Automating Repetitive Work Tasks

Will Accounting Be Replaced? 🤖 💼 Everyone's asking if AI will replace accountants... Let me settle this once and for all. ➡️ WHAT WILL TRANSFORM ADVISORY SERVICES are becoming the heart of what we do. Gone are the days when accountants just crunch numbers. Now we guide strategic decisions using real data insights. Companies need advisors who understand both numbers AND business strategy. FORENSIC ACCOUNTING gets supercharged with advanced analytics. Finding fraud used to be like searching for a needle in a haystack... With AI-powered anomaly detection, we spot patterns humans would miss. The fraudsters are getting smarter, but so are our tools. AUDIT & RISK ASSESSMENT will never go away, but everything about it is changing. Instead of sampling transactions once a year, we're moving to continuous auditing with real-time data. AI review systems flag issues as they happen, not months later when it's too late. FINANCIAL ANALYSIS & FORECASTING is where accountants shine brightest. Sure, AI can run calculations, but humans bring context to numbers. Our forecasting is getting enhanced by predictive analytics and scenario modeling that processes variables faster than ever before. CLIENT COMMUNICATION is shifting completely. We're moving from transaction processors to trusted advisors. ➡️ WHAT WILL BE REPLACED Let's be honest... some parts of accounting are tedious and perfect for automation. MANUAL DATA ENTRY is already on its way out. AI-driven data capture and OCR tools process invoices and receipts in seconds, without the errors humans make after hours of monotonous work. ROUTINE BOOKKEEPING tasks are getting automated through cloud accounting software. Bank feeds, automatic categorization, and machine learning mean the days of manually reconciling every transaction are numbered. BASIC TAX PREPARATION for standard situations will be handled by smart platforms. E-filing tools get smarter every tax season. The complex tax strategy work? That's still all us. INVOICE MATCHING & RECONCILIATION is perfect for automation. AI bots can match thousands of invoices to purchase orders in minutes, with real-time reconciliation systems keeping everything in sync. COMPLIANCE MONITORING no longer needs accountants to manually check every rule. Automated alerts and built-in compliance checks flag issues instantly, letting us focus on solving problems rather than finding them. ➡️ THE FUTURE ACCOUNTANT The accountants who will thrive aren't fighting against technology... They're embracing it. The future belongs to those who combine technical accounting knowledge with: - Strategic thinking - Business acumen - Technology fluency - Communication skills === What parts of your accounting job do you think will change the most with AI? Which skills are you developing to stay ahead? Join the discussion in the comments below 👇

How we shrank 30-40 hours of weekly manual work into just 2-3 hours 🤯 (Automation Tip Tuesday 👇) This home services company was struggling with their invoice reconciliation process. They received numerous vendor invoices via email (PDF format) and needed to manually match them against jobs in ServiceTitan. Their team was stretched thin, discrepancies and overpaying were daily occurrences, and one day, they had enough. We worked on a three-step automated solution: Step 1: Finding the PDFs Zapier monitors the inbox for invoices. When it detects an invoice with a PDF attachment, it proceeds to Step 2. Step 2: Parsing the Data Nanonets uses AI to extract data from the PDF. Step 3: Data Comparison The extracted data is compared with jobs in ServiceTitan. Any discrepancies are added to a spreadsheet for internal review. 30-40 hours of weekly manual verification time is now just 2-3 hours. With instant discrepancy flagging, their system allows for better vendor management, improved billing accuracy, and more time for the team to pursue higher-value tasks. Which manual task that can be automated is currently taking up too much valuable time? If you’re thinking of one, it’s time we spoke. Book a free call (link in the comments 👇) and let’s see what we can do for your workflow. -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ These tips I share every Tuesday are drawn from real-world projects we've worked on with our clients at Flow Digital. We help businesses unlock the power of automation with customized solutions so they can run better, faster and smarter — and we can help you too! #automationtiptuesday  #automation #workflow

