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AI Leadership & Business Transformation

Lead AI adoption through strategy, governance, operating models, and human change.

Leadership sees many AI possibilities but lacks a prioritized roadmap, governance model, adoption plan, and ROI discipline. From scattered experiments to a company-wide AI roadmap with accountable opportunities, safeguards, and change leadership.

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Author
Nathan Thiyagarajah
Chapters
15
Reading time
About 7 hours
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Book contents

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  1. 01
    Where Should a Business Start With AI?Select one contained, useful, measurable AI pilot instead of launching a company-wide technology programme.
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  2. 02
    How to Identify Your Best AI OpportunitiesFind, define, and rank AI opportunities by business value, process readiness, feasibility, consequence, and learning value.
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  3. 03
    Build Your Company's AI RoadmapSequence use cases, capabilities, controls, skills, and change around business outcomes rather than a list of tool launches.
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  4. 04
    How to Calculate AI ROICalculate AI return using accepted outcomes, total cost, a credible baseline, adoption, quality, risk, and attribution—not generated-output volume.
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  5. 05
    Build vs Buy AIChoose build, buy, configure, or partner based on differentiation, requirements, readiness, control, total cost, operating capability, and exit.
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  6. 06
    Choosing AI VendorsEvaluate an AI vendor through requirements, evidence, representative trials, contract controls, operating fit, claims validation, and exit readiness.
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  7. 07
    Creating an AI PolicyCreate a short, usable policy that helps people distinguish permitted, conditional, and prohibited AI use and know who decides exceptions.
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  8. 08
    AI Governance for Small BusinessesGovern AI in a small business with a current inventory, proportionate tiers, named decisions, lightweight evidence, incidents, and regular review.
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  9. 09
    AI Privacy and SecurityLead AI privacy and security from data purpose and threat paths through least privilege, safe testing, monitoring, response, and deletion.
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  10. 10
    How to Get Employees to Adopt AIEarn meaningful adoption by solving a real employee problem with participation, practice, support, workload protection, feedback, and visible improvement.
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  11. 11
    AI Skills Every Employee Will NeedBuild practical AI literacy for everyone and job-specific proficiency through representative tasks, feedback, evidence, and refresher learning.
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  12. 12
    Creating an AI-First CultureCreate a culture where people improve work with AI thoughtfully, share evidence and failures, protect one another, and challenge unsafe or wasteful use.
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  13. 13
    Redesigning Jobs Around AIRedesign a job task by task so AI removes friction while human judgment, relationships, growth, workload, and accountability remain explicit.
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  14. 14
    Building an AI-Native CompanyBuild reusable AI capability around product teams, governed data and tools, shared evaluation, lifecycle operations, and business outcomes.
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  15. 15
    From Departments to Human + AI TeamsDesign teams around outcomes, verified capacity, clear decision rights, AI service limits, exception work, learning, and human accountability.
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