As we’ve written about before at the APEX of Innovation, many organizations are investing more heavily in AI as part of their post-pandemic recovery. While the technology has incredible potential to deliver value and drive competitive advantage, these and other benefits are only possible if a solid foundation is in place to support it from the pilot to the operational phase.

With that in mind, the following are five essential strategies for supporting your AI initiative and ensuring that it’s primed for long-term success.

1. Determine Why You Need AI

Given all the hype surrounding the technology, many organizations are under competitive pressure to adopt AI. But before developing an algorithm or investing in any new solutions, companies should first determine what they hope to address with AI—and whether they have enough data to solve the problem.

2. Don’t Scrimp on Data Quality

Speaking of data, another important foundational element of AI success is ensuring good quality data. Inconsistent, inaccurate, incomplete, or duplicative data can easily lead to poor recommendations or faulty conclusions from the AI engine. As such, prior to any AI implementation, it’s critical to ensure the quality and availability of data, as well as to define clear and measurable KPIs.

3. Remember that Lab Results and Real-World Results May Differ

Some AI pilots may be successful in the lab but not work as well in the real world, as the latter is much more random and complex than a controlled environment. In a similar vein, it’s not uncommon for AI to perform well in one scenario but fail to deliver when applied to another use case. 

It’s important that organizations understand the limitations of lab AI, discern what they need to drive value from it in the real-world setting, and finally, how they can deploy this in a scalable fashion throughout the company.

4. Align AI with the Product Roadmap

Remember not to view AI as the product but rather as an enabler of new products. This strategic perspective necessitates ongoing collaboration between product development teams, data scientists, and tech leaders to develop quality AI tools. Data science roles can educate the product teams about what is possible with AI, while the product teams bring market knowledge and customer expertise to ensure the problem is actually solved.

5. Monitor Models for Drift

As new data enters the organization, it’s not uncommon for models to drift. In order to ensure they remain accurate, companies must monitor their models and tune or retrain them as needed.

For more on the above and other important considerations for AI success, check out this InformationWeek article.