Artificial Intelligence (AI) is powering a new level of understanding and a product personalization experience at a rapid scale. The use of machine learning in making smarter, intuitive and better product experiences is the key to this growth and adoption.
Machine learning is not new, nor is it necessarily a competitive advantage in and of itself. The whole idea of machine learning is to take an algorithm, apply it to data and learn something from it. This is actionable insight and not mere data; the insights of an individual’s deep profile level are now available and producers of goods and services need to be consumer aware at this new, more detailed level.
AI must be integrated into a platform to work well, accessing foundational data, the intelligence layer, execution layer and deployment engines. Just like a human brain, software has an infinite capacity for intelligence. Unlike product features and UI elements, choices about intelligence do not have to be ‘either this, or that’. Social footprint derived intelligence works as a perfect complement to owned or CRM data derived intelligence. Together, they complete the story.
The age of technology has undoubtedly broken barriers to make the unimaginable, imaginable. But does it have the capability of getting smarter? It is no longer enough to have efficient workflows and an intuitive UI to make your product compelling for your users. Your users today expect more. They demand more. They expect your software to be intelligent so that it can make recommendations, and sometimes even decisions.
Along with the enhancement of technology and software, people, too, are growing and changing with time. This makes it tricky for organizations to know what people will respond to right now, while planning for what they may be interested in headed into the future. Industries that hold people at the crux of their business operations need to think about a way to get to know these people more deeply.
In the area of customer care, a gap exists between what we could potentially call, ‘The customer we know’ and ‘The customer we deeply know’ and if we have the required gaps and breaks filled out using state of the art insights and proactive recommendations, the customer relationships will be far more enriching on either side.
Use of AI in the Hiring Segment
One example is the ongoing struggle of choosing the wrong person to fit into the right job has been an age-old complaint. Employers use the best recruitment procedures in order to select the best candidates, and , to an extent this is successful.
A recent survey by Robert Half showed that one-third (36%) of 1,400 executives surveyed felt the top factor leading to a failed hire, aside from performance issues, is a poor skill match. Only the second most common reason (30%) was unclear performance objectives.
The most common hiring blunders are instinct-based. Many hiring managers make the mistake of choosing someone based on an instinctual “gut feeling” or because they “liked” a particular candidate, only to find out later that the candidate was not a good fit for the position for which he or she was hired.
When a recruiter is evaluating a candidate for any role, the candidate’s skills and expertise are only half the story. There is a great deal about the candidate that is not covered in a resume — factors like personal drive, flexibility, energy levels, dependability, and more.
Recruitment is undergoing a sea change for both the recruiter and the recruit. Recruitment once consisted of a written test and some interviews, but now it’s about analyzing GitHub contributions and understanding social activity over years.
If we specifically take the software space, recruiters care far more about abilities beyond capabilities. That is where AI comes in, helping recruiters understand the softer attributes of a candidate’s personality, as top grade performance is a lot more about EQ (emotional quotient) rather than just IQ (intelligence quotient).
When a hiring manager has the benefit of the knowledge of one’s behavioral attributes, it makes it easy for a hiring manager to know their candidate better, with a level of precision that only machine intelligence can provide. These include learning ability, team play dynamics, relationship skills, attitude, energy levels, dependability, flexibility and various other personas.
An article from [email protected], the online journal of the Wharton School at the University of Pennsylvania, said that a 2011 survey by the Society for Human Resource Management showed that nearly 20% of organizations used personality or emotional intelligence tests in hiring or employee promotion.
If you’re still not sure, according to TalentSmart, the leading organization in this area said EQ is responsible for 58 percent of job performance; 90 percent of top job performers have a high EQ; and people with high EQ make $29,000 more annually than their low EQ counterparts.
Through machine learning recruiters can strategically construct a platform that makes hiring an easier process. Going beyond understanding demographics, the software focuses on the personality and behavior of the candidate (the EQ), making it much easier to validate the potential job fit.
Breaking down the construct, AI will leverage certain employee personas that help the hiring manager understand the applicant. Now it’s not just about ‘first impressions last’, it’s about a deeper and clearer picture of a candidate, assessing whether a gamble should be made or not.
The bane of our human existence is the gap between expectations and reality. Today, it can be difficult to convey the ideal communication for a particular situation. We’re all different and have myriad experiences that shape how we communicate. What we say might mean something different to someone else. The faces we present to the world can only be judged up to a certain extent. The remaining parts of one’s personality that are slowly revealed over time is something to strive to know, and makes us believe that the future and AI will expose what was a mystery as of today.
Customer care and hiring are two fronts that are seeing potential in the utilization of knowledge and deep understanding of people in the most intimate manner. What if you were told that dating could begin with two people that could actually begin with a deep conversation about something they both found meaningful and interesting, and not filled with small talk? What if they knew the person’s behavioral and personality traits before beginning the conversation? Being compatible, having a rather interesting opening to a conversation, truly understanding a person, are only a few attributes in the list of many that can be provided by AI.
In the case of travel, this type of deep knowledge about a person’s preferences could help hoteliers and travel agencies customize a holiday experience to the customer’s needs. The selections would be based on his/her personal attributes, enhancing the their travel experience, making it one that would be truly personalized based on insights and not mere bookings.
The future, as we see it, is a diamond mine waiting to be discovered. We possess the necessary empirical value, we all know it. The tools to dig are ready. All that’s required now is a group of people ready to gather the necessary resources, to be able to change the world, one strike at a time.