Can AI Adoption be Catalyzed?

At the World Economic Forum 2017 in Davos, business leaders and technologists met to discuss the impact of the fourth industrial revolution on various aspects of industries. Many, multi-faceted, issues were brought to panel discussions – ranging from efficacy of AI to ethics, and even the impact on the workforce.

One thing is clear – the most important driver for the 4th industrial revolution is the data or the ability to extract information from the data overload presented to us today. Leaders across the globe are sharing their viewpoints on this phenomenon and a common consensus is being sought. At the same time, there is a lot of focus on learning from each other.

In such an interesting time, the OpenText CEO Blog is apt. Mark Barrenechea in his book “The Golden Age of Innovation” points out items that have time and again become the bedrock for the future. Whether it’s the skills or ethical practices or the intent – everything is of importance when we look at AI and its implementation in the industry. Mark’s cogent points are synonymous with the business leaders across the globe and across the industries.

At the WEF 2017, in an interesting panel discussion with Ginni Rommety (CEO – IBM), Satya Nadella (CEO – Microsoft), Joi Ito (Director, MIT Media Lab) and Ron Gutman (Founder & CEO, HealthTap) were asked to share views on their learnings from the revolution fueled by AI. This discussion was thought provoking and well moderated by Robert F. Smith (Chairman & CEO, Vista Equity Partners).

Some of the key takeaways from this panel discussion are summarized here –

  1. Element of trust – Every new technology or a change is successful when it can generate trust in the stakeholders. This trust can only be gained through transparency and practices that synchronize with the team.
  2. Knowledge of the business domain – Making sense of the vast datasets and an ability to extract information is crucial in the validation of the technology. Unless the information provides meaningful insight to the end user, the system will be treated as noise and the acceptance level will be low. Users will have more confidence in a solution when trained using data from their own domain and so proven.
  3. Understanding of the purpose – During the planning or inception of the solution, one must ask and understand the question – “What is the purpose of my solution?” or “What problem do I want to resolve?” The purpose or the intent provides a clear direction to the efforts. This also helps in boosting the trust and confidence of the stakeholders involved.
  4. Redevelopment of skills – An interesting comment was made that at a macro level there are billions of jobs available across the globe and there is a lack of working population in many countries. But what hurts is when one’s job is moved. As Mark points out in his series of articles, skills need to be redeveloped. Everyone is required to re-skill themselves as needed with the advent of new technologies. Even though some jobs may lose their value, new jobs will take their place.
  5. Sense of ownership – Joi Ito pointed out an outcome from a survey about autonomous cars. During the survey, people were asked should the car compromise its riders while avoiding a major accident involving many others or should it save the riders at the cost of others? The survey answers – the car should compromise the riders to save others, but the survey takers would never buy this car! The example here shows that technology will experience a better adoption rate when there is an ownership behind the same. When the outcomes are tied to responsibilities and ownership, the trust factor would improve tremendously.

Yet, the AI technology that enables a machine to control other machines and provide algorithms beyond the human mind, still has a long way to go when it comes to wide adoption. Given the challenges and responsibilities, some have started to call AI as “Augmentative Intelligence” than “Artificial Intelligence”.

EIM providers today stand at an interesting juncture wherein they can help the users understand their own data, augment their decisions, help them make better models and train their models. This is a time not just to look at AI, but your own EIM strategy! After all, the industry is undergoing a revolution.

Nitin Rastogi

Nitin Rastogi is an EIM professional with over 19 years of experience in finding patterns and use cases for technology to solve problems. Nitin has been working great deal identifying business challenges for his clients - internal or external and providing solutions through technology and tactical measures. You may want to reach out to Niitn for his perspectives and views on your own story!

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