Artificial intelligence and automated learning can lead to new solutions for companies. This is what IT leaders need to know to launch and maintain a successful machine learning strategy.

“Machine learning” (ML) is fast becoming a decisive test for advanced CIOs. Companies that do not adopt automatic learning for product development or business operations run the risk of lagging behind more agile competitors in the next decade. That’s according to Dan Olley, who as CSE of Elsevier, the health and scientific information unit of RELX Group, has increased the adoption of its organization of ML technologies in recent years, “I think fundamentally that we are in a point turning with automatic learning and that will change the way we interact with the digital world over the next decade. ”

That’s a reasonable assumption. The growth in computing power, the growing sophistication of algorithms and training models, and an apparently unlimited source of data have facilitated important innovations in artificial intelligence. AI, which includes any technology in which a machine can mimic the behavior of the human mind, includes subfields such as ML, in which statistical-based algorithms automate knowledge engineering. Google, Amazon, Baidu and others are spending more money on AI and ML . On the other hand, the business activity triggered by these events attracted three times more investment in 2016 – between 26,000 and 39,000 million dollars, according to the McKinsey Global Institute.

The time to adopt AI and ML is now

The adoption of AI outside the technology sector is mostly at an early, experimental stage, with few companies deploying it to scale, McKinsey reports. Companies that have not yet adopted IA technology at scale or as a central part of their business are not sure of the benefits they can expect from such investments, according to McKinsey. But Olley, whose ML efforts at Elsevier have helped pharmaceutical clients discover drugs and deliver relevant medical information, says that ML use cases abound in talent management, sales and marketing, customer care, and other areas.

Dan Olley and Gartner offer the following tips:

1. Understand where data science fits

You do not need to centralize your data science and ML operations. In fact, it may make sense to integrate data science and automatic learning into every department, including sales, marketing, human resources, and finance.

2. Getting Started

Gartner says he should encourage small experiments in different business areas with particular AI technologies for learning purposes, not ROI.

3. Treat your data as if it were money

CIOs should treat their data as if it were money by administering, protecting and obsessing them.

4. Stop looking for perfect people for the job

Data scientists tend to be people who have a high aptitude in mathematics and statistics and are experts in finding ideas in the data, not necessarily software engineers who can write algorithms and craft products.

5. Build a data science training curriculum

Not everyone who practices data science is going to be a data scientist or requires a black belt in the field. Gartner advises you to identify AI knowledge and talent gaps and develop a training and hiring plan to build your skills.

6. Endorse Data Science and ML Platforms

Companies that catch up with AI and ML or are unsure about how to tackle a data science problem can dump their data onto data science platforms.

7. Beware of “derived data”

If you are going to share your algorithms with a partner you understand that they are viewing your data keep in mind that your data is the new currency.

8. Do not always try to solve the whole problem

Solve every bit of the problem. Get more data. Build over time.

9. Do not think too much about your data models

It is more important to get the right training sets than to perfect the data models.

10. Educate the CEO and board that their pilot testing is a priority.