How AI Will Transform Work in Finance

AI is transforming finance — and CFOs need to be ready. In a recent interview with Adam Zaki of CFO.com, I shared some key insights from my book "AI Mastery for Finance Professionals," and how finance leaders can navigate the rapidly evolving AI landscape. Here are the highlights: 1️⃣ Data Readiness is Critical Generative AI offers incredible potential, but without mature, clean, and well-governed data, it’s not a technology that can be fully leveraged. CFOs must prioritize their data infrastructure first. 2️⃣ Start Small, Think Big Success with AI isn’t about automating everything overnight. Focus on incremental wins—projects that demonstrate impact, gain buy-in, and build momentum for broader adoption. 3️⃣ Understand the Tool, Not Just the Output AI isn’t a magic box. CFOs don’t need to be developers, but understanding how AI works is crucial to asking the right questions and trusting its insights effectively. 4️⃣ Bias Awareness Matters AI models are only as good as the data they’re trained on. Proactively test for fairness and ensure your datasets are free from bias. 5️⃣ CFOs as Strategic Leaders Today’s CFOs are more than financial stewards—they’re strategists and innovators. AI enhances this role, providing tools to forecast, predict, and guide with creativity and precision. 💡 Final Thought: AI adoption isn’t about replacing people — it’s about empowering teams and creating new efficiencies that drive long-term value. The future is here, and it’s time for finance leaders to embrace it. https://lnkd.in/emBQtfHR

Your AI Copilot Isn’t Replacing You — It’s Promoting You 🚀 Remember when Excel first landed in offices? The people who mastered it didn’t get replaced. They got promoted. We’re living through that moment again—only now, it’s with AI. Your AI copilot—whether it’s ChatGPT, Claude, or a custom tool—isn’t here to take your job. It’s here to multiply your impact. Take my week, for example: 🧠 Summarized a 20-page whitepaper in 90 seconds ✍️ Drafted 3 client emails—in my voice, not some generic template 💡 Reframed an investor pitch deck using insights from a different industry None of that replaced me. It amplified me. And what I’m seeing personally? It’s happening at scale in fintech. AI in Fintech: Quiet Revolution, Massive Impact The same AI that’s helping me move faster is now transforming how fintech operates — not someday, but right now. 1. Smarter Risk Management ↳ AI flags fraud in real time, predicts loan defaults before they happen. ↳ JPMorgan cut false positives in fraud detection by 40%. 2. Personalization That Actually Works ↳ Hyper-relevant offers, proactive chatbots, AI-driven wealth advisors. ↳ Result? 5–10% uplift in revenue through more engaged customers. 3. Less Ops, More Innovation ↳ KYC checks, compliance reviews, documentation—automated. ↳ Your team spends less time chasing files, more time chasing growth. PwC predicts over $1 trillion in AI-driven value for financial services by 2030. Deloitte shows major gains in both cost reduction and revenue growth. This isn’t just an upgrade. It’s a shift in how fintech runs. At Netevia, we are already making this a reality. We are currently integrating AI into two core fintech processes: risk assessment and underwriting. These processes are being enhanced with AI to improve accuracy, speed, and decision-making. This integration enables our teams to focus on higher-level insights while AI handles complexity at scale. 💬 If you treat AI as competition, you’ll get left behind. 💡 If you treat it as a collaborator, you’ll move ahead. So let’s make this real: How are you using AI as your copilot? Drop your favorite use case in the comments—let’s crowdsource the next fintech playbook. #AI #Fintech #FutureOfWork #ArtificialIntelligence #ChatGPT #Productivity #CareerGrowth #BankingInnovation

