This post originally appeared at forbes.com
To Make Or Buy AI: Two Often Overlooked Factors
BY: Sandra Carrico, Vice President Engineering & Chief Data Scientist at GLYNT.AI
Artificial intelligence (AI) continues to change the competitive landscape, and corporate spending on AI is expected to increase by 62% in 2019, according to research by Gartner. For many companies, the first AI decision is whether to make it or buy it.
Two years ago, my company faced the same dilemma. We decided to build our own machine learning system, and it turned out so well that we now sell it as a product. Now, our customers and prospects make the “make or buy” AI decision. From this vantage point, I know it is easy to miss two key factors that drive cost and benefits.
1. Watch The Errors
At its core, AI makes predictions. Those predictions may be more accurate than ever before, but they always come with some error. This can feel like statistics, but it actually has huge business implications.
First, ask the basic question, and verify for yourself: What is the error rate of this AI solution? Whether from an in-house team or from a vendor, there should be an expected accuracy level. Too often, the reality of an AI solution falls short of what’s advertised.
Second, every AI model is optimized to produce a result. Is this optimization objective consistent with your business objectives? Are the right types of errors being minimized? For example, medical mistakes are costly, so you want an AI system that minimizes false positives. On the other hand, errors about online buyer sentiment are not life-threatening, so you might want a system that maximizes stickiness, even if it has a high rate of error.
Third, the statistical modeling of errors may not be consistent with how errors are minimized in your business. Discussions can get confusing, and the root cause could be a case of comparing apples to oranges. Consider a model that minimizes mean squared error (an average difference between estimated and actual values) by accepting many, many small errors in lieu of infrequent large errors. After all, it’s a lot easier to find large errors.
Putting consideration into the errors first helps drive the rest of the “make or buy” discussion. Can the in-house team produce results that are acceptable? Is there a vendor that can stand up to this selection criteria? What are the total costs of ownership? Looking closely at the errors opens up the black box of the cost of AI.
2. Going AI-Everywhere Is Expensive
Typically, there are two phases to the rollout of an enterprise AI strategy. First, you develop competency in AI modeling and deliver good results. Then, you push AI thinking and access through the organization, embedding it in daily operations as well as a cycle of constant innovation. It is the second stage that has much higher costs than are typically expected.
In the first phase, traditional analytics models are supplemented or displaced by AI-powered results. The AI advantage is delivered within the current database and workflows, slipping into an existing system. The cost of AI is focused on the AI team and supporting modeling technology itself. There are a number of companies offering AI modeling platforms for subscription, so hiring the AI team becomes the key challenge. After some months or years, once the results are in and at an acceptable performance level, phase two planning begins.
This is where things get expensive. To push access to AI throughout an organization, consider two types of employees within a business unit: business analysts, the users of AI results, and software developers who support the AI users. The analysts need access without coding, thus you need to invest in data management, user interface, governance, security and more — all encased in a user-friendly experience. The software developer, who may be only lightly schooled in AI, must support the users by assembling databases, releasing data silos, applying models to data, reviewing results and so on. The developer needs an efficient workflow to do new tasks.
Phase two calls for an entirely new technology infrastructure. Most of this spending is for non-AI technologies and talent. The move to AI creates a huge need to hire other software engineers. From our experience, this ratio could be one AI software engineer to 15 other software engineers. It’s no wonder that technology vendors supporting these type of initiatives, such as Talend and Docusign, are growing at 35% to 40% per year.
3. The Tipping Point
Not every company should invest in AI. But here are four questions that can help define the decision tipping point.
• Risk. What AI performance level is needed? If a low rate of accuracy is acceptable, then there is much less risk. But if you need 95% accuracy or more, you’ll need to build a larger, more skilled team. This is exponentially more expensive. And don’t forget to add in the risks of building and deploying the technology stack to support your AI ambitions.
• Focus. Is your company ready to be “AI-first?” Starting an AI endeavor in-house is not just about building an AI team and starting a project, but reworking the entire technology stack in addition to the AI platform itself so that it fully leverages the power of AI. AI-first is needed to unleash amazing benefits, but touches everything. Is the company culture and leadership ready?
• Timing. What is the competitive threat from AI in your industry? Is there an option to wait on the AI investment? Perhaps use a fast-follower strategy, letting others go first and educating vendors, so you don’t have to build it all in-house.
• Core Competency. Which part of your service offering is the key advantage and must be your AI? Can you use AI as a service from outside vendors for the other parts? Not doing it all could take out a lot of risk and expense
“Make versus buy” is a familiar strategic choice. But AI is different, shaping new questions for this key decision.