The major topics that will be covered in this course include:

• The changing finance technology landscape: continuous processing, modular ecosystems, automated controls, and real-time visibility (and the risks these shifts introduce)

• Technology trends and “AI” definitions: automation vs. machine learning vs. generative AI vs. agentic AI—what each means for governance and auditability

• Five common challenges in finance tech projects and how to address them:

  • Cutting through vendor claims and AI hype

  • Integration complexity and hidden costs

  • Change management and proving ROI

  • Data quality readiness for AI and automation

  • Risk, security, and governance

• Vendor Evaluation Scorecard: 20 criteria across five categories, including control “deal-breakers” (audit trails, segregation of duties, logging, SOC reporting, error handling)

• Five-Year Total Cost Framework: software, implementation, data migration/cleanup, integration build & maintenance, internal costs, training, and ongoing operations (plus common underestimates)

• Industry-specific considerations (manufacturing, distribution/wholesale, SaaS/tech, professional services)

• Four-phase implementation roadmap with checkpoints and red flags: Assessment & Planning; Selection & Negotiation; Implementation & Testing; Optimization & Ongoing Governance

• Working with external advisers (CPA/auditor involvement, implementation partner expectations, and common deficiencies to avoid)