Notes & Key Takeaways from Deloitte Digital in Hong Kong I spent the day listening to the curation by Deloitte's Digitial Day, deep-diving into how AI is transforming customer service, marketing, and business workflows, and here’s what stood out to me from a business leader and founder’s lens: 1. Customer Conversations Are the New Growth Engine: AI-driven messaging across WhatsApp, email, and live chat isn’t just support—it’s conversion, loyalty, and retention. Smart agents can now respond 24/7, pull purchase history, personalize suggestions, and create deeper customer journeys in real time. 2. Email Isn’t Dead.. It’s Smarter Than Ever: From OfferFit by Braze to Deloitte’s enterprise stack, one thing is clear: AI is transforming how we communicate. You can now: Personalize every subject line and call-to-action A/B test dynamically Trigger campaigns based on live customer behavior 3. Decision Intelligence Is Now C-Suite Ready: Deloitte’s C-Suite AI platform gives executives instant access to forecasting, risk models, and investment simulations, automating 50–60% of tasks that typically take weeks... AI isn't just a tool, it’s becoming an advisor! 4. Finance Is Becoming a Strategic Powerhouse: We saw AI applied across 6 finance pillars: forecasting, working capital, cost optimization, scenario planning, investment decisions, and operating models. Finance teams are moving from reporting what happened → to shaping what happens next. 5. Data + Apps + AI = A Unified (and Greener) Platform: Deloitte’s GenAI stack emphasizes energy-efficient architecture, scalable integrations, and reduced operational waste. The goal: sustainable innovation that doesn’t overload infrastructure or cost. 6. AI Isn’t Just for Big Enterprise Anymore: From personalized wedding planning agents to dynamic subscription models, the tools showcased: from ByteDance’s 'Seedream' to Mastercard’s loyalty platform, are accessible, efficient, and designed to scale across industries and business sizes. *Final Thought* This was a clear reminder that every part of a business: from customer service to executive strategy, can be elevated through the right AI tools. But what truly matters is how we apply them: not to replace human connection, but to deepen it. This experience only sharpens my focus on what I’ve been building.. an ecosystem that helps businesses discover and integrate the AI tools that are actually right for them. Whether it’s a sales agent for WhatsApp, a personalized email engine, or finance automation, the market is flooded with options, but few businesses know which solutions will work for their unique goals, workflows, or budgets. I’m even more committed to creating a curated marketplace that connects businesses to tailored, plug-and-play solutions, because scaling smarter shouldn’t mean hiring an in-house AI team from day one. Ultimately we have come to a point where we don’t need more AI noise. We need more clarity, more curation, and more real-world results.

A Major Finance Reshuffle is Coming - Are You Ready? AI is rapidly transforming finance, but its impact within Finance varies across different sub-functions. Some areas will see high automation, while others will still heavily rely on human intelligence for decision-making, judgment, and strategic thinking. Here is a quick analysis of changing mix of AI verses Human Intelligence required in Finance: 1. Accounting & Bookkeeping - High Automation AI can automate journal entries, reconciliations, payroll processing, tax filing, and financial reporting. Human role will be limited to reviewing complex transactions, handling exceptions, and making strategic accounting decisions. 2. Audit: Internal & External - High Automation AI can detect anomalies, fraud risks, and compliance violations. Human role is limited to final decision-making, critical evaluations, and handling complex investigations. 3. Costing & Management Accounting - High Automation AI can perform cost allocation, variance analysis, and predictive cost modeling using variety of data. Humans will focus on interpreting cost insights, making strategic cost-cutting decisions, and communicating strategic analysis. 4. Treasury Management - Moderate Automation AI can optimize cash flow forecasting, liquidity management, and foreign exchange hedging. RPA can streamline bank reconciliations and fund transfers. But risk assessment, managing complex financial instruments, and relationship management with banks remain a domain of humans. 5. Financial Planning & Analysis (FP&A) - Moderate Automation AI can automate forecasting, budgeting, and variance analysis, but humans are still required to interpret data, make recommendations, and influence key business decisions. 6. Corporate Finance (M&A, Capital funding, Investments) - Low Automation Mergers & Acquisitions (M&A) involve negotiations, strategy, and relationship-building, which AI cannot replicate. AI can assist in financial modeling and deal analysis, but humans will drive the final investment decisions. 7. Strategic Finance & Business Partnering - Low Automation AI can provide data-driven insights, but strategic finance leaders must interpret results, advise executives, and shape corporate strategy. CFOs & Finance Business Partners will continue to play a key role in long-term financial planning, business expansion, and risk management. Finance is facing a major staffing reshuffle in the days to come. Some have to leave but majority will be absorbed somewhere in the organization. Where will you settle, depends on your skill set. This is the time to make a critical evaluation of your skills and bridge the gap before it is too late. #CFO #FinanceSkills

