Embrace knowledge automation to operationalize the knowledge economy and stay relevant
The successful IPO of Aramco which has seen the Saudi oil giant become the most valuable company in the world was as spectacular as it was mindboggling. Yet, despite this stupendous success, the direction of travel for the world economy continues to go in a different, or more precisely in the opposite, direction. The industrial era, with its focus on scale and standardization, is heading for the sunset. Today, 8 out of the 10 most valuable global companies are technology companies that blend digital assets with the network effects of platforms. If anything, the news that Tesla has overtaken the largest German car manufacturer, Volkswagen, in terms of market capitalization encapsulates one of the fundamental challenges for most companies. How can incumbents stay relevant in the face of the onslaught of the platform economy and disruptors like Tesla?
Suffice it to say, market capitalization is just a snapshot of investor sentiment, but the strategic imperatives for the established economy are glaring. While not every company can turn into a platform company or emulate the innovations of Elon Musk, the case of Tesla crystalizes another fundamental question. How do operations and processes have to be set up to thrive in a period of disruptive change? As the established economy doesn’t have the luxury of greenfield operations, any potential answer comes down to how operations can be transformed despite technical debt. The ultimate goal for operations has to be to become flexible enough to adapt to any change or disruption in the chosen value chain. Not only that, but operations has to be able to enable new business models to compete with the strategic headwinds. And as Tesla exemplifies time and again, this also includes supporting completely new and consequently untested products and services. All this must be part of clearly articulated goals of the transformation journey. These are the discussions that we at arago often get involved in. Below we highlight some of our thinking and the strategic levers that we offer to our clients.
Knowledge Automation as the next frontier
What the examples in the introduction have highlighted, is that we are in the midst of a seismic shift away from industrial processes that focus on producing goods at scale and aimed at standardization, and towards knowledge-based processes where mass customization and consequently high variability of processes must be supported. Yet, traditional automation such as runbooks or RPA deals largely with static processes and highly repetitive tasks. Similarly, tools such as AIOps can detect patterns in data because processes are standardized. As a result of these technology constraints, processes remain largely deterministic. All these tools require scripts which are big cost drivers in operations and consequently an inhibitor for adaptability. However, for the process variability that is inherent in the Knowledge Economy, we need new approaches to overcome those static and deterministic processes. Within that context Figure 1 depicts this seismic shift with the direction of travel toward the Knowledge Economy while highlighting the technology building blocks that help to scale the automation deployments:
Fig. 1. Pathway toward Knowledge Automation
Source: arago 2020
While organizations have made progress on their automation journeys by more effective ingestion of data and by leveraging methodologies such as process mining to better understand process flows, many still struggle to scale and only a few have been able to articulate where “Digital Transformation” is meant to lead them. This is where arago’s capabilities come in.
At the heart of arago’s approach is knowledge automation. It is not just a marketeer’s play to instill the aura of innovation for “data” and “information”. It is much broader than that, encapsulating the experience and judgment of folks who run processes. And just for transparency, it has nothing to do with “content automation” or the much-maligned knowledge management systems. Equally, it is not just a different moniker for Digital Transformation but a methodology to capture and digitize knowledge of employees with the ultimate goal of automation.
This knowledge is being retained by storing it in discrete units called “Knowledge Items”. Arago’s ground-breaking AI then figures out how to apply these Knowledge Items for different problems, which is the core of the Knowledge Automation approach. In other words, knowledge becomes reusable. By dynamically recombining Knowledge Items, we are able to increase end-to-end process automation rates from the current best of breed 30% to 90%, a threefold increase.
We fundamentally believe that the direction of travel is not toward incremental digitization of tasks and activities, but a profoundly different approach. Instead of defining and standardizing the process flow, with our HIRO™ platform clients need to describe the desired result with the engine finding the best solution to get there. Crucially, HIRO™ integrates into the existing enterprise applications, thus ringfencing clients’ investments.
Traveling toward the Knowledge Economy means reconciling scale and customization
Perhaps not surprisingly people are starting to get tired of the moniker “Digital Transformation”, not least as digital capabilities are pretty much everywhere these days. Yet, with the same token, the term “transformation” should imply a major change in form, nature or function. Thus, the strategic imperative should not just be digitizing of information, but a profound change of business models to be able to compete with the platform companies. Unavoidably, this means open environments and almost infinite process variants which in turn necessitate a rethinking of exception handling.
To support such a fundamental change, organizations must find ways of reconciling the rigidity of process swim lanes that visualize process steps and responsibilities, with the complexity of knowledge graphs that structure disparate data. Such knowledge graphs are at the heart of the success of platform companies like Facebook, Google or Netflix. The real value of those graphs lies in having quality data in a semantic structure so that the data can be reused in different environments. Very few people will know that arago has one of the largest semantic data pools in Europe.
This is what the right-hand side of Fig.1 is trying to highlight. As they progress with Intelligent Automation, organizations need to ingest knowledge on top of largely tactical automation toolsets. This knowledge is enabled by integrated AI platforms that can deal with and ultimately automate a vast set of process variety and the complexity of a semantic data pool. But only by advancing with Transfer Learning, i.e. the ability to store and transfer knowledge to new conditions, will organizations be able to make real progress in overcoming their siloed operations. It is around the notion of Transfer Learning where advances of Arago’s AI capabilities really come to the fore. And it is here where the differences to narrow approaches of AI, in particular Machine Learning, need to be called out. To bring it back to the world of operations, the fundamental differences that Knowledge Automation offers to customers is a far cry from the progress with image recognition and process mining in the RPA world or the analytics capabilities of most AIOps tools. It literally is a different way to run your operations. Another way of thinking about this is that Arago provides the automation of automation. By ingesting non-standardized knowledge Arago allows clients to adapt to and withstand the disruptive change the Knowledge Economy is creating.
Bottom-line: Knowledge Automation helps you to progress beyond static and deterministic processes
Whatever moniker we choose for the direction of travel, for many organizations the most pressing strategic imperative is to stay relevant in face of constant change and disruption. Boards must be able to convey the strategic direction and provide actionable mandates for their operational teams to support the transformation journey. Yet, for most sectors, an incremental tinkering will not be enough to deal with the strategic headwinds. Knowledge Automation is an approach that will help the established economy to compete with the technology capabilities of the platform economy without requiring open-heart surgery on its core operational processes. To succeed on that journey two issues are paramount: An innovation mindset and the ability to drive change through an organization. These are the issues that we at arago would love to discuss with you!
