One creative and highly effective way we leveraged automation was for an auto dealership in Poland, where we built a custom workflow that connected Facebook Lead Ads directly to Google Sheets via n8n—an open-source automation platform. This eliminated manual lead processing and created a near real-time response system that significantly improved sales follow-up and conversions. The Problem The dealership was running Facebook Lead Ads to promote used cars, but the leads were landing in Facebook's native interface. Sales reps had to manually download them, often with delays of several hours—or worse, days. By the time they followed up, many leads had already moved on or bought elsewhere. The Solution: Automated Lead Flow We set up a workflow using: Facebook Lead Ads to capture user data (name, phone number, car interest) n8n to act as the integration hub Google Sheets as the central lead tracker, updated in real time Whenever a new lead came in, n8n automatically: Pulled the lead data from Facebook Ads Cleaned and standardized the fields Pushed the data to a Google Sheet shared with the sales team Triggered an email + SMS notification to the assigned rep Created a follow-up task in their CRM if integrated What Made This Unique Instead of relying on paid tools like Zapier (which can get expensive at scale), we used n8n as a fully self-hosted, cost-effective automation engine tailored to the client's infrastructure. We also added logic to: Assign leads based on the car brand selected Flag duplicate entries Automatically add UTM parameters to track ad performance by car model The Results Response time dropped from hours to under 5 minutes Lead-to-sale conversion increased by 22% in the first month Sales reps became more efficient, focusing only on fresh, high-intent prospects The dealership had better visibility on ad performance and lead quality Takeaway: This approach shows that automation isn't just about saving time—it's about creating a seamless experience between marketing and sales. By customizing the flow with n8n and connecting it to tools the team already used (Google Sheets), we built something scalable, cost-efficient, and incredibly effective.

We developed a "competitor intelligence automation" that monitors our clients' competitors for content gaps and business changes, then automatically generates personalized opportunity alerts with ready-to-execute marketing strategies. This system goes beyond basic social media monitoring by analyzing competitor website updates, job postings, press releases, and content publishing patterns to identify strategic opportunities our clients can exploit. The automation tracks 15-20 competitors for each client, using web scraping tools and AI analysis to detect significant changes like new product launches, leadership departures, pricing adjustments, or content strategy shifts. When it identifies opportunities; like a competitor removing a key service page or posting job openings that signal strategic pivots: the system automatically generates a detailed brief explaining the opportunity and suggesting 3-5 specific marketing tactics our client could implement within 48 hours. One client gained 23% market share in their local market after we detected their main competitor's website went down for maintenance, triggering an automated alert that led to a targeted PPC campaign capturing their competitor's search traffic. This approach is unconventional because most businesses only monitor competitors reactively, missing time-sensitive opportunities that require immediate action. Our automation creates competitive advantages by identifying windows of opportunity and providing actionable intelligence while competitors are vulnerable. The system generated $180k in additional revenue for clients last year by spotting strategic openings that would have been missed through manual monitoring. Automating competitive intelligence transforms it from periodic research into continuous strategic advantage.

Most people think automation means set it and forget it. That's dead wrong. One unconventional move we made at Martal was combining AI-driven data analysis with human intuition in real time. Our proprietary sales platform is informed by extensive data from millions of sales emails, buyer intent signals, and years of campaign insights. It helps our reps craft outreach and tailored messaging across email, LinkedIn, and calls, all dialed in for the ICP and buyer persona. Not a generic copy, but messaging tuned for each company's ICP and buyer persona. Every SDR on our team uses AI daily, for prospecting, outreach, and follow-ups. But AI doesn't replace strategy. Our reps review and refine every output. While AI provides scale, humans provide context and judgment. What makes it unique is that it's automation with a brain, driven by reps who know how to read the room and make the right moves. Pro tip. Automation that doesn't work with your people is just noise. Build systems that amplify your team's skill, not replace it. That's how you win.

