Machine Intelligence Ascending (Part 1)

Soumya Chakravorty

Part 1 in the “Machine Intelligence Ascending” series

New digital technologies, including Internet of Things (IoT) devices, promise to revolutionize your business—and to overwhelm it with data. How do you sift through the expanding ocean of data to glean actionable insights and turn bits into value? The short answer is that you don’t always have to. You can let the machines do the work for you.

Mature machine intelligence technologies that include robotic process automation (RPA), machine learning, and artificial intelligence are providing today’s organizations with critical tools they can use to make sense of data and to leverage it to generate new value—to automate insights, business processes, transactions, customer engagement, and more. As machine intelligence helps transform today’s enterprise into a kinetic enterprise—one that can quickly adapt and overcome organizational inertia—what opportunities emerge? And how do existing solutions align with new intelligent technologies? Here are a few things to know.

These machines aren’t robots with arms, but they are doing some heavy lifting

RPA involves business processes that are automated by means of robotic software—software that can act robotically on high-volume, objective processes, and structured data. RPA is not a truly “smart” technology. RPA capabilities can help you automate traditionally human-involved processes such as invoice processing, taking on rote processes that require straightforward, objective actions.

Machine learning and artificial intelligence (AI) capabilities can do even more. At its core, machine learning is the process of automatically discovering patterns in data and then using patterns to make predictions. AI takes things a step beyond—applying more humanlike intelligence to “think” about patterns, to anticipate possibilities, and to perform more complex tasks such as natural language processing, visual identification, and other activities that involve learning or problem solving.

Ultimately, machine intelligence capabilities can help create automated systems of transaction and interaction—in which smart software gets smarter, providing actionable data-driven insights and executing decisions. And these systems can get smarter over time. Possibilities include applications that engage with customers to anticipate needs and provide answers, or a set of image-recognition and natural-language processing tools that serves effectively as a smart digital assistant for workers in the field.

Machine intelligence empowers people

Across many organizations, there’s a rising concern that new cognitive technologies will replace workers. To some extent and within some organizations, technologies can render some jobs obsolete. But the real power and potential of machine intelligence is its ability to free up human workers from mundane, tedious, or time-consuming activities—simple tasks such as routing information or complex challenges such as spotting business trends or opportunities hidden within piles of data. With machine intelligence tools, humans can spend less time on traditional support and maintenance activities and spend more time on strategy—to support a kinetic enterprise that can respond proactively to market forces.

Expect modern enterprise solutions to be front and center

What does machine intelligence mean for organizations that run SAP solutions or that want to leverage SAP technology to get ahead of disruption? First, the recently launched digital innovation system can provide a portfolio of solutions to help you rapidly deploy cognitive capabilities. Second, adding machine intelligence to the mix does not require you to alter ERP solutions. It is minimally invasive—external software that works with SAP solutions, not changing it. Rather, machine intelligence enhances what you can do with other enabling solutions.

Machine intelligence owes its maturity to a few big enablers

Cognitive technologies aren’t exactly new, but what they can do today is—thanks to a handful of enablers. For one, an in-memory database can support the type of computing power required for evolving needs. Machine intelligence tools can rapidly process data to generate insights and take action.

Second, the amount of data available today is a game-changer. Big Data brings lots of data—to detect patterns, to identify trends, to self-learn. By processing millions of online images of cars, for example, or millions of recordings of a language being spoken in different accents, these technologies can self-learn. They can improve their ability to identify a specific model of car on a crowded highway. They can improve their ability to understand human speech. Thanks to Big Data, they can get smarter. And cloud computing has helped significantly, too—enabling organizations to store the vast amounts of data that fuel machine intelligence.

Yet another enabler involves today’s APIs. Solid APIs for speech recognition and machine learning, for example, mean that you don’t have to be an expert to build cognitive apps. Many of the pieces you will need are readily available—just waiting for you to use a solution to bring the pieces together and to extend the natural boundaries of your core ERP.

These enablers—as well as continued investment from commercial sources, not just government—means that machine intelligence has become far more relevant than it was just a few years ago.

The next blog in this series will explore next steps. Look for it on Thursday, Dec. 28.

For more on how emerging technology will change business, see Futurists On Robots At Work: Whose Job Is It Anyway?

This article originally appeared on Deloitte Tech Trends and is republished by permission.

Follow me:




Soumya Chakravorty

About Soumya Chakravorty

Soumya Chakravorty is managing director and CIO Fellow in Deloitte’s SAP Service Line and serves as practice leader for the re-platforming and modernization capability. Soumya is responsible for Deloitte’s ‘Migration to SAP S/4HANA’ services and digital transformation services across various industry sectors and geographies throughout the world. Soumya has over 20 years of experience in ERP-enabled business transformation and enterprise architecture, integrating solutions around business process enablement, information flow, mergers and acquisitions, mobility, and decision-making including analytics. Soumya holds an MBA from Mumbai University and a bachelor’s degree in Electronics Engineering from NIT, Kurukshetra, India.