Integrating Human Capital and Artificial Intelligence for Organizational Effectiveness

As machine learning and artificial intelligence rock the business world, we must think beyond data analytics, and instead, about a holistic approach to running our businesses. We must include the people who fuel our operation, and how we can best deploy them, to achieve our vision. How will we bridge the chasm between the traditional knowledge worker and a new paradigm, wherein, machines forecast and do predictive modeling in a more affordable and efficient manner?

Organizational structures must be aligned with emerging technology and skills, as they evolve. We should focus not only on the skills and technology, but workplace preparedness, for managing the impacts of machine learning and AI. People will need to re-skill, and we must all bear the trade offs of time and money for doing so. But that’s only one part of the equation. It goes without saying, that AI and ML are now critical to business survival. The businesses that will be most successful are those that can marry the technology with people and structure.

Human Capital Implications:

We need to focus more on data integrity, as the machines perform predictive modeling. How do our QA processes change, and what skills are required for validating accuracy in outputs? These are skills that will secure future employment, as task work tapers off.

In times like these, employees often wonder if their skills will be become obsolete or if they’ll be displaced, altogether. We saw such worry with the advent of the personal computer. In the end, we saw job growth. Theoretically, this would assuage the fears of today’s knowledge workers. However, this history has done little to ease the worry among employees I know.

I recently spoke with a massage therapist who said, “I don’t think a robot could do my job.” The operative word was “think.” He wasn’t 100% sure, like many people. I also heard a fear-inspiring commercial alerting CPAs that they should pursue a CMA (Certified Management Accountant) designation to demonstrate competency in decision making and strategy, over quantitative CPA skills. In fact, some groups are advocating for a universal basic income as a counter-measure to jobs displaced by machines. As you can see, fear abounds. This makes the case for integrating Artificial Intelligence with organizational effectiveness.

Without strong Quality Assurance, the machine becomes an echo chamber of false information. This sabotages the integration of Artificial Intelligence and organizational effectiveness.

(See the NYT link below that shows the real-life implications of machine learning in the justice system as an example of this.)

Human Capital and Artificial Intelligence in Talent Management 

Identifying talent is one of the tasks that machine learning has infiltrated most pervasively. We can learn from the Applicant Tracking System (resume filter) as one of the more familiar machine learning/AI models. Being that the ATS can filter out age, race, gender, etc., it stands to reason that the workplace should become increasingly diverse. However, according to MIT researchers,

I’ll elaborate. Assuming that the ATS is successful at identifying the best candidates (and I have strong opinions on that for another time), we’ll need to continually recalibrate the ATS models to predict the success and retention of this newly identified machine learning skilled workforce. Are the skills we assumed were needed, like the CMA accountant, truly what we needed? Will CMAs be successful in their new roles, and can we retain them? There must be a woman or man behind the curtain to make the final hiring decisions, right? With the ATS generating the ideal candidates (on paper) managers are currently making decisions around culture fit.

Making decisions around culture fit introduces its own complexities, and the downstream effects impact the efficacy of the ATS. If I’m a hiring manager, I just might think I‘m a great culture fit. I really know how to get things done around here, and I might hire a top contender with characteristics similar to mine. My candidate should be a great culture fit! The hiring outcome feeds back into the ATS. This suggests that this candidate I chose is just the type to be successful! This hiring decision trains the ATS.

The ATS becomes inherently biased. A diverse team should be building and tweaking the tool to ensure it’s not reinforcing its own biases. This will reduce the probability of unconscious bias within the programming. After you select your dream candidate, you must now immerse them in your culture to ensure their satisfaction and integration. We must redeploy our resources, to create these fantastic employee experiences. We cannot nurture these relationships and retain our employees with machines, alone.

Integrating machine learning and AI will require new skills, new organizational designs, effective QA and feedback loops. People, processes and technology need to create and sustain organizational effectiveness. The organizations that harness efficient designs, QA, feedback loops and harmonious organizational deployment will have the sustained success we’re all seeking.

Here is an interesting article about how AI and machine learning are impacting the lives of US citizens: https://www.nytimes.com/2017/10/26/opinion/algorithm-compas-sentencing-bias.html