As part of my job, I meet on a daily basis with enterprise leaders who tackle the challenge of implementing AI in their business. These are typically executives in charge of their organization's AI transformation, or business managers who wish to gain a competitive edge by improving quality, shortening delivery cycles and automating processes. These business leaders have a solid understanding of how AI can serve their business, how to start the AI-implementation process and which machine-learning application fits their specific business needs. Despite their understanding of AI and its potential, most managers seem to lack understanding in key technical areas in AI adoption at scale. Managers that strive to overcome these blind spots, which currently derail successful implementation of AI projects in production, should address the following five questions.

Machine learning (ML) has become foundational across many industries over a few years. Supervised learning is currently enjoying the lion's share of machine learning. For example, it is concerned with a prediction like whether a customer will churn or not. Supervised learning is also concerned with a classification such as if an email is spam. It ensures whether a tumour in a diagnostic image is benign as well.

In a previous blog post, we explored the importance of machine learning (ML) and delved into the five most important things that business leaders need to know about ML. First, recall that supervised learning is concerned with the prediction and classification of data. Now it's time to dive deeper. We saw that accuracy (the percentage of your data that your model predicts/classifies correctly) is not always the best metric to measure the success of your model, such as when your classes are imbalanced (for example, when 99% of emails are spam and 1% non-spam). Another space where metrics such as accuracy may not be enough is when you need your model to be interpretable.

In order to make educated decisions in this fast-moving field, all managers should have a basic understanding of AI. Here are four key facts that will give you an edge. AI systems learn from the data and feedback that they receive in response to their earlier decisions. Their predictions and actions are only as good as the data they have been trained on. This characteristic makes AI systems very different from traditional deduction- based programming. A traditional program processes data but does not learn from it.

For Deep Knowledge Ventures, the Hong Kong-based venture firm that added a machine learning algorithm named VITAL to its board in 2014, it was about adding a tool to analyse market data around investment opportunities. For global professional service firms experimenting in this space, machine learning could allow deeper and faster document analysis. And though you may not think you are competing with Silicon Valley salaries for talent, you are if you want great people: a great data scientist can easily be 50 times more valuable than a competent one, which means that both hiring and retaining them can be pricey. As the machine learning ecosystem evolves, companies will find interesting ways to combine in-house industry experience with a range of off-the-shelf tools and open source algorithms to create highly-customised decision-support tools.

The excitement around artificial intelligence (AI) has created a dynamic where perception and reality are at odds: everyone assumes that everyone else is already using it, yet relatively few people have personal experience with it, and it's almost certain that no one is using it very well. This is AI's third cycle in a long history of hype – the first conference on AI took place 60 years ago this year – but what is better described as "machine learning" is still very young when it comes to how organisations implement it. While we all encounter machine learning whenever we use autocorrect, Siri, Spotify and Google, the vast majority of businesses are yet to grasp its promise, particularly when it comes to practically adding value in supporting internal decision making. Over the last few months I've been asking a wide range of leaders of large and small companies how and why they are using machine learning within their organisations. By exposing the areas of confusion, concerns and different approaches business leaders are taking, these conversations highlight five interesting lessons.