Redesigning Process Improvement in the Age of AI & the Customer
Long ago you could win in business if you had standardized processes and your competition did not. In essence, if you standardized your processes, you maximized efficiencies and drove consistency into your product creation and service delivery. Those standardization benefits are best exemplified by Henry Ford. Pity for his competitors who lacked Ford’s assembly line standardization and operated in a more random or haphazard way.
Many analysts suggest that in the 1970’s – with advances in information technologies (IT) and business process reengineering (BPR) – a tectonic shift occurred in the direction of automation. In fact, to be successful and efficient since the 1970’s leaders have deployed automation to reduce manual process errors and variations. But how about today? What is needed from a process perspective in the modern day world of big data, artificial intelligence, and machine learning?
Human + Machine
In the book, Human + Machine: Reimagining Work in the Age of AI, Paul Daugherty and James Wilson suggest that automated processes are giving way to a future dominated by “adaptive processes.” While adaptive processes are defined in very complex ways by information systems designers, I’ll cull the concept to its essence by suggesting, that adaptive processes deploy real-time data to readily and continually guide improvements in all aspects of business.
Daugherty and Wilson put it this way:
Leading firms in many industries are now reimagining their processes to be more flexible, faster, and adaptable to the behaviors, preferences, and needs of their workers at a given moment. This adaptive capability is being driven by real-time data rather than by an a priori sequence of steps. The paradox is that although these processes are not standardized or routine, they can repeatedly deliver better outcomes. In fact, leading organizations have been able to bring to market individualized products and services (as opposed to the mass-produced goods of the past), as well as deliver profitable outcomes.
Daugherty and Wilson’s insights warrant a quick summary. Businesses moved to process standardization in the days of Henry Ford then to automation in the 1970’s. Collectively those business approaches drove increased consistency and efficiency. Today artificial intelligence provides real-time insight that can guide actions that aren’t necessarily standardized or routine but are instead individualized and profitable.
Adaptive Processes Success
So how do business leaders drive these modern adaptive processes? To answer that question fully, I encourage a thorough read of Human + Machine: Reimagining Work in the Age of AI. If your time is short, I would suggest Bob Morris’ synopsis of the book provided on his site Blogging on Business. To get you started, I will highlight Bob’s take on the five key themes in Human + Machine, namely:
Mindset: “Assuming a radically different approach toward business by reimagining work around the missing middle.” That is, humans and machines help each other to perform at their best. “People improve AI and, in turn, smart machines give humans superpowers.”
Experimentation: “Actively observing for spots in processes to test AI and to learn and scale reimagined process from the perspective of the missing middle.”
Leadership: “Making a commitment to the responsible use of AI from the start.”
Data: “Building a data ‘supply chain’ to fuel intelligent systems.”
Skills: “Actively developing the eight ‘fusion skills’ necessary for reimagining processes in the missing middle.”
But What About the Customer?
For me (as a customer experience designer) the end goal of all process improvement (whether that improvement is driven by standardization, automation, or adaptivity) is to do more that will increase efficiency. In fact, the ultimate goal (from my worldview) of process improvement is increased customer value and effectiveness. Every process improvement effort should seek to effectively add value to customers.
With real-time insights that come from big data, artificial intelligence, and machine learning, companies are driving customer value in something author Patricia Seybold described back in 2002 as dynamic effectiveness. Patricia noted,
…in the customer economy, the efficiency-based approach to process automation and management doesn’t work. Why? Because today’s business processes must support customers’ goals and contexts (or scenarios) rather than merely codifying internal operations. Processes must be defined from the customer’s point of view, and the customer’s goal and context must be carried across and throughout all activities that make up the process. And the goal, context, and current status of the process should dynamically determine the next step in the process. In a world where customers’ goals and contexts are continually changing–even ever so slightly–it is next to impossible to anticipate every scenario and outcome that a customer needs.
Nimble, Effective, Customer Value
Thanks to AI and other emerging technologies we are getting better at anticipating customer needs and nimbly making process changes to drive dynamic effectiveness. Based on the research of Daugherty and Wilson, the good news is that dynamic effectiveness only occurs when humans leverage technology tools. It does not occur in applications where technology displaces humans or where humans fail to analyze rapidly available data.
My questions to you are as follows:
- How would you describe your approach to process enhancement – standardization, automation, or adaptation?
- What are you learning from real-time customer data that is helping you dynamically and effectively personalize process solutions that meet rapidly changing customer wants, needs, or desires?
- How are you investing in both people and technology to better position a competitive advantage through adaptive process improvement?
My team and I are here to understand and strategize ways for you to improve your adaptive processes, customer value, and dynamic effectiveness. To schedule a time to talk, simply send us an email.