August 2026
Intermediate
368 pages
8h 6m
English
Content preview from AI Powered Digital Twins
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Chapter 16 AI and Data Strategy
Most organizations struggle from absence of coherent strategy translating aspiration into operational reality. Executives announce AI initiatives generating enthusiasm, yet 18 months later pilots languish, budgets exhaust without measurable outcomes, and skepticism replaces optimism. The gap reflects strategic failures more than technological limitations. Successful AI deployment requires systematic approaches addressing organizational readiness, use-case prioritization, governance frameworks, and resource alignment.
Effective strategy aligns top-down vision with bottom-up execution. Leadership provides mission, resources, and organizational air cover. Frontline teams identify applications serving genuine needs. Middle management translates between levels. Quarterly reviews assess progress, learn from failures, and adjust priorities. Strategy must respond to rapid AI evolution rather than rigidly adhering to obsolete plans.
Mission, Goals, and Success Metrics
Clear mission and goals prevent AI from becoming technology in search of problems. Investment must be tied to defined business outcomes—revenue growth, cost reduction, or risk mitigation—that are specific, measurable, achievable, relevant, and time-bound. “Leveraging AI to transform our business” is aspiration; “reducing customer service costs 25% within 12 months by automating tier-1 support” is strategy.
Cost-benefit analysis should define success before major investment. Organizations ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.