We are excited to announce our partnership with Skan AI alongside Zetta Venture Partners, Bloomberg Beta, Firebolt Ventures, Cathay Innovation, and Citi Ventures.
Future of Work - think outside the ‘bots’
The value of a resilient operating model is obvious to organizations everywhere right now as companies recognize that the old ways of working are not as effective as they once were. Senior leaders are adopting new future of work initiatives every day, but how often have you seen digital transformation efforts fall short of the expected target?
70% of all digital transformation initiatives did not reach their goals... equating to $910B in spending missing the mark
For example, one piece of research found that 70% of all digital transformation initiatives did not reach their goals - a shocking number considering spending on digital transformation will hit $1.3 trillion this year equating to $910B in spending missing the mark. It is a familiar story in the age of modernization. Companies spend approximately $1.6B billion on automation tools without understanding what problem they need to solve, and wasting a lot of time, money, and resources along the way. Contrary to popular belief, automation cannot transform every aspect of a business because not all activities can lend itself to automation. Parts of the workflow may involve interactions, exceptions, and handoffs that cannot be automated because doing so may create new inefficiencies in itself, chipping away any new benefits gained from automation. Moreover, the automation initiative may subscribe to a narrow perspective of eliminating human work. Such focus offers only incremental gains of cost reduction.
The real opportunity presented in the future of work goes beyond bots or automation that competitors can easily imitate. To create a durable advantage, companies should use their digital transformation initiative to improve customer journeys end to end, which requires a system of record for work. Such broad visibility will allow companies to identify key moments that matter and assess what technologies make sense for a given workflow, if a process requires reengineering, whether parts of the process can be outsourced, and if automation is indeed the right approach here. Enterprises seem to have a system of record for customers (i.e. Salesforce), finance (i.e. SAP), and labor (i.e. Workday), though they lack records for the nature of work. Teams can point to SOPs, but such records capture how work ought to happen, not how it actually happens. Such antiquated records also miss the reality that includes, reworks, exceptions for special scenarios, and the fact that different people perform the same tasks differently. Often, such elements come to light only when companies have visibility to break down the process and examine different parts.
You can’t redesign what you cannot measure nor understand
While working at Genpact, a global business process outsourcing firm, Avinash and Manish recognized that building resilient operating models required an accurate assessment of current processes, but there was an enormous gap between how people think a process works and how it actually works.
To create a record of work, digital transformation teams spent hours interviewing employees or hiring consultants, but employees struggled to communicate the steps and procedures. This communication gap is known as Polanyi’s Paradox, which underlines that we know more than we can tell. So if you ask ten people at the same company, “what are the steps for getting XYZ task completed?”, you will get ten different responses, and none of them will be completely accurate. Some tasks may have nuances, exceptions, preferences, and ad hoc digital human interactions. These invisible or subconscious steps are challenging to capture. Moreover, a task might involve multiple stakeholders from different departments who use various systems and work in different time zones, making it difficult to analyze a process at scale.
That's why it is imperative to create a system of record for work
Skan AI was born after Avinash and Manish personally experienced these problems within large organizations. The company uses computer vision to understand business processes nuances by watching the on-screen interaction of humans with digital applications to generate a digital process twin, a virtual replica of the process to provide full transparency as to how work gets done across hundreds of employees within an enterprise. By using machine learning and observing ad-hoc human actions on computer screens, Skan can infer a process metamodel comprising all variations in processes. Such models become useful in simulation and modeling, intelligent automation (RPA), effective transformation, precision training, process redesign, and etc. For privacy concerns or regulated industries, Skan can also anonymize the users and selective screen level masking or field-level redaction that protects confidential information.
Skan's use of cognitive technologies and approach built on data science is both simple and powerful. Unlike the first-generation process mining companies who primarily focused on application logs, Skan can work with any system - legacy or modern - and does not require any deep integration or access to sensitive data. The approach allows them to capture the nuances and exceptions of work that do not leave a trace in backend data or logs. These invisible steps are critical to a process but are completely missed by their competitors using data-log centered approaches. By capturing these steps, Skan can create the most granular description of work across a business function.
Skan's technology is foundational and is an integral part of any enterprise digital transformation. Their AI-powered process discovery and continuous monitoring platform can be used to (1) uncover optimization and automation opportunities in shared services such as customer service, accounts receivable/accounts payable, etc. and (2) identify process variants and how work happens in core processes such as claims processing, mortgage origination, policy administration or asset servicing. In addition to discovery and visualization of work as it happens, enterprises are also utilizing Skan for process monitoring, continuous improvement, process conformance, and AI-augmented supervisory.
We knew Skan was a special company after meeting with Avinash and Manish. Some of our early conversations always diverged to discuss challenges around digital transformation strategies, its shortcomings, and the potential impact that a system of record for work - like Skan - could have not just with digital transformation efforts but also the future of work. Since meeting with the team, Skan has received stellar feedback and gained traction with leading insurers and banks through the Plug and Play ecosystem. We are very excited to support Avinash and Manish on their journey that is only just beginning.