user needs before they start building products fully. After all, if internal users are unhappy with the tools they’re given to do their job, that will spoil the experience for their customers. It’s also crucial to think beyond the new tool’s technical output. An enterprise that is focused entirely on developing an AI algorithm to improve customer segmentation, for example, can inadvertently blind itself to the real problem it needs to solve. Key questions about why the enterprise wants to build that tool can help to reframe the issues. For instance, if the sales team isn’t using the current segmentation tool, why is that happening? Perhaps they have their way of segmenting customers and have no interest in using a segmentation tool. If that’s the case, the segmentation algorithm isn’t the problem. Behavior change is the issue that needs to be addressed and better segmentation is just one aspect of that. By reframing the problem, the focus turns to understand what matters to the sales team. Identifying their needs and helping them improve the way they segment customers – and then translating that into actions, including a better way of defining segmentation. Ultimately, the successful adoption of enterprise AI starts with putting the human being at the heart of the problem. After all, scientific evidence indicates that people’s choices are based on emotions in the ‘hot’ state of decision- making. When the person wants to justify those decisions, rational factors come into play later. A traveling salesperson, for instance, lives a life of uncertainty because finishing a job or closing a deal all depends on their client’s response. They spend most of their day in the field, waiting outside clients’ offices with all their sales collateral in the boot of their car. While they wait, they may be worrying about how to close a deal in time for the quarterly bonus or how to juggle their work and childcare commitments. The technical output or business value of the new app they’ve been asked to use is the least of their priorities; however, if the app helps them achieve that bonus and strike that
work-life balance, they’ll pick it up. Of course, the tool needs to create value for the business too. But to do that successfully, it’s crucial first to understand the user’s context, emotions, and motivations. It’s a simple equation: if the tool makes users’ life easier, they will adopt it; if it doesn’t, they won’t. “For the technology to deliver real value, the problem it’s intended to solve must be framed at the intersection of the enterprise’s need and the user’s need,” says Francesca Passoni, Principal Consultant, Experience team at Fractal. “That will avoid the need for top-down directives such as pushing people into training programs or introducing incentives to drive adoption. These strategies are often bolted on after deployment and they don’t work in the long term. Instead, look at the success of technology like Apple’s touchscreen devices: they don’t require a training manual or awareness-raising exercises because they deliver intuitive value for users. Enterprise AI tools enable human decision- making in the enterprise, not replace it, and they should be just as simple and intuitive for users to adopt . Fundamentally, the process of design thinking and behavioral science is about looking through human eyes and putting aside any preconceptions from a business, technology, or organizational perspective. Those perspectives are important but they come later – it’s important to first look at the issue through a human lens.”
Ultimately, the successful adoption of enterprise AI
starts with putting the
human being at the heart of the problem.
3 KEYS TO INTERNAL AI ADOPTION Deep, early-stage research is a must Allocate resources for deep early-stage research into users’ needs. View the project through a human lens • Understand the emotional needs and context of the people who will use the tool.
Follow the path of least resistance
People will naturally adopt a tool that makes their lives easier – if it doesn’t, no amount of top-down edicts will force them to use it.
• Think beyond technical output – consider the business impact you want from the technology. • Fo cus development efforts at the point where business needs and users’ needs meet.
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