Article

We once believed that an organization's success rested primarily on the rational intelligence of its leaders and staff - intelligence measured by intelligence quotient (IQ). However, the business world has evolved to highlight the multidimensional aspect of human intelligence. The theory of multiple intelligences, which advocates various forms of intelligence, places emotional intelligence (EI) at the top of the list. But in today's digital age, the list includes a new form of intelligence: artificial intelligence (AI).

In an increasingly digitized and accelerated world, organizations are more determined to find ways to improve the employee experience (EX) - to attract, retain and mobilize talent and ensure organizational sustainability and growth. Although EI and AI are distinct, they can complement and even combine significantly to achieve this goal.

Let's explore the critical impact of EI on a quality employee experience, and how AI can complement it to create a harmonious, productive and value-added work environment.

Emotional intelligence and the work environment

Popularized by Daniel Goleman, EI refers to the ability to recognize, understand and manage our emotions and those of others. We can break it down into five main components:

1. Self-awareness : the ability to recognize our emotions and their effects
2. Self-control : the ability to control or redirect disruptive impulses and moods
3. Motivation : a passion for work beyond money or status
4. Empathy : the ability to understand the emotions of others
5. Social skills : the ability to manage relationships to achieve positive results

We can also measure EI to obtain a person's “emotional quotient” (EQ).

Is emotional intelligence essential to a positive employee experience?

EI plays a key role in creating a positive work environment. Several studies demonstrate the central role of EI in staff and leaders' performance at work.

Effective communication

Leaders and employees with high EQ can communicate more effectively. They can express their thoughts and emotions clearly and constructively, limiting misunderstandings and conflicts. They can better understand and communicate their feelings and share the intense emotions that might interfere with (or help!) a constructive conversation. We can't stress this enough: healthy communication is essential to fostering collaboration and productivity in a team.

Positive interpersonal relationships

EI helps develop strong, positive interpersonal relationships. Commitment and motivation increase when staff feel understood and appreciated. Furthermore, high EQ fosters a supportive and understanding work environment, which can reduce stress and improve overall well-being.

Conflict resolution

Conflict is inevitable in any organization. A certain amount of conflict and disagreement can even contribute to productivity - when properly managed! People with a high EQ are generally better equipped to manage conflict constructively. They can recognize and understand the emotions involved and find solutions that facilitate a positive resolution.

Stress management

Stress can dramatically affect staff satisfaction and performance. EI helps to recognize the signs of stress and use effective management techniques, for the individual and others. Leaders with high EQ can better support their teams in times of stress and welcome their experiences, creating a more resilient work environment.

The limits of artificial intelligence

Understanding human emotions 

While AI has made significant progress in numerous areas, it is limited in understanding and managing human emotions. AI algorithms can analyze data and identify patterns but cannot feel or understand emotions like human beings. This limitation means that AI cannot replace IE in human interactions. If we believe that AI can (or soon will) understand and feel human emotions, we still have a long way to go. 

Personalization and empathy 

AI can provide personalized solutions based on data. However, the ability to empathize, essential to positive human interactions, remains impossible. For example, AI can recommend interventions based on data analysis but cannot offer the emotional support that empathetic interventions require. AI recommendations will always have to be implemented by a human being, who will add his or her personal touch and warmth and execute the recommendations in his or her own way. 

Contextual judgment 

AI can struggle to understand the complex context of human interactions. For example, contextual factors unapparent in data can influence human emotions and behaviours. IE enables humans to understand and react to these contextual nuances - something AI cannot do. 

Complementing IE and AI 

The theory of multiple intelligences states that each type of intelligence enhances the others. The more “competent” a person is on various dimensions of intelligence, the higher their overall intelligence is perceived. So why not use AI to go even further? 

Automating repetitive tasks   

AI excels at automating repetitive, routine tasks, freeing up time to perform higher value-added tasks. For example, chatbots can handle basic support requests, enabling teams to concentrate on more complex problems. Automating data processing or parts of business processes also frees up time. 

Data analysis and decision-making 

AI can analyze vast quantities of data to identify trends and patterns. These analyses can help leaders make informed decisions based on hard data. For example, AI can analyze staff satisfaction surveys to identify areas for improvement, leaving IE to judge and lead the required improvement actions. 

Improving the employee experience  

AI can complement IE by providing tools and information to improve the employee experience. For example, AI platforms can capture real-time well-being indicators and flag signs of stress or demotivation among staff members. Leaders can then use AI to intervene appropriately and empathetically. 

Straining and skills development  

AI can be used to tailor training and development programs to employees' individual needs. For example, machine learning algorithms can analyze performance and recommend specific training to fill skill gaps. Leaders can then use AI to coach and support each member of their teams throughout their development journey.

Case study: combining AI, EI and EX 

Here are three case studies (anonymized) where combining AI and EI helped create a more harmonious, productive and value-added working environment. 

A solution to a labour shortage 

Fast-growing Montreal-area manufacturing company 

The company is struggling with accounts payable issues, as the two current resources are not enough to handle the thousands of invoices received monthly. Despite several job postings and attractive working conditions, no reinforcements arrived. Processing delays are piling up, and penalties abound. Overtime is the order of the day, resources are running out and tensions are running high. 

