Optimizing Financial Reporting Through AI

Finance leaders are under pressure to deliver precision, speed, and compliance while keeping costs in check. Manual reconciliation, reporting, and transaction processing consume up to 60% of analysts’ time and increase the risk of financial errors. AI automation is changing that reality. With AI, enterprises can automate up to 80% of repetitive finance workflows while maintaining 99.99% accuracy across reconciliation, validation, and reporting cycles. The outcome is consistent, transparent, and real-time financial control. Global enterprises adopting AI-led finance automation have reported measurable results: • 45% faster month-end closure • 35% lower compliance risk exposure • Up to 50% reduction in financial operation costs • ROI within 90 days A no-code platform enables finance teams to deploy intelligent agents without technical complexity. It integrates with more than 1,000 ERP, CRM, and API endpoints, ensuring seamless adoption across SAP, Oracle, and cloud ecosystems. This shift is redefining the finance function. CFO offices are moving from transaction execution to data-driven advisory. Finance professionals now have more time for forecasting, scenario planning, and strategic decision-making that drive growth. AI amplifies human judgment by uniting accuracy, compliance, and agility to help finance teams scale with confidence. If you are exploring how AI can modernise your finance operations and deliver measurable value in 90 days, DM to start the conversation. . . . #AI #FinanceAutomation #DigitalTransformation #EnterpriseFinance #FinTech #AIAutomation #FutureOfFinance #OperationalExcellence #DataAccuracy #FinanceLeadership #AIAdoption #BusinessTransformation #IntelligentAutomation #CFOLeadership

How Adobe’s CFO is Using Agentic AI to Transform Corporate Finance. The Transition from Financial Reporting to Real-Time Predictive Orchestration: Adobe’s Blueprint for an AI-Driven Finance Function From "Bean Counters" to "Bot Commanders" Adobe’s finance department is no longer just tracking the money; it’s building the machines that manage it. In a deep dive into the company’s internal transformation, CFO Dan Durn reveals how Adobe has effectively turned its finance wing into a high-stakes AI laboratory. While other companies are cautiously piloting "chatbots," Adobe has deployed a fleet of autonomous agents to handle the heavy lifting of closing the books, tax compliance, and multi-year forecasting. The result? A "continuous close" environment where the quarterly scramble is being replaced by real-time, AI-driven strategic steering. Key Takeaways: The "Zero-Day" Close: Traditionally, closing a global company’s books takes weeks of manual reconciliation. Adobe is moving toward a "zero-day" close, where AI agents monitor transactions in real-time, flagging anomalies and reconciling accounts instantly. This allows the finance team to spend 90% of their time on strategy rather than data entry. The "Agentic" Budgeting Cycle: Adobe has replaced static annual budgets with a dynamic "agentic" model. Thousands of internal agents ingest real-time market data and internal performance metrics to suggest budget reallocations on the fly, allowing the company to pivot capital toward high-growth areas in days, not months. The Rise of the "Finance Engineer": Durn is aggressively hiring for a new hybrid role: the Finance Engineer. These are professionals who understand GAAP accounting but are equally proficient in prompt engineering and agentic workflows. In this new lab, "Excel proficiency" is a baseline, not a differentiator. Ethical Guardrails as a Moat: A key part of the "lab" is the testing of "Financial Trust Layers." Adobe has built proprietary systems to ensure that AI-generated financial insights are auditable and compliant with SEC regulations, solving the "black box" problem that keeps most CFOs awake at night. Read more here: https://lnkd.in/ejg7whXF Who Should Care: - CFOs & Controllers – Seeking a roadmap for moving beyond legacy ERP systems. - Financial Analysts – Who must upskill into "AI auditing" to remain relevant in a flattened organization. - Enterprise Software Vendors – Facing a world where customers (like Adobe) build their own agentic tools rather than waiting for "off-the-shelf" updates. - Audit & Compliance Firms – Who must now learn to "audit the agent" rather than just the spreadsheet. - Business School Deans – Needing to integrate data science deeply into the core accounting curriculum.

