You may have heard before about the “age of AI,” the “digital age” or the “information age,” sometimes in negative terms and sometimes in glowingly positive ones. But the period that’s actually dawning on us as we make our way through the beginning of the twenty-first century is the “augmented age.” The augmented age is closely related to AI, but it’s not the stuff of movies we’ve seen: AI is not about to automate all of our jobs, become sentient, or take humans’ place in the world. The augmented age is all about humans using AI to our advantage, not the other way around, in a process often called “augmented intelligence.”
This doesn’t mean that humans will incorporate AI into their brains to become superintelligent cyborgs. It means that humans will begin using AI solutions to automate low-level tasks that are repetitive, boring, time-consuming, and/or not particularly skilled. When humans are faced with these types of tasks, our progress is hindered in two key ways:
- We waste time. Humans are brilliant, innovative, and able to make creative connections and decisions in a way that AI can never rival. So why should we waste our time on dull tasks that don’t challenge us or move us toward our goals?
- We make errors. Boredom and repetition rarely produce precision in human beings. Instead, we become more prone to mistakes because we are not engaged.
Embracing the augmented age will help us reduce these problems by allocating the tasks that don’t highlight the best things about our human intelligence and capabilities to AI. Augmented intelligence will improve our ability to innovate and help open up our creative potential.
But are you aware of how your industry will transform when you embark on a digital transformation and begin adopting AI? Do you have a clear vision of how you will navigate this transition and how you will make it work for you? The first steps are to be prepared and know where you’re starting from.
Before an enterprise transforms to compete in the augmented age, it’s necessary to carefully evaluate your unique challenges and build a strategy that will make your digital transformation painless, profitable, and scalable for the long term. There are five key considerations or steps that are crucial to success, and most companies have only accomplished one or two at best!
The 5 Key Considerations in Assessing AI Readiness
1. Data InfrastructureTransformation
This is what most people mean when they use the phrase “digital transformation.” Although the process looks different for every individual business, the most basic and important component of digital transformation is transitioning business infrastructure from traditional structures to cloud-based structures. Moving to the cloud is the foundation of any effective digital transformation because it enables businesses to take advantage of the two most significant cloud offerings: speed/agility and security. Customers are seeking fast, on-demand access to products and services, and cloud-based offerings make this possible because they’re perpetually available and accessible. Moving to the cloud also means enhanced security and reliability for both the business and its customers. Cloud computing is generally thought of as safer and more secure than traditional methods of storing and transferring data.
2. Data Collection Transformation
Transforming your business’ ability to collect data is crucial to reducing reliance on low-level human labor. By securely collecting important data through any number of IoT devices, including video cameras, sensors, audio devices, and mobile devices, an enterprise can reduce the possibilities of human error, compromised data integrity, and lack of security. One of the most error-plagued aspects of business is the “simple” process of manual data entry. The introduction of IoT solutions like proximity sensors, air gap sensors, pressure sensors, artificial vision systems, and control systems that align with them to perform corrective actions can eliminate many layers of human mistakes and make company data more precise, not to mention more quickly collected and more securely transmitted and stored.
3. Data Structuring Transformation
Transforming the data structuring process goes hand in hand with the aforementioned process of transforming data collection. Once information has been quickly and accurately gathered from IoT devices and whatever other sources the business might still rely on, it must be stored in a way that lends it to use in the digital transformation process. Many businesses have massive backlogs of unstructured data, some of it stored untouched for decades. Unfortunately, even if this data is securely and accurately collected and stored in the cloud, it can still be useless to the company if it isn’t properly structured and then leveraged to gain business insights. If the end goal of digital transformation is the application of AI solutions to augment human efforts and achieve business growth, data must be ready for AI applications to access and use it. This means implementing automated solutions for data storage that can handle any backlog of unstructured data and maintain advantageous structuring as new data flows into the enterprise so that there’s no backslide as new data accumulates. Continuous structuring enables ongoing transformation.
4. Business Process Transformation
The transformation of business processes goes back to what we discussed at the beginning of this article: redefining the roles of humans and machines in business processes to take the greatest advantage of the capabilities of each. AI isn’t implemented with the goal of eliminating human labor, but rather with the goal of reducing human error and freeing up human resources to do tasks that AI can never do. Artificial intelligence is best suited to completing repetitive tasks, prioritizing human actions, making small day-to-day adjustments, and reducing errors that result in lack of efficiency, lack of precision, and wasteful re-work. Humans, on the other hand, are best suited to making high-level decisions, inventing creative solutions to problems, and innovating existing processes. After all, what is AI but an innovation invented by creative human intelligence?
The end goal of digital transformation is to have humans and machines working side by side to drive a business forward. AI solutions churn through terabytes of data to prioritize actions and make tiny adjustments, while humans define large-scale objectives and make the most important business decisions.
5. Continuous Improvement
Just as we hope to use data structuring solutions that continue to work long-term after their initial implementation, we hope to achieve augmented AI solutions that are open to continuous improvement as the business and its environment grows and changes. Much of this improvement process can be automated, too. AI solutions can be implemented with feedback loops that ensure machines can make increasingly accurate decisions, relying on machine learning to become better trained. Because they can be adjusted or self-adjust so quickly, solutions can be rapidly adapted to suit shifts in business goals and industry needs.
As we make our way into the augmented age, every industry will be forced to adjust their data infrastructures, data collection and structuring practices, and their business processes to achieve continuous improvement and maintain their relevance to customers. If businesses are willing to embrace the possibilities of augmented AI, they can take greater advantage of both automated solutions and human capabilities.
This piece originally appeared on CrowdANALYTIX.