Algorithms and human can work seamlessly provided machine has the last word

Much has been written about AI and its interfaces in journals and newsprint. I too have been swept in its allure and I thought of adding five cents to the growing interest in the AI conversation.

AI, machine learning, algorithms have become the latest indices for organizational success as much as running marathon has become a must trait for a professional to be seen successful in the financial landscape.However, if we sit down and reflect, machine learning or algorithm or AI cannot be considered as sole criteria for defining organizational success unless convenience, efficiency and cost are the only means; whenever an organization looking for sustaining the success we consider parameters beyond hits on our website or social media connects. Since even for customer and employee centricity factors like empathy do count. Before we pledge by AI for every task we perform we need to define our criteria’s for success . e.g. Recommendations on facebook and you tube are based solely on the basis of the pages you have visited earlier since the criteria there is as a consumer you should be spending more time. It does not reflect your choices always or preferences.Likewise is if our personal success criteria based on happiness and well being, will the likes on Facebook or the number of messages on what’s app matter or would it compete with cocaine in giving us a false sense of well-being, not true happiness.

Consider a team of computer scientists creating an AI application to support the talent acquisition process. The AI specialists probably aren’t experts on the people. They’ll need line managers and HR professionals, working at their side to identify where the AI can best support the human asset manager, help design and train the AI to provide that support and be willing and able to use the AI effectively.And since the talent world is in constant flux, once the AI is up and running, it will need continual customizing and tweaking. For that too, functional specialists—not programmers—will have to lead the way.AI works best in the sharing and processing of information. Through this methodology we are getting all the people impacted with the change in the same place at the same time, are provided with the same input, have the same level of opportunity to participate and contribute and everyone involved knows everything that happens during that time.

Unfortunately, in the fixing mode do we spend enough time on defining the success criteria and what it would mean to the ecosystem within the organization. Let’s take employee engagement, every CEO,HR manager thinks it's important all they do is surveys, have drivers and outcomes of engagement generated followed by eight-page reports which operational managers cannot decipher. As a result engagement scores over the last few years has shown very little progress. The only people who have benefitted are the consulting firms and those who helped to sell tools helping organization to analyse faster from annual survey to doing pulse survey on real-time basis without realizing that engagement is not a destination but a journey, hence a continuous process and possible employee experience around the milestones and associated analytics across the employee journey would benefit the organization. Hence the success criteria' s for engagement has to be reframed since it's still rooted in the Industrial era and associated AI or machine learning would have a limited impact. On the other hand, we have not reflected enough to realize every technological revolution gives rise to an organizational revolution. Digital era demands placing the “employee” in the center of the design and creating the kinds of easy, digital experiences that people expect in their daily lives for everything from ordering products to paying bills to connect with friends on social media out in the workplace would mean how do the milestones in the employee journey interface with human, physical and digital interfaces to create a seamless and positive employee experience. e.g.Employee's want consumer level technology and not enterprise level technology experience. To achieve this level of centricity, data captures from different points though people analytics and technology needs to be transformed into insight and strategy however it will be the organizational culture and its biases and not algorithm biases which will take a final call and hence define success.AI works best when it brings together data and teams from different disciplines. It also requires structures and skills for human-machine collaboration. But most organizations keep data in cartels and teams in silos. Few have started work on giving employees the basic AI skills that they’ll need. The average enterprise isn’t ready for what AI is about to demand of it.