The diagnosis indicates an optimal process, but the accounting data entry into the ERP system is cumbersome. The company's traceability obligations and the wide range of suppliers and products in its assembly chain limit invoice payment process streamlining. It's time to look outside the usual frame of reference. Specialists are turning to one of AI's skills: process automation through software robotics (RPA - Robot Process Automation). 

The project duration: twelve weeks to cover the technical aspect and a few months for the human element. 

Now, the organization receives all invoices by e-mail to a single address in PDF format. The robot saves each invoice (and the e-mail), recognizes the data (different invoices for each service provider) and enters the data in the ERP. Following rigorous testing and monitoring to substantially reduce the error margin, human validation is limited to sampling including random selection and new supplier accounts or new products or services. 

Gradually, over time declines, resources blossom, tension disappears, and climate improves. The time gained is used for collaboration and supplier relationship management. After a few months, the people responsible for accounts payable learned to work in value-added mode. With a greater sense of self-awareness and self-mastery, they regained a healthy sense of motivation and are more fulfilled professionally. 

A decision-making tool for optimizing tasks and workload management 

A major French supermarket retailer with tens of thousands of employees 

The retail sector must constantly tackle numerous external factors that modulate the efforts required to deliver expected services and appropriate volumes. External factors such as time of year, weather, public holidays, roadworks, local cultural events, current events, or economic climate will influence customer traffic and consumption. Forecasting workforce requirements becomes an art that combines intuition and error margin. “Planners” forecast workforce requirements, but talented planners are few, making their contribution even more critical. Clear forecasting is essential because overstaffing will generate disproportionate operating costs, while a shortage will affect customer experience quality and possibly profitability. It is best to get it right. 

To secure its operations and facilitate business volume growth, the company procured an AI-based tool to identify labour requirements using complex, multi-factorial algorithms, drawing on data from various sources. Once it establishes forecasts with a visible error margin, the tool issues one or more assignment hypotheses and generates work schedules aligned with prevailing conditions and agreements. 

The project required several months of work, major investments - in AI and the integrated Human Resources Management System (HRMS) - and significant change management efforts. But the gains far outweighed the efforts, and the need for planning resources melted like snow in the sun, as did the gap between the estimated need for manpower and the actual need. This benefitted the employee experience for many as the occupancy rate of resources was rebalanced. Previous experience showed that over- or under-utilized resources were more likely to deal with a tense or unhealthy work climate, whereas optimal occupancy rate contributed to a more harmonious climate. Furthermore, better planning of staffing requirements substantially reduced the number of last-minute schedule changes, promoting a better balance between employees' professional and personal lives. 

Data analysis to support integrated human capital management 

A major North American airline operating mainly in the United States 

Since we now widely recognize that human capital is the most important organizational asset and that its management must be optimal, HRIS software publishers have all, without exception, taken the HR analytics turn. Feedback from the field and employee experience is invaluable in guiding decisions that can have a major impact. Whether it's the perceptions of specific projects, the pulse of unionized employees during collective bargaining negotiations, or understanding the issues and objectives involved in a reorganization, knowledge of perceptions and concerns often helps guide the way forward. 

HR analytics is the gathering, analyzing and reporting of data that surrounds the management of human resources. It is the method of getting a better understanding of the people within an organization and how well the human resources team is performing. The analysis of this data can be a huge help to giving an organization the right direction to move forward to maximize payroll, benefits, its ability to hire or keep employees and more. [...] HR analytics is considered to be a systematic identification and analysis of the people drivers of any specific business outcomes.

Source: HR Analytics: Definition, Best Practices & Examples. Forbes

For a company operating in a congested sector such as air transport, the ability to hire or retain staff is critical. Every percentage point increase in staff turnover generates instability and additional costs. As a result, one must assess their staff regularly to ward off the setbacks - and the costs. Knowing how the relationship between staff and the organization is perceived, and being able to adjust elements constitutes a prudent and responsible approach. Employee engagement surveys are increasingly used and appreciated, provided they are deployed adequately and administered properly. However, time and effort must be invested in compiling, analyzing and following up on the data collected. 

The company regularly conducted employee engagement surveys to assess satisfaction and identify areas for improvement. However, analyzing these surveys was becoming difficult given the amount of data generated. The company decided to use AI to analyze the survey results. AI allowed to accelerate analysis, identify themes dear to staff, and produce summaries, results and comments. AI even allowed to compare teams and help identify advancement levels. It opened discussions on how to share the successes of each person for the benefit of all and strengthen recognition practices for staff. Above all, the advanced analyses generated invaluable information enabling managers and leaders to exercise their judgment and interpersonal skills to support their teams according to their needs. 

Conclusion 

EI and AI are complementary forces which, when combined, can significantly enhance the employee experience. With its ability to understand and manage emotions, EI creates a harmonious work environment conducive to employee well-being. Concurrently, AI's power of analysis and automation optimizes processes, freeing up time for higher value-added tasks. 

The three case studies above perfectly illustrate this synergy. Organizations that integrate AI solutions to automate repetitive tasks and analyze data and capitalize on their staff's EI skills dispose of new levers. These enable them to improve the quality of their services, mobilize their staff and satisfy them as they do their customers and business partners. 

In the future, organizations that know how to take advantage of the complementarity between IE and AI will be better positioned to attract and retain talent, while creating a resilient and engaging work environment. Ultimately, this alliance between humans and machines will propel the employee experience to new heights.