The use of Artificial Intelligence (AI) is expanding and continually changing.  The power of AI to accumulate considerable amounts of data for analytics, speech recognition, visual perception, learning, reasoning, inference, planning, decision-making is unlimited.  

The technological disruptions in this new age of rapid business transformation have moved corporate executives to pay greater attention to the challenges their analytics teams are facing and, to gauge their actions as AI is increasingly transforming the nature, scope and scale of work, affecting traditional business models, industries and culture in unprecedented ways. As a result, driving the adoption of AI must involve the C-Suite in the decision process.  As the technologies continue to evolve, leaders, especially the Chief Data Officer (CDO) are elevated and have become increasingly crucial to lead the strategic direction, execution performance, change management and, business ethics for their companies.  Companies that are successful are those who can address their data strategy with their overall enterprise strategy.  A recent IBM Global study surveyed more than 13,000 C-suite executives in 20 industries worldwide about data and the value gained.  Only about 9% of those surveyed are effectively approaching data in a way that creates real value for their companies.   

Businesses across all industries must evaluate and, prioritize the value of data and analytics.  
The strategic use of data, analytics is a critical element to an organization’s competitive position.  Gartner predicts that “By 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.” In related studies about strategies to enhance the value throughout the enterprise, Gartner also indicated that “Data and analytics will become the centerpiece of enterprise strategy, focus and investment.”  Amongst the many areas that companies are addressing during the AI journey, there are three mission critical aspects that cannot be overlooked these include:

• Regulatory compliance 

While Testing, and monitoring models are essential, strong data governance is crucial in order to deliver the right outcome.  Every industry -- from healthcare to financial services -- is supervised by some form of regulatory body.  Regulations such as the General Data Protection Regulation (GDPR) and, the new California Consumer Privacy Act (CCPA) targeted for January 1, 2020 are in place to protect the interests of the public.  Leaders are to be aware of the regulations that are applicable to their industries and organizations and ensure that their teams are in compliance.  To alleviate the burden on the stakeholders, it is imperative that leaders make sure that their data-scientists and legal teams cooperate to outline well-defined metrics for AI initiatives and to pay close attention to how customer data is being utilized.    

Applying technologies such as analytics and AI in regulatory compliance is every corporate leaders’ responsibility.  Such technology is able to assist business leaders in understanding compliance requirements for appropriate action.  Companies can also benefit from regulatory technology or regtech, to leverage regulatory focused data to better understand and manage their compliance risks. The use of regtech within the financial industry as example, promises to provide technologically superior solutions to the ever-increasing demands of compliance in the Industry.  Regulatory Reporting, Risk Management, Identity Management & Control, Compliance, and Transaction Monitoring are areas of focus.  Regtech similar to AI in regulatory compliance helps both regulators and the regulated. 

• Workplace Cultural Sensitivity and data set suitability

When Digital transformation undertakings do not take into consideration cultural and human aspects; the risk of failure increases. This is an area that is creating a lot of buzz, placing the responsibility concerns at the heart of the ongoing AI dialogue.  Particularly, in facial-recognition technology it is crucial to ensure that data sets correctly reflect and recognizes all peoples, underrepresentation of groups of people, misrepresentation or assumptions can lead to serious societal impacts. Algorithms have no context on the effects of their results. They rely on patterns detection; the recommendations are generated from data and experiences.  In February, 2019 Joy Buolamwini; computer scientist and, founder of the Algorithmic Justice League published an important video piece entitled “ AI, Aint I a woman?“  in which she displays how AI incorrectly categorizes several individuals.  In a similar study the ACLU reported that Amazon’s face recognition falsely matched 28 Members of Congress with Mugshots.  

Having data sets that are representative of the real world, reflecting the population can be helpful in uncovering issues early during the testing process.  In some cases, understanding and addressing how historical human biases might affect AI is an important factor.  This requires the collaboration of Data scientists and their business counterparts to produce hypotheses and, to identify the necessary data and testing plan for validation. Undoubtedly, this conversation will continue as many organizations are paying more attention to their data and models. 

• Responsible data acquisition

Data is what powers AI.  The acquisition of data is now top of mind for many leaders in several   industries.  Creating new data ecosystems for collaboration is key to success.  What is driving this need for data is that the more data is utilized to train internal systems, the more precise the forecasts.  There are however in some industries, privacy concerns by many organizations over the use of external data for AI.  The key is to use both internal and external data responsibly since ultimately brand reputation is in the balance.  Corporate leaders must be vigilant to assure that their data-science teams perform proper due-diligence in acquiring data and, internally how the data will be used for customers and business.  

Data and analytics and, AI are pervasive – these are at the heart of digital transformation, having profound impact in all aspects of businesses and individuals.  The ability to wisely address the potential impacts, and communicate the potential, will help data and analytics leaders and corporations drive better results.   Many organizations remain challenged by how to realize the value of these technologies across their businesses.  Companies have a choice, they can either lead in their industries with digital transformation or risk to be left behind by the competition.   For a competitive advantage, organizations should consider the following:

• Build a culture that makes data and analytics core to the business and, that reaches across the enterprise to the customer and community for maximum impact.  
• Create new leadership roles that help maximize the value and use of data assets, with responsibility to understand and address customer needs, as well as the cultural and social impacts.  
• Develop strategies to enable key business initiatives around digital transformation.