F E A T U R E S T O R Y
Dancing to your algo-rhythm
Enterprise technology projects often falter when internal users don’t adopt the new tools. Combining design thinking with behavioral science provides a powerful framework for success.
Enterprise artificial intelligence (AI) investment is accelerating fast. For instance, in its latest CIO and Technology Executive Survey, Gartner predicts global revenue will reach $62.5 billion in 2022, increasing more than 21% from 2021. Enterprises are driving that growth, with 48% of CIOs saying they have already deployed AI and machine learning or plan to do so in the next 12 months. However, ensuring the adoption of those new tools can be another matter. Gartner also points out that while enterprises are keen to experiment with AI, many struggle to establish the technology as part of their standard operations. It lists reluctance to embrace AI, lack of trust in the technology, and difficulties delivering business value from AI investments among the causes. Behind those figures lies a fundamental reality: enterprises invest millions of dollars in developing AI business tools. Once they’ve deployed those tools, many discover that most of their people are not using them. And no tool can deliver value if people don’t use it. So, where are all those good intentions going astray? In most cases, the issue can be traced back to early assumptions about what users really need. “People often assume they know their teams well, so when developing new AI tools, they don’t research internal users’ needs as rigorously as they would for an external client,” says Benis Kumar Moses, Director, Experience team at
Fractal. “That assumption leads to early-stage mistakes which proliferate during development and are eventually built into the finished product. Poor adoption is the result. Without a full understanding of how the technology meets the needs of its users, enterprises are planning their AI projects to fail despite their good intentions.” Even when enterprises realize a problem, their assumptions about users’ needs often prevent them from resolving the issues early on. Many enterprises assume that a better user interface, training program, or awareness-raising exercise will get more people using their new AI tool – but those theories are usually wrong. Only rigorous research can reveal the root cause of poor adoption and the steps needed to resolve it. That means going right back to the project’s discovery phase and engaging with stakeholders to get a deep understanding of their needs. It can be a costly lesson for many enterprises to learn. But applying a few simple principles to AI development can help them drive internal adoption and maximize the value of their investment. Resourcing deep, early-stage research is a must. Still, while this is common practice for external-facing AI projects, it’s often overlooked when developing tools for internal users. Typically, internal enterprise AI projects are developed by the IT, HR, and administrative departments – none of which have the research team, design team, or budget to understand
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