Getting the best value from your AI investmentSyah Ismail
A lot of companies are realising the potential of artificial intelligence (AI). However, many of them are asking the same question – How do I get the best value from artificial intelligence (AI) investment?
There are three steps to realise the most business value from your AI projects.
Step 1: Align AI projects with business priorities and find a good sponsor
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 a great chatbot.
To ensure alignment, start with your organisation’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 machine learning (ML). 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. 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.
AI projects are more likely to be successful when they have a senior executive sponsor that will champion them with other leaders in your organisation. 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 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.
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.
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. It is recommended to gather user needs to help you choose the right technique for model explanation.
Step 3: Broaden expertise in data analytics and data engineering within your organisation
To realise the full potential of AI, you need good people with the right skills. This is a big challenge for many organisations given the acute shortage of ML engineers. 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.
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. 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.
Powerful but simple ML products such as Cloud AutoML 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 operationalise 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.
You don’t need to hire a large team of ML engineers to be successful. 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. 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.
Google provides a wealth of ML training from Qwiklabs to Coursera courses like Machine Learning with TensorFlow on Google Cloud Platform Specialisation or Machine Learning for Business Professionals. These courses offer great avenues to train your business analysts, data engineers and developers on machine learning.
With these three steps in place, your organisation will be able to maximise your AI investment. Hence, your business can grow thanks to the automation and insights gathered from AI.