Digital Decision Framework

Framework to Digital Automation Decisions

Creating a workplace that attracts high-caliber digital workers requires a progressive and forward-looking organizational culture. The impetus to set this culture has to come from leadership. The traditional attributes that set a good leader apart remain relevant, but today’s leaders also need to be well-versed in digital and how it’s disrupting their business. CEOs such Jeff Immelt at GE, Microsoft’s Satya Nadella and Marc Benioff of Salesforce.com are moving away from hierarchical, autocratic top-down approaches and looking instead to create more open, collaborative environments, powered through digital collaboration tools. This resonates well with millennials.

Recent technological advances are removing any grounds for complacency among the human workforce, as technology
is increasingly acquiring skills that previously only humans could master. Vast advances in computational capabilities in areas such as image and speech processing are redefining human versus computer roles. Technology such as cognitive computing is now a viable substitute for routine human perception tasks, which can range from sorting items on a factory floor to identifying specific features in images. Cognitive technologies are set to automate or augment a wide range of work activities that today are largely done by humans, including manual workers and knowledge workers.

Organizations can use the framework below to identify where they should automate:

Digital Decision Framework

Digital Decision Framework

When work complexity is low and data complexity is low, there is a wheelhouse for automation. In contrast, automation is still applicable in scenarios where both work and data complexities are high. However, the types of relevant automation are altered.

Efficiency model. This characterizes more routine activities based on well-defined tasks that can clearly be
understood by computers.
• Expert model. The expert model should be used to classify more complex cognitive computing tasks that are harder
to automate and non-routine. They do, however, have consistent data.
• Effectiveness model. The work identified in this quadrant is highly knowledge-based and requires a high level of
interpersonal skills, making it harder to automate.
• Innovation model. This identifies cognitive computing solutions that enhance creativity and ideation by humans.

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