OasisLMS

Finance technology decisions (ERP, FP&A, AP/expense automation, close/consolidation, bank integrations, and AI-enabled tools) can dramatically improve close speed, forecasting, and control execution-but vendor demos and "AI" marketing often hide integration complexity, data readiness gaps, governance risk, and long-term total cost. This course provides a practical, vendor-neutral approach for CPAs and finance leaders to evaluate and implement corporate finance technology with an audit-ready mindset. Participants learn how to cut through AI claims using an AI capability framework, apply a weighted vendor evaluation scorecard focused on controls and assurance, build a five-year total cost model that captures hidden and ongoing costs, and follow an implementation roadmap with checkpoints and red flags to reduce failure risk and improve ROI realization. This event may be a rebroadcast of a live event and the instructor will be available to answer your questions during the event.

CPAs in corporate finance and controllership roles; internal audit and compliance professionals who advise clients on finance systems selection, integration strategy, and implementation governance.

After attending this presentation, you will be able to...

• Differentiate automation, machine learning, generative AI, and agentic AI claims in finance software and identify related audit/control implications.

• Apply a control-focused vendor evaluation scorecard to compare finance technology vendors using consistent criteria (including potential deal-breakers).

• Develop a five-year total cost model that includes hidden and ongoing costs beyond Year 1 licensing.

• Identify key implementation checkpoints and red flags across a four-phase roadmap to reduce risk, support adoption, and improve ROI measurement.

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)

• Finance

Group-Internet-Based

Leeland Rogers, Ph.D.