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How to build IT competencies for the AI era

Diana Bersohn | April 12, 2017
CIOs are already managing massive disruption – from analytics to cloud – and AI presents opportunities for IT organisations to drive change and enable business capabilities.

IT groups have long been an underutilized resource for perspectives on how enterprise processes could be improved to optimize top or bottom line results. Leaders should build competencies in the workforce around both deep business process knowledge and how machine learning can improve processes and outcomes. For example, anti-money laundering processes can be greatly improved by the ability of AI to process multiple streams of information in ways that humans cannot. This means that AI can augment human decision-making in process flows by synthesizing data, making basic decisions and deferring more complex ones to humans. This means humans have to reengineer process and competencies to support for integrated approaches and more complex decisions. AI is already transforming customer-focused and internal processes in ways that humans have not yet been able to. There is clearly a convergence between traditional business process management and RPA as seen with Pega’s recent acquisition of RPA company OpenSpan. More broadly even, there are numerous examples beyond RPA such as voice-based customer authentication that can vastly improve customer service interactions in some industries.

Specific competency examples: Business process knowledge, industry knowledge

 

3.    Platforms and data stewardship

The technology workforce must develop strong information management and technology platform (e.g. big data) skills. Machine learning methods only produce predictive models as good as their data. Organizational silos and data quality are certainly not a new challenge for companies. However, people run the risk of becoming a bottleneck to AI if they do not have the skills to support the models and platforms. As IT re-invents itself as an organizational cloud provider, new technologies and architectural concepts require that IT teams serve as enterprise stewards of data and ultimately break down silos to harness the power of machine learning.

Specific competency examples: Data systems management, API management and development, information strategy

 

4.    Algorithm awareness

Not everyone must be a data scientist, but it is critical for technologists to have basic statistical competencies and the ability to articulate how AI algorithms are created, improved, and ultimately output data. There are two core benefits to companies. First, IT can articulate AI capabilities to the business and can work in partnership with the business to continually improve models. Second, a foundational understanding of the mathematical concepts that drive machine learning enables an essential degree of knowledge and creativity. This creativity can support IT organizations to create positive business outcomes as they build AI capabilities. In an example, Accenture has partnered with the Stevens Institute of Technology to develop advanced analytics skills in critical areas of its workforce.

 

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