Why Finance Needs a Human-Centric Approach to AI: Insights from Trintech’s CMO

Artificial intelligence is everywhere in finance, splashed across trade show booths, woven into vendor pitches, and pressed into budgets but the organizations that succeed are the ones making AI work for people. In our SAPinsider Live Executive conversation, Tamir Sigal, Chief Marketing Officer at Trintech, the real breakthrough with AI isn’t faster automation; it’s unlocking intelligence that helps humans make better decisions. Finance processes were built for humans, he noted, which means leaders must redesign policies, controls, and workflows with AI in mind, so intelligence shows up in measurable value, not just pilots and proofs of concept.

That human-centric stance starts with trust. Finance is the backbone of organizational confidence, and any AI initiative lives or dies on the quality and transparency of its data. Sigal cautioned: “Teams drawn to the shiny new thing can underestimate the hard work of governance, data lineage, auditability, and explainability, especially when black-box models obscure how results are produced.” He sees this tension every day, “Excel remains beloved precisely because people can trace formulas and understand how numbers are generated, AI has to meet that bar with clear narratives and an audit trail.”

Data readiness is equally non‑negotiable. Without cleaning up years of ERP data, intelligence will stall. Sigal calls this “data debt,” and it’s one of the most common reasons AI programs underperform. He warned that if leaders skip the unglamorous work of data cleanup and governance in systems like SAP, their AI ambitions will stall no matter how advanced the tooling appears. When the data foundation is solid, AI can finally move from accelerating tasks to automating decisions like where to invest, where to divest, and how to prepare the board and shareholders with forward‑looking insights.

Sigal’s most provocative challenge to finance teams is a career imperative:

“AI isn’t going to take your job. The person who knows AI might.”

Tamir Sigal

Chief Marketing Officer

Trintech

This reframes AI as a professional skill set. The risk isn’t that machines replace people, it’s that people who embrace AI will outpace those who don’t. Leaders should create programs that build literacy prompting, validation and control design so accountants and analysts learn to collaborate with AI, not fear it.

He also grounds adoption in practical change management. Sigal describes three groups you’ll meet on the journey: enthusiastic champions who will help you change it, skeptics who will fight it, and a middle group that wants to change but needs proof and handholding. His advice, “spend your energy empowering the champions and supporting the middle with training, validation, and small wins. The skeptics will self‑select over time.” This pragmatic approach keeps momentum high and reduces the fatigue that comes from trying to “boil the ocean.”

On the capability front, the gains are already tangible. Matching transactions across systems, once a manual, rule‑based slog, can now be driven to near zero‑touch with learning models that adapt patterns dynamically and reduce hours spent chasing immaterial differences. Journal entries can become intelligent workflows: AI validates documentation, flags anomalies, and blocks errors before they hit SAP, compressing cycle times while improving control quality. Just as importantly, AI can generate narrative insights that explain what changed week‑over‑week and where teams should focus attention, turning raw output into action.

Looking ahead, Sigal envisions a self‑driving financial close. In that model, AI won’t just detect issues, it will predict them, explain root causes, and guide humans to resolution before problems escalate. Agentic AI agents will act as a traffic cops performing reconciliation, documentation, and orchestration across the close. None of this happens overnight. It requires phased roadmaps, clear KPIs, and cadence sprints that deliver incremental value while steadily increasing maturity.

The headline for finance leaders is simple: treat AI as a human‑centered transformation, not a technology install. Clean data and strong governance build trust. Transparent, explainable workflows earn adoption. Practical change management creates capacity. And continuous learning gives your teams the confidence to wield AI as a strategic advantage. When you take that path, automation becomes the byproduct and intelligence becomes the outcome, which is exactly what the Office of the CFO needs now.

What This Means for SAPinsiders

Clean and Govern Your Data: Before deploying AI, address “data debt” by cleaning up years of ERP data and implementing governance frameworks. Accurate, structured data is the foundation for predictive insights and risk scoring. Without this, AI initiatives will stall.

Embed AI into Processes, Not Just Tasks: Redesign financial close workflows and controls with AI in mind. Move beyond automating manual steps, focus on embedding intelligence into decision-making processes for forecasting, risk management, and exception handling.

Invest in Cultural Readiness and Training: AI adoption is a change management exercise. Build programs that train finance teams on AI literacy and explainability. Empower early adopters, support those who need guidance, and create a culture that trusts AI outputs.

Start Small with Phased Rollouts and KPIs: Avoid boiling the ocean. Begin with quick wins—such as automating reconciliations or journal entries, then scale gradually. Define clear KPIs and checkpoints to measure success and maintain momentum.