Last week we talked about some of the “soft” issues around AI; this week, I want to share some insights I gained from an Enterprise Connect 2026 presentation, offering hard truths about what it takes to succeed with AI.
Sinead Aylward is a veteran contact center technology decision-maker, and as senior director, customer experience and collaboration platforms at Johnson Controls International, she’s been at the epicenter of this global building systems company’s AI adoption. Her presentation at EC2026 was entitled, “Forget the Moonshot: Build your AI Strategy One Micro-win at a Time,” centering on applying the “fail fast” mentality to enterprise AI success. Sinead described the key principles:
- Use data to identify [potential] use cases
- Focus on small well-defined use cases
- Articulate the KPIs that will demonstrate success
- Very short-term small group pilots
- If we fail, we fail fast, we learn and we move forward to the next hypothesis
But behind this proven approach are the steps that will determine whether even a well-conceived use case will actually prove out. At Johnson Controls, Sinead and her team established the following practices to support AI pilots:
- Ask The IT Expert -- A weekly session and discussion forum designed to foster an open dialogue between IT and employees by offering short presentations on tips and tricks, upcoming changes, important topics, and short trainings.
- Digital Champions – A community sponsored by IT to help increase digital literacy, improve technology rollout and adoption, and inspire global collaboration to share knowledge across [Johnson Controls]
- Modern Workplace - One-stop-shop for information on how to navigate the modern workplace while making sure that employees are connected, secure and productive anywhere.
Combining the “fail fast” approach with this robust user adoption and support structure is a time-tested way of increasing the likelihood of success, and Sinead discussed a few examples of such project success at Johnson Controls. However, one thing I really appreciated about her presentation is that she closed out by focusing on a few areas that IT organizations may often be tempted to either overlook, or may simply be stuck with and have to work around.
For example, she emphasized the importance of flexibility in your contracts as key to providing, in turn, the flexibility you need to experiment and scale up or down. Relatedly, she reminded IT decision-makers not to overcommit on volume in contracts where you have yet to see results. She also noted that implementation work generally represents a higher cost than migration work. Finally, she noted the hard reality that, if your use case is built around ROI to the business for automating a process with AI, the dollar savings from a successful deployment will come back to the business – while the costs of the implementation stay stuck with IT.
One of AI’s biggest challenges to the enterprise has been fitting it into existing processes and governance. It’s important to be clear-headed about what this entails and how to make it work, and I’m grateful to Sinead for giving our audience a real-world example.
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