Investment in Artificial Intelligence (AI) is growing rapidly and is increasingly affecting organizations well outside the tech sector. You probably suspect AI can dramatically improve your business, but perhaps you are not sure exactly how -- perhaps you are even worried that a start up or tech company is gunning for your business.
One question at the top of folks list I often get asked is: “How do I spot AI opportunities in my business?” My response is to start asking questions. What am I looking for during my line of questioning? I’m ultimately trying to get down to two things: 1. what are the high level business problems that could be transformational if solved and 2. which business problems are feasible to solve with AI to some meaningful degree in the short term and with increasing effectiveness over time.
Understand your existing data assets
My first line of questioning is directed at understanding what data you have today. For example, is your data largely images, video, audio, text or some other business data? What is your data describing? Is it customer profile data? Does it contain aerial imagery of crops? Is it images of patient skin lesions? Is it texts between politicians and their constituents? If your data is unstructured (e.g. imagery, audio, text), I might ask about metadata, that is, data about your data such as author, title, origination_date, or sender. If your data is structured (tabular) or semi-structured (tabular with some referenced unstructured data), is your data scattered around your business in different business unit databases? If so, what do each of those business units do, and how difficult is it to access this disparate data? The answers to these and similar questions help us to understand what data you have, how readily accessible it is, how difficult it will be to transform into AI ready data, and how we can improve its AI readiness and predictive impact over time.
Discuss and document your data intuition
Most folks have some intuition about high value things that can be done with their data. For example, an insurance company executive might have intuition that recordings of claims conversations can reveal insights about whether claims are being fairly addressed in a standardized fashion. A manager of a roof top construction company might think inspections of their roof top drone footage could be automated. An HR leader in a company might believe they can predict when an employee is no longer engaged based on how they communicate in platforms like Slack. A physician might realize they are not that great at understanding exactly how to classify a skin lesion anomaly like a purpura. One rule of thumb in developing and understanding your intuition about what can be down with your data is to
look for wherever humans are acting more like robots, that is, spending a lot of time looking for patterns.
So what do we mean by looking at patterns? It might help to think about this in the context of a particular type of data. For example, looking for patterns in an image might mean counting objects, like the number of people on a bus, or the density of fish in a school. In a video, looking for patterns might include quantifying things over time, for example, how much coral is in an area being studied by marine biologists, how much fat is in a human body as seen from an in body camera, or how much vertical head oscillation is present in a runner.
Looking for patterns in text, might be about how many times a person or topic is mentioned over time, for example, one of our customers included a group of historians trying to understand what was known and by whom in the state department during the Iranian revolution. In text, we also might look for sentiment patterns, that is, many categories of ways to say something positive, and many categories of ways to say something negative. Finally, in business data, the
patterns might manifest themselves in a playbook of rules like heuristics for determining unhappy customers or knowing which customers are likely to stick around for a long time.
Often times, departments explicitly document these playbooks, even iterating and improving them overtime. AI algorithms are great at matching and often outperforming humans at predicting specific things, like customer happiness, likelihood of quitting, or total lifetime value. In addition, AI algorithms are often more dynamic than traditional, manually intensive, statistical techniques and usually outperform humans, if provided the right training data, at regularly adapting playbook rules, such as which attributes are most predictive of say, customer happiness, likelihood of quitting, or fraudulent activity.
List your high value business problems
I usually start by looking for one of the following:
a major efficiency improvement that might yield new capabilities and result in important new business opportunities
a significant business cost which is imposed or will be imposed if efficiency improvements are not found
an impactful action if taken at the right time that will save or make significant money or positively affect an important outcome
Let's look at how we can identify significant efficiency improvements. Brainstorming answers for a few questions might help tease these out:
What are your employees spending a lot of time on?
When servicing your customers, where do you find bottlenecks occurring?
Are there activities where your employees just cannot keep up?
And thoughtful discussion around these questions might tease out impactful actions that could, if taken at the right time, make a significant impact:
Which outcomes really matter to your business?
What actions, however difficult or expensive, could help improve these outcomes?
Here are a few example statements that came out of similar lines of questioning above of high value business problems. These statements might help illustrate what you should be after:
Physicians currently fail to diagnose heartbeat anomalies 40% of the time.
We currently lose 18% of our transactions to fraud.
Our customers currently spend $10 on average and we need to raise it to $100.
Our reviewers just can’t keep up with the new content they must review
Many of our customers purchase the wrong plans, then quit a few months later. If only we could direct them to the right ones up front.
Experts teach students 1 on 1 in our premium service. This only works in the wealthy west, if only we could drop this cost by 10x, we could address entirely new markets in poorer nations.