One unconventional way we've seen automation unlock major value is through enabling our customers to fully automate the management of their cloud infrastructure — not just provisioning, but everything that happens after deployment. Traditionally, Infrastructure as Code (IaC) tools like Terraform are used to define cloud environments, but once deployed, those environments tend to drift. Manual changes, legacy resources, and compliance blind spots build up quietly over time — especially at scale. What we've helped organizations implement is a continuous automation layer that keeps their cloud and code aligned, remediates drift, enforces guardrails, and ensures infrastructure stays compliant without slowing developers down. What makes this unique isn't just the technical challenge — it's the shift in mindset. Automation isn't just about moving faster; it's about creating a system that can sustain scale, avoid configuration sprawl, and reduce the operational burden on DevOps teams. We're seeing more companies recognize that post-deployment infrastructure control is where real risk and complexity live — and automation is the only scalable answer. This is becoming the new normal: IaC isn't a one-time activity anymore, it's a lifecycle that needs continuous oversight, delivered automatically. Looking ahead, we believe the future of cloud automation lies in closing the loop between code, cloud, and compliance — with minimal human intervention. That's what's letting our customers move faster, with confidence.

We let an AI agent take the first swing at recruiting and it worked. Instead of wading through every resume, we set up SmythOS to parse applications, and grade them against key skills or keywords. The automation process even includes drafting personalized emails to the most promising candidates. Most people reserve automation for back-office busywork but here, it kicked off a deeply human process. The process paid off! We had faster access to high-fit candidates, quicker engagement, and way more freed-up hours for the hiring team. The best part about using AI in your hiring process is that it's time-saving. Just give it clear criteria and let AI handle the sift without sacrificing the personal touch needed later in the process.

Automation is synonymous with time-saving for everyone. We used it to save emotional energy. Homeschooling parents can be overwhelmed, so we built an AI-powered onboarding flow that not only handles logistics, but also walks families through the first two weeks with personalized messaging, milestone check-ins, and nudges written in a warm, human tone. It didn't so much feel like automation as having a very sympathetic, very orderly friend in your pocket. The result? A 40% decrease in early-stage support tickets and a quantifiable improvement in student retention. The unconventional twist? We developed the system backward—from feeling to function. We started by mapping how a parent would feel at every stage along the journey—confused, hopeful, anxious—and only then did we write what the system ought to do. Automation should not just make business easier. It should make people feel understood. That's where technology becomes revolutionary.

We use an auto-summary engine for long discovery calls, trained to highlight pain points and desired outcomes. It plugs directly into our Notion system and adds structured notes under each client. Our strategist can then jump in and build a pitch based on that synthesis. It sped up our proposal process from three days to a few hours. The standout here was cutting down context gathering, not cutting out the conversation. We kept the human connection but automated the data capture. This let our team move fast without losing nuance. Clients felt seen, not sold to, and we closed more work.

As the CEO of Tenet, a product development and growth marketing company, I automated client reporting using "predictive narratives" that generate written explanations of metric movements. Instead of static dashboards, our system identifies why performance changed. When conversion rates drop 15%, it automatically analyzes traffic source changes, seasonal patterns, or technical issues, providing context-rich explanations. One fintech client saved 12 hours weekly previously spent interpreting data, while our client retention increased 60% because they understood performance drivers without explanation calls. The unique approach: combining data analysis with natural language generation. Most agencies automate data collection; we automated data interpretation. Clients receive actionable insights, not just numbers, making them feel smarter about their marketing investment and significantly less likely to churn to competitors offering similar services.

One creative way we used automation was by setting up an hourly "live report" for one of our biggest clients, showing the current run rate for daily and monthly revenue, along with insights like top-performing products. What makes it unique is that it runs so frequently it should be annoying—but instead, it became the heartbeat of the company. If the report doesn't go out, or sales is low in one interval, I often get messages like "Is the site down?" It keeps everyone involved, boosts morale on good days, and serves as a subtle motivator on slower ones. It also creates real-time transparency around which campaigns or products are driving results.

One unconventional way we leveraged automation was by creating automated, personalized follow-ups for candidates based on how they performed in assessments—not just generic "thank you" emails. Instead of sending a single response to every applicant, we built logic that sent different follow-ups based on their scores, skill gaps, and even response behavior (like time taken or incomplete tests). Top scorers would get a fast-tracked scheduling link. Mid-level candidates received upskilling resources. Those who didn't complete the test got a gentle nudge and a flexible retake window. What made it unique was how we used automation to keep the human touch—something most companies lose. The result? Higher completion rates, better candidate experience, and a 22% reduction in time-to-hire. Automation isn't just about speed—it's about being smart with how you scale personalization.

One unconventional way we leveraged automation at CoSupport AI was by using our own AI platform internally. Most companies deploy AI just for customer-facing support, but we turned CoSupport into an internal AI assistant across our sales, product, and customer success teams. By training it on our internal docs, case studies, product specs, and workflows, we gave our team instant access to everything they need, directly in Slack. No more digging through knowledge bases or pinging team leads. What made this approach unique is that we didn't just automate support for customers, we automated support for ourselves. The result? Faster onboarding, smoother sales calls, and a more aligned, high-performing team.

