How to Engineer Automation for ROI

The A.C.T.S™ Framework : From AI Pilots to Measurable ROI A leadership system for AI, Agents & Agentic AI Most organizations have an ROI accountability problem. AI pilots get approved. Demos look strong. Adoption feels “promising.” But when the CFO asks, “Where is this in the P&L?” there’s no clear answer. That’s because AI is being managed like technology, not like a financial system with ownership. For AI leaders, this is the shift: AI is no longer experimentation. It’s economic governance. A : AUTHORITY ROI Defined (Economic Permission) Should this system be allowed to act? Define: - Decision/action + autonomy level - Value of correct vs cost of wrong decisions - Volume (to justify scale) - Guardrails (limits, reversibility) Mandate: No authority without clear ROI and bounded risk If you can’t price the decision, you shouldn’t automate it. Goal: Define ROI for AI decisions C : CASH ROI Proven (Financial Impact) AI scales on money, not potential. Require: - One workflow, one financial metric - Baseline vs 10–20% improvement - Real usage (not pilots) - Finance-validated impact For Agentic AI: - Track autonomous execution - Track override rates Mandate: No $$ in 90 days → no scale Accuracy doesn’t fund programs. Financial impact does. Goal: Prove ROI with real impact T : THROUGHPUT ROI Sustained (At Scale) This is where most AI fails. At scale, hidden costs appear: - Human corrections - Escalations - Exception handling Measure: - % human intervention - Minutes per case - Cost per case (AI + human) Mandate: Scale only if cost per case declines If human effort grows, ROI collapses. Goal: Sustain ROI at scale efficiently S : SELF-LEARNING ROI Compounded (Over Time) Agentic AI either improves or repeats mistakes faster. Ensure: - Decision history is captured - Failure patterns are reviewed - Fix cycles are fast (<2 weeks) - Exceptions decline QoQ Mandate: No learning loop → no scale Without this, you automate repetition, not improvement. Goal: Compound ROI through continuous learning Leadership Reality (2026) - AI fails at Cash (no visible ROI) - Agents fail at Throughput (human drag) - Agentic AI fails at Self-Learning (no learning loops) More autonomy = tighter economics. The Leadership Rule AI scales only when every stage holds: - ROI is Defined (Authority)   - ROI is Proven (Cash)   - ROI is Sustained (Throughput)   - ROI is Compounded (Self-Learning) If any stage fails: → Do not scale   → Step back   → Fix ownership, economics, or design   Final Thought AI that answers questions is useful AI that takes action creates value AI that learns compounds advantage But none of it matters without economic discipline For AI leaders, the mandate is clear: > Don’t just deploy AI   > Govern it like a P&L system That’s the difference between pilots and profit! Next Post : The AI Trust Quadrant™: Most AI Is in the Wrong Zone

The Treasury Technology ROI Blueprint For years, treasury technology was sold as “strategic,” “innovative,” or “transformative.” But today’s CFOs and Treasurers want proof of value in numbers, not adjectives. So, how do you quantify the ROI of your TMS or automation project? Here’s the framework 1. Efficiency ROI – Productivity & Cost Savings Formula: Time Savings × Average Treasury FTE Cost × 12 months Metrics: % reduction in manual reconciliations % reduction in time spent on cash positioning & forecasting Headcount reallocation from operations → strategy Example: Automation that saves 3 FTEs at $120K each = $360K+ annual ROI in productivity gains. 2. Liquidity ROI – Optimized Working Capital & Interest Impact Formula: Trapped Cash Released × Weighted Average Cost of Capital (WACC) Metrics: Cash visibility improvement (e.g., from 60% → 95%) % of idle/trapped cash repurposed Reduced reliance on short-term borrowing Example: Releasing $50M in trapped cash at 5% cost of capital = $2.5M liquidity ROI annually. 3. Risk & Control ROI – Compliance, Accuracy & Exposure Reduction Formula: Cost Avoidance + Risk Reduction Impact Metrics: % reduction in manual/payment errors FX exposure minimized through real-time visibility Audit findings eliminated or improved rating Example: 40% fewer manual errors = $500K+ avoided losses from fraud or compliance risk. 4. Strategic ROI – Decision Quality & Business Agility Formula: Qualitative Value × Quantifiable Business Impact Metrics: Forecast accuracy (+20%) Faster liquidity decisions (hours → minutes) Improved cost of capital via better debt planning Example: Better forecasting frees $10–$20M in working capital for redeployment. The Complete ROI Picture TMS ROI = (Efficiency + Liquidity + Risk Reduction + Strategic Gains) – Total Cost of Ownership (TCO) Most well-executed treasury tech projects achieve ROI in 18–30 months post go-live. The takeaway: Treasury technology ROI isn’t abstract but it’s measurable. When you quantify efficiency, liquidity, risk, and strategic impact, you stop managing cash and start creating enterprise value.

