Interview with Mark Moir, CEO at Acclaro, The Workforce Intelligence Company

Mark – we would like to give you some space to introduce yourself. In a nutshell, who are you, and what do you do?

So, a colleague and I co-founded Acclaro out of a desire to help organizations constructively and scientifically work with the interrelated dynamics of employee experience and an organization’s environment. Our desire was an experiential response – we had lived through product limitations in terms of evidence-based design, as well as the underwhelming impact that vendors were providing to the organizations that we served, and so we felt compelled to act. 

As organizational psychologists, we felt there was a more comprehensive and evidenced-based way to understand the holism of organizational life and experience, so we embarked on the development of a platform that would help organizations make the needed connections between environment, experience, and performance.

Our partners at the Center For Evidence-Based Management (CEBMa) are committed to helping people in organizations make better decisions. Recently, you’ve joined CEBMa as Professional Member; how do you help your clients improving their people and organizational decisions?

We strive to create meaningful insights for leaders and organizations by illuminating the linkage between contextual uniqueness and employee experience. We also work to shed light on the organization’s relative agility or adaptive capacity.

Every organization has a unique social dynamic that influences perception, mental processing, meaning making, and other socio-cognitive activity. We believe that to understand experiential issues, you need to account for this environmental condition.

Accordingly, we created a cloud-based system that analyzes both structured and unstructured data to more deeply understand the social or cultural underpinnings that influence the employee experience. Our process provides insight into outcomes like employee engagement and agility, but also surfaces the primary drivers of these states. This can provide insight for leaders and organizations into ways of intervening that are driven by evidence, rather than supposition or another organization’s “best practice”.  

And I can tell you that one of the great benefits of being a Professional Member of CEBMa, is that I have access to a deep well of knowledge that helps us continue to not only learn about new evidence-based insights and tools, but also to stay connected to a community of practitioners and scholars who have the same desire. That is both personally and professionally stimulating and wildly rewarding.

Your expertise seems to tap into organizational evidence in its soft elements of employee perceptions of organizational culture and attitudes. How does Artificial Intelligence is helping you revealing knowledge of an organizational context?

Great question. So, our model leverages the Cognitive Capacity within IBM Watson. We capture qualitative data through our workforce intelligence assessment that helps us get at culturally infused issues like leadership, relationship, change, etc. That data is processed using Natural Language Processing which provides a view of the employee’s psychological experience of what we call the Tone and Tenor of the organization’s cultural dynamic, and the meaning they attach to this experience.  This experiential view captures emotions, social tendencies, values, and needs that are present within the data.  

© Center for Evidence-Based Management

Let’s take leadership for example. Along with qualitative data, our assessment captures quantitative data through valid and reliable items that focus on the activity and behavior of leaders – behaviors that we know from the extant research literature on leadership, as well as our own research over many years, drive outcomes like commitment, discretionary effort, intent to stay, organizational advocacy, etc. We analyze these findings against climate related dynamics that are discerned from the qualitative data to understand the relative impact of leadership (as well as other driver categories) on environmental dynamics, and how these dynamics are impacting the employee experience.  

For example, with a recent client, our climate analysis revealed a deep Need for Closeness and a Need for Structure within the employee population – basically there was a strong concurrent desire for community and clarity. There was also a strong sense of Sadness discovered within the situation as well. These Needs and Emotions were extracted through the Natural Language Processing aspect of our process. Within the quantitative data, endorsement on leader related items were relatively low – specifically on trust and communication. In conducting Driver to Indicator analysis, it was clear these dynamics were negatively impacting commitment, burnout, as well as intent to stay.  

Within the accompanying developmental effort for the accountable leader, it was discovered that the leader’s primary strategy for managing stress and uncertainty was detachment – to basically create emotional space for the benefit of reducing stress. We discovered this proclivity using Hogan Assessments. These strategies were experienced by employees as the leader being physically and emotionally unavailable, as well as uncommunicative. Regardless of this leader’s relative intentions, the causal chain created an environment and experience of disconnectedness and a general lack of community, along with a lack of clarity in terms of direction, expectations, and vision. Putting these points together allowed for a data driven approach for course correction.

This is both impressive and insightful – Mark! Now, the techno-sociologist Zeynep Tufekci gave a TED Talk on the Ethics of Artificial Intelligence which is worth watching. From an ethical standpoint, what’s the point of using AI in the context of employee experience?

We take the ethical implications of our AI or Cognitive Computing process very seriously. It’s important to maintain perspective in terms of limitations as well as primary focus.  

We approach the ethics of Cognitive Computing in the same fashion as the organizational commitments that exist when data are collected through an organization’s employee survey activities. Practically speaking, we aggregate climate dynamics only where privacy of participation can be ensured. We also commit to ensuring that our sample of data is sufficient to ensure integrity of output.

The focus of what we discover is always directed at enhancing and improving. Our view toward what we generate is that it should serve conversations around making sense of what organizations have created so they can juxtapose against what they aspire to achieve. So, our hope is to open conversations of possibility that can reveal practical, evidence-driven interventions that lead to better employee experience, organizational performance, and positive change.

We are talking about putting technology to serve people, and not the other way around. Should organisations focus on helping people make sense of their work and find meaning, also by leveraging technology?

Our primary goal is to work with organizations to understand what has positive benefit for their stakeholders in terms of what they are trying to create from a cultural and organizational perspective. Our deepest belief is that the purpose of technology is to serve the work of improving the employee experience as well as the organization’s opportunity for greater sustainability.  

We place great emphasis on the moral and ethical obligation of leaders in organizations to create and foster meaningful experiences by tending to the organization’s environmental condition. Leadership can have such a tremendous impact on the state of an organization’s environment. And we think our platform can help them gain a deeper appreciation for this responsibility.  

And with the pace of technological change, we certainly hope that the focus of further technological development efforts is on augmenting and enhancing the experience of people, versus displacing and encumbering people.

Dr. Mark Moir, thank you for this meaningful conversation around work–and possibly its future, and congratulations for joining the Center For Evidence-Based Management in this exciting journey to make work better, and matter more.

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Selected Resources

Barends, E., Rousseau, D.M., & Briner, R.B. (2014). Evidence-Based Management: The Basic Principles. Amsterdam: Center for Evidence-Based Management

Burton, E., Goldsmith, J., Koenig, S., Kuipers, B., Mattei, N., & Walsh, T. (2017). Ethical considerations in artificial intelligence courses. arXiv preprint arXiv:1701.07769.

Moir, M. (2017). Contextual Leadership: Context as a Mediator of Leader Effectiveness. Psychology and Behavioural Science International Journal , 3(4): 555617.

Tufekci, N. (2016, June). Machine intelligence makes human morals more important [Video file]. Retrieved from

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