Tech giants like Google and Amazon were built around data and AI. The nature of their businesses enable them to leverage data and technology to drive decision-making and innovation, ushering in dramatic changes in the way the world finds information and shops. But virtually any type of company can leverage data and AI to gain a competitive edge.

That’s why businesses in industries of all types are putting data at their core, using digital representations of physical objects to track processes and the flow of goods in real time. They know AI drives transformative change, giving users a way to create scalable impact across the business, identify trends early, and receive recommendations for continuous improvement.

AI’s ability to learn and drive improvement over time is its most valuable quality. Companies that deploy an AI solution are making an investment that will keep paying off. The more data the AI solution takes in, the better it can identify and recommend opportunities to generate revenue and reduce costs. To name just a few examples, AI can recommend ways to improve supply chain efficiency, increase marketing and sales productivity, and drive personalized service.

With the right approach to AI, companies can create a baseline framework that delivers results almost immediately. This can scale across the organization, driving improvements in virtually every business unit. That’s why AI has such transformational potential—it’s also why companies should adopt AI sooner rather than later, to scale the benefits over time.

But before adopting an AI solution, you need to decide on the best approach for your organization. Will you build or buy a solution? Which data sources should be involved? And who has ownership of the algorithms the AI solution generates?

Let’s explore each of these factors…

The build-or-buy dilemma

There are at least two factors to consider when deciding whether to build or buy your AI solution. The first factor is time. Since early adoption provides a lasting competitive advantage, partnering with an AI vendor may be the best approach simply because it’s faster. The second factor is the complexity of implementation.

A Databricks survey found that users rated just one in three AI projects as successful. Getting to value quickly requires three crucial elements: an analytical framework in which AI can operate (i.e., a business problem to solve and relevant data); context that enables AI to factor in business rules and information; and a scalable technology infrastructure. Whether built or bought, the AI solution has to address these elements to succeed.

The right data sources

All companies use analytical frameworks to solve business problems. Imagine your goal is to sell a line of pasta sauces to a grocery chain—to identify which SKUs to offer, you’ll review information about the stores, look at past sales, evaluate shelf placement, get information on upcoming promos, etc.

That same data can help you get to value faster with an AI solution. With the analytical framework described above, AI can recommend product mixes, sell-in offers, store segments, etc. But best of all, AI continues learning as new data flows in, layering in information, decision-making criteria, etc. This develops smart algorithms that keep learning as long as they have access to fresh data.

Algorithm ownership

The question of who owns the algorithms the AI solution generates is critical because self-learning algorithms are the most valuable asset AI creates. If you opt to work with a vendor, make sure it’s clear that your company owns the algorithms, which prevents the vendor from licensing algorithms generated by your AI solution, which can undercut your marketplace advantage.

When considering these three factors, remember that speed is critical. The faster you implement an AI solution, the quicker you gain a competitive edge and compound that advantage over time as your solution gets smarter.

Anil Kaul is cofounder and CEO of Absolutdata.