Rolling Out Generative AI in Your Organization
A CFO’s Playbook
Finance is splitting into two camps.
One is moving fast and using generative AI to close the books 33% faster (IBM IBV, Jan 2025), cut uncollectable debt by 43% (Billtrust, Apr 2024), and auto-generate board-ready reporting in minutes.
The other? Still stuck in spreadsheets, manually reworking variance narratives and chasing down data.
The early adopters aren’t waiting for permission. They’re already redefining what finance can do.
65% of U.S. CFOs say GenAI is now part of their business strategy — up from just 15% a year ago (Billtrust/CFO Dive, Apr 2024). But only 4% have scaled beyond early pilots (Hackett Group, Oct 2024). That’s a massive execution gap, and a rare chance for forward-leaning finance leaders to pull ahead.
But for many finance leaders, the question remains:
Where do I start?
This playbook offers a clear, practical framework to move from experimentation to impact. Each phase is designed to build momentum, mitigate risk, and deliver real business results.
Phase 1: Strategy & Readiness
GenAI rollouts don’t start with prompts and a vague "magic sprinkling of AI" across departments. They start with purpose. Many CFOs are now expected to steer AI strategy, even if they’re still catching up on the tech. This phase sets the tone: real business value, not buzzword bingo. Get the team aligned, the expectations clear, and the first use cases scoped. Lead with value, not voltage.
Audience-first framing: Frame the conversation in terms of business outcomes: faster close cycles, fewer manual handoffs, better visibility for leadership. Avoid leading with model specs or token limitss. Focus instead on how AI drives better business outcomes.
Educate, don’t evangelize: Make sure the team understands what GenAI can and can’t do. Clarify hallucination risks, data sensitivity concerns, and why GenAI is better suited for drafting than for fact-checking.
Start small, aim smart: Focus on use cases that are low-risk and high-volume like generating AR follow-ups or summarizing budget variances. Skip the moonshots; earn trust with wins that make a visible difference in week one.
Example: Auto-generated board reports or variance summaries (Workiva, Microsoft).
Position AI as a co-pilot: Frame GenAI as an accelerant for your top performers, not a replacement for them. Emphasize how it reduces drudgery, sharpens insights, and elevates team capacity.
Phase 2: Discovery & Assessment
You can’t automate what you can’t map. This phase is about finding the friction(e.g. manual workarounds, dirty data, tech that doesn’t talk to each other). Get a clear-eyed view of what’s fixable, what’s fragile, and what’s ready for AI. Skip this, and you’re building on sand.
Map your workflows: Trace end-to-end processes and document where time is lost — especially in manual handoffs or tools that don’t integrate. Highlight “swivel-chair” workflows where employees move data between systems by hand.
Audit your data: Evaluate the cleanliness, accessibility, and ownership of critical datasets. AI depends on trustworthy inputs; so start by surfacing where the gaps and inconsistencies live.
Assess your stack: Catalog your systems and software, noting what’s already API-friendly or has built-in GenAI capabilities. Identify overlaps, gaps, and integration potential.
Run a risk check: Look closely at where data flows today, and where it shouldn’t. Flag any areas where bias, hallucination, or compliance breakdowns could pose material risk to the business.
Over 50% of CFOs cite data security as their top concern (CFO.com, KPMG).
Phase 3: Prototyping & Prioritization
This is where ideas meet reality, and where momentum starts to build. Forget five-year roadmaps. Think AGILE here. Quick wins beat perfect plans. Build something small. Test it. Learn fast. Iterate faster. We're not looking for a fully automated finance function. We're looking to prove the concept, build confidence, and show what’s possible.
Pilot the obvious: Start with a workflow everyone understands (e.g. variance memo or revenue forecast). Choose something high-visibility but low-risk. The win is in proving it works.
AR automation cut uncollectable debt by 43% in some orgs (Billtrust).
Stack-rank with a rubric: Use a simple scoring system to rank use cases by potential value, ease of implementation, and stakeholder enthusiasm. Make your next step obvious.
Use a sandbox: You don’t need a dedicated test environment or dev team. For most finance teams, this means using web-based tools like ChatGPT or Claude in a browser window. Start with anonymized or fictional data copied into Excel or Google Sheets, and walk through the workflow manually. The goal is to test the logic, pressure-check the outputs, and get feedback before connecting anything to live systems. Think of it as a safe, scrappy dress rehearsal.
