The best way to collaborate with IT for AI in finance is to align on outcomes first, co-own governance, and run a two-speed delivery model: a fast lane for validated use cases and a platform lane for secure, scalable integration. Tie the work to shared KPIs, use standard integrations, and operationalize AI Workers—not one-off tools.
Quarter after quarter, finance is asked to move faster without sacrificing control. AI promises that escape velocity—shorter close, tighter working capital, sharper forecasts—but most initiatives stall in cross-functional friction. IT needs security and scale; finance needs speed and results. You do not have to choose. With the right operating model, you turn that tension into your advantage: IT establishes the guardrails and platform, while finance designs and scales the automations that deliver value.
This playbook shows you how. You’ll learn how to align with IT on business outcomes, stand up governance that accelerates rather than slows, adopt a two-speed delivery model, co-design the data and integration backbone, and prove value in 90 days with high-ROI finance use cases. We’ll also explain why moving from generic automation to AI Workers changes the collaboration dynamic—so both teams can do more with more.
Finance–IT collaboration on AI stalls when speed and control are treated as trade-offs; it unblocks when outcomes, governance, delivery cadence, and integrations are co-owned with clear roles and shared KPIs.
If you’ve lived “pilot purgatory,” you know the pattern: finance has urgent use cases, IT has essential guardrails, and both are right. Traditional approaches force false choices—custom builds that take quarters, or shadow tools that can’t scale. According to Gartner, CFO–CIO partnerships succeed when both functions jointly optimize technology for measurable business value, not when one “owns” AI outright (Gartner: Create a Strong CIO–CFO Partnership). McKinsey likewise urges CFOs to lead on enterprise AI outcomes while prioritizing function-level wins (McKinsey: Gen AI—A guide for CFOs).
The shift is architectural and operational: define the value (cash, close, compliance) up front; encode risk rules once; let finance run fast within those guardrails; and build integrations that any approved AI Worker can reuse. That’s how you get dozens of safe, production-grade wins instead of one expensive science project.
You build a shared AI outcome map by translating finance objectives into measurable AI targets, co-owning KPIs with IT, and prioritizing use cases through a value–feasibility lens.
Finance and IT should share outcome metrics that tie directly to enterprise value: days to close, forecast accuracy, DSO/working capital, audit exceptions, fraud losses prevented, and hours freed per role. These unify priorities and prevent “tool for tool’s sake” projects.
Start with a one-page outcomes compass: top three value levers for the next 90 days, owners on both sides, and baseline/target metrics (e.g., “Close in T+3,” “DSO -8 days,” “Forecast MAPE < 5%”). Align this with CFO priorities and CIO platform plans so every build ladders up. For practical framing, see how outcome-first roadmaps accelerate adoption in this guide to AI execution sprints (AI Strategy Planning: Where to Begin in 90 Days).
You prioritize AI use cases using a value x feasibility matrix—favoring processes with high business impact and available, governed data and systems access.
Examples that typically score high in finance: reconciliations, variance analysis, close checklists, collections outreach, invoice triage, and policy compliance checks. These are repeatable, rules-aware, and data-rich. Build a joint backlog, rank it monthly, and greenlight 2–3 per sprint. For inspiration, scan finance-ready ideas here (25 Examples of AI in Finance).
You stand up joint governance by defining non-negotiables once (identity, data boundaries, logging, model risk), templating them into your platform, and letting teams ship within those guardrails.
Non-negotiable governance includes least-privilege access (SSO/MFA), data minimization and redaction, environment segregation, audit trails with immutable logs, human-in-the-loop for material judgments, and model/change approvals proportional to risk.
Lock these into reusable templates: approved connectors, PII handling policies, prompt/input sanitization, and standardized review flows. Codify “critical-path” steps (e.g., journal postings) with mandatory approvals. This keeps high-stakes actions safe while allowing routine automation to move fast. For IT’s perspective on readiness, Forrester highlights strengthening data and AI foundations as a top CIO mandate (Forrester: The CIO’s Guide to AI Readiness).
You prevent shadow AI by offering a better, safer alternative: an approved platform, pre-cleared connectors, fast reviews, and visible wins that make “rogue” tools unnecessary.
Publish a “how to ship AI safely in finance” path: 1) pick from the approved backlog, 2) configure in the platform, 3) connect through vetted integrations, 4) run UAT with audit logging, 5) go live with monitoring. When the safe path is the fastest path, adoption follows. For data sensitivity in payments, AP, and treasury, this practical checklist helps (CFO Guide: Securing AI for Payments, AP & Treasury).
You adopt a two-speed delivery model by separating a fast lane for low- to medium-risk use cases from a platform lane for reusable capabilities, with explicit SLAs and handoffs.
The fast lane handles contained, reversible processes (e.g., collections outreach drafts, flux analysis memos, close checklist orchestration) that can be proven in weeks and rolled back easily.
The platform lane builds shared assets: ERP connectors, data contracts, identity and secrets management, observability, policy packs, and model registries. IT owns these, so every new AI Worker inherits security and scale by default. This mirrors how leading organizations balance speed with enterprise-grade foundations (Gartner: AI in Finance—What CFOs Need to Know).