One of the most effective and, at the same time, non-trivial automation solutions we've implemented at DreamX is the automation of management and analytical processes - particularly within the sales department. Previously, a lot of time was spent manually consolidating data, analyzing team performance, and preparing weekly and monthly reports. Now, all of this is automated: data from our CRM and financial spreadsheets is automatically collected into a single analytics environment. This allows us to see, in real time, what's happening with leads, the cost of acquisition, and more. This solution has become not just an optimization, but a tool for strategic management. Thanks to this automation, we respond to changes faster, adjust our tactics, and stay focused on growth.

At Watsi.org, we didn't just adopt automation—we built an entirely new model around it. Traditional nonprofits often separate engineering from core programs. We did the opposite. We invested early in in-house engineering, keeping our developers close to the problem: how to fund healthcare for people around the world, transparently and efficiently. We created a custom platform that allows our medical partners—across 34 care centers in 8 countries—to securely submit patient stories, track funding progress, and report on outcomes. We've automated everything from intake to updates, layered in real-time dashboards using Metabase, and minimized manual data entry through structured templates and training. That's allowed us to mobilize over $18M to fund care for 30,000+ patients globally—at a scale and speed most nonprofits can't reach. In 2025, we're building on this foundation with AI tools that help us spot trends, speed up internal workflows, and keep improving the donor experience on our website. Automation wasn't an add-on for us but the engine that made our entire model work. And that's what's made it stick and sustainable.

For anyone in the trading space, the biggest fear is being blindsided. You can have a perfect strategy, but if your broker or prop firm suddenly changes its conditions, experiences server issues, or tightens payout rules, you're the one who pays the price. The information available is almost always reactive—by the time you read a bad review, the damage is done. Our most game-changing automation was built to solve this exact problem. We developed a proprietary suite of AI and automation tools we call the "Broker Risk Scanner." It's not just a data scraper; it's a 24/7 sentinel that constantly monitors the digital ecosystem of brokers. The system pulls in dozens of data streams around the clock—we're talking real-time spread fluctuations, server latency from various global nodes, subtle changes to the text on a firm's Terms of Service page, and even sentiment analysis from niche trading forums and social media chatter. But the real magic isn't just in the 'what' we collect, its the 'how' we use it. Our AI isn't programmed to just report the data. It's trained to recognize patterns and precursors to problems. For instance, it won't just tell us "Broker X had high slippage today." It will alert us that "Broker X's server ping times in London have been degrading by 8% over the last 48 hours," which is a leading indicator of future slippage and execution problems. Or it might flag that a prop firm has quietly removed the phrase "no restrictions on EAs" from its FAQ page. What made this approach unique? This automation isn't about replacing human analysis; it's about arming our team with intelligence that's impossible to gather manually. Our analysts get alerts on their dashboards that are essentially early warnings, allowing us to advise our community and adjust our own strategies before a problem becomes common knowledge. The results have been profound. It has become our single greatest competitive advantage and, more importantly, the biggest source of trust with our user base. They know our recommendations aren't based on last month's news, but on what's likely to happen next week.

For my ecommerce store, I automated how I optimized my product pages, category pages and blog pages for SEO. At first I used ChatGPT prompts to create SEO-focused product and category pages, but quickly realised that it was not the most efficient use of my time (as I had to copy and paste back and forth), especially with a Shopify store which had no direct integration with ChatGPT. I then found a Shopify integrated tool in the app store (Plug In AI) that helped me populate the relevant content inside my Shopify store. This approach was unique because it's slightly different from how other ecommerce store owners might approach AI-optimized store content. It bypasses the need to copy and paste and further smoothens out the workflow between generating prompts for my store and actually implementing the results.

