There’s a tendency among many entrepreneurs and other executives to separate strategy from technological development. Many companies want to figure out every stage of their product’s evolution without thinking through what it will take to realize those steps. But if the centerpiece of your product is artificial intelligence, technology and strategy are inseparable.
This is largely because of the fuel that powers algorithms: data. Data are not a simple production input like raw materials or labor; there are limits to what a particular data set can do and challenges to acquiring the right data for any particular algorithmic task. Making product decisions without considering the data needed to support them will lead to great ideas with poor performance.
Narinder Singh, the cofounder and CEO of LookDeep Health, a startup that plans to use A.I. to “transform the capabilities and cost of telemedicine,” has had to embrace the notion that every strategy choice is simultaneously a technology choice, and vice versa. LookDeep’s focus is on using cameras in patient rooms, combined with computer vision technology, to improve patient monitoring in inpatient care settings. The core source of value it offers customers—better patient care when medical providers aren’t in the room—has applications for myriad settings and iterations. But pivoting an algorithm isn’t as straightforward as pivoting a product idea, and Singh’s approach to strategy has been disciplined by that reality. As a result, LookDeep has deprioritized some opportunities to add new products or functionality, not because of hurdles in the competitive landscape but because of hurdles inherent in the way algorithms work.
LookDeep’s story illustrates how strategy and technology go hand in hand, and how A.I. expertise comes from an understanding of the intersection of the two fields. (Disclosure: we know the company’s story well because two of us, Naila Dharani and Sendhil Mullainathan, have acted as advisors to LookDeep.) This expertise does not require an advanced degree in computer science—one needn’t be able to code an algorithm to think strategically about it. Rather, smart leaders of A.I. companies simply need to understand how algorithms work, and then use that understanding to keep their plans tethered to reality.
It’s all about the data
In essence, A.I. is a method of using data to make a prediction. That’s it. There are some technical details around needing an x and y variable, and there are different types of machine-learning models, but the most fundamental principle of A.I. is that you need to have good data to get good results. This applies to the data the algorithm learns from, known as the training data, as well as the data it’s asked to use to make predictions.
As the LookDeep team was building out its tech, it was also getting business advice from outsiders. One of these advisors suggested that an opportunity for scaling the business would be to take the product—built for a single patient in a hospital room—and use it in an assisted-living facility. This sounds great on paper because it could dramatically increase the customer base and grow the market. But in A.I., a seemingly small adjustment can imply a fundamental change in the algorithm and in the training data.
Because the current models are tuned to work in a hospital room with a single patient, the training data contain only one bed and assume that providers come into the room from only one point of entry. This precise match between the training data and the setting in which the algorithm is deployed is a hallmark of good A.I. design, but it means the algorithm won’t work well outside of that setting—in a room with multiple entry points and multiple beds, or even in a single-occupant room in which the bed is in the corner of the field of view.
There is a need for businesspeople who understand the process of building an algorithm and know what questions to ask.
To apply this technology to an assisted-living facility and accomplish the same things that the single-patient tool is meant to do, you would need to build completely new A.I. models from scratch, starting with acquiring and curating a new set of training data.
Why? Because A.I. models are often incredibly sensitive to the training data they use. If you only use training data that consist of daytime images, the model will produce errant results on images taken at night. If you use data from one hospital, it could produce errant results for a different hospital. And if you use images from one camera, it could produce errant results with images taken using a different camera.
This may seem strange—images from one camera are fairly similar to images from another camera. But since the training data provide all the information the model has to produce results, it uses signal from anywhere it can, and that could be something seemingly minute, such as the field of view or the color density of an image. This sensitivity of models complicates every discussion about adapting a product for an adjacent opportunity.
To the business strategist, expanding into assisted living seemed obvious; from a tech standpoint, it would have required a full rebuild. Leaders of A.I. companies need to think about both the market opportunity and the technical implications of a new idea before deciding which path forward makes sense.
Don’t forget the workflow
Traditional strategy emphasizes the importance of making sure the product fits within the customer workflow. There are lots of examples of this—gyms, for instance, tend to want locations near offices because most people work out before or after work or during their lunch break. Setting up a gym far from office buildings wouldn’t take into account the customer workflow.
Similarly, LookDeep has had to make sure its product integrates with the daily routines of doctors and other healthcare providers. But with A.I., considering the workflow means confronting another data-acquisition challenge. For example, if a hospital using LookDeep wants to alert a nurse every time a patient has been lying still in bed for more than two hours (and is therefore at risk of a pressure injury), it needs to consider the nurses’ workflow to make sure the product isn’t disruptive to their day-to-day operations. A nurse may be on break, not on shift, in a different part of the building, or with another patient, or there could be an altogether better person to alert based on the nurses’ workflow. Simply detecting when a safety check is warranted is just the first hurdle to clear; considering all parts of the operational context so that the right person is alerted at the right time and in the right manner may be as challenging or even more so.
As a result, for LookDeep to provide this functionality without impacting workflow, it would need identifying data not only about when patients are static and in bed, but also where their rooms are located and who the nurses are they’re matched with, as well as all nurse staffing and shift records, just for starters.
The nuance with A.I. is that even though it is still important to think about how a solution works for the customer, your data-acquisition goals change based on the customer workflow. This means you have to identify how the solution aligns with the workflow up front when deciding what functionality to offer and how to build the training data for your algorithm.
The code isn’t the key
We started out by saying that strategy and technology are inextricable for A.I. companies. That may sound like a problem, as researchers continue to move the science of A.I. forward at a rapid pace. But aligning strategy with technology doesn’t mean shaking things up with every new innovation in computer science and machine-learning methodology. The aspect of A.I. that corporate leaders need to keep in focus is not so much the code as the data.
When LookDeep decided to build a posture detector (to determine whether patients are sitting upright), for example, the company’s team spent most of its time on the project defining the data and the problem. They then selected from several options for classification models—they did not create a model from scratch.
What differentiates LookDeep’s product is the hours spent building a data set and ensuring its robustness (e.g., testing it with multiple people in a room, at different times of day). This arduous approach to data generation not only increases the accuracy of the algorithm dramatically but also serves as a strategic and competitive advantage. Anyone who wants to compete with LookDeep’s product will have to make the same investment in data.
Singh’s decision to invest more in data than in code underscores the notion that successful executives at A.I.-oriented companies don’t need an advanced understanding of the technical minutiae but rather a working knowledge of the principles of A.I. and the way they affect strategic decision-making. As industries continue to find new means by which A.I. can improve operations and lower costs, there is a need for businesspeople who understand the process of building an algorithm and know what questions to ask. Without those people to connect technology to strategy, companies can end up making expensive mistakes as they try to integrate A.I. into their business models.
Naila Dharani is principal consultant for Chicago Booth’s Center for Applied Artificial Intelligence.
Jens Ludwig is the Edwin A. and Betty L. Bergman Distinguished Service Professor at the University of Chicago Harris School of Public Policy.
Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth.
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