Last quarter, we spent $1,404,619 on AI tokens - an all-time high - and the ROI wasn’t what we expected… Most of the ROI didn’t come from “flashy AI”, it came from boring AI doing boring work at scale. Here’s where our spend went and what actually moved the needle: 1. Telling reps who to call today (and why) We’re using AI to sift through millions of signals and tell reps who to talk to today and why. The signals that we’ve found matter: Job changes (new decision makers = new opportunities), buying committee changes and intent signals (active web research and pricing page visits). The big ROI driver is helping our customers with daily prioritization so they don’t have to go fishing for actionable info. At ZoomInfo, We’ve seen a 25-33% increase in meeting quality and opp creation when AEs are sourcing using our AI tools. Win rates also jump from 16-20% to 30%. 2. Writing outreach that doesn’t sound automated We’re moving from “20 segments of 1,000” to 20,000 segments of 1. Not “VP IT at enterprise insurance” messaging… but John at State Farm, who we talked to last year, who competes with three of our customers, with context pulled in automatically. Customer ROI here ultimately comes from better response rates and higher close rates by being more relevant. Buyers care when you show you care. 3. Turning sales calls into usable data Every sales call (ours and customers) is recorded using @Chorus and becomes structured data: objection patterns, competitor mentions, deal risk, coaching moments. We’ve found the benefits of this are huge - 25-30% faster ramp time for new reps, and 10-15% larger deal sizes through better discovery and value articulation. The average rep sells more like the best rep. 4. Speeding up low-value engineering work Every engineer at Zoominfo has Intellij and VS Code w/ Cline. AI handles the unglamorous stuff: Boilerplate code, refactors, test coverage. We’ve seen ~25–30% faster execution on these routine tasks, which frees senior engineers to focus on system design and real product innovation. Our biggest lesson so far has been that if your data foundation is garbage, AI just helps you move faster in the wrong direction. You won’t get AI “working” until you have contextual customer/prospect data centralized, and you can actually build on top of it. We’re still early and we’re trying a lot of things but these have been the highest ROI drivers by a mile. If you’re testing AI in your GTM stack, drop a comment with what’s actually working for you - I’m all ears.

Everyone says AI will transform finance, but no one tells CFOs how to make it actually pay off. AI pilots are everywhere… but measurable ROI is rare. If you’re a CFO or FP&A leader, you don’t need another tool, you need a framework that connects AI to business outcomes. Here are 5 that actually work: 1) The 4R Framework Recognise → Identify real finance pain points. Redesign → Integrate AI and automation into the process. Run → Pilot with real data and defined KPIs. Realise → Quantify time, cost, and error reductions. 2) The VALUE Framework Vision – Automate – Learn – Use – Evaluate. Start small, build literacy, then scale what delivers measurable impact. 3) The 3P Framework People. Process. Platform. Train your team, redesign workflows, and choose scalable tools (Python - available now in Excel, Copilot, ChatGPT Enterprise, Power BI). 4) The ROI Loop Measure → Deploy → Measure again → Reinvest. Treat AI like any other capital project. Expect a return, not a headline. 5) The MIND Framework Model – Interpret – Narrate – Decide. Turn deterministic Python outputs into GenAI-powered insights that drive action. BONUS: The FOUNDATION Framework Before deploying AI, build a clean, automated, and standardised data layer. Then: a) Define the real business problems to solve. b) Deploy a standardised, repeatable solution that uses not only AI, but also automation, data governance, and integration across your systems. Because AI is only as powerful as the data and the discipline behind it. These frameworks can help you move finance from AI hype to measurable value. Sharing 3 More Resources to make this happen: https://lnkd.in/erM6KiNv https://lnkd.in/eTgrPPec https://lnkd.in/eTVnDvKQ

