Build Vs. Buy AI: The Answer Is To Build, With Someone You'd Buy From

Among technical teams, high-ranking executives and business leaders, the question of "build vs. buy" is an age-old debate. However, as companies look to adopt AI and analytics, one side is gaining steam. And that’s to build, with someone you’d buy from.

Recently, in an era of modern software that’s affordable, many companies have been buying technology from providers and gaining rapid results in the process. The trend notably reduced the appeal of building analytics from the inside, which has traditionally been considered costly and time-consuming. Yet, as it relates to AI and analytics needs, companies should take a strong look at building alongside outsourced software development services partners.

Through an outsourcing approach, companies learn from experts quickly, speeding up how they build customizable offerings internally. The trend is changing the build vs. buy debate because companies are seeing remarkable benefits. In fact, research from Bain & Company found that 60% of engineering executives plan to increase outsourcing over the next three years.

Everyone’s undertaking analytics and AI projects.

How many companies buy software products? It depends on the category, but companies that lead in their sub-sector—and the ones that have allocated resources across the organization—tend to sit in a position of undertaking analytics projects and in-house solutions instead of licensing software products.

For example, in the world of consumer brands, Colgate-Palmolive has developed an in-house, full-stack application for its sales, category management and revenue growth management strategy. And in the world of AI, Gartner surveyed corporate strategists, finding nearly 8 in 10 see AI as being critical to their business success.

These examples speak to a trend in organizations actively identifying whether they can build an analytics and AI platform or need to buy a solution.

Organizations and IT teams who build internally are starting to use specialized software development and services partners. This is partially because the costs associated with using data science services and staff augmentation partners have come down, so even companies of smaller sizes can engage specialized service partners with the right sized engagement.

Key factors when building an analytics solution include:

• Creating A Competitive Advantage: A bespoke technology project can be a way to differentiate a business, offering a quality of exclusivity.

• Owning The Intellectual Property (IP): By building internally, a company can ensure they retain ownership of the IP or potentially license or sell the technology to other organizations, creating a revenue stream.

• Customizing To The Business Needs: An analytics platform or AI solution developed internally can allow companies to have control over the design and functionality and tailor the experience to their most pertinent nuances.

But how do these benefits compare to the benefits of buying a technology, such as the better cost and speed to value? Plus, organizations must consider that buying a solution instills confidence in the executive team by leveraging a technology that has been proven to work well, has succeeded in solving key business challenges and has been adopted by stakeholders.

The trick is leveraging and keeping the software.

Organizations that have wanted to build an analytics solution can partner with a small number of product-based software companies focusing on AI or analytics. These software companies are licensing software products but also starting separate services and staff augmentation divisions. In this way, organizations can pay for their experienced data scientists, AI developers and data engineers.

In that process, organizations with an eye on building analytics internally can see outsourcing as a shortcut. They can learn from the data experts and AI developers, train in-house teams and use new knowledge to build a customizable solution.

Additionally, some software companies can optionally include parts of their technology software, libraries, components, algorithms and code packages into the services initiative. The use of such sub-products helps complete organizations’ projects at a faster pace, higher quality and lower cost.

In the consumer goods industry, for example, large brands need to make insight-driven decisions quickly. A company with software products—already productized with capabilities that incorporate syndicated data, strategic business planning, category management, internal commercial dynamics and execution—is a huge plus. Borrowing talent, combined with the use of the existing software components, represents a significant step up from simply picking up offshoring teams to work on stop-start projects.

Outsourcing is a key to the build vs. buy debate.

If an organization finds a software company with products focused on AI and analytics that would be open to services projects or staff augmentation, they must ensure:

• They can keep some of the technology. By partnering with a proven solution, companies get applications that have already been tested repeatedly. Ensuring long-term access and availability is key.

• Knowledge transfer will happen. Solution partners can train internal teams to understand new technology and help IT teams eventually build or manage solutions internally. In this way, a provider quickly gets a company up and running while they then build or customize tools alongside.

With the right partner, the answer in the build vs. buy debate shifts naturally toward build.

Working with the services division of a software product company can truly capture the benefits of better assurances, speed, cost and expertise. Companies that are reviewing the next steps in leveraging AI and analytics should consider partnering with and learning from software companies.

Source: forbes.com