Successful artificial intelligence programs rely on capabilities that take time to establish. This can make building AI programs seem like a daunting task. The key is to take an enterprise -- rather than local -- perspective as AI project teams learn and mature, according to researchers from the MIT Center for Information Systems Research. When companies identify and accumulate expertise and practices from their AI teams, they can create reusable and refinable practices and build capability. This speeds up new AI projects and sets future teams up for success.

With the research showing growth and transformation are the top priorities for their organisations, this puts technology leaders and the exploitation of data capabilities at the heart of digital business agendas. The research also found that data analytics was the area where technology leaders expected their investments to grow the fastest, and is only behind cybersecurity for where the most technology investment is going in the coming year. Members of the Technology Leaders Agenda 2021 advisory panel of CIOs and CTOs said that after years of hype around AI, innovations in the data ecosystem had finally reached enterprise maturity. As such, it was no surprise that AI and automation initiatives are increasingly being implemented. "AI and automation are now solidly delivering the use cases and value promised three years ago," says Nadine Thomson, global CTO at MediaCom.

As the digital world continues to modernise, and new technologies are developed, the AI and Data Strategy book simplifies the complexities and introduces a structured way to update your strategy to include AI and Data capabilities The book defines the technologies and why they are important and this includes Automatic cars, drones, robots, blockchain, data science, machine learning and internet of things. Most of us are unsure what this all means and may think it's a bit technical and over our head. In the end we really just want to manage our information more securely and be able to access it anywhere anytime any place. The book makes the connection between what the technology is and how it can help us achieve your goals in accessing information. The AI and Data strategy book will get you started on the what, how, who and why to enable you to build and implement an AI and Data strategy.

As 2018 rolls to a close, it's time to turn our attention to 2019 and the possibility that it holds. What will happen next year is anybody's guess, which is half the fun in assembling (and hopefully reading) predictions from leaders and experts in the big data and data science fields. Machine learning had a good year in 2018. But enterprises will embrace machine learning in new and profound ways in 2019, envisions Hilary Mason, GM of machine learning at Cloudera. "Next year we'll see a new step in maturity in the enterprise ML transformation as companies advance from proof-of-concepts to production capabilities," Mason says.

The next big trend was a key topic in the fourth World Internet Conference held in Wuzhen recently. During the event, Alibaba founder Jack Ma said humans "have explored less than 10 percent of our brains, and the machine created by that 10 percent knowledge won't beat humans". Tencent founder Ma Huateng noted that internet plus advanced manufacturing will create smart manufacturing that combines software, hardware and services. Meanwhile, Baidu founder Li Yanhong said that the population dividend for the internet industry is ending, and artificial intelligence will be the new growth engine. We are in an era of information explosion thanks to mobile internet and different applications.

In their HBR Big Idea feature, Erik Brynjolfsson and Andrew McAfee argue that AI and machine learning will soon become "general-purpose technologies," as significant as electricity or the internal combustion engine. They represent a landmark change in our technical capabilities and will power the next wave of economic growth. But how will we put them into practice? Where in the organization will these new capabilities sit, and how will companies take advantage of them? To get a practical, on-the-ground view, HBR senior editor Walter Frick spoke with Hilary Mason, the founder of Fast Forward Labs, a machine intelligence research firm.