People have asked me what my AI productivity stack looks like. Here's a list of my 3 best AI tools that I use to scale myself, as a startup founder. 1) Zapier Zapier helps me automate workflows across different apps without coding. Put simply, you can eliminate a lot of mind-numbing, mundane tasks like data entry. I've used it to automate tasks like: (i) pinging users on WhatsApp after they've signed up on Peek, and (ii) updating call summaries into our CRM. They've made it possible to create these workflows by simply telling its AI zap creator what you want it to do. 2) Fireflies.ai As a founder, some days I have to be on calls for hours; with existing clients or onboarding new ones. But taking calls is the easy part. Generating summaries to keep track of what was discussed, and following up on actionable items is where it gets harder to keep up. Fireflies uses AI to help me transcribe, summarize, and follow up on calls with the help of Zapier. 3) Cursor (by Anysphere) I've previously written about how I build mini webapps to validate demand for a feature idea - before committing any engineering time and resources to build it into the product. Cursor is an AI code editor that helps me write code using natural language. So even as a non-technical founder, I can quickly build "minimally viable features" without having to distract my tech team. Personal finance is one area of our lives where AI can help you stay on top of otherwise very messy, and frustrating tasks. At Peek, we're trying to use AI to be your personal CFO. I'm always curious to know how you are using AI tools in your own work to supercharge your productivity. Let me know the best hacks you've discovered!

This one’s for Founders & Sales folks. I built an AI agent that cut my sales follow-up time by 90%. Not kidding. From 30 minutes per email... to 2 minutes. And I actually enjoy it now. Let me back up. I hate writing sales follow-ups. → Re-reading call notes → Trying to remember context → Spending hours wordsmithing Even with my system of organized ChatGPT folders with custom deal context, it still took forever. So I did what any founder would do. I built a tool. It sounds much harder than it actually was. I hadn’t built an AI agent before and it only took me 2 hours end to end. Here’s what I used and how it works. ⚙️ Built with: Relay.app (shoutout to Jacob Bank - love what you’re building!) Step 1: I trigger Relay to follow up with a particular deal in Hubspot. Step 2: Relay retrieves deal context from Hubspot (it’s made me much more diligent about making sure my data is up-to-date here) Step 3: Agent reviews the deal and decides if a follow-up is needed. It gives me the following output: Is a follow up required? Yes / No response What kind of follow-up is required? General check-in email, breakup email, nudge with resources (I provided these options for it to choose from). Why did it make this decision? This is really helpful because it gets me up to speed on the deal quickly—when did we last check in, what were their objections or concerns, when is the next expected touch point, and so on. Step 4: I approve or tweak. I tell the agent if it’s right or wrong, or provide context it may not have. Step 5: AI writes a draft email. The first draft hits me within ~20 seconds. I give high-level feedback (e.g., “focus more on timeline urgency”) if necessary. Step 6: AI revises the draft based on by input. At this stage I have an almost perfect draft. I make minor edits if at all and hit send. The whole process takes 2–3 minutes max. Are we all getting replaced by AI in 2 years? Probably. But for now, I’ve outsourced an annoying part of sales and it's amazing.

Here's exactly how I structure my follow-ups to stop deals from slipping or ghosting at the last minute. Buyers ask themselves 5 crucial questions before they spend money. So we match our follow ups to each different question of the buying journey. The questions: 1/ "Do we Have a Problem or Goal that we Urgently need help with?" Follow up examples: Thought Leadership emphasizing the size / importance of the problem. Things like articles from Forbes, McKinsey, HBR or an industry specific publication. Screenshots, summations or info-graphics. NOT LINKS. No one reads them. 2/ "What's out there to Solve the Problem? How do Vendors differ?" Follow up examples: Sample RFP templates with pre-filled criteria. Easy to read buying guides. Especially if written by a 3rd party. 3/ "What Exactly do we need this Solution to do? Who do we feel good about?" Follow up examples: 3 bullets of criteria your Buyers commonly use during evaluations (especially differentiators.) Here's example wording I've used at UserGems 💎: "Thought you might find it helpful to see how other companies have evaluated tools to track their past champions. Their criteria are usually: *Data quality & ROI potential *Security (SOC2 type 2 and GDPR) *How easy or hard is it to take action: set up/training, automation, playbooks Cheers!" 4/ "Is the Juice worth the Squeeze - both $$$ & Time?" Follow up examples: Screenshots of emails, texts or DMs from customers talking about easy set up. Love using ones like the Slack pictured here. Feels more organic and authentic than a marketing case study. 5/ "What's next? How will this get done?" Follow up examples: Visual timelines Introductions to the CSM/onboard team Custom/short videos from CSM leadership When we tailor our follow ups to answer the questions our Buyers are asking themselves - Even (especially!) the subconscious ones Our sales cycles can be smoother, faster and easier to forecast. Buyer Experience > Sales Stages What's your best advice for how to follow up? ps - If you liked this breakdown, join 6,000+ other sellers getting value from my newsletter. Details on my website!