A lot of finance teams are waiting for a magic box. A plug-and-play AI solution that solves all their modeling and forecasting challenges... out of the box. But here's the truth: 𝘛𝘩𝘦 𝘳𝘦𝘢𝘭 𝘰𝘱𝘱𝘰𝘳𝘵𝘶𝘯𝘪𝘵𝘺 𝘪𝘴𝘯’𝘵 𝘪𝘯 𝘰𝘶𝘵-𝘰𝘧-𝘵𝘩𝘦-𝘣𝘰𝘹 𝘢𝘯𝘺𝘵𝘩𝘪𝘯𝘨... It’s in evolving your finance processes and team with automation and AI together. Because AI won’t replace your FP&A team. But it can help: • Automate recurring models • Enhance variance and scenario analysis • Assist decision-making with smarter insights • Help the team see beyond their current sight, creating more capable professionals For AI automation to work, companies need to stop thinking like tech consumers... And start thinking like process designers. Here are key things to consider for a successful AI + automation project: ➤ Start with clarity Know which processes are repetitive, time-consuming, and rules-based. Automate them. ➤ Identify the biggest bottlenecks for a successful automation. They might be good use cases for AI ➤ Don’t skip the human layer AI can assist with insight, but you still need finance judgment to interpret and act. ➤ Data quality is everything Bad inputs = bad outputs. Garbage in, garbage out. Clean, consistent, structured data is key. ➤ Integrate, don’t isolate AI should sit within your tools and workflows, not float in a separate app. It should part of an existing process and not a process created apart. ➤ Implement measures to keep data safe. Governance, policies and compliance. Create guardrails in the processes. ➤ Measure impact, not hype Track real ROI: time saved, accuracy improved, insights gained. The future of FP&A isn't a robot doing your budget. It's smarter tools helping humans do finance better.

Crossing the GenAI Divide: Why 95% of AI Pilots Fail and How Leaders Can Fix It According to the new State of AI in Business 2025 report, 95% of enterprise GenAI pilots deliver essentially zero P&L impact. In other words, only about 5% of projects are producing millions in measurable value. This divide isn’t due to AI models or regulations, but how companies approach implementation. The report finds that the few who succeed do three things differently: they buy versus build, embed AI deeply into workflows, and focus on high-ROI use cases. Key insights for leaders: * Buy, don’t build: Top-performing firms partner with AI vendors instead of developing tools entirely in-house. In our interviews, twice as many vendor-supplied solutions reached full deployment (66% vs 33%) compared to internal projects. Treat AI providers as strategic collaborators (think BPO models, not just software licenses) and require solutions to learn from your data and adapt to your processes. * Prioritize back-office automation: Nearly half of GenAI budgets currently flow to marketing and sales, but the highest ROI often lies in operations, finance, and other support functions. Automating mundane admin tasks, report generation or customer support workflows can deliver clear cost savings (for example, $2–10M annual BPO spend reduction in best-in-class cases). Don’t chase shiny front-office demos at the expense of these workhorse opportunities. * Align with real needs: Only deploy AI that integrates seamlessly into existing workflows and drives measurable outcomes. Successful buyers demand deep customization to their processes, benchmarking tools on operational metrics – not just model features. If a GenAI tool can’t learn from user feedback or fit into the day-to-day, users will abandon it. Takeaway/Call to action: Enterprise leaders must rethink their GenAI strategy. Shift spending from one-off pilots to strategic buys: select learning-capable AI systems that remember and evolve with your business. Start with high-ROI, back-office use cases and empower front-line managers to drive adoption. Hold vendors accountable to real business KPIs, and insist on deep workflow integration. By buying the right tools (not building static proofs of concept) and aligning them with concrete needs, you can cross the GenAI divide and turn AI pilots into profit. Chinmay Hegde | Chandrashekar SK [CSK] #GenAI #AI #ArtificialIntelligence

I just saved a company $9.7M on their AI project. Here's the $300K solution they almost spent $10M on: [The Million-Dollar AI Implementation Truth] Their original plan: • $5M AI transformation • $3M cloud migration • $2M digital transformation What they actually needed: • Month 1: Fixed Excel hell ($50K) • Month 2: Cleaned data ($50K) • Month 3: Basic automation ($100K) Results: • 300% ROI in 90 days • Problems actually solved • No AI magic required Reality check: Your "$10M AI project" is probably: • Messy spreadsheets • Broken processes • Bad documentation • Excel doing heavy lifting • Someone manually copy-pasting Why companies waste millions: • "AI" sounds better than "Excel" • "Transformation" beats "cleanup" • "Innovation" > "maintenance" • Executives need big projects • Consultants love big budgets The truth about enterprise AI: Small team + clean data > Big budget + AI dreams Want proof? • Company A: Spent $10M on AI, failed • Company B: Spent $300K on basics, won • Same problem, different approach Save this before your next AI project. Your career might depend on it. #EnterpriseAI #DigitalTransformation #RealTalk P.S. VPs of Innovation are typing angry responses. But first, show me your ROI metrics.

