AI and Automation: Leading the Resurgence of Re-Engineering in the Enterprise

Purposeful AI

The key to surviving in a highly competitive environment has always been innovation. In the early twenty-first century, this took the form of enterprise-class software, namely ERP, CRM and SCM, which re-engineered the enterprise and led to the radical redesign of processes, structures and culture.

Today, the technologies powering innovation are Artificial Intelligence (AI), robotics, Natural Language Processing (NLP), IoT, and more. Unlike the early twenty-first century, innovation is no longer incremental and linear, rather it is rapid, disruptive, and seminal. From smart manufacturing to predictive analytics for legal institutions, and from hedge fund management to advanced digital service providers in telecom, enterprises across industries are turning to today’s innovative technologies to create near zero latency, hyper-efficient business models and to stay relevant in the market. As updates on the latest feat by these innovative technologies hit the newsstands, one cannot help but wonder just how smart will AI become? I suppose you wouldn’t be surprised if I said, a lot smarter. AI, for instance, will not only be able to do increasingly complex tasks, but also interact with us just like another human would. Making this happen, of course, is data and it comes from you and me. 

People are increasingly living digitally-intermediated lives – through their digital assistant, smart devices, social media platforms, and even browsers. As AI accesses all this data and integrates it with machine learning capabilities, data lakes, cloud computing, robotic process automation, and mobile and voice interfaces, it becomes not just a toolset to an enterprise, but can also amplify human cognition by potentially taking the form of a friendly avatar or a coworker.

Re-engineering of the enterprise

The convergence of AI capabilities has the potential to not just push the boundaries of human experience, but to also enhance human productivity, and even launch new business models. Pricing, speed, and quality are all immediate reputation risks for enterprises in the older world order, but with AI they finally have the tool to put the genie back in the box. For instance, a nerdy data scientist sitting in a dingy corner of an office can answer questions like how an enterprise can drive 20 percent growth in market share by better sensing and shaping customer demand and experience across digital channels, or how an enterprise can improve customer satisfaction by 15-20 percent by leveraging IoT for predictive maintenance, or how it can harness automation to accelerate revenue and cost synergies for an M&A integration.

Powering this enterprise level re-engineering are millions of structured and unstructured data points which constantly scale and throw forth intelligent insights and knowledge that inform human and machine actions. The algorithm economy is also capable of extracting knowledge from one part of the enterprise and placing it in another, thus linking sectors and enabling them to learn from each other. To facilitate this re-engineering, cognitive solutions will continue being woven into the very fabric of the enterprise, transforming our ability to engage, experience, and influence our environment like never before. 

Another big positive: AI will create new jobs that do not exist today

As the re-engineering in the enterprise gains momentum, jobs will be impacted and will have to be re-envisioned. This means enterprises will need to retrain their employees for new roles just how they did it at the onset of the industrial revolution. Employees will have to abandon tasks that are repetitive and reskill to do those that require creativity, leadership, critical thinking, and innovation. Here are some of the new jobs employees will have the opportunity to do in the near future.

1. Training AI systems: To understand nuances in speech, such as detect sarcasm, match payments to invoices, or develop an algorithm to be ‘fair’ even against the backdrop of cultural nuances.
2. Determining the need to deploy AI: Assessing the business impact of using AI algorithms and becoming context designers to enable smart business decisions.
3. Evaluating: The cost of poor machine performance, including non-economic factors as an automation economist.

Even as machines are doing increasingly complex tasks, and humans are being called upon to re-envision work, AI is unearthing customer knowledge, assessing scarce resources, and finding profitable adjacencies. As in the case of Amazon which not only leads an exceptionally profitable online business, but much to our surprise, branched out into the store model as well. The enterprise is being re-engineered with AI and automation. The endeavor is to simplify customer experience. The question to every enterprise is, are you ready for this zero latency, hyper-efficient business environment?

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