To my CFO friends who have not yet started with AI - from a fellow CFO who has only dabbled in it. From my experience over the last few years, for companies greater than $10 million annual revenue, the app that provides a clear payback you can measure and is also relatively easy for your accounting clerk to adapt to is - AP Automation. With the vast majority of A/P invoices received by email, the app will optically scan the invoice for the key data, route it to an approver or verify against a PO, format it for posting, apply the correct G/L account number, and integrate to your ERP system for posting. AI comes in when you have a vendor invoice the system does not know what G/L account number to post to. You tell it which account and then going forward it remembers and "learns". After time, you will have as many as 90% of your emails coming into your email getting posting directly to your ERP system, freeing up tons of time your A/P person spends doing the same thing. People have asked me - which AP Automation tool is the best? Frankly there are a bunch of good ones out there and they are improving by the month. But your first filter should be the ability to integrate to your ERP system. If that is not part of it, you'll be missing out on a lot of time savings. That was a very rudimentary description as I've tried to simplify it for this post. Has anyone else implemented AP Automation or anyone else have something to add that I've missed?

AI is so freaking cool y'all! We're 9 days into 2024 and my newly built AI-automations have already both saved me a ton of time and improved my client onboarding experience. Here’s a peek into how I've used AI to improve my most popular AI marketing team training package in 2024, for both me and my clients: ⬅️In 2023, these trainings began with a one-hour call with my client to identify the most helpful AI use cases for their team, followed by 30-45 minutes of planning and email drafting on my end. Although effective, this process was time-consuming. While I loved the conversations, figuring out the necessary assets I needed for personalizing each team's training was my least favorite part of my work. ➡️This year, I've leveraged AI to transform my approach so I can focus on what matters most: delivering an impactful AI skills training for my clients' teams! Here's how I've used AI automation to enhance the experience for EVERYBODY: 1️⃣ First I analyzed all of my 2023 trainings and feedback to create a brand new 'AI use case menu' of 29 popular and highly impactful options. Now, my busy marketing leader clients select their use case preferences through a streamlined onboarding form, that also gathers the other information I'll need to personalize their training. This eliminates the need for a logistics meetings and is quick for clients to complete, saving us both time. 2️⃣ The onboarding form links to Zapier, triggering a cascade of automated actions when it's submitted. The responses go to the OpenAI API, where GPT-4 meticulously matches them to the necessary items for personalizing a perfect training for my client. Once completed, Zapier emails me the exact details I need for each client's personalized training. I then perform a crucial human-in-the-loop review before sending the personalization needs list to my client. ⏱️ Time Saved, Value Added: This isn't just about cutting down my workload. It's a dual victory. My clients now breeze through onboarding, investing mere minutes where they once needed an hour. Meanwhile, I have taken 1.5 hrs of my own work down to only 5 minutes. 🌊The ripple effect? My clients now enjoy a richer, more focused engagement. The time previously reserved for preliminary discussions is now rechanneled into a valuable 30-minute post-training debrief session, offering marketing leaders space to ask any questions they may have around supporting their teams' AI adoption. 🧠Implementing this system wasn't instantaneous. It took several hours of work gathering and creating the data necessary, considering the automation steps, creating and testing detailed prompts for GPT-4, ensuring consistent, accurate outputs, and guaranteeing a positive client experience. But now, having seen it in real-world action, I am immensely grateful for the time and energy invested in building this system (which is just one of many I've developed to streamline my efficiency in 2024)!

