July 28, 2022 • 4 min read
Intelligence augmentation or IA are technological tools designed to enhance human intelligence by improving learning, decision making, cognitive performance and new experiences.
The difference between Artificial Intelligence (AI) and Intelligence Augmentation has to do with AI machines being autonomous without human intervention, while IA is where human interaction is the critical part to enhance a humans job.
According to (Reportlinker), as of 2022 the augmented intelligence market hit $17.9 billion. Within the next 5 years it’s expected to grow to $54.7 billion, making up a compound annual growth rate (CAGR) of 25.1%.
The forecast shows an increase in business adoption, highlighting the increased volume of complex business data and the need of IA to automate that growth.
Full automation in time will lose steam. As technological automation matures, tasks that are either dull, dirty or dangerous will be automated. Complex tasks where human intervention is a must, will remain and even grow. Thus, putting humans on jobs where decisions and creativity is needed all while being supported by Augmented Intelligence.
In fact, automation is what will be pushing the growth of augmentation. As more people are freed up from what will be considered pointless, unnecessary routines, new roles will emerge that will enhance our working output.
The concept that robots will replace all jobs is a common fallacy. The economy pays workers from the revenue generated. Workers in return buy products out of play or necessity. Humans need the money from jobs to pay for the products they are producing. If you automate all jobs, no one has money to buy the products they produce. Thus both the business and the consumer loses.
In a capitalist economy, competition fuels the need to find innovative ways to take the markets from competitors by shifting roles that speed growth. In a time of exponential technology growth, not adding new innovations created by humans will ultimately have the opposite effect and lose out on competition.
A great example came from when ATMs were invented in 1969. Many claimed that these autonomous teller machines would relieve a vast majority of cashiers and teller jobs. In fact the opposite happened. Handing cash out was a repetitive job, which has now been replaced by new services between customers and tellers. These new jobs free up additional sales channels to increase revenue. In fact, there has been a 10% increase of these jobs since 2000, and more branches opening since ATMs were invented.
The most popular uses of IA are through online softwares that can run chatbots, assessment tools, playbooks, search tools, smart FAQs and configurators.
Let's dive into what they do:
Chatbots are knowledge management tools that replicate human interaction to gain information. By writing or selecting an action, a chatbot allows one to gain the knowledge to perform tasks. It can either be customer-facing, B2B or internal support.
Assessments allow one to map a group's skill sets and knowledge. This allows the ability for a quick search by connecting people to the correct knowledge experts.
Playbooks are used to make company guides easy for selective searches, instead of a massive book from which people must accurately follow and memorize.
Search tools are documentations of knowledge where users can search between folders, docs and videos aggregated in one system.
Smart FAQs allow customers to select answers to questions through personalization. Instead of contacting customer support, which often wastes time, they can gather the exact info without the cost of using humans to help.
Configurators are used to support complex customer processes. The configurator allows for a seller to take a question from a customer and guide them to ask the customer more specific to the exact purchase they need.
Logictry has become a leader in creating the next generation of Knowledge Management Systems. With the use of Logic Maps, Logictry can take any data knowledge set from an organization and turn it into a decision tree that leads you to the correct answers by selecting actionable statements.
Logictry uses a new declarative syntax called Logic Markup. It allows logical conclusions to be predicted from previous statements. Therefore a Logic Map follows a path based on the user’s previous answers. The approach drastically cuts down time for individuals to seek answers to do their job.