How (and whether) to implement machine learning in your organization

Machine learning is making substantial impacts on businesses around the world, but many organizations struggle to understand where and when to optimally use ML. To enable successful deployments, businesses must first recognize which problems are most amenable to ML and, second, ensure the right processes are in place to evaluate its impact.

In general, ML algorithms build relationships between inputs and outputs by leveraging statistical properties of the data. As researchers expose the algorithm to more data, it learns and adapts. Eventually, the relationship becomes accurate enough that the algorithm generalizes to predict outputs from new inputs. Companies can use these predictions to uncover new insights and power business automation.

Above: Vertical sample use case

Finding problems ML can solve

So how can you identify a business goal you can address with ML? Here is a typical set of steps to consider.

1. Is the goal substantive, quantifiable, and measurable?

First, the goal you are looking to achieve using ML should be meaningful to your business. Identifying substantive goals typically requires engaging business owners who have a broad understanding of the value generated by an ML solution.

Next, the prediction goal should be quantifiable and well-defined. For example, one of the most common types of ML frameworks is supervised machine learning. In supervised machine learning, researchers give the algorithm an input ‘X and an output Y, and ask it to find the functional mapping Y=F(X) between the X and Y.

If you’re looking to maximize engagement on your site, the target Y that you optimize might correspond to onsite click-through rate, total time on site, or a combination thereof. A supervised ML problem requires the business owner to explicitly quantify what they’re optimizing the function for.

Finally, the output of the ML algorithms should be measurable on an ongoing basis. The best ML algorithms adapt as researchers expose them to new data in order to minimize the error rate of predictions. This enables the system to continually learn and adjust its algorithms to optimize business goals.

2. Is machine learning the right approach?

Broadly speaking, businesses can approach developing intelligent systems (AI systems) in two ways: expert systems and ML. In expert systems, humans explicitly program actions often based on “if this, then that” rules. Such systems, in general, don’t have the same data requirements as ML algorithms, and they benefit from the developer having a more explicit understanding of the final algorithm. ML, on the other hand, uses data to learn rules. For nuanced problems whose solutions analysts cannot encode in rigid rules, machine learning can uncover relationships that expert systems may miss. But this flexibility comes at a cost: You need data.

Above: Expert systems vs. machine learning: Medical diagnoses

3. Do you have the necessary data?

If ML is a viable path to solving your business problem, then data is required. Both quantity and quality are important. Historical data establishes a reliable input and output relationship to train the model. Beyond the model-training phase, infrastructure is typically needed to collect new data from which to learn over time.

Evaluating machine learning in your business

After a company identifies an ML project, it is important to evaluate the broader impact of ML on the business.

An active area of ML research focuses on interpreting why an algorithm derived the output that it did, and researchers can decompose many algorithms to provide these insights. For instance, knowing that a user will churn is valuable, but knowing why that user churns allows businesses to enhance their products or develop automated mechanisms to prevent them from churning. Ensuring that your business is actively engaged in the “why” is important to optimizing the broader impact of ML on your organization.

It’s important to measure how the ML algorithm affects broader business goals. For example, a Facebook feed focused on maximizing engagement might initially focus on maximizing content clicks. This ML goal may have unintended consequences, namely adding click-bait to a content feed, ultimately leading to a decrease in CLV across the user base. Over time, it may be necessary to modify the engagement goals to a quantifiable metric associated with long-term customer retention.

A recent survey of 1,000 business leaders found that while 66 percent of organizations use AI to automate routine tasks, 80 percent of C-level executives indicated that the future of their businesses “will be informed through opportunities made available with AI technology.” It is crucial that business owners are able to effectively identify and measure the impact of ML in order to maximize the business opportunities the technology brings.

Alex Holub is the cofounder and CEO of Vidora, a real-time machine learning platform used to optimize marketing and product automation.