Chapter 7: Digital and AI-Enabled Solutions
Nine months into the transformation, a vendor arrived with a demo. An AI-powered student success platform: predictive analytics, personalized intervention recommendations, institutional retention dashboards. The visualizations were striking. Sandra saw faculty-level visibility into student struggles. Claire saw operational efficiency. Even James, usually sceptical of technology promises, noted that the retention improvement figures would generate significant tuition revenue recovery. Marcus asked four questions: Which specific capability gap does this address? What happens when the prediction is wrong? What data does the model need, and do we currently have it integrated and reliable? Who would act on the predictions, through what process, with what authority? The vendor answered the first confidently. The other three revealed a 23% false positive rate, data that was not yet integrated, and an organizational process the platform had not considered. This chapter teaches you how to evaluate digital and AI proposals from the business architect's perspective -- not whether the technology works, but whether it serves the architecture.
What You'll Be Able To Do
By the end of this chapter, you'll be able to:
- Evaluate any AI or digital proposal against the capability gap, distinguishing solutions that genuinely enable a capability from solutions that add complexity to one that already works.
- Ask the four questions of any AI proposal that reveal whether the organization can actually act on what the system produces.
- Identify the data governance implications of a proposed solution that the technical assessment will not surface.
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