Many organizations are determining how to strengthen their teams amid economic uncertainty and skills shortages. Building technical talent is key to helping teams withstand the challenges of undergoing digital transformation. Your approach can differentiate your organization and set it up for success. Whether you're focusing inward or hiring, what matters is a well-defined strategy to help make informed decisions with a positive long-term impact. Melanie Kalmar, CIO at Dow, recently wrote about why you should be focused on building digital acumen; "Building digital acumen is essential across our organization if we're going to realize the true potential of what we're trying to do with digitalization – from the CIO and information systems teams to sales, supply chain, communications, manufacturing, R&D, and more."

The Defense Department's recent efforts to raise its artificial intelligence game have revealed a few obstacles. There are no cohesive goals across the military branches, and there is no way of knowing whether each service has enough people with the right skills. DOD should work with the services to establish AI-specific goals for cultivating technical talent, make it easier for all personnel to learn about AI and put it to use, and enable AI "rock stars" to succeed. It is currently impossible for the DOD to assess its AI posture, let alone assert leadership in AI. That's because posture assessment requires measurement.

As we noted at the beginning of this series on the AI bank of the future, disruptive AI technologies can dramatically improve banks' performance in four key areas: higher profits, at-scale personalization, smart omnichannel experiences, and rapid innovation cycles. The stakes could not be higher, and success requires a holistic transformation spanning all layers of the organization's capability stack. Our previous articles have focused on the capability stack's technology layers: reimagined engagement, 1 1. Leveraging these capabilities to create value requires an operating model combining structure, talent, culture, and ways of working to synchronize all layers of the stack. Synchronizing these layers is not easy. Any organization undertaking an AI-bank transformation must determine how to structure the organization so that its people interact and leverage tools and capabilities to deliver value for each customer at scale. In this article, we take a closer look at the need for a platform operating model, the categories and scope of operating models, and the building blocks of effective models.

In the coming year there will be less isolated experimentation around AI; many companies will move out of the pilot phase and toward enterprise-wide deployment. Although AI at scale is rare today, many companies are setting this as a core objective over the next three years. To rapidly pursue AI, many companies are looking to third-party AIaaS vendors. While embracing third parties does not eliminate the need to build up internal capabilities, it opens up more options. In a sample we surveyed of large global companies, we estimated that roughly two-thirds of their total AI spending is currently allocated to internal build – and only one-third is dedicated to buying services from vendors.

Deloitte Global predicts that in 2019, companies will accelerate their usage of cloud-based1 artificial intelligence2 (AI) software and services. Among companies that adopt AI technology, 70 percent will obtain AI capabilities through cloud-based enterprise software, and 65 percent will create AI applications using cloud-based development services.3 Further, Deloitte Global predicts that by 2020, penetration rates of enterprise software with integrated AI and cloud-based AI platforms will reach an estimated 87 percent and 83 percent, respectively, among companies that use AI software. Cloud will drive more full-scale AI implementations, better return on investment (ROI) from AI, and higher AI spending. Importantly, we'll see the democratization of AI capabilities--and benefits--that had heretofore been the preserve only of early adopters.

On Feb. 11, the White House released an executive order on "Maintaining American Leadership in Artificial Intelligence" (AI)--the latest attempt to develop a national strategy for AI. The order envisions the United States taking significant steps to increase research and development efforts while reforming its executive agencies to better compete with the Chinese government's investments in AI development through its Made in China 2025 plan. Although the order is full of promising language and constructive suggestions for executive agencies, it is unlikely to have much of a long-term effect without further support from Congress. The executive order has three basic prongs. First, it charges executive agencies to "prioritize AI" across several dimensions.

Deloitte Global predicts that in 2019, companies will accelerate their usage of cloud-based1 artificial intelligence2 (AI) software and services. Among companies that adopt AI technology, 70 percent will obtain AI capabilities through cloud-based enterprise software, and 65 percent will create AI applications using cloud-based development services.3 Further, Deloitte Global predicts that by 2020, penetration rates of enterprise software with integrated AI and cloud-based AI platforms will reach an estimated 87 percent and 83 percent, respectively, among companies that use AI software. Cloud will drive more full-scale AI implementations, better return on investment (ROI) from AI, and higher AI spending. Importantly, we'll see the democratization of AI capabilities--and benefits--that had heretofore been the preserve only of early adopters.

The transformative impact of artificial intelligence (AI) on every industry is indisputable, as is its effect on the labour market. Will AI benefit the labour market over the next decade and beyond, or will it change it and replace humans? The continuous interaction and integration of data, algorithms and use cases are driving AI development. AI has cut positions, broken the bottleneck of human efficiency, reduced standardized and repetitive work, changed the nature of work and enhanced work efficiency. At the same time, it has created new jobs.

Google today announced a new cloud service that's designed to make it easier for companies to create custom machine learning algorithms for processing images. Called Cloud AutoML Vision, the system allows developers to upload a bunch of images to Google's cloud and receive a custom model in return. It's based on Google's research into training machine learning models to construct models that perform particular tasks well. In theory, companies should be able to feed the system a set of sample images and, within a day, get back an automatically trained model that's optimized for their specific data. Cloud AutoML, which will eventually expand beyond images, is supposed to help bridge the gap between the companies that need custom machine learning tools and the handful that are able to pay top dollar for the technical talent needed to implement those tools.

Artificial Intelligence is changing the way we think of technology. It is radically changing the various aspects of our daily life. Companies are now significantly making investments in AI to boost their future businesses. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020. "Artificial Intelligence" today includes a variety of technologies and tools, some time-tested, others relatively new.