A few weeks ago, I took my friend to the emergency room of a well reputed local hospital with a pretty severe eye infection. He had to narrate the same exact story of his symptoms and associated medical history to the check in person, the nurse, the doctor, the specialist and probably 2–3 other people over the 2 hours that we were there. All while enduring the pain and discomfort he went to the emergency room for.
I’m sure many of you have experienced this scenario at the hospital, with customer service, tech support- its confusing, frustrating and highly inefficient for both yourself and the company’s bottom line: your precious time is wasted, and the company is at risk of losing a valued customer.
What if he called the hospital while we were driving over and explained his symptoms to a virtual assistant enabled with voice commands (just like Alexa) via a guided q&a. Once documented he would not have to repeat himself again and again but communicate only the changes in how he was feeling.
It’s not something that’s too far out- we already have pretty smart recommendation engines like Amazon shopping and Netflix that streamline our lives by intimately anticipating our entertainment or shopping needs.
A Looming Digital Transformation
Enter artificial intelligence (AI). To be clear, this is not the malicious mechanical antagonists often represented in popular culture. It is also not “superintelligent” agents that bring on the Singularity in which exponentially growing AI overtakes humanity in a zero-sum game. Rather than conscious, emotional machines, these days “AI” is more often co-opted to refer to a network of digital tools that automate tasks to augment human capabilities.
We all are well aware of them: the virtual assistants embedded in your phone’s operating system, or the online chatbots that parse your questions to better resolve queries. With the rise of big data and deep learning, the technologies are also increasingly embedded across entire technological frameworks to predict, promote and cater to the individual needs of customers and consumers.
There is no doubt that digital tools are set to overhaul entire industries. As widespread advancements in machine learning (ML), natural language processing (NPL) and computer vision trickle from academia into industries — and tech giants progressively developing new algorithms in-house — the fact that automation is now augmenting human behaviors and capabilities will only become more salient. One can even argue that, with proper implementation, we are already at the crossroads of human-machine hybrid intelligence for practical use. Rather than purely designating tasks to AI, what we’re witnessing is extended intelligence, in which automation combines with, or frees up, human intelligence to tackle today’s complex problems. More specifically, in this win-win situation, computers take over repetitive tasks, which frees the human mind to tackle intricate and complex problems with sensitivity and compassion.
A recent SalesForce Report found that the majority of customers now consider their experience with a company as important as the product that it offers. By offloading mind-numbing processes to tireless machines, AI has the curious ability to further promote human-to-human interactions.
Nevertheless, despite obvious advantages of incorporating AI into business intelligence, customer relationship management, managing assets or streamlining internal business processes, a 2018 report by Forrester found that 22% of 1,600 surveyed North American and European enterprises are hesitant to transform digitally. Some of the reluctance is rooted in old-school technophobia; however, much more stems from little understanding of the concepts of automation, augmentation and paths forward to best integrate these practices as complete strategies into existing business paradigms.
Here we take a deeper look into these concepts and their current impact on hypercompetitive markets across industries — a first step towards exploring a practical and optimal path towards digital transformation.
Automation and Augmentation
Although often discussed as separate — if not competing — processes, automation and augmentation are better thought as synergistic forces that intricately collaborate to empower human creativity and innovation. One does not preclude the other; rather, one enables the other.
Automation generally refers to machines performing specified tasks with minimal supervision. At its most basic, these include robotic arms re-stocking shelves in warehouses, web browsers auto-filling forms, or software that automatically scans resumes for required keywords.
Basic automation removes the need to manually tackle repetitive, rule-based tasks in a structured setting, freeing human workers from boredom. As underlying algorithms and technologies further advance, automation is increasingly covering more sophisticated tasks that deal with complicated, unstructured data. These tasks may still remain simple to a human — for example, reading a customer’s chat record to distill his or her basic needs and goals, or gaining an intuition for when to best post content on various social media platforms. However, until recently, parsing normal human language has been difficult for machines. Automation has gained traction in small steps, but these add up — similar to the industrial revolution, we can expect automation to further support human workers by freeing their time and energy.
