Artificial Intelligence, and Machine Learning for Business
A NoNonsense Guide to Data Driven Technologies
by Steven Finlay
Reading Profile
Should I read this?
This is a business-oriented primer that frames AI and machine learning as operational tools companies can deploy to cut costs, boost revenue, and improve customer experience. Expect clear, use-case-driven chapters that map common commercial problems to types of models and vendor options rather than line-by-line code or heavy math. The most useful parts are decision-focused: limitations, timelines, and where executive attention matters. The main limitation is surface-level treatment of algorithms and engineering; technically inclined readers will find few implementation details.
Read this if...
- •product manager at a mid-size retailer deciding whether to fund an inventory-vision pilot — provides business cases and investment trade-offs to frame a proposal to leadership.
- •operations leader in healthcare evaluating workflow automation — helps translate AI possibilities into measurable cost or safety outcomes to discuss with clinical and IT teams.
- •company founder weighing vendor solutions versus building in-house models — offers a business-centric lens to compare expected ROI, timelines, and organizational impact.
Skip this if...
- •you'll likely put it down when the prose should turn into concrete implementation steps but stays at vendor and strategy level — it is not a developer manual.
- •annoying if you prefer deep technical or mathematical explanations; the coverage stays at the business/strategic layer and can feel superficial on algorithms and architecture.
- •frustrating if you want step-by-step implementation playbooks or project-level blueprints; the book focuses on decisions and trade-offs rather than granular execution plans.
Artificial Intelligence, (AI) and Machine Learning are now mainstream business tools. They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Consequently, organizations which understand these tools and know how to use them are benefiting at the expense of their rivals.Artificial ...
Before You Buy
Reading Specifications
Difficulty:hard
Audience Fit
- product manager at a mid-size retailer deciding whether to fund an inventory-vision pilot — provides business cases and investment trade-offs to frame a proposal to leadership.
- operations leader in healthcare evaluating workflow automation — helps translate AI possibilities into measurable cost or safety outcomes to discuss with clinical and IT teams.
- company founder weighing vendor solutions versus building in-house models — offers a business-centric lens to compare expected ROI, timelines, and organizational impact.
- you'll likely put it down when the prose should turn into concrete implementation steps but stays at vendor and strategy level — it is not a developer manual.
- annoying if you prefer deep technical or mathematical explanations; the coverage stays at the business/strategic layer and can feel superficial on algorithms and architecture.
- frustrating if you want step-by-step implementation playbooks or project-level blueprints; the book focuses on decisions and trade-offs rather than granular execution plans.
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View available editions on AmazonKey themes
Why recommended
appears in Machine Learning, Technology, and Nonfiction.
Recommendation Signals
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
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Appears In
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