But as your data scientists and data engineers quickly realize, building a production AI system is a lot easier said than done, and there are many steps to master before you get that ML magic. At a high level, the anatomy of AI is fairly simple. You start with some data, train a machine learning model upon it, and then position the model to infer on real-world data. Unfortunately, as the old saying goes, the devil is in the details. And in the case of AI, there are a lot of small details you have to get right before you can claim victory.

The age of automation is upon us. While businesses have started embracing artificial intelligence (AI) and machine learning (ML) to help automate many of their business systems, some companies persist in using manual methods for processes that could be more efficiently and cost-effectively managed through automation. This can lead to a range of problems, including human error, time wastage and delays in product delivery. Businesses should explore all the possibilities and benefits of automation and seek to implement AI and ML wherever possible to increase operational efficiency. Below, 11 members of Forbes Technology Council offer valuable advice on functions that businesses should look into automating.

Before any AI/ML deployments, organisations need to marry their data science research efforts with their project management best practices.

Financial terms of the deal were not disclosed. The chain has been using technology to stay ahead of the competition. In March, McDonald's announced the acquisition of Dynamic Yield, which specializes in personalization and decision logic technology. People familiar with the matter said the deal was worth more than $300 million, making it McDonald's largest deal in two decades. After closing the Dynamic Yield acquisition, McDonald's quickly deployed its machine learning tech in more than 8,000 U.S. drive-thrus.

Chatbots and voice assistants powered by artificial intelligence are starting to gain traction in the workplace of large organisations, according to a report from tech firm Spiceworks. The report, based on a survey of 500 IT professionals in organisations across North America and Europe, found that within the next 12 months, 40 percent of large businesses – those with more than 500 employees – expect to implement one or more intelligent assistants or AI chatbots on company-owned devices, compared to 25 percent of mid-size companies and 27 percent of small businesses. The findings indicate that although adoption is on the rise, some organisations are holding back due to a lack of use cases in the workplace and privacy concerns. Across all company sizes, Microsoft Cortana is the most commonly used intelligent assistant in the workplace, likely due to its native integration into Windows 10. Among organisations that have implemented intelligent assistants or chatbots on company-owned devices and services, the results show 49 percent are currently using Microsoft Cortana for work-related tasks, followed closely by Apple Siri at 47 percent.

Companies are collecting more data than ever, and decision-making is now heavily based on insights from all of that data. Primarily, the person in charge of putting together these valuable insights has been the data scientist, driving up the demand for data scientists worldwide. However, as machine learning and artificial intelligence continue to develop an increasing role in the workplace, there's also a lot of talk about the role of the data scientist becoming obsolete. Bots are providing new, powerful ways to automate many of the data science-related tasks that are being performed today. But just much of the data scientist role will new AI technologies take over?