You run 6-week sprints by scoping one process per sprint, defining a clear success metric, and moving from sandbox to pilot to production with predefined gates.
Week 1: map the process and guardrails; Week 2–3: configure and integrate; Week 4: UAT with finance SMEs; Week 5: pilot with controls; Week 6: production with monitoring. Recap results publicly. Then clone the pattern to adjacent processes. For a blueprint of rapid execution, see this overview of AI Workers in action (AI Workers: The Next Leap in Enterprise Productivity).
You co-design the backbone by defining finance data contracts, normalizing critical objects, and publishing approved, reusable integrations to your core systems.
Finance data architecture should expose “good enough” governed access rather than waiting for perfect MDM—publish curated views for close, cash, and compliance, with lineage and access controls.
Create canonical finance entities (invoice, vendor, customer, GL account, cash application) and document their sources, freshness, and controls. Provide read scopes for analysis and write scopes for approved automations with checkpointing and rollbacks. The goal is pragmatic reliability—what your people trust, your AI can trust.
The highest-value integrations connect your ERP/GL, AP/AR, bank/treasury portals, CRM (for collections), procurement, and document repositories.
Publish these as pre-approved connectors with standardized authentication, sandbox data, PII handling, and throttling. Every new AI Worker should point-click into this library—no bespoke plumbing. For a concrete overview of finance automations that benefit from these connectors, review this finance automation explainer (AI Accounting Automation Explained).
You prove value fast by selecting use cases that compress time-to-close, unlock cash, and improve control quality within 90 days.
Consistent 90-day candidates include: month-end close orchestration (checklists, reconciliations, flux narratives), collections and dunning workflows, invoice intake and coding, spend anomaly detection, and policy compliance checks.
These use cases combine structured rules with judgment, are measurable, and leverage existing data. Many organizations start with close acceleration to build trust, then expand to cash acceleration and risk controls. Case patterns and step-by-step examples are summarized here (AI Strategy for Business: A Complete Guide).
You measure impact with before/after metrics: days to close, percentage of reconciliations completed autonomously, variance explanation coverage, DSO reduction, right-party contact rate, and disputed balance cycle time.
Complement financial outcomes with control metrics: exception rates, audit-ready evidence completeness, and remediation time. Maintain a running “value scoreboard” published to finance and IT leadership. Sharing wins creates momentum—and resourcing follows momentum. For a research-heavy use case example, see how investment reporting can be reimagined end-to-end (How to Generate Investment Reports with AI).
AI Workers outperform generic automation because they execute multi-step, policy-aware finance processes end-to-end, inherit IT’s governance by design, and can be configured by finance without custom code.
Most teams have tried scripts, bots, and chat assistants. These help, but they rarely move the needle on close, cash, or compliance because they aren’t orchestrating the full process with context, decisions, and controls. AI Workers change that: they read policies, reason over data, call systems through approved connectors, request human approval when materiality thresholds trigger, and produce audit-ready evidence. IT loves them because security and observability are built in. Finance loves them because the behavior is configurable, not hard-coded.
This is the “do more with more” moment: empower people with capable AI Workers and a governed platform so every high-value finance process can be reimagined and scaled. If you can describe it, you can build it—and you can build it together. Explore what this looks like across functions here (AI Workers: The Next Leap in Enterprise Productivity).
The fastest way to align Finance and IT is to learn a common language for outcomes, governance, and delivery—then apply it to your top five use cases immediately.
The best way to collaborate with IT for AI in finance is to align on value, template governance, and run a two-speed model where finance moves fast within guardrails IT defines once. Prove it with close and cash in 90 days, then scale what works everywhere.
As you build momentum, keep your compass steady: cash, close, compliance—and capability compounding over time. Use a reusable backbone of connectors and controls. Celebrate shared wins. And keep expanding the circle of builders in finance. This is how Finance Transformation earns the mandate to lead AI—by turning collaboration into competitive advantage.
The AI budget should be co-owned: Finance funds outcome-driven use cases; IT funds the shared platform, governance, and integrations that make every use case safer and faster.
No, you need governed access to “good enough” data for the first use cases, with clear lineage and controls; you improve data quality iteratively as value accrues.
Finance should build process design, prompt/policy configuration, measurement/analytics, and change management; IT brings security, integration, observability, and platform engineering.
You avoid pilot purgatory by selecting measurable 90-day use cases, running a two-speed model with pre-approved connectors, and publishing a shared value scoreboard to sustain executive support.
Further reading from trusted sources: strengthen CIO–CFO partnership essentials (Gartner), frame finance-ready AI priorities (Gartner), orient CFO leadership on gen AI (McKinsey), and gauge IT readiness for safe scale (Forrester). For more practical playbooks, review finance-focused EverWorker resources: outcome-driven 90-day roadmaps (guide), accounting automation patterns (overview), and secure payments/AP/treasury practices (checklist).