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Digital Transformation & Automation Operating Systems

2022-07-29
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Most enterprises are having a difficult time definitively achieving full-blown digital transformation. Simultaneously, the entire software industry is also struggling to figure out how to best speed up the transformation. The reasons why digital transformation projects fail are complex enough to grasp. One explanation may lie in the approach adopted by the tech industry.

'A real-time automation OS that more specifically enables deployment of low-code orchestration from any system and device or data record to any other system can result in an incredible end customer experience.' -WaylayClick To Tweet

Digital Transformation: The Intrinsic Problem

As humans, we know what we want to achieve. Our brains tell us anything is doable. But when faced with building the ultimate solution in software and where we need to cope with the heterogeneity of things very quickly, we run into quicksand very fast. 

The subsequent cost, time, and resource loss, not to mention the impact on organizational confidence, is simply huge. For example, it’s not atypical to see hundreds of millions spent on digital transformation projects only to flounder. According to TechTarget, $1 trillion had been invested in digital transformation projects with the Boston Consulting Group estimating at least 70 percent of these initiatives fell far short of their original goals by March 2021. The problem has become so apparent that it is not unusual for a CEO to dodge analysts’ questions in earnings calls on Digital Transformation.

So, what is the intrinsic problem? Humans with a passion for building tend to also love three things:

  1. Complexity
  2. Originality
  3. No clock ticking in the background.

However, maybe the more important question is rather than building software that fails to deal with the complexity of things fundamentally, why not take a shortcut and focus on the integration of things?

I started my career in the software industry, building network management systems in the 1990s, followed by cloud software and IoT platforms, and so on. I always accepted that my tried and trusted engineering colleagues always knew best. In all fairness, the engineers did know best, but they struggled when challenged to ponder the sheer complexity of the computer science challenges they were facing and how things were becoming exponentially complex. 

Then, I really started to get very curious about ways to solve the mountainous challenges being faced in digital transformation. Not only given the great quantum leap in the complexity of business logic modeling required, but also a new methodology in software that could mirror the aspirations of a human’s creative mind into solutions that would actually work.

So with that goal in mind, I started looking at automation engines to solve digital transformation problems.

The Future of Digital Transformation

In the 1980s and ’90s, computing took a big shift forward with the introduction of operating systems (OS). Therefore, why not look for an automation engine that functions like an OS? One where its role is to orchestrate software workflows from disparate data sources in real-time in unison and is built on the basis of the user/designers’ needs, namely low-code and even better no code.

An easy-to-use, visual canvas connecting to pre-existing “any-to-any” applications/systems of record automating mission-critical workflows and speeding up actions via an ultra-fast rules engine may be necessary. For the data scientists, a real-time automation OS that more specifically enables deployment of low-code orchestration from any system and device or data record to any other system can result in an incredible end customer experience. Many industry commentators believe there has been a missing link between operations and the massive investment in AI. I also believe in automation.

Today, the automation industry is in full flight robotic process automation (RPA) software companies where “hyper-automation” is the new buzzword. An entirely new generation of automation technologies is appearing in the market with RPA being the most common for solving simpler digital transformation problems.

Reaping the Benefits of Automation

As the world braces for a very tough economic backdrop, the biggest win to focus on is the enormous savings that will be reaped from automation at a time of record software skills shortages globally. Other target metrics to look for from an Automation OS are 10x faster time to market, 100x less code, 15x less development time, and up to 20x operating expenses (OPEX) and capital expenditures (CAPEX) cost savings. And finally, break-even ROI within nine months or less.

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  • Automation
  • Digital Transformation

  • Automation
  • Digital Transformation

参考译文
数字转换和自动化操作系统
大多数企业都在艰难地实现全面的数字化转型。与此同时,整个软件行业也在努力思考如何最好地加快转型。数字转型项目失败的原因非常复杂,我们很难把握。一种解释可能在于科技行业采取的方法。作为人类,我们知道自己想要实现什么。我们的大脑告诉我们任何事情都是可行的。但是,当我们面对在软件中构建最终解决方案,并且需要快速处理事物的异质性时,我们很快就会陷入流沙之中。随之而来的成本、时间和资源损失,更不用说对组织信心的影响了,简直是巨大的。例如,在数字转型项目上投入了数亿美元,结果却陷入困境,这并不罕见。根据TechTarget的数据,数字转型项目已经投资了1万亿美元,波士顿咨询集团估计,到2021年3月,这些项目中至少有70%远远低于最初的目标。这个问题已经变得如此明显,以至于首席执行官在数字转型(Digital Transformation)的财报电话会议上回避分析师的问题已经司空见惯。那么,内在问题是什么?热爱构建的人往往也喜欢三件事:然而,也许更重要的问题是,与其构建无法从根本上解决事物复杂性的软件,为什么不走捷径,专注于事物的集成?我的职业生涯开始于软件行业,从90年代开始搭建网络管理系统,之后是云软件、物联网平台等。我一直认为,我那些经过努力和信任的工程同事总是最了解情况。平心而论,工程师们确实是最懂的,但当他们被要求思考他们所面临的计算机科学挑战的绝对复杂性,以及事情是如何变得指数级复杂时,他们感到很吃力。然后,我真的开始非常好奇如何解决数字转型中面临的巨大挑战。不仅考虑到业务逻辑建模所需要的复杂性的巨大飞跃,而且还考虑到软件中的一种新的方法,这种方法可以将人类创造性思维的愿望反映到实际可行的解决方案中。带着这个目标,我开始研究自动化引擎来解决数字转换问题。在20世纪80年代和90年代,随着操作系统(OS)的引入,计算发生了巨大的变化。因此,为什么不寻找一个像操作系统一样工作的自动化引擎呢?其中,它的角色是协调来自不同数据源的软件工作流,并基于用户/设计师的需求构建,即低代码,甚至没有代码。可能需要一个易于使用的、可视化的画布连接到预先存在的“任意对任意”的记录应用程序/系统,自动化关键任务工作流并通过超快的规则引擎加速操作。对于数据科学家来说,一个实时自动化操作系统能够更具体地实现从任何系统和设备或数据记录到任何其他系统的低代码编制部署,可以带来令人难以置信的最终客户体验。许多行业评论人士认为,在人工智能的运营和大规模投资之间存在一个缺失的环节。我也相信自动化。如今,自动化行业正在蓬勃发展的机器人过程自动化(RPA)软件公司中,“超自动化”是一个新的流行词。市场上出现了全新一代的自动化技术,RPA是解决更简单的数字转换问题最常见的技术。 随着世界准备迎接非常严峻的经济形势,在全球软件技能短缺达到创纪录水平之际,最大的胜利是关注自动化将带来的巨大节省。从自动化操作系统中寻找的其他目标指标是更快10倍的上市时间,100倍的代码,15倍的开发时间,高达20倍的运营费用(OPEX)和资本支出(CAPEX)成本节约。最后,在9个月或更少的时间内实现收支平衡。
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