Phase 4: Deployment & Scaling
Pilots are proof points. This phase is about getting GenAI into the real tools and workflows your teams already use. No hero projects. No black boxes. Just clean integrations, smart automation, and strong documentation.
This is also where many finance teams need outside help. Connecting GenAI to ERP systems, CRMs, file storage, and messaging tools often requires integration expertise. Most finance professionals aren’t expected to build these bridges themselves. Consider tapping internal IT partners, consultants, or off-the-shelf GenAI platforms that already integrate with your stack (e.g. Numeric for financial close, Vic.ai for AP automation, or Datarails for FP&A overlays.
Build integrations: Connect ChatGPT to the systems your team already uses (ERP, CRM, email, file storage). Even basic automations like generating a draft response or filing a document can free up hours every week.
Fail-safe everything: Use human-in-the-loop review where accuracy and context matter—like regulatory reports or customer communication. Let AI take the first pass, but keep a human touch on the final output.
Train your people: Adoption won’t happen without enablement. Equip teams with prompt packs, role-specific use cases, and clear guidance on where GenAI fits. The more practical and accessible, the faster they’ll put it to work.
Some orgs redeployed 40% of finance capacity to higher-value work (IBM IBV).
Phase 5: Governance & Monitoring
AI without governance is a reputational risk waiting to happen. From hallucinations to data leaks, unchecked GenAI can create more problems than it solves. This phase is about building trust across all stakeholder groups (internal, external ... not to mention with regulators). Governance ensures your GenAI outputs are grounded, consistent, and explainable. It's also what separates experimental tools from operational systems.
Ground your outputs: Avoid hallucinations and misalignment by giving GenAI the right context at the right time. That means injecting relevant business data (e.g. current KPIs, recent transactions, or company policy language) into your prompts or workflows. Whether you're doing this manually or using tools with embedded context windows, the goal is the same: smarter, more accurate results.
Capture feedback: Build structured feedback loops into AI workflows. Let end users flag bad outputs and continuously refine prompts and logic.
Stay compliant: Apply access controls, enable audit logging, and document use cases. Make it easy to show who did what, when, and why — especially in regulated environments.
Phase 6: Value Realization & Iteration
You can’t just launch and pray. You need to measure what works, socialize the wins, and use the data to drive the next phase. This is where generative AI shifts from experiment to operating model. Finance leaders who revisit, refine, and build on their early GenAI wins tend to see faster adoption, stronger results, and more staying power across the organization.
Show the ROI: Track concrete gains: reduced close times, fewer errors, faster stakeholder turnaround. Share results early and often to keep momentum.
Monthly close times dropped ~33% in early adopters (IBM IBV, Jan 2025).
Model the impact: Build simple cost-benefit models that show value over time. Consider time savings, error reduction, and labor reallocation—not just licensing costs.
Build momentum: Package successful use cases into repeatable playbooks. Use each win to justify the next investment, whether that’s a new workflow, a tool upgrade, or added headcount.
Advanced Moves (Optional but Powerful)
Once your foundation’s in place, you’re ready to scale with style. These aren’t for day one, but they’re how you ultimately unlock speed, autonomy, and a finance operation that learns, adapts, and scales with less manual effort and more strategic focus.
Agentic workflows: Move beyond single-use prompts. Let AI string together multi-step actions, like identifying anomalies, drafting memos, and routing them for approval via customer workflows.
No-code democratization: Equip analysts with tools like Zapier, Power Automate, or n8n so they can build, test, and modify GenAI workflows without waiting on IT. This is how finance becomes self-sufficient.
Train evangelists: Identify high-trust, high-curiosity team members and empower them to lead. Give them early access, support, and visibility, adn they’ll drive adoption far better than a top-down mandate.
Lead from the Front
Use this playbook to get moving, then scale what works. Start with one high-ROI use case, show results, and build from there. The gap between experimentation and execution is closing fast. Make sure you're on the right side of it!
Curious how GenAI could reshape your finance team?
By helping finance leaders navigate these rollouts firsthand, I’ve seen what works (and what doesn’t). If you’re thinking about taking the first step or scaling what you’ve started, I’d love to hear where you are in the journey.
🔓 From Roadmap to Rollout
The Pro version includes the full execution toolkit:
Use Case Prioritization Rubric: score and select high-ROI GenAI pilots
Pre-Deployment Rollout Checklist: align data, people, and process before launch
Governance Framework for Finance Teams: stay compliant, auditable, and in control
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