Stop using your FP&A team to "download csv - copy - paste". A $75M-revenue CFO shared: “Every month we burn hours updating the same reports, and have no time left to think strategically.” That’s not a workflow; it's a tax on decision speed. The shift is underway: finance is moving from report production to decision production. What it looks like in practice: - GL line items and pipeline data are automatically pulled from source systems. - AI runs the first pass: flags variances, explains drivers, spots trend breaks, suggests where to dig. - Your team spends its calories on scenarios, actions, and trade-offs. Not on formatting. The payoff: - 70–80% of time goes to forward-looking analysis, not backward-looking compilation. - Faster board and exec cycles with clearer “do this next” recommendations. - A finance function measured by decisions made and dollars avoided/captured; not decks shipped. This isn’t sci-fi. Teams are already doing it with AI-native platforms (yes, including Payflow) that automate the grunt work and surface the “why” behind the numbers. If your smartest people are still stitching CSVs at month-end, you’re leaving strategic alpha on the table. Flip the calendar: let machines build the package and let finance drive the plan. #FPandA #CFO #StrategicFinance #FinanceTransformation #AIinFinance

90% of finance teams still think AI means “just ChatGPT.” So why is that your only priority? AI tools already determine speed, accuracy, and cost savings in finance. Yet most businesses ignore Pigment, Ramp, and Causal... That's why I mapped this AI stack for finance pros to stay ahead. Steal it to maximize leverage 👇 🔎 Generative AI ↳ Summarises reports + policies. Use cases: • Policy documentation • Answering finance queries • Drafting accounting memos    KPIs: • Reduced analyst workload • Hours saved per report • Faster reporting cycles    📊 AI for Forecasting ↳ Predicts revenue, cash flow, and costs. Use cases: • Scenario planning • Expansion modeling • Real-time cash tracking    KPIs: • Forecast accuracy % • Variance vs. actuals • Cash visibility    📈 AI for FP&A ↳ Scenario + driver-based models. Use cases: • Budget vs. actuals • Sensitivity analysis • Driver-based forecasting    KPIs: • Accuracy of NRR / CAC • Forecast cycle speed • Scenario coverage    💳 AP & AR Automation ↳ Invoice capture + payments. Use cases: • Vendor payments • Bank reconciliation • Automated reminders    KPIs: • DSO (days sales outstanding) • Payment errors reduced • Collections speed    💼 Expense Management ↳ Policy enforcement + parsing. Use cases: • Fraud prevention • Receipt scanning • Category automation    KPIs: • Reimbursement time • Expense accuracy • Policy adherence Adopting AI isn’t optional. But it doesn’t have to be overwhelming. That’s why I'm helping SaaS founders & operators. Join 5k folks at: thestartupfinance.com PS. Are you already using AI in your finance stack?

If you're still manually exporting data to build reports in 2025, you're doing finance wrong. It used to drive me insane. When I needed to pull a simple report from a client's billing system...and it would take 30 minutes. Export the data. Clean it up in Excel. Build the damn report. Then next month? Do it all over again. I see this constantly as a fractional CFO. Someone wants to understand what's driving a revenue spike. Or they need to analyze customer cohort performance before a board meeting. Or they're trying to validate subscription data. And instead of just...getting the answer...they're exporting CSV files and rebuilding spreadsheets. It's 2025 and we're still doing this manually. The data EXISTS. It's sitting right there in your billing system. But getting to it? That's the problem. → What Maxio just launched I've been using Maxio for years to manage billing and revenue for my clients. They just released something called Maxio MCP. You connect your Maxio data to AI tools like ChatGPT or Claude. Then you just...ask questions. In plain English. "Show me churn by cohort for Q3" DONE. The AI pulls the data using Maxio's financial logic and returns an answer you can actually use. Generate cohort reports. Summarize revenue performance. Validate subscriptions. Create customers. All through typing questions like you're texting a coworker. No exports. No rebuilding. No hunting through dashboards for 20 minutes. → But what about security Finance needs boundaries. You can't just let AI run wild with your revenue data. Maxio built this with role-based permissions, OAuth authentication, scoped access tokens, full audit logs. Every action is logged. Every permission is enforced. So you get the speed and convenience of AI...but with complete control over who can access what. → Why this matters Finance teams are being asked to move faster. Boards want answers NOW. Leadership wants insights THIS WEEK. But we're stuck spending hours generating the same reports over and over. This removes that bottleneck. You stop wasting time on data extraction and start spending time on what the data MEANS. I'm excited to use this with clients because I know it'll save me hours each week If you're using Maxio and you haven't checked this out yet...go look. 👉 https://www.maxio.com/mcp

