Return to page


Making AI a Reality


By Ellen Friedman | minute read | October 16, 2020

Blog decorative banner image

This blog post focuses on the content discussed in more depth in the free ebook “ Practical Advice for Making AI Part of Your Company’s Future”. 

Do you want to make AI a part of your company? You can’t just mandate AI. But you can lead by example.

All too often, especially in companies new to AI and machine learning, team leaders may be tasked by their managers to “start using AI” without having clear goals set about how AI may help the business or how to build an effective AI team. It’s understandable that executives want to take advantage of the great potential value they see that AI and machine learning are delivering for other companies, but successfully putting AI to work in your own enterprise requires more than passing down the order to do it. Whether you are the executive choosing to add AI to your business or a team leader responsible for making it so, be aware that there are practical steps to put in place  before you plunge into hiring someone to make models. One of the first practical steps to develop and implement AI is to build a data-aware culture within your organization.

Data-Driven Choices

One of the challenges in developing serious data skills is to get buy-in about the importance of data-driven decisions. People with extensive experience in their particular business sector hold valuable insights – knowledge that is important to the success of AI projects built to address essential business goals. But this experience may result in people relying on “gut feeling” about business processes, predicted performance and the potential for new lines of business. A key step in building the data-aware culture needed to support AI systems is to develop an appreciation among stakeholders in your organization for what data can tell you.   Show – rather than tell – how data can augment, reinforce or refute their experienced view or gut feeling.

One way to do this is to make use of data in making your own decisions  and to provide transparency to your teams about how data is influencing your decisions. Develop the habit of asking, “What do we know and how do we know it? ” This habit may involve collecting more metrics about essential business processes than you already do, but more often it means making use of the data you already have. Help people within your organization to see data as a way to expand understanding, not just as a report card on their own performance.

Data sources have proliferated in the last few years – data is literally everywhere. This variety of data provides a rich resource for AI and machine learning. You also can raise awareness within your organization of the wide range of data sources you have available and how they can inform you not only about your own business processes but also about the behavior of your customers.

The proliferation of data including from new data sources such as IoT devices and real-time streaming data provides rich opportunities for AI and machine learning.

Using automated, machine-based learning systems makes it feasible to make decisions based on data at a level and speed that may not be practical for humans to do. This, in turn, lets you take advantage of how data tells you about conditions in the world around you, and how they affect your business.  It also is the best way to inform you about how your business needs to adjust to changes in the world, a situation underlined dramatically by the COVID pandemic .

Why is it important to get buy-in from stakeholders? One of the biggest barriers to building effective AI systems is to set the business context for AI in realistic ways. Getting buy-in from stakeholders is important to ensure cooperation in defining business goals for AI and for ensuring adequate resources are assigned to develop, implement and maintain AI systems.

AI as a Team Sport

Getting up-to-speed with AI may require hiring talented data scientists or selecting the right company to provide data science  as a service. But whether you build an in-house data science team of AI and machine learning experts or work with external talent, there’s more to building an AI system than just the specialists who code and train the models.

Who needs to be on your list of AI talent? Data engineers are a critical part of the success of AI and machine learning projects. In fact, it’s essential to budget a major part of the time and resources allotted to a particular AI project to the effort needed to handle the logistics, from data preparation for training sets to deployment and management of AI models.  The following figure shows the relative effort of model building to all the other parts of a machine learning project.

A major part of the effort needed to build learning systems is outside of the model training itself. Doing this well involves data engineers as well as data scientists. Figure based on the widely read Google paper “Hidden Technical Debt in Machine Learning Systems”.

DataOps helps bring AI to life

Making AI practical and profitable for your business requires the cooperation and collaboration of people with different skills. One of the best ways to achieve this is through a DataOps approach in which you assemble a cross-skill team that has members collectively focused on a shared goal. That style of work improves intra-team communication and avoids the sense that asking someone with a particular specialized skill to do their part is “asking a favor” or imposing on their time. Instead, people are more apt to work together efficiently when they share a goal and understand what needs to be done.

DataOps teams include cross-functional skills and rely on better communication and better focus to improve development and production for machine learning projects. (Figure based on Machine Learning Logistics by Ted Dunning and Ellen Friedman © 2018 (published by O’Reilly Media). Used with permission.

Technology can help with this team approach as well. Tools that improve machine learning interpretability  make it easier for data scientists to communicate clearly to others how models are making decisions and how data is influencing those decisions. The H2O Driverless AI platform is an example of technology that not only makes it easier to develop AI models but also easier to explain them.

A sample of the wide range of visualization and metrics provided by H2O Driverless AI. This platform offers robust interpretability of machine learning models to explain modeling results.

Next Steps

Creating a data-aware culture across your organization is a powerful foundation for successful AI. But there are other concrete steps you can take to make AI practical for your business. Many of these are discussed in the ebookPractical Advice for Making AI Part of Your Company’s Future  that you can download free courtesy of

Another useful approach is to think about where in your business could AI make a difference. One way to do this is to look at how other companies across different sectors are taking advantage of AI. Here’s collection of use cases organized by sector that you may find helpful.

And if you want to discuss specifically how AI can help in your business, reach out to experts at  who can help you identify those aspects of your business where AI can make a difference.



Ellen Friedman , PhD

Ellen is Technical Evangelist at She is an international speaker, author, and scientist with a PhD in biochemistry from Rice University. Ellen has been a committer for Apache Drill and Apache Mahout projects and previously a laboratory researcher in molecular biology. In addition to authoring publications in technical fields from genetics to oceanography, she is co-author of data-related books published by O’Reilly Media, including AI & Analytics in ProductionMachine Learning Logistics, Streaming ArchitectureIntroduction to Apache Flink and the Practical Machine Learning series. Ellen has been an invited speaker for keynotes at JFokus in Stockholm, Big Data London, the University of Sheffield Methods Institute (UK) and NoSQL Matters in Barcelona as well as invited talks at Nike Tech Talks (Portland OR), Berlin Buzzwords and Strata Data conferences in San Jose CA and London. She's also an artist with not-enough-time for the paint box.