AI just made its move into financial services. Anthropic announced a new tailored offering: Claude for Financial Services. Let’s break it down. • Claude connects directly to your internal data stack: Snowflake, Databricks, S&P, PitchBook, FactSet, and more. • It’s not a consumer chatbot. It’s a task-specific analyst, tuned for high-stakes environment. • It doesn’t train on your data. Privacy and compliance are foundational. • Oh yeah, and it can do Monte Carlo simulations. Where it creates value: • Investment teams can analyze portfolios, trends, and risk exposures in real time, without toggling across 12 dashboards or waiting on data prep. • Compliance and audit functions can use Claude to summarize regulatory updates, track adherence, and flag anomalies, before the next quarterly fire drill. • Client-facing teams can generate custom pitch decks, scenario models, and account insights on demand, without pulling an associate off a deliverable.    For CFOs • Increase visibility into financial drivers by asking natural-language questions across systems and models • Pressure-test scenarios in real time using up-to-date financial and macro inputs • Generate investor-ready insights faster and more consistently For FP&A Transformation leaders • Automate recurring analysis cycles such as forecast variance, budget rollups, and board package creation • Embed Claude into planning workflows to assist with driver modeling, commentary, and contextualization • Scale insight delivery without increasing headcount For GenAI Transformation leads • Operationalize AI within high-stakes workflows without reengineering existing systems • Launch proof-of-concepts with measurable productivity impact in under 90 days • Build a business case grounded in time saved, accuracy improved, and risk reduced Real results: • AIG accelerated underwriting by 80% while increasing data quality from 75% to 90% • Norway’s NBIM saved over 213,000 hours in a single deployment with a 20% productivity lift across finance teams If you’re leading a team inside a Fortune 500 and wondering where to start: Identify high-friction, high-repetition tasks in finance, ops, or risk. Don’t wait for a firm-wide transformation plan. Start small with one workflow Claude could automate or accelerate. Pilot. Measure. Expand. ----------------------- Follow me for GenAI Transformation, Training, and News.

5 Ways AI Is Reshaping Finance Right Now (Banks and financial firms are using AI to cut risks, boost profits, and make smarter decisions.) 1. Fraud Detection ↳ AI scans millions of transactions in real-time, flagging suspicious activity instantly. Banks using AI for fraud prevention have cut losses by 50%. 2. Algorithmic Trading ↳ AI-driven systems execute 60%+ of stock trades, reacting to market shifts in milliseconds. This improves accuracy, reduces human error, and maximizes returns. 3. Credit Risk Assessment ↳ AI-powered credit scoring analyzes thousands of data points, helping banks approve loans 30% faster while reducing default risk. 4. Personalized Banking ↳ AI chatbots and virtual assistants handle 80% of routine banking questions, cutting wait times and improving customer satisfaction. 5. Wealth Management ↳ AI-driven robo-advisors manage over $1 trillion in assets, offering smart investment strategies with lower fees. AI is transforming finance - are you using it to stay ahead? ______________________ AI Consultant, Course Creator & Keynote Speaker Follow Ashley Gross for more about AI

In recent conversation with IT leaders across a range of industries, the topic of business model transformation has come up more than ever due to AI and AI Agents. Most companies are rapidly thinking through what the impact of their business will be in an AI-First world. Not all of the impact will be the same, and it’s clear that industries will evolve in different ways, including how each of the players in these industries adapt with AI. There are a variety of factories to consider, like whether your business model historically sold services by the hour vs. by outcome, how information-centric your product is, the level of critical thinking required to deliver your service, and more. For instance, if you’re a law firm today, AI Agents have the potential of compressing the hours needed for particular legal work. The industry often bills hourly, so fewer hours certainly can put more risk on revenue per account. However, firms are starting to think through multiple ways AI begins to drive growth or benefits firms. You can now expand with more customers because you can deliver more work at a lower rate, or you could deliver even better work in less time, which ironically could mean fees go up even over time. You can extend out this type of dynamic to a variety of other professional services firms, from marketing agencies to systems integrators. Or, take financial services, where large organizations like financial advisory firms are thinking through what AI Agents do to their business model. In this industry, client relationships and value add is the biggest imperative. Even as AI may lower the barrier to getting financial advice for anyone, AI equally provides the potential for even smarter investment decisions and closer customer relationships between the advisor and the client, which leads to greater stickiness. Ultimately, there isn’t a single industry that won’t be impacted in some small or large way due to AI. Some companies will use AI to win more customers, and others will be forced to compete with new AI entrants which deliver services at a lower cost. Not every firm will adapt to this new reality, however, and those will be at the greatest risk. One big implication to all of this transformation is it puts the technology department more in charge of determining the long term business model and execution of a company ever before. The right moves and partnership right now by those implementing AI in their companies are in a critical position to execute on this.

