Phase 3: Scale Your Success
Congratulations! You’ve completed your pilot project and measured real business value. Now it’s time to scale. You can look for additional areas of your retail business that can benefit from AI and analytics.
1. Remember to keep business value and data front and center. Whether it’s your second or hundredth AI project, business value and data are remain the two foundational pillars of success.
2. Build a platform instead of pursuing individual point solutions. To maximize return on investment, speed deployment and reduce costs, choose technology that can scale across a variety of use cases. For example, cameras and sensors can be used for loss prevention. They can also be used for customer tracking to identify behavior patterns that can drive merchandizing. And they can be used for self-checkout. One platform that supports all three use cases is more effective than deploying three separate sets of cameras with different software solutions.
3. Plan for the right amount of compute. Your first project probably had a modest amount of data and analysis, and in some cases proving out a use case can be done using cloud computing resources. But when you scale across a variety of use cases or thousands of stores, the amount of data and analysis grows quickly. When scaling AI solutions, consider whether you need to invest in more compute power at the edge, to decrease latency and bandwidth costs associated with transferring data to and from the cloud.
4. Be open to disruption. Harnessing the power of data and AI is crucial to digital transformation. But with transformation comes disruption. Disruption can be a powerful driver of improvements, but it can also be disconcerting. Be willing to change how your business model works when necessary and keep exploring the art of the possible.
5. Expect some resistance to change. Engage in open communication about the benefits of each project. Consider providing a forum for management and employees to exchange ideas and concerns. Consider who all your stakeholders are: the way you approach change management at franchises may differ from the approach used in a top-down management structure. Also, don’t forget to communicate with customers as well, emphasizing the benefits of new approaches (like self-checkout, which some people may initially dislike, seeing it as impersonal).
6. Share your stories. Consider publishing case studies and success stories that outline the business problem, how you solved it and the results you have achieved. Doing so can establish your brand as forward-thinking and digitally savvy and can also help lift all boats by sharing best practices across the industry.
7. Establish a strategy for physical management and maintenance of devices. Most devices need periodic maintenance or upgrades and even the highest-quality devices occasionally fail. Choose technology that enables remote device monitoring and maintenance to help cut costs and speed recovery in the event of an issue.
8. Plan for model monitoring and retraining. AI models are dynamic creatures. You can’t simply deploy them once and ignore them. The AI lifecycle includes monitoring the accuracy of an AI model and retraining the model using new or additional data. For example, a computer vision-based solution may need to be retrained if the inventory it is tracking changes. Or an NLP-based solution may need to be retrained if it is deployed in a new region where accents are noticeably different.
Overview
Find out how to enhance customer experience, boost in-store efficiency and employee productivity, and enable more informed strategic decision making.
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Phase 1: Prepare Well
Follow these preparatory steps to lay the foundations for AI success.
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Phase 2: Prove Business Value With Your First AI Project
With the groundwork complete, take these steps to prove the business value of your initial AI project.
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