AI Integration For Companies

You learn what AI can reliably do inside a company today, how to tell real value from hype, and how ready your own organization is. You finish with a scored readiness self-check you can share with colleagues.

You learn to map a business process, spot the steps where AI can help, and rank ideas on value and feasibility. You leave with a ranked list that separates quick wins from larger bets.

You learn where company data lives, how to judge its quality, and who should own and control it. You finish with a data inventory for your top use case and a short list of fixes.

You learn the main vendor types, how foundation models differ, and how open and closed options compare. You practice estimating total cost of ownership and reducing lock-in before you sign anything.

You learn how data leaks happen with AI tools, what KVKK, GDPR and the EU AI Act ask of companies at a high level, and how to deal with shadow AI. You draft an acceptable-use policy that staff can actually follow.

You learn to turn an approved use case into a small, measured experiment with a hypothesis, a baseline, clear metrics, a tight scope and a go or no-go rule. You finish with a one-page pilot plan ready for sign-off.

You learn how AI changes real processes in customer service, finance, HR, legal, sales and software, and how to describe a process before and after AI. You leave with a workflow map you can bring to your own team.

You learn why people, not software, decide whether AI succeeds, and how to plan training, role changes and a champions network. You practise handling resistance honestly and build a literacy plan for your own team.

You learn what AI really costs to run, how token and licence pricing work, and how to measure time saved without fooling yourself. You build a simple, honest cost and benefit case for one use case and set up FinOps habits to control spend.

You learn how a company organises itself to run AI over years: who decides, who builds, who is accountable, which policies apply and how vendors are managed. You draft a lightweight operating model for your own organisation.

You learn how to keep AI systems safe and reliable as use grows: monitoring, human oversight, incident response, vendor lock in and model change management. You write a monitoring plan and an incident playbook for one use case.

You combine the work of eleven weeks into one roadmap for your organisation, with a 90 day plan and a steering committee deck. You learn how your roadmap will be judged and how to review it before you present.