by Kumar Srivastava

Developing leaders for managing AI-driven businesses

Nov 01, 2017
Artificial IntelligenceCIOIT Leadership

A long-term approach and investment is required to train the next generation of business leaders who can leverage the state of the art to make the best decisions for their customers, users, employees, stakeholders and shareholders.

digital leadership gap primary
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As most businesses feel the need for and strive to become AI-driven, there is bound to be a growing need for professionals who understand what it takes to run an AI-driven business. Enterprises can leverage AI across the board; from using AI- to run their sales and biz dev to using AI to enhance product development to using AI to revamp operations and using AI to market, support and improve their products and services. However, as AI is introduced in these various functions, managing these functions to generate business value efficiently becomes a whole new ball game and requires skills that neither current business curriculums nor on the job experience provide.

The impact of AI on a business

AI will have a disruptive impact across the enterprise and the ability to leverage, adopt and assimilate into the planning, decision-making and action workflows in the enterprise will form the basis of sustainable competitive advantage for the next decade. A subset of enterprises will choose to ignore this shift and will cede market, mindshare and profits to other enterprises that do adapt. Even though there will be growing pains and massive cultural upheaval, business leaders must continuously push for the transformation to AI-First.

Transforming to AI-First requires the adoption and use of AI across the following key six areas in the enterprise.

Enhancing customer experience and customer request processing

AI offers a great use case in understanding and improving the customer experience and the ability of the enterprise to offer the best possible customer request processing experience. Being able to predict customer needs, intent and the ability and quality at which the enterprise will be able to service the customer need can greatly increase customer satisfaction and at the same time, improve resource utilization in delivering on the customer needs.

Increasing employee productivity and accuracy.

AI can be leveraged really well to understand and boost employee productivity by removing repetitive, manual tasks and/or improving the information available to an employee to empower them to make better decisions. AI driven predictions can boost automation that enables a more consistent output in terms of throughput and accuracy. The best application of AI to this area have shown that not only manual costs associated with servicing customers go down but the accuracy at which customers are serviced and that in turn, conserves resources, increases customer satisfaction and reduces compliance, audit costs.

Improve service quality through automation, resiliency and process reengineering

Another incredible use case of AI is its ability to improve service quality through focus on process automation, service resiliency and process re-engineering. AI algorithms can enable manual breaks in processes to be automated through predictive determinations. Processes and services can be made more resilient through predictive issue identification and resolution, predictive maintenance and intelligent recovery. Processes that are broken or error prone can be reengineered through workflow analysis and the identification of factors that indicate downstream problems.

Understanding and mitigating risk

Perhaps the most well-known and verified use case for AI in the enterprise is using it to understand and mitigate risk where the risk can range from business risk to security risk to safety risk to operational risk and financial risk. The ability to process large amounts of data and uncover signals that can predict risk, quantify it and its impact and enable quick corrections that can not only reduce but often mitigate the risk. In cases, where the risk cannot be mitigated, AI can help generate corrective strategies and highlight potential decisions and actions that can enable the enterprise to recover from and prevent similar situations in the future.

Training a new generation of business leaders

Given the broad and deep impact of AI to the enterprise operations, product and services and its finances, there is a need for new business leaders who understand and appreciate AI and its impact on decisions and actions. There is an acute need for existing business leaders to understand and adapt their organization, decision and product strategies to AI driven mechanisms and even larger need for the future generation of leaders who can intrinsically leverage AI across all enterprise functions.

To achieve this, colleges and universities not only have to recognize this need and start preparing through custom curriculums and teaching staff but also have to define programs and specializations that enable business students to understand and prepare themselves to leverage AI in the industry. Namely, universities need to:

  • Appreciate the limitation of the traditional business programs and develop technical management programs to address the gap between business and technical programs. This MBA program is an example of this approach.
  • Offer opportunities for students to specialize in specific enterprise operational areas (as mentioned earlier) with a focus on how AI impacts that area.
  • Bring in industry experts and enterprises breaking new ground in the adoption of AI in the enterprise into these programs and use their real-world success and failures to train the next generation of leaders.

This change in focus is not easy or time bound. Rather, this is a long-term approach and investment that is required to train the next generation of business leaders who can leverage the state of the art to make the best decisions for their customers, users, employees, stakeholders and shareholders. This is a shift similar but larger than the previous generation of leaders who became adept at using descriptive analytics, spreadsheets and decision science to drive enterprise strategy and operations.