Artificial intelligence (AI) and machine learning are increasingly powering workplace platforms and tools. The sophisticated automation tools have been widely promoted as relieving workers from tasks that are “dirty, dull, or dangerous,” unleashing them to do higher-level work and create. PwC research estimates that AI will contribute $15.7 trillion to the global economy by 2030, driven primarily by productivity gains and AI-fueled product innovation.

In various categories, it’s beginning to deliver on its promise. Financial services companies are using such technologies in ways that range from chatbots that answer basic customer questions to AI-powered platforms that help prevent fraud and money laundering. Human resources (HR) applications help companies sort through resumes, find talent, and even conduct initial interviews. It can be used for maintenance alerts and prevent equipment and vehicle failure in automotive fleets. Purchasing algorithms can help sort through data to make better procurement decisions. In healthcare, promising applications range from robotic surgery to diagnoses of various conditions to AI-powered preauthorizations and other medical certifications.

But realizing the benefits of AI requires thoughtful planning, says Soumendra Mohanty, executive vice president and chief data officer at LTI, a global IT firm. Effective AI applications “elevate the work,” allowing humans to do “higher-order work,” he says. That works best when the technology is implemented the right way.

Build a framework that works

A scattershot approach to AI usually isn’t the right way to make a difference, says Dan Priest, technology strategy leader with PwC’s Strategy&, the strategic consulting arm of PwC. In the past, automation typically came in big rollouts like ERPs that had a very distinct value proposition. “AI is smaller. It gets introduced in fast sprints, in a more decentralized model, and so the companies need to manage that type of automation differently, having some guardrails in place,” he says. Across its various applications, there are some common steps to get it right.

Start with the task—not the job

Effective AI applications start with a business problem rather than a specific role, says Jeanne Meister, founding partner of Future Workplace, an HR advisory and research firm providing insights on the future of learning and working. Collect data on the business problem, then educate business leaders about what you’re trying to solve is the first step to successful AI application. Identify the specific tasks that AI can improve and what you hope the tool or platform will achieve in terms of productivity, efficiency, accuracy, or other goals.

“Why are you doing this? How are you going to move forward, what are you going to do, and how are you going to track it?” she says. “We forget to keep it simple.” That means starting with the problem that needs to be solved and tapping a cross-functional team that can help identify the potential—and possible consequences—of adopting AI in a given area.

Get the data right

AI is only as good as its data. What data does the tool need? What data is necessary to train it? “[I]t’s interesting because a lot of people think the challenge is the algorithm, and yes, those are getting better and better and continually improving as we learn more about this space. But the biggest challenges actually lie in the data,” says Malcolm Silberman, a director and blockchain and AI practice lead with accounting and advisory firm Grant Thornton.