AI's $2.4T Banking Revolution: The Hidden Story Behind 200,000 Job Cuts Here's what's really happening in banking's AI transformation → A deep dive that shocked me: The Scale is Staggering: → $770B value creation in Risk & Legal alone → $642B in Corporate Banking → $612B in Retail Banking But here's the fascinating part ↳ While headlines scream about 200k job cuts, the real story is about value creation: • Banks project 12-17% higher profits by 2027 • 54% of jobs will transform (not disappear) • 80% of banks expect 5%+ productivity surge The Hidden Pattern I Found 🔍 ↳ Back office automation isn't the end game ↳ Banks are using AI for value CREATION, not just cost-cutting ↳ JPMorgan's AI chief confirms: "AI is augmenting, not replacing" The Most Surprising Discovery: Risk & Legal functions → Highest AI value potential This suggests the real revolution isn't in customer service, but in the complex decision-making backbone of banking. Key Insight: We're witnessing the largest transformation of financial services since the 2008 crisis, but this time it's about building value, not just surviving. 🎯 My Prediction: Banks that view this as a transformation opportunity (not just cost-cutting) will capture the lion's share of the $2.4T value creation. 🔥 Want more breakdowns like this? Follow along for insights on: → Building with AI at scale → AI go-to-market playbooks → AI growth tactics that convert → AI product strategy that actually works → Large Language Model implementation Happy Sunday! #AI #Banking #FinTech #Innovation #DigitalTransformation #FutureOfWork

Over the past year, I've tried to predict AI trends, and while we've been right about the direction, we've consistently underestimated the pace of change - here's what I'm seeing now about AI's evolution in financial services: To preface: AI’s progress is following an exponential growth curve, just like Moore’s Law, where essentially every 18 months, the number of transistors on a microchip doubles, leading to more computing power. These AI systems are continuing to scale and scale, with some capabilities appearing out of nowhere. Here are a few things I’m excited about, especially for Zocks: 1. Software to sidekick I’ve noticed a shift in user experience. One of our customers described Zocks as a “sidecar” - like someone hanging out with you. Instead of feeling like a “software” where you ask it to do something and then wait, it’s now doing things for you on the side. And it’s just there all the time. Some of those things still go to the advisor or their team for review. Others can just be done automatically for them. 2. Deep reasoning In the last six months, deep reasoning has come into the forefront. It allows us to take on more long-running, multi-stage tasks that were really hard to do before, like pulling data from multiple sources, and then running an analysis across them or comparing them. We’ve built a lot of this into Zocks already. But as the core models improve, we can go even deeper. 3. Integrations One of our big strategies is to go deep on integrations (not shallow, click-through push things). And I’ve noticed that the longer an advisor has Zocks running, the more data we capture – and the more we learn where we can help. The depth is really starting to pay off. We’re seeing a continual increase in the types of things we can help with, like putting unique analytics data into the CRM or datalake that people can join with their own data, querying custom data, following custom workflows and even integrating with custodial systems and custom platforms. 4. Multi-agent architecture We use a number of different AI systems underneath. Why? Because some are just better at certain things. Some LLMs are better at extracting specific data, some are better at generating emails, and some are better at structuring specific different data (email, voice, financial records, etc…). And because we’re operating at the platform layer, we can bring those together and use the best one for each task, even if a customer brings their own. I don’t think of it as a bifurcation but as a specialization. Our multi-agent architecture has turned out to be really strong. Any AI stuff you’re excited about?