about the “promise of the blockchain.” We all know that the Internet of Things will continue to “revolutionize the way that we interact with technology.” Jargon fatigue can pose a real problem to creating meaningful interactions with new tech, but let’s examine a particularly meaningful technology in a new light: big data.

We’re not just going to discuss the basics, though. In order to fully take advantage of technologies like artificial intelligence that are becoming more viable for large-scale use, companies will need to redefine how they handle their data. It’s big data with a purpose–the goal is to build out massive datasets with a specific use case in mind.

If we’re going to utilize the substantial quantities of data that we have available to us, we need to set up realistic strategies for engaging artificial intelligence (AI). We’re not anywhere near the point where it would
be feasible to outsource all or even most decision-making to machines, but it does make sense to learn how to better work with them. By shifting your organizational focus from artificial intelligence to augmented intelligence, you can set yourself up to incrementally adopt the technology. The key difference here is in realizing that the adoption of AI practices for your business will not happen all at once, and will likely start by making your decision-makers’ jobs easier in small ways that promote, rather than replace, humans working with machines.

Before we dive into what augmented intelligence means for your business, we should set a common vocabulary for what we’re trying to do with this data. We can break predictive analytics down into four major groups: descriptive, diagnostic, prognostic, and prescriptive. Descriptive and diagnostic analytics are focused on the past, while prognostic and prescriptive are future-facing.

Let’s assume that the goal is to move your business towards prescriptive analysis; here’s what’s going to happen and what you should do to either avoid or encourage that outcome. As you can imagine, there is
an immense range in the scale of these decisions. The data may tell you to change something small like a line of copy in your product description, or it may tell you to completely shift your production to focus on a
different goal. Because of the variance in these decisions, businesses need to make sure that they’re enabling everyone in the organization, not just executives or analysts, to interact with the data in a meaningful way.

While we should all be open to being challenged by our own data, it’s not always a comfortable position to be in. One person who knows this process intimately is Tom Schenk, Jr., the Chief Data Officer for the City of Chicago. Tom and his team led the charge to make as much city data as possible easily consumable by the average citizen, as decisions made based on the data impacts everything from school funding to wastewater management. Seemingly disparate data, such as recent temperatures and neighborhood rodent complaints, can help their team predict outcomes like health code violations in restaurants.

In fact, food inspection is a great example of a modern partnership between artificial intelligence, data, and human workers. Chicago has over 15,000 active restaurants, but only 32 food inspectors for the entire city. By using the data open to every citizen and partnering with Allstate, Tom and his team were able to cut the response time to health code violations by a week and improve accuracy by 14%. The data insights augmented a workforce already spread thin in order to increase their productivity and help the organization’s overall goal. While a food inspector’s job may not be able to be automated yet, it can be more impactful by embracing the technology and trusting the data that we have access to.

The food inspection example provides a few key points that set a roadmap for increasing the usefulness of augmented intelligence. Firstly, Chicago started by making the data open in traditional formats through the Data Portal and is now transitioning to an easy-to-consume map-based format with a new offering called OpenGrid. No one can use data if it’s limited to a small team of “data professionals” or archived in spreadsheets that only exist on someone’s laptop. Giving people the right tools to make sense of the data makes a huge difference and can foster innovative ideas in your organization from unlikely places and collaborations. If we’re moving business towards a heavier reliance on data, that data needs to be open to as many people as possible.

The last point involves Chicago’s experimental approach to taking actions based on their data. Although technology aims to better predict the future and remove some of the subjectivity out of business decisions, the suggestions it yields are ultimately still predictions. Tom and his team launched a small trial run of their food inspection model to test assumptions and outcomes before investing more money on a roll out to the entire city.Experimenting and quickly trying new ideas is imperative to the success of innovation-focused endeavors.

So what can you do to make sure that your organization is taking the right steps towards effectively using AI and data? Set a target for what you want to do. Interested in using augmented reality to help train your workforce? Collect a catalog of images to train a computer-vision algorithm to tell the computer what it’s looking at. Want to better understand how staffing effects your organization? Get that data centralized in a common system and out of spreadsheets, then start storing it for long-term analysis.

As mentioned before, artificial intelligence will not spring up in your company overnight. By focusing on ways to use augmented intelligence to help your workforce now, you take the initial steps towards shifting artificial intelligence from jargon to a core part of your business.