I've been using AI heavily in finance for a year. Here's what I've found actually matters: 1️⃣ If your accounting system doesn't connect easily with an external AI (ChatGPT, Claude, etc.), you're using the wrong accounting system. Built-in AI tools in accounting systems like QBO are rarely as capable as the real thing. 2️⃣ Know the privacy risks. My rule: strip out identifying information before uploading any document. 3️⃣ Treat it like a new staff accountant. You have to check its work. You have to train it. It will make mistakes. 4️⃣ Direct connectors to Google Drive or cloud storage are hit or miss. If accuracy matters, export the reports and upload manually to the AI model. 5️⃣ Before you review anything, ask it to double-check its own work first. It catches its own errors more often than you'd think. 6️⃣ Reconciliations and matching tasks are where it shines. Add agents into the mix, and you're automating a significant chunk of staff-level accounting work. 7️⃣ The future belongs to finance professionals who understand how these models think, know roughly what the answer should look like, and can verify the math. Your experience-based gut instinct matters more now, not less. 8️⃣ Cash flow forecasting is where I'd start for most businesses. It's the single highest-impact use case I've found. 9️⃣ For FP&A work specifically, Claude is the clear winner — and worth the paid upgrade. 🔟 Information overload is real. My fix: ask it to cut the output by 50%, then ask it to simplify further. Forces it to distill down to what actually matters. AI won't replace experienced finance professionals. But it will replace those who don't know how to use it. #Finance #AI #Accounting #FPandA #CFO

Finance leaders know the pain: siloed data, slow reports, and insights that arrive too late to matter.   That’s exactly what Hewlett Packard Enterprise CFO Marie Myers set out to change by embracing a transformation with purpose mindset. As most recently covered in Fortune (https://deloi.tt/4kHtwJ1), her focus shifted to technology adoption, grounded in strong data foundations, governance, and change management. And that’s where Deloitte came in.   Together, and with NVIDIA, we co-developed CFO Insights, a product combining agentic and GenAI technologies in one solution powered by Deloitte’s Zora AI™ platform and running on HPE Private Cloud AI. CFO Insights, known internally at HPE as “Alfred”, replaces static reports with live insights, guiding finance leaders on what matters and what to do next.   An important breakthrough is the deterministic outcomes behavior. GenAI tools can give slightly different answers each time you ask the same question. That’s fine for some types of work, but finance needs the same answer every time, with full traceability. Getting large-language models to behave that way is much harder than it sounds, and seeing it work in a real environment is a big step forward.   What’s even more exciting is how fast HPE is extending agentic solutions into areas like credit, collections, audit, procurement, and payroll. To date, we expect CFO Insights to cut financial reporting cycle time by about 50%, lower processing costs by 25% and fuel sharper, more focused conversations around performance. The impact is real: faster reporting, lower costs, and a CFO role built for an AI-native future. 🚀   Get the full scoop on how Marie and our Deloitte teams transformed HPE’s finance function into a true enterprise intelligence powerhouse here and reach out if this is something your organization is looking to take on: https://deloi.tt/4rQ6x0H.

Many finance leaders I talk to are stuck in the first phase of AI adoption. They're using it to write cleaner emails or build formulas in Excel. Getting more productive individually is great, but compared to software engineering or go-to-market teams, the office of the CFO has fallen behind. So I sat down with our finance team at Maxio to review how we were doing our Management Discussion and Analysis (MD&A) reporting. Every month, we produce a thoughtful readout of what happened with the business so our investors can get that perspective. But too often, these reports end up being a simple regurgitation of the numbers. Leadership and investors don't just want a summary of the data, they can read a spreadsheet as well as anybody. The narrative, the accountability, and the through-line is what’s valuable. So we ran an experiment to test what AI could really do for finance. We loaded every official document we've ever given to our board into a Claude project. Management packs, historical reporting, and previous quarterly presentations became our single source of truth. Then we asked it to draft our upcoming MD&A. The output was impressive. It hadn't just summarized the data, it was building on a historical narrative! It remembered what we promised in Q2 of last year, connected the dots, and celebrated compounding wins we would have completely missed. More importantly, it caught the blind spots. It looked at the data and pointed out that we said we were going to do one thing, and now we're doing another. It forced us to answer the hard question: What’s your story? Instead of spending days compiling historical data, our team spent that time looking ahead, debating where we needed to go and how to allocate capital to get there. The new job of finance. The books still need to balance, debits & credits need to add up, and audits need to be passed. AI has mastered hindsight, and it can help with all of that. But foresight? That requires a human at the helm.