Artificial intelligence(AI) is emerging in the organizations without considering its effects on employees. If employees do not pay value to this technological change, it would be difficult for business to align employees with AI. Now the question is ‘How to align employees?’ expectations with AI. AI is considered the 4th industrial revolution (Syam & Sharma, 2018). AI will become the most important part in every business in the coming time. Greater efficiency, faster results and reduced errors in strategic outcomes are imperative in business AI introduction (Davenport & Kirby, 2015).
Every organization is spending time, resources and efforts to adopt AI. AI also has many limitations. Researchers found that 47% of AI projects were considered difficult to align in the workplace (Deloitte, 2017). There was a decline in AI projects during 2019 to 2020 from 20% to 4% (The Economist, 2020).
This study will explain three approaches
These approaches will be helpful to align employees with AI. Real-life Examples will help to understand AI in daily routine alignment.
Cognitive Approach:
AI is to help not to put employees in trouble. Although, adoption change is not an easy way. Smoothing the relation at every level is imperative for the effective performance of both employees and AI. In processes and operations, fairness and transparency reduce biases in business (Satell & Sutton, 2019). Effective performance can be achieved through cooperation. Failure of AI can be avoided, if employees are ready to cooperate. As AI has speed decision search, decision making, better processes and search, more alternatives, better capability (Shrestha et al., 2019), employees can use it for their performance improvement. Choudhury et al. (2020) identified that biases are already present before AI is even applied. If employees are ready to put better input, then there would be better outcomes. AI adoption training must be provided with every step of launching AI. AI applications must be easily adaptable and context based rather than special task automation (Davenport et al., 2020). Alignment of AI with employees must never be ignored. In the complex environment there must be alignment between human thoughts and AI processes to meet the upcoming challenges (Jarrahi, 2018).
Relation and cooperation Approach:
Mutual trust and corporations in relations always makes tasks easy. Higher trust and cooperation results in higher AI integration (Glikson & Woolley, 2020). Hard tasks can be made easy and error-free, if there is timely implementation of AI. Accurate time to implement AI helps to develop the trust between AI and employees. There are lots of reasons for the failure of AI. One of the biggest failures of AI is due to lack of accurate leadership skills. So managers must learn a new set of leadership skills to understand and lead the complex AI↔employees alignment (Agrawal et al., 2017).
Structural implication:
In the modern era technology, every business wants to adopt AI to reduce production cost, increase productivity and stay in competition. There is a need for a Job redesign to align AI with production (Barro & Davenport, 2019). Redesigning job descriptions of employees would protect employees from confusion in the long-run.
AI integration throughout an organization is not an easy process. If AI is involved in decision making to level organizational structures, employees’ empowerment must be appreciated at lower levels (Fountaine et al., 2019). As technology is an ecosystem, AI introduction will open the door for new and upcoming technologies. Human AI alignment will solve many mysteries and complexities of business. Machines are to help humans in processes to get their jobs done. Fear of unemployment, more burden, change adoption are challenges of AI. As AI creates new job types (Wilson & Daugherty, 2018), integration of AI with humans is the only key for future survival.
Figure 1.1 shows the continuous process to adopt AI for the business uncertainties and improving old processes. Change adoption step is the most complex and full of mysteries about the responses from human capital. These adoptions convert human capital to socio-technical capital.
Examples form real-life
- Organizations are using Marketing Chatbots to connect to the end user of their products. Chat Robots not only improves their business but also improves their customer support system.
- There is a Google Assistant in every mobile. This assistant answers every thing which is asked within no time.
- Businesses use automated Manufacturing Robots in production which saves time, cost, and increases productivity.
- In hospitals, Health-care Robots are used for diagnoses, treatments, surgery, records keeping, data analysis, payments etc. Modern treatment cannot be imagined without these robots.
- Automobile industry also uses robots in production plants. But Self-driven cars are the masterpiece in the automobile industry.
- Businesses especially shopping malls use Expression sensors to judge the expressions as feedback of their customers.
- AI made shopping easier. Shopping assistant provide full product consultation like feedback, ratings and variations of usage.
- Writing assistant and Voice recognition is the revolution of the content and creation industry.
Conclusion:
Artificial intelligence adoption and changing organizational structures are complex issues. This shift needs lot of time, efforts and resources. Keeping in mind employees’ morale artificial intelligence adoption options must be kept safe and effective. Organizations has already enabled AI in marketing, production, healthcare, virtual assistant etc. Enabling AI and making employees socio-technical capital needs more safety measures. COVID-19 has already highlighted the need of AI. AI must be designed more user-friendly to integrate human with machines successfully. Better understanding and executing negative perceptions of AI can make integration easier (Makarius et al., 2020). The successful adoption, execution and integration is tough without employees cooperation and trust. If employees are not able to adopt that change in the learning phase, they will go back to that uncertainty phase for which Artificial intelligence was adopted. These failures can be avoided through new job design, developing trust relation and adoption of new leadership skills to integrate artificial intelligence with humans. If AI implementations become helpful for employees, they will become socio-technical capital for the organization. This socio-technical capital has a high scope of new job types for organization, as AI obsoleted old jobs and created new job types.
References
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