Digital transformation is no longer an “if” but a “when” for enterprises across both public and private sector.
The promise of greater efficiency, customer-centric products and services, rapid response to changing regulatory or economic requirements – and the chance to compete with disruptive start-ups percolating every sector of the economy can no longer be overlooked.
The challenge is how to get started and get the runs on the board that build innovation momentum.
Google Cloud’s Nigel Watson believes machine learning and artificial intelligence (AI) offer the most straightforward way to demonstrate what digital transformation can deliver.
Watson is Head of Cloud Technology Partners, Japan and Asia Pacific for Google Cloud. He acknowledges that neural networks, AI and machine learning have been around for years – but notes their progress was stymied by the high cost of supporting infrastructure.
“What has changed over the last decade is the emergence of cloud computing that makes petabyte scale storage available at very low cost – literally a few cents per gigabyte and also provides access to supercomputer scale machines at really low cost compared to just a handful of years ago.”
At the same time the platforms for machine learning and AI are being democratised. Google Cloud for example has released its machine learning platform TensorFlow under an open source licence that allows developers to create solutions for image processing, speech recognition and natural language processing.
Accessed via API it makes machine learning solutions more accessible and affordable says Watson. And that makes them a valuable proof point for digital transformation.
Internet of Things (IoT) and blockchain will, he says, also spur innovation – but the hurdles are higher; IoT devices need to be deployed which takes time, while most blockchain applications involve multiple parties – again it takes time.
This may explain why Gartner’s 2018 CIO survey reveals that only 1 per cent have yet deployed a blockchain. Meanwhile the analyst predicts that the global business value derived from AI this year will surge 70 per cent to $US1.2 trillion.
According to John-David Lovelock, research vice president; “AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data as well as advances in deep neural networks.”
Critically also, says Watson, machine learning applications can be spun up swiftly. “All of a sudden you’ve got this situation where have the horsepower and storage to push out the boundaries of what is possible.
“You don’t have to be a data scientist to leverage machine learning as long as you can call an API and feed it some data. The platforms are democratised and readily available for anyone to use.”
Once the business has identified a use case, and a technology to support it, the real fun begins. Watson explains; “The trick to supporting experimentation with emerging technology is to lower the cost of failure – not overinvesting in the IT. It’s about finding the minimal viable and least cost ways to explore an idea without having to build a massive business case to run a longer pilot program.
“If you are able to reduce the cost of failure it means you are able to conduct more experimentation – [you] can try lots of different things rather than getting fixated and overinvesting in one idea before finding out that it’s just not going to work.”
In his experience it’s this approach coupled with a supportive innovation culture that bears fruit most frequently. “Have an idea – a hypothesis – run an experiment and if it doesn’t work – the next step is to take the learnings of that. Modify the hypothesis or do something completely different.
“The more you are able to do that the more you are able to push out the boundaries of what you might be doing as an organisation.”
- Read more from SAP: Making the most out of Machine Learning: 5 lessons from early adopters
Watson also recommends enterprises seek out start ups and collaborate with them in order to gain a fresh perspective, engage with vendors and take advice from seasoned consultants such as Deloitte which have a broader view of transformation across multiple and adjacent sectors.
“We have the Machine Learning Advanced Solutions Lab – a facility to allow organisations to partner closely with Google engineers to solve high impact business challenges.” While currently only available in New York, Mountain View and Dublin, Watson says local enterprises can still work with Google Cloud ANZ engineers on innovative ideas.
Mapping the future
While Watson believes machine learning will prove a useful catalyst for broader digital transformation, he says there is value in considering an enterprise’s longer term digital journey and how different technologies can be deployed to drive enterprise outcomes.
Mapping that journey allows enterprises to plan for the future and predict the sorts and scale or resources that will be required. While there are specific use cases for individual emerging technologies, “They are at their most powerful when factored into an overall business strategy to take advantage of emerging business technology in general,” says Watson.
That holds true for both the public and private sectors, he says. While there is nuance for the public sector particularly around regulatory requirements with regard to storage and management of data, emerging technologies like AI and ML have the potential for enormous benefits in government, and fostering a culture of innovation is just as important in those organisations.
About the author
Beverley Head is a business and technology journalist. SAP is a corporate member of the Which-50 Digital Intelligence Unit. Our members provide their insights and expertise for the benefit of the Which-50 community. Membership fees apply.