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We are in the midst of a massive transformation which is affecting the future of the workplace as AI-based technology surrounds today’s workers and creates new ways to maximize their interactions with customers and increase their output or productivity.  AI is augmenting the workforce and creating value like never before at both the individual and enterprise level.

Data Was Step 1, AI is Step 2

First, we have to recognize that we could not reach this stage of AI transformation if we hadn’t already gone through the data transformation.  For AI to work you need a massive amount of data and the digital transformation stage of the last 15 years has enabled businesses to amass enough data to warrant and utilize AI.  Enterprises have gathered the data and identified which are signal and which are noise and now that data can be structured in a way where machines can use it to feed experiential algorithms and establish baselines for AI.  AI can then be integrated into all aspects of the business and can make decisions based on experiences rather than rules-based programming. This provides for the AI transformation process to begin.

Becoming #VoiceFirst with AI

At the core of the AI transformation is the concept of becoming “voice first”.  Voice First is the new paradigm for the development of consumer-oriented UI. Originally businesses began to transform by becoming online-first, then they transitioned to being mobile-first, and now companies like Amazon and Apple and leading the evolution towards voice-first. Voice is the way consumers are going to be engaging with technology across new platforms for many years to come… their phones, their cars, the internet of things, etc.  It is also how businesses are going to interface with their systems, though that adoption will follow behind the consumer adoption. For decades we have been trying to make machine interfaces easier to understand and more accessible to more people. Voice represents an inversion in that paradigm, where we start asking machines to understand us, and not the opposite. Because of this, voice is one of the most important step function changes in the democratization of interfaces.   

The first interfaces with computers were painfully low level but as computational capabilities,  and our ability to design more abstract languages evolved so did the user experience. The speed at which adoption occurs has paralleled this evolution. Look at the progression from the modern desktop GUI to mobile’s simple touch interfaces. That jump brought billions of new users into the digital world.  Now we have voice. Asking Alexa, Siri or Google to do things via voice has become commonplace at home. This will only accelerate when voice is integrated into the enterprise where it can impact productivity. Enterprise UIs are rapidly becoming consumerized and the development of voice-activated UI lowers the bar and makes it easier for everyone to engage with enterprise applications.  

In the next 12-24 months, we’ll see new applications that allow enterprise users to employ AI and voice that goes beyond consumer interfaces and interactions and ventures directly into their workplace.  Look at products like Slack that iterated on technology which came before (in their case it was an iteration on instant messaging and file sharing).  They created an improved interface that leads to increases in productivity, but if you take that and couple it with AI that can immediately add people to conversations or recommend attachments that would be helpful, you further increase productivity.  What’s further, you could add a voice-activated interaction component and Slack becomes a much more easily activated platform, increasing the speed at which information is exchanged and shared.

The above example is a small one.  When you generally couple voice with advanced AI to capture, analyze and understand intent, enabling machines to augment the modern workforce becomes extremely powerful in more than just a conceptual stage.  It is not about replacing people – it is about making them better, faster and more productive.

AI is about augmentation

AI has historically been something in science fiction and futurist presentations, but in reality, it has been operating alongside you every day for years and you weren’t taking notice.  Consumer companies like Amazon and Netflix have been using AI in their core recommendation engines for many years, creating more personalized consumer experiences. These are now looking to venture into the workplace as AI can be used by the individual, on a more immediate scale, to automate everyday responsibilities and free up knowledge workers to allocate time to more profound uses of their time.  AI is used in tools like Grammarly for automating grammar correction and spelling. It can be used on your computer or your phone for autocorrect. AI can be seen being used in the same way as Marvel’s “Iron Man” armored suit, surrounding the worker to make them faster, stronger and more efficient. These tools are inexpensive and easily integrated with one another.

AI is about a new type of competitive development advantage

AI has the ability to deliver compounding competitive advantages at an enterprise-scale level. While we are all familiar with the standard network effect, AI opens doors for teams to develop compound network effects.  The compounding effect comes from self-deploying AI that automatically upgrades its own capabilities to create a slightly better interaction every day. These can be used to provide increased output for customer service, development and more  While classic software has more intermittent and slower upgrade cycles, continuingly deploying AI has a clear advantage. While this seems obvious, this type of system is incredibly hard to deliver. The reason is few AI teams invest in all the infrastructure needed to have automatically deployed continuous learning systems. For teams that take the risk, they will reap significant competitive advantages. Companies that embrace the learning part of AI and the associated hype but neglect the deep infrastructure needed for continuous learning, will be left behind.

AI might be omnipresent in consumer news, but we are about to see a significant sea change in how it impacts businesses. AI infrastructure and depth has made significant strides in the past five years due to changes in the cost of processing and the availability and ease of capturing data.  We are in the midst of a data renaissance that has been brewing for the last decade.

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