Augmentation highlights the ability of machines to further improve human decisions and behavior — often, but not always, through automation. These can be data insights. For example, analytics from A/B testing or eye-tracking that identify webpage elements that best draws attention, or algorithmic radiologists that confer a diagnostic opinion for a medical doctor to further evaluate. Machine-supported trading decisions already dominate Wall Street, and car manufacturing companies gather data to predict when parts could fail or need services to uphold safety records. Because of their extraordinary power to process, distill and extract patterns from massive amounts of data — far beyond the ability of any given human — computers can offer invaluable insight by analyzing real-world feedback.
Notably, automation and augmentation are intricately intertwined, and efficient integration of machines into human workforces touches on both. For example, picture a customer service scenario that begins with an automated virtual assistant. Using natural language processing, this assistant asks a series of questions designed to tease out the customer’s main concerns and intent. When optimized, this step efficiently gathers basic data and automatically fills out a ticket, without the digital assistant ever becoming exhausted or exacerbated with the customer, ensuring a pleasant interaction. With simple problems, the bot can direct the customer to relevant online information; with difficult problems, the bot then passes on the ticket to a human service agent. The human agent can then skim through a ticket, summarize the main problem and respond accordingly, without having the customer to repeat his or her narrative.
Thanks to both human and machine, the customer’s experience is pleasant, seamless and efficient.
Extended Intelligence is Already Here
We’ve already touched on some examples in which digital help, when successfully integrated into the human workforce, extends the reach and efficiency of intelligent output. These are not isolated cases. Although relatively nascent, concepts of automation and augmentation are already deeply rooted in various industries from marketing to business operations, and from healthcare to journalism.
Social media and online marketing are now indispensable avenues for broadcasting a company’s mission and connecting on a personal level with customers. Marketing departments deal with repeat tasks in email, social media and website management, with the goal of attracting prospective customers, establishing loyal relationships and converting casual clicks into delighted brand followers.
Automation supports this process by scheduling regular tweets, Facebook and Instagram posts. A good pipeline follows custom-built templates to generate relevant, optimized content and helps organize and track campaigns across platforms and text messaging. Subsequent data analysis, which creates measurable outcomes such as ROI, can then augment marketing professionals in developing future strategies, or modify outreach methods mid-campaign to achieve better results.
With machines helping employees to repeatedly press the “send” key hourly, daily and over months, humans are free to handle higher-order roadmaps, concepts and strategies, while reducing typos and other human errors.
The force of this type of integrated, extended intelligence has further shaken up business operations. For example, automation can streamline employee onboarding by filling out paperwork, scheduling training sessions or even setting up bank accounts. Back-office processes also benefit from digital transformation, with AI distilling relevant information from receipts, purchase orders and other documents to generate and track invoices. More sophisticated systems can even monitor customers and vendors over time to identify problematic collaborators, alert the responsible departments and provide optimal times to reach out to the customer, potentially resulting in more constructive interactions. Personnel scheduling, deliverables tracking, IT support and other repetitive administrative tasks are all pressure points that don’t necessarily improve a company’s market competitiveness — software is in a perfect position to assuage these points.
Healthcare is another field ripe for a digital overhaul. Automation can simplify the patient in-take process: a digital doctor assistant follows the list of questions normally used for patient history and automatically generates an intake form for physicians to look over. This frees up precious time for clinician-patient human interaction, with love, empathy and care. In terms of journalism, experiments are underway to automatically generate articles to support local news coverage, which persistently suffer from lack of human reporting due to time restraints.
The above examples make it clear: even with relatively simple automated procedures and low rates of adoption, digital processes are ushering in a new era in workflow, business organization and customer (or patient) interaction. It’s no surprise that industry leaders are drawn to digitalization to improve efficiency; but perhaps more importantly, they also celebrate the power of automation and augmentation to liberate their human workforce, allowing them to do their best and emotionally fulfilling work.
To nail it home: rather than miring ourselves in discussions of a man versus machine singularity, it pays to focus on what’s already prominent and beneficial — a man plus machine extended intelligence that will not only augment existing tasks, but also arm us with a powerful tool to tackle today’s grand challenges, such as the economy, environment and health.
Of course, appreciating the impact of intelligent transformation is only the first step. Realizing its implementation, without stumbling into big data roadblocks such as “garbage in, garbage out,” is an arguable harder problem. Both automation and augmentation rely on large, organized dataset architectures as their foundation, and this is what we will explore next in my post titled- There is no AI without IA (Information Architecture).
I look forward to your feedback questions and comments.
Partnering in your AI automation projects
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