“The future of business process improvement is on making them intelligent. Machine learning is the driving force”
Machine learning is on a steep adoption curve and making its inroads in our daily lives and work. The application of the technology won’t be an issue at all. There’s an abundance of meaningful value propositions for many functional areas, business processes and roles across multiple industries.
Software vendors of enterprise business solutions are focusing their product development on machine learning and other related artificial intelligence technologies. CEO Bill McDermott of SAP said that intelligent applications will fundamentally change the way you do work in the enterprise in the next decade. He mentioned that we need the system to tell us what to do.
In many publications about machine learning we read about IBM Watson beating humans at Jeopardy, or Google’s AlphaGo beating a Go world champion. There are predictions, for example from Nick Bostrom, that indicate that singularity, or the moment in time when a computer will be as intelligent as a human, is going to happen around 2040.
We also know that machine learning solutions work with structured and unstructured data. Data is growing at a fast pace and doubles every 2 years. 80% of the data is unstructured and 20% is structured. The technological advancements of the last 5 – 10 years removed barriers that artificial intelligence has been struggling with for many decades. Computing power being the most important one. As an indication, IBM Watson can read 200 million pages in 3 seconds and understand the content.
Machine learning solutions are coming our way. A fundamental principle is that they predict based on past behaviour.
Think about weather predictions. IBM acquired the digital assets of The Weather Company in 2016 and is leveraging IBM Watson platform to provide meaningful services to businesses and consumers. The weather notifications you can receive on your smart phone are coming from IBM Watson, a machine learning solution.
Machine learning solutions aren’t always perfect and there are ways to go. Facebook is using machine learning to recognize and understand photos, video and audio that are posted by its users. Content that is not meeting Facebook’s standards is removed. An example of this is the recent removal of a 1973 world press winning photo of the Vietnam war. After a public outcry Facebook reversed its decision.
A few weeks ago I had a personal experience with Facebook’s machine learning solutions and how it influenced a post. I was using an iPhone app to splice a few videos and thought it would be great to add audio to it. I selected the tune from the app library. When I posted the video, Facebook rejected it, because it believed that I did not have the rights to use the audio. Facebook’s interpretation was wrong, because the tune was general available to the app users. It’s an indication that machine learning solutions can learn on their own, but need human intervention to train them.
Machine learning is going to change the way we design and optimize business processes and functional roles. Automation will shift the role of humans more to exception based interactions and real-time, evidence-based decision making. The level of people, process, technology and data integration will further increase. Standardization of end-to-end processes in the supply chain will further manifest.
My expectation is that the software vendors of enterprise business solutions like SAP, Oracle and Salesforce will put their focus on functional areas where there is a high volume of routine transactions first. Think about the order-to-cash process where recurring orders flow through the order entry, fulfillment and delivery processes with limited human intervention. Think about the customer service process where the scheduling of work orders is further automated. Through internet of things technologies, real-time data becomes available that enable machine learning solutions to schedule service orders at the right time with the right spare parts ordered and skilled technician assigned.
There will be niche solutions too where machine learning is augmenting the human capability in a specific area. Think about travel and expense management solutions, where the processing of entries is for the greater part done without human intervention. Another example is recruitment. Machine learning solutions will take over the steps of identifying and screening candidates. Recruiters will receive a short list of qualified candidates and can focus more on the softer aspects that do require human interaction, for example determining if the candidate is a good fit with the organization.
Interaction with customers through call centres and other channels like email, apps or internet is another example. Machine learning solutions can understand text and speech and process simple transactions. Audible from Amazon does that to process refunds for audio books that the customer does not like.
Machine learning and other artificial intelligence solutions are at the top of Gartner’s Hype Cycle for Emerging Technologies, 2016. The evolution of the technology in the next decades will be fascinating, because it is coming so close to our existence as human beings. The potential to apply it in a meaningful way to our live and work will be enormous.
Bas de Baat
