Many customers have asked us this profound question: How do we realize business value from artificial intelligence (AI) initiatives after a proof of concept (POC)? Enterprises are excited at the potential of AI, and some even create a POC as a first step. However, some are stymied by lack of clarity on the business value or return on investment. As a result we have heard the same question from data science teams that have created machine learning (ML) models that are under-utilized by their organizations.
At Google Cloud, we’re committed to helping organizations of all sizes to transform themselves with AI. We have worked with many of our customers to help them derive value from their AI investments. AI is a team sport that requires strong collaboration between business analysts, data engineers, data scientists and machine learning engineers. As a result, we recommend discussing the following three steps with your team to realize the most business value from your AI projects:
Step 1: Align AI projects with business priorities and find a good sponsor.
Step 2: Plan for explainable ML in models, dashboards and displays.
Step 3: Broaden expertise within the organization on data analytics and data engineering.
Step 1: Align AI projects with business priorities and find a good sponsor
The first step to realizing value from AI is to identify the right business problem and a sponsor committed to using AI to solve that problem. Teams often get excited by the prospect of applying AI to a problem without deeply thinking about how that problem contributes to overall business value. For example, using AI to better classify objects might be less valuable to the bottom line, than, say, a great chatbot. Yet many businesses don’t start with the critical step of aligning the AI project with the business challenges that matter most.
Identify the right business problem. To ensure alignment, start with your organization’s business strategy and key priorities. Identify the business priorities that can gain the most from AI. The person doing this assessment needs to have a good understanding of the most common use cases for AI and ML. It could be a data science director, or a team of business analysts and data scientists.
Keep a shortlist of the business priorities that can truly benefit from AI or ML. During implementation, work through this list starting with the most feasible. By taking this approach, you’re more likely to generate significant business value as you build a set of ML models that solve specific business priorities. Conversely, if a data science or machine learning team builds great solutions for problems that are not aligned with business priorities, the models they build are unlikely to be used at scale.
Find a business sponsor. We’ve also found that AI projects are more likely to be successful when they have a senior executive sponsor that will champion them with other leaders in your organization. Don’t start an AI project without completing this critical step. Once you identify the right business priority, find the senior executive to own it. Work with their team to get their buy-in and sponsorship. The more senior and committed, the better. If your CEO cares about AI, you can bet most of your employees will.
Step 2: Plan for explainable ML in models, dashboards and displays
An important requirement from many business users is to have explanations from ML models. In many cases, it is not enough for an ML model to provide an outcome; it’s also important to understand why. Explanations help to build trust in the model’s predictions and offer useful factors with which business users can take action. In regulated industries such as financial services and healthcare, for example, there are regulations that require explanations of decisions. For example, in the United States the Equal Credit Opportunity Act (ECOA) enforced by the the Federal Trade Commission (FTC), gives consumers the right to know why their loan applications were rejected. Lenders have to tell the consumer the specific reasons why they were rejected. Regulators have been seeking more transparency around how ML predictions are made.
Choose new techniques for building explainable ML models. Until recently, most leading ML models have offered little or no explanations for their predictions. However, recent advances are emerging to provide explanations even for the most complex ML algorithms such as deep learning. These include Local Interpretable Model-Agnostic Explanations (LIME), Anchor, Integrated Gradients, and Shapley. These techniques offer a unique opportunity to meet the needs of business users even in regulated industries with powerful ML models.
Use the right technique to meet your users’ needs for model explanation. When you build ML models, be prepared to provide explanations globally and locally. Global explanations provide the model’s key drivers, and are the strongest predictors in the overall model. For example, the global explanation from a credit default prediction model will likely show the top predictors of default may include variables such as number of previous defaults, number of missed payments, employment status, length of time with your bank, length of time at your address, etc. In contrast, local explanations provide the reasons why a specific customer is predicted to default, and the specific reason will vary from one customer to another. As you develop your ML models, build time into your plan to provide global and local explanations. We also recommend gathering user needs to help you choose the right technique for model explanation. For example, many financial regulators do not allow the use of surrogate models for explanations, which rules out techniques like LIME. In this instance, the Integrated Gradients technique would be more suited to this use case.
Also, be prepared to share the model’s explanations wherever you show the model’s results — this can be on analytics dashboards, embedded apps or other displays. This will help to build confidence in your ML models. Business users are more likely to trust your ML model if it provides intuitive explanations for its predictions. Your business users are more likely to take action on the predictions if they trust the model. Similarly, with these explanations, your models are more likely to be accepted by regulators.
Step 3: Broaden expertise in data analytics and data engineering within your organization
To realize the full potential of AI, you need good people with the right skills. This is a big challenge for many organizations given the acute shortage of ML engineers — many organizations really struggle to hire them. You can address this skills shortage by upskilling your existing employees and taking advantage of a new generation of products that simplify AI model development.
Upskill your existing employees. You don’t always need PhD ML engineers to be successful with ML. PhD ML engineers are great if your applications need research and development, for example, if you were building driverless cars. But most typical applications of AI or ML do not require PhD experts. What you need instead are people who can apply existing algorithms or even pre-trained ML models to solve real world problems. For example, there are powerful ML models for image recognition, such as ResNet50 or Inception V3, that are available for free in the open source community. You don’t need an expert in computer vision to use them. Instead of searching for unicorns, start by upgrading your existing data engineers and business analysts and be sure they understand the basics of data science and statistics to use powerful ML algorithms correctly.
At Google we provide a wealth of ML training — from Qwiklabs to Coursera courses (e.g. Machine Learning with TensorFlow on Google Cloud Platform Specialization or Machine Learning for Business Professionals). We also offer immersive training such as instructor-led courses and a four-week intensive machine learning training program at the Advanced Solutions Lab. These courses offer great avenues to train your business analysts, data engineers and developers on machine learning.
Take advantage of products that simplify AI model development. Until recently, you needed sophisticated data scientists and machine learning engineers to build even the simplest of ML models. This workforce required deep knowledge in core ML algorithms in order to choose the right one for each problem. However, that is quickly changing. Powerful but simple ML products such as Cloud AutoML from Google Cloud make it possible for developers with limited knowledge of machine learning to train high-quality models specific to their business needs. Similarly, BigQuery ML enables data analysts to build and operationalize machine learning models in minutes in BigQuery using simple SQL queries. With these two products, business analysts, data analysts and data engineers can be trained to build powerful machine learning models with very little ML expertise.
Make AI a team sport. Machine learning teams should not exist in silos; they must be connected to analytics and data engineering teams. This will facilitate operationalization of models. Close collaboration between ML engineers and business analysts will help the ML team tie their models to important business priorities through the right KPIs. It also allows business analysts to run experiments to demonstrate the business value of each ML model. Close collaboration between ML and data engineering teams also helps speed up data preparation and model deployment in production. The results of ML models need to be displayed in applications or analytics and operational dashboards. Data engineers are critical in the development of data pipelines that are needed to operationalize models and integrate them into business workflows for the right end users.
It is very tempting to think that you have to hire a large team of ML engineers to be successful. In our experience, this is not always necessary or scalable. A more pragmatic approach to scale is to use the right combination of business analysts working closely with ML engineers and data engineers. A good recommendation is to have six business analysts and three data engineers for each ML engineer. More details on the recommended team structure is available in our Coursera course, Machine Learning for Business Professionals.
As many organizations start to explore AI and machine learning, they are confronted with the question of how to realize the business potential of these powerful technologies. Based on our experience working with customers across industries, we recommend the three steps in this blog post to realize business value from AI.
To learn more about AI and machine learning on Google Cloud, visit our Cloud AI page.