If you think regular customer updates aren't crucial, think again. Timely communication is the backbone of customer retention. Using automation tools like HubSpot, we ensure every customer feels valued and informed. Here’s what makes it truly effective: 1/ Segment your audience: We use data to segment our customers based on behaviors, preferences, and past interactions, allowing for more targeted communications that truly resonate. 2/ Automated triggers: Our system creates triggers based on customer actions—or inactions. For instance, if a customer hasn't interacted with our emails for a while, we initiate a re-engagement campaign automatically. 3/ Drip campaigns: We've set up drip email campaigns that send messages at just the right times or in response to specific user actions, keeping our communication flow consistent and reducing team workload. 4/ Regular monitoring: Automation isn't a set-it-and-forget-it tool. We continuously monitor and optimize our automated campaigns to improve engagement and conversion rates based on performance analytics. 5/ Feedback mechanisms: We automatically send surveys and feedback forms at different stages of the customer journey, making our customers feel heard and helping us quickly identify and act on areas for improvement. This strategy not only saves time but also enhances the overall customer experience, leading to higher satisfaction and loyalty. #hubspot #customers #engagement #automation

Last month, I had a call with a CEO who was about to make a $50,000 mistake. He wanted to hire a new employee to handle their growing client onboarding process. "We're drowning, each new client takes 40+ hours to get set up properly." I asked him one simple question: "Can you walk me through your current process?" What followed was painful to hear: → Manual contract creation (2 hours per client) → Back-and-forth email chains for signatures (5+ days) → Manually setting up 12 different software accounts (3 hours) → Creating folder structures in 4 different platforms (1 hour) → Scheduling multiple onboarding calls (30+ minutes of coordination) The most insane part: his team was re-entering the same client information into 7 different systems. The same exact information seven times. Instead of hiring a new person at $50K, we built a simple automation system in 2 weeks: ✅ Smart intake form that captures everything once ✅ Auto-generates contracts with client data ✅ Triggers signature requests automatically ✅ Creates all software accounts simultaneously ✅ Sets up folder structures across all platforms ✅ Schedules onboarding calls based on client preferences Onboarding time dropped from 40+ hours to 2 hours. Client satisfaction increased (they loved the smooth process). His team could focus on actual value-add work instead of data entry. Total cost: $8,000 Annual savings: $50,000+ Before you hire more people, ask yourself: "Are we solving the right problem?" Sometimes the answer isn't more hands. It's smarter systems. Follow me Luke Pierce for more content on automations, AI, and scaling systems that actually work.

Workflow v.s. AI Agents II: Get the Best of Both Worlds In my last post, I unpacked the differences between workflow systems and agentic systems and showed how both have propelled contact‑center AI forward. Each comes with clear pros, cons, and use‑case sweet spots. Today, I want to describe two patterns I’m seeing in real‑world deployments that capture the best of both worlds. 1. Workflow as a Tool to AI Agents Think of refund or authentication flows: you need them to be reliable, precise, and deterministic, no imagination, no exceptions. The right approach is to wrap each of those flows in code and let the LLM call it only when the conversation reaches the correct step. It’s the same strategy an LLM uses when it calls a calculator. The model handles natural language, then hands off to deterministic code. Because these calls rarely exist in isolation, you also maintain a lightweight global‑state store, e.g. customer ID, authentication status (e.g. failed codeword, 2nd attempt, need last 4 digits of SSN) , open‑case number, refund amount, and so on. Both the agent and the workflow read from and write to that state, so every turn starts on the same page. 2. Agentic System as a Fallback‑and‑Healing Layer Rule‑based workflows dominate high‑volume, repetitive back‑office tasks.  An invoice‑processing pipeline is a classic example, because cost and reliability matter more than creativity. The problem is that even the most battle‑hardened workflow eventually hits an edge case: an OCR misreads a field, a vendor changes a PDF layout, or a UI update moves a button or turns one text field into a drop down box. When that happens, route the exception to an LLM‑powered agent. The workflow raises a “can’t‑proceed” flag and passes the partial context. The agent reasons through the anomaly: asks a clarifying question, consults a knowledge base, rewrites the input, or tries to process the updated UI with an vLLM action model. The agent writes the corrected data back to the global state, then nudges the original workflow to resume. In effect, the deterministic layer handles the 95 % happy path, while the agentic layer patches the 5 % that rule‑based code can’t anticipate, and every successful patch becomes new training data for further hardening. In my next post, I will talk about test case management, evaluation to achieve determinism over underlying probabilistic models.