Accelerate the digital transformation agenda with AI

digital-acceleration

Rapid innovation and productivity breakthroughs require an accelerated digital transformation strategy that melds people, business processes, advanced analytics, and new human/machine interaction technologies.

Today, it is the supervised machine learning segment of AI that is generating the most economic value. But as digital transformation accelerates, the abundance of data that AI can consume will drive the speed of AI adoption even faster, including its unsupervised learning segment.

Ask Alexa to summarize the meeting minutes

We need only look at how quickly conversational AI (CAI) has become part of our everyday lives as we query Alexa, Siri or Cortana. But in the enterprise the interactions can be extremely complex, such as “Hey <CAI>, summarize the minutes and action items from the recording of the last board meeting.” We are limited by only our imagination and — significantly — access to high-quality, well-organized data.

The accelerated AI adoption will in turn drive better understanding of how to customize AI for the relevant business context and drive digital transformation to new levels. It will provide instant measures of business performance down to the smallest task, leading to more predictable business outcomes, as well as enhance productivity and 24×7 business operations through automation of business processes and algorithmic work.

Manage advanced analytics as assets

As AI permeates every facet of the organization, organizations will need industrialized AI with strong governance and data quality. They will need to manage analytics models as assets to avoid algorithmic bias, retrain analytics models in a timely manner and ensure that data privacy and regulatory policies are properly implemented.

As we become better at blending advanced analytics technologies with how we think and work, there will be massive implications for how we run our companies and live our lives. It will be up to all of us to make sure that advanced analytics are used for ethical purposes.

Organizations should define their long-term AI objectives, clearly understand where and how new business value will be created, and design their digital journey maps. Once a business outcome and measurable business value is identified, organizations should proceed with developing analytics and AI/Machine Learning models and implement them in business operations.

Read more in the position paper, Embed advanced analytics into every aspect of your digital business.


Dragan Rakovich, DXC Technology’s chief technology officer for analytics and Distinguished Technologist, leads the company’s analytics technology and innovation strategy. Dragan brings strategic advice and thought leadership to customers in actionable analytics, machine learning (ML) and AI, business intelligence, internet of things (IoT) and analytics platform domains to create advanced analytics solutions. @AnalyticsCTO

Comments

  1. Shanaya Sharma says:

    Great Blog !

    Like

  2. Great post, Dragan. I think simpler user interfaces for front-line workers to build bots and machine learning models will make the difference between conceptual possibilities and adopted technologies. DIY makes the difference at the edges.

    Like

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