Most AI strategies are theatre, not ROI. Because they start with models, not money. You don’t need another pilot or data lake. You need one business decision to improve. Budgets get burned, teams stall, trust erodes. Boards keep asking, where is the value? Here’s how to turn AI from slideware to cash flow. I use the SPARK framework with leaders. It’s simple, practical, and built to scale. Use this playbook: 1. Specify the Problem Pick one metric to move 10–30%. Map the work. If value < $250k/yr, pick a bigger problem. 2. Propose a Solution Design the workflow, not just a model. Start with the simplest tech that proves value. 3. Assess AI Readiness Score data, platform, people 0–5. Fix red gaps in 2 weeks before you pilot. 4. Run a Pilot Thin‑slice: 1 use case, 1 team, 4–8 weeks. Pre‑register metrics. Run shadow or A/B. 5. Keep or Kill Decide with a stoplight: keep, pivot, kill. Kill fast if lift <5% or ops burden spikes. 6. Scale What Works Productize and integrate with core systems. Roll out in waves. Track drift and unit costs. 7. Pitfalls to Avoid Start with money. Not models. Not a data lake. No owner = no project. Optimize decisions. 8. Metrics That Matter North star: value per decision. If metrics stall by week 3, unblock or stop. Save this for your next QBR or steering meeting. Remember: Simple beats flashy. Which step is your bottleneck right now? ⬇️ Let me know in the comments Want to scale your business with AI? Join AI‑Empowered Leaders: My weekly newsletter with actionable AI and strategy insights from my work as an AI advisor, trainer and coach. Sign up here 👇 https://lnkd.in/dar5M9p7 ♻️ Repost to help your network pick and scale AI use cases that drive real ROI.

𝐀𝐈 𝐑𝐎𝐈 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐬𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐦𝐨𝐝𝐞𝐥𝐬. It starts with business clarity. Too many AI initiatives stall because teams jump straight into tools before defining outcomes. Real impact comes from treating AI like any other business investment - with ownership, metrics, and execution discipline. 𝐓𝐡𝐢𝐬 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐬𝐡𝐨𝐰𝐬 𝐡𝐨𝐰 𝐭𝐨 𝐦𝐨𝐯𝐞 𝐟𝐫𝐨𝐦 𝐚𝐧 𝐢𝐝𝐞𝐚 𝐭𝐨 𝐦𝐞𝐚𝐬𝐮𝐫𝐚𝐛𝐥𝐞 𝐢𝐦𝐩𝐚𝐜𝐭 𝐢𝐧 𝟏𝟎 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐬𝐭𝐞𝐩𝐬: Start by identifying a real business problem - where costs leak, decisions slow down, or risk is high. Then translate that problem into a clear ROI hypothesis with measurable targets like cost reduction, revenue lift, accuracy gains, or time saved. Before building anything, assess data readiness. Validate availability, quality, ownership, and access early to avoid silent failures later. From there, prioritize AI use cases based on feasibility, business impact, and adoption readiness - not novelty. Run controlled pilots to test assumptions against baseline metrics. Design human-in-the-loop workflows so teams can supervise, validate, and override AI outputs. Adoption depends as much on trust as on technology. Enable change through training and operational alignment. Measure ROI continuously across both financial and non-financial outcomes. Compare results against the original hypothesis. Once value is proven, scale with governance - clear controls, monitoring, and compliance. Then keep optimizing models, workflows, and metrics as systems mature. 𝐓𝐡𝐞 𝐜𝐨𝐫𝐞 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: AI delivers returns when it is treated as a business system, not a technical experiment. Clear problems. Measurable outcomes. Disciplined execution. Continuous improvement. That is how ideas turn into impact. ♻️ Repost this to help your network get started ➕ Follow Prem N. for more

What Restaurant Tech Actually Delivers ROI? Every operator wants to cut costs and boost profits, but not every piece of tech gets you there. Let’s break it down: 🍽 POS – Essential for service, but it won’t reduce your admin burden or boost margins. 📅 Scheduling Tools – Helps with labor, but that’s just one piece of your cost puzzle. 💻 Back-Office Automation – This is where the real ROI lives. Here’s what that looks like in practice: 📥 AP Automation – One group I work with cut 20+ hours/month from manual invoice processing. That’s $1,000+ in labor saved—every month. 🏦 Bank & Credit Card Recs – What took days now takes minutes. That’s more time for strategy, less time chasing transactions. 📊 Consolidated P&Ls – Ownership gets real-time insights, no more waiting weeks for a clean financial picture—helping them make faster, smarter decisions. 📦 Inventory Controls – No more guessing margins. One operator tightened COGS by 2–3% just by having visibility into actual usage. Multiply that across all of your locations, and the ROI is massive. Bottom line: The right back-office system doesn’t cost you. It pays you back. If you’re scaling or just want to stop pouring time into manual work so you can actually enjoy the fruits of your labor, let’s talk. I’m happy to run a quick ROI analysis based on your specific numbers. DM me.