When most people think of automation, they think of speed. I think of scale and soul. I led one of the first education companies in Australia to go fully paperless and automated—long before it was trendy, or even common. At the time, the VET sector was buried in binders, whiteboards, and manual admin processes. We knew we had a big vision: to build fast, and to build differently. What made our approach unique wasn't just the tech—it was why we used it. We weren't automating for efficiency alone; we were automating for impact. Within 24 months, we built a $30 million business in a highly regulated industry—not by undercutting the competition, but by collaborating with our five biggest competitors. That alone was disruptive. But automation was the critical piece that made it all work. Here's where it got unconventional: instead of using automation to cut costs or replace people, we used it to elevate our people. We automated every repetitive, manual process—enrolments, compliance, data validation, assessment submission, reminders. This freed up dozens of staff who would've been stuck chasing paperwork. Instead of letting them go, we retrained them as customer engagement officers—a move that defied industry logic. And it worked. Not only could we handle the massive surge in enrolments (thanks to our competitor-collaboration model), but we also did what most thought impossible: raised our student completion rate from the industry average of 3% to over 85%. Automation didn't replace connection. It created it. We gave students human support, fast service, and clear pathways—powered behind the scenes by smart systems. That's what made it different. In hindsight, automation was our quiet co-founder. It allowed us to scale with integrity, lead the sector into the digital age, and most importantly—turn what's normally an isolated online experience into a human-centred one. That's not just tech. That's transformation.

I've spent 30+ years implementing CRM systems, and one of our most unconventional automation wins came from reversing the typical data flow. Instead of just pushing data from CRM to email marketing, we automated the *reverse* - automatically creating CRM records from email engagement patterns. Here's what we built: when someone clicks multiple links in our newsletters or downloads resources, our system automatically creates opportunity records and assigns them to our sales team with pre-populated context about their interests. Most companies just segment email lists, but we used email behavior to trigger actual sales processes. The twist was automating the qualification itself - the system scores engagement patterns and only creates opportunities when someone hits specific behavioral thresholds. We're not just capturing leads; we're identifying sales-ready prospects automatically. This eliminated about 70% of our manual lead qualification work. The results were immediate - our conversion rate from email to actual sales meetings jumped dramatically because we were only pursuing genuinely interested prospects. One client told us they'd never seen such targeted follow-up, and we've closed over $2M in projects that started from automated email behavior triggers.

I've been helping blue-collar service businesses for years, and most automation focuses on the obvious stuff—scheduling, invoicing, basic follow-ups. The most unconventional automation I implemented was for a water damage restoration company called Bone Dry Services in Denver. Instead of automating customer communications, we automated their *lead intelligence gathering*. When someone calls about water damage, they're usually panicked and don't give complete information. We built a system that automatically cross-references the caller's address with property records, insurance databases, and even local weather data from the past 48 hours to predict job complexity before the technician arrives. The system generates a "job profile" within minutes—estimated square footage affected, likely insurance coverage, equipment needed, and even suggests pricing based on similar properties. This helped them prepare the right equipment on the first visit and quote more accurately. They went from generating unpredictable referral-based revenue to $500K in tracked pipeline within three months. What made this unique was automating the *thinking* behind the service, not just the scheduling. Most companies automate the paperwork; we automated the intelligence gathering that happens before the real work begins.

I automated our 3D rendering pipeline after watching my team manually recreate the same lighting setups and camera angles for every product launch. We built a system that automatically generates multiple render variations from a single 3D model - different angles, lighting conditions, and backgrounds - all triggered by uploading the base file. The unconventional part was programming it to create marketing assets for different customer personas simultaneously. For our Robosen Optimus Prime launch, one upload generated tech-focused renders with detailed specifications for adult collectors, plus action-packed hero shots for younger audiences, and clean product shots for retail partners. This cut our visual asset production time from 3 weeks to 2 days per product launch. The Optimus Prime campaign alone generated over 300 million impressions partly because we could flood every channel with perfectly optimized visuals immediately after product announcements. The automation didn't replace creativity - it amplified it by letting our designers focus on concept development instead of repetitive technical execution.

I automated our donor wall updates by creating a "gratitude cascade" system that triggers personalized thank-you sequences based on donation patterns. Instead of just sending standard receipts, our system automatically generates custom video messages featuring real student beneficiaries when donors hit certain thresholds or anniversaries. The unconventional part was connecting it to our interactive displays - when donors visit campus, the touchscreen recognizes their approach (via QR codes we sent them) and displays their personal impact story in real-time. We also automated quarterly "impact reports" that show exactly which students benefited from their specific contribution, complete with photos and updates. This eliminated 15 hours of manual thank-you work weekly while increasing our donor retention rate by 25%. The automation freed up our team to focus on relationship building instead of administrative tasks, and donors started bringing friends to campus just to show off "their" display. The revenue impact was immediate - we saw a 20% jump in annual giving because donors felt personally connected to outcomes rather than just sending money into a void. The key was making the automation feel more personal, not less.