Distill high value business problems to AI opportunities
The next step can be awkward for those not used to applying AI solutions to a wide variety of problems. But first, we discuss some high level AI basics. AI solutions typically boil down to a few types of solutions. The first type are supervised learning problems where many examples of specific outcomes are provided and the system is then trained and evaluated on its ability to predict outcomes for examples it has never seen. The second type is called unsupervised learning where patterns are naturally discovered in the data. These patterns can in turn be used to determine other things to predict, to find additional training examples for known things to predict, or to better understand the thing being studied, e.g. reasons a customer might quit, types of scenes in a video, types of images in a collection, etc.
Using this basic AI knowledge, we can cycle back to some of our example statements above and stack rank a set of AI opportunities.
Stack rank your AI opportunity candidates
Once you have your list of AI opportunities, and you understand which high value business problems they address, you are ready to stack rank them. You are, however, missing one key ingredient, namely, feasibility. You next need to determine the feasibility of any AI solution you can build for your problem. Assessing AI solution feasibility has many aspects. To name a few:
Likely achievable efficacy (how accurate will it be) in the near and far time horizons.
How many resources (people and money) are required for a solution
How difficult is it to acquire and label (where applicable) ground truth (i.e. training and test) data
Assess your achievable efficacy
Regarding assessing achievable efficacy, the obvious thing to do is try to actually build and evaluate models. Often times I see folks become paralyzed by the size or breadth of their machine learning problem. I recommend sampling early on to deal with size as the key at this stage to simply assess feasibility. Scaling training is something I see too many people worry about prematurely, it is almost always best to start small and on one machine to assess whether the problem is worth investing in. Breadth is another aspect where it is easy to be overwhelmed. I was recently working on a problem with a few hundred columns of data, about 20 of which were potentially viable as target variables (things to predict). It is easy to spend too much time making sure all potential feature variables are incorporated into an analysis, or all potential targets are addressed. I recommend at this stage, do the absolute minimum amount of work in a first pass to check for signal and sanity check efficacy numbers. You may need to make another pass or two, but you may disprove feasibility very quickly, and it is important not to spend excessive time on the wrong business problem.
Assess your resources
Once you have an idea of efficacy expectations, it is much easier to determine resources. For example, you will likely know whether you are talking about a few models or many. You will likely know whether a general model is sufficient, whether you need many independently fine tuned models, or whether you need both. You will also likely have a much better idea of your computational requirements, due in large part to the number and type of models, as well as lessons gleaned from observing your models in action during assessment.
Assess your ground truth development
In many cases, you will need a growing body of examples (ground truth) from which to train and test your models. To assess feasibility, I recommend just diving in and labeling a few hundred items yourself or with your data scientists. This is a great way to understand the cognitive complexity of the task as well as to understand the data much more intuitively. One classic mistake I see often are data scientists unwilling to label their own data. Obviously once you’ve hit some scale, this is a necessity, but in the early stages, while the label distributions are still being determined, task complexity is still being understood, it is essential that data scientists get hands on with the labeling process. While labeling, you will understand how to scale the labeling process. For example, we often rely on our trusted annotators to label text into hundreds of categories, this is a cognitively complex task. When our data scientists actually label themselves, and are confronted with making category boundary guidelines, such as when to label something as a negative vs neutral (is “you guys were fine” negative or just something more explicit like “you guys were bad”. Part of assessing the development path of your ground truth really comes down to questions like:
how many labels can one human do in what period of time?
how much does the time it takes to label select items vary?
how much human time does it take to correct model errors?
Complete ranking AI candidate opportunities
With an idea of achievable efficacy and the resources required to obtain it, you should now be in a good position to rank your AI opportunities by feasibility. You can make a light pass through the above three steps and only iterate as you get serious about pursuing a particular AI opportunity. For example, an experience data scientist can just look at a few hundred images or video frames and know roughly what type of accuracy they can expect in a few weeks, months or years of time.
Sanity check your AI opportunities
Now that you have a stack ranked list of your AI opportunities and a good idea about their feasibility, you can take a look at the most important again, in the context of your high value business problem and see whether pursuing the AI opportunities really will move the needle a lot for your business. Note, this is not always the case. Sometimes we tease out an AI opportunity, but realize after further reflection that solving it does not really move the needle for the business. And of course, once you have identified your great AI opportunities, and they stood up to significant critique, go forth and get a prototype into the field as fast as you can.
According to a recent McKinsey report, “You don’t have to go it alone on AI -- partner for capability and capacity. Even large digital natives such as Amazon and Google have turned to companies and talent outside their confines to beef up their AI skills.” Obviously we at Xyonix agree.