“Can you share one specific AI implementation that significantly improved your business operations? How did it impact your efficiency, and what advice would you give to companies considering similar AI integration?”
Here is what 10 thought leaders had to say.
AI Mockups Cut Proposal Time by 70%
One specific AI implementation that significantly improved our business operations was using AI-generated client proposal mockups for digital out-of-home (DOOH) campaigns.
At our agency, we own a 3D digital billboard in Warsaw. Pitching campaigns to brands used to require our design team to manually create realistic mockups showing how a client’s ad would appear on our screen. This took 2-3 hours per concept, limiting how many custom proposals we could send and slowing down our sales cycle.
The AI-Powered Shift
We now use a combination of:
ChatGPT to write creative concepts and generate detailed prompts
Midjourney and DALL*E to produce high-quality visuals of the 3D billboard with the client’s brand
Photoshop/GIMP to make small final adjustments and polish the layout
We can now generate a polished, professional pitch visual in under 20 minutes—down from hours. That means we can pitch 4-5x more often without additional team resources.
The Impact
60-70% time savings per proposal
Closed more deals by showing clients instant visuals of their brand on our screen
Enabled our team to focus on strategy, not just production
Helped smaller clients “see the vision,” increasing close rate on cold leads
Maksym Zakharko, CMO, maksymzakharko.com
AI-Driven CRM Transforms Deal Flow Management
We implemented an AI-driven CRM and pipeline scoring tool that fundamentally changed how we manage deal flow and client interactions. Before that, I’d spend hours manually reviewing founder decks, email threads, and investor feedback. It worked, but it was far from scalable. The AI tool now tracks engagement signals, predicts investor interest levels, and nudges us when a conversation is going cold. What used to be instinct-driven is now data-backed, freeing up bandwidth for more strategic work—especially during crunch times like live fundraising rounds.
One time, we were juggling four startups prepping for investor outreach. Normally, follow-up would’ve slipped somewhere, but the system flagged dormant leads and helped us prioritize effectively. That alone saved us from potentially losing two warm investor conversations. If you’re considering AI, start by pinpointing areas that drain your time but don’t require deep human judgment. Don’t overcomplicate it from the start. Integration should feel like an assistant, not a full-time job to manage. And test rigorously—what works in theory might need serious tweaking to fit your workflow.
Niclas Schlopsna, Managing Consultant and CEO, spectup
Predictive Analytics Cuts Retail Stockouts by 34%
One of the most impactful AI implementations I have overseen was the integration of predictive analytics into a global retailer’s e-commerce operations. The business faced a familiar challenge: extensive product catalog, inconsistent inventory turnover, and frequent overstock or out-of-stock situations. Rather than relying on intuition or basic reporting, we deployed an AI-driven forecasting engine that analyzed historical sales, seasonality, promotional calendars, and external factors like weather patterns.
The practical result was a shift from reactive inventory management to a proactive model. The AI system provided clear, actionable forecasts at the SKU and regional level. Inventory planning meetings stopped revolving around guesswork and shifted to scenario planning based on model outputs. Within six months, the company reduced stockouts by 34 percent and cut excess inventory by over 20 percent. Cash flow improved, but just as important, the team was able to refocus: instead of wrestling with spreadsheets, they spent more time on supplier negotiations and campaign development.
From a leadership standpoint, the greatest impact was operational clarity. AI did not replace decision-makers; it equipped them with a sharper lens to assess risk and opportunity. Efficiency gains followed not only from automation, but from freeing up talent for higher-value work.
For companies considering similar AI integration, my advice is direct: do not underestimate the groundwork. AI delivers value only when your data is reliable and your teams are engaged with the process. Start with a tightly defined business problem, ensure cross-functional alignment, and keep expectations grounded in tangible outcomes. In my consulting experience, the best results come when AI is treated not as a magic fix, but as a tool for disciplined, data-informed decision making. The organizations that succeed are those that balance technology investment with the operational discipline to act on what the models reveal.
Eugene Mischenko, President, E-Commerce & Digital Marketing Association
AI Matchmaking Slashes Partner Search by 94%
Our most transformative AI implementation has been our proprietary matchmaking algorithm that connects eCommerce businesses with ideal 3PL partners from our network of 600+ vetted providers.
When I launched Fulfill.com, finding the right fulfillment partner typically took businesses over a month of research, RFPs, and evaluation. Our AI solution now completes this process in just 48 hours with a 90% success rate – nearly double the industry average.
The technology initially focused purely on quantitative factors: order volumes, SKU counts, and geographic requirements. But we quickly learned that successful partnerships depend equally on qualitative elements like company culture and communication styles. This realization transformed our approach from a standard matching algorithm into what I call a “tech-enabled relationship builder.”
Our AI now processes thousands of data points across both dimensions, continuously learning from each successful match to refine future recommendations. The efficiency gains have been remarkable – we’ve reduced partner search time by 94% while dramatically improving match quality.
For companies considering similar AI integration, my advice is twofold: First, recognize that AI works best when enhancing human expertise rather than replacing it. Our most successful implementation combines algorithmic precision with our team’s industry knowledge and relationship skills.
Second, be prepared to evolve your approach. We initially overvalued the technical aspects and undervalued the human elements. The willingness to adapt our algorithm based on real-world feedback ultimately created our competitive advantage. The best AI implementations are never static – they continuously learn and improve from each interaction.
Joe Spisak, CEO, Fulfill.com
AI Scheduling Boosts Daily Service Calls
When we rolled out an AI-driven scheduling tool last winter, it was the first time I’d seen our route planning shift from guesswork to data-driven precision. Instead of manually plotting calls based on ZIP codes and gut instinct, the system analyzed historical travel times, appointment durations, and even local traffic patterns to assign jobs dynamically. Almost overnight, our technicians were spending 18% less time behind the wheel, which freed up capacity for an extra service call every day without adding staff.
The boost in efficiency was clear: our average calls per truck climbed from 5.2 to 6.1 daily, and we shaved nearly 90 minutes off weekly overtime per technician. My advice for companies eyeing similar AI integrations is to start with clean, reliable data, and involve your field team from day one. Let them test the tool on a handful of routes, gather their feedback, and iterate before a full rollout. When your people trust the AI as much as the leadership does, adoption goes smoothly and you’ll see results faster.
Joel Miller, President, Miller Pest & Termite
AI Uncovers Hidden Marketing Campaign Insights
Something that we’ve used AI to help with is campaign success analysis. After we run a marketing campaign of any kind, we always want to analyze it to see how well it performed as well as any specific insights we can derive from it. AI has been helpful in deriving unique, valuable insights alongside the insights we discover on our own. It helps us see the full picture and identify any findings that might have slipped under the radar on our end.
Edward Tian, CEO, GPTZero
NLP Tools Triple Grant Writing Capacity
The most transformative AI implementation I’ve integrated into my grant writing practice is using natural language processing tools to analyze successful grant proposals and identify winning narrative patterns. This AI system processes thousands of funded proposals to extract key phrases, structural elements, and persuasive language that consistently resonate with specific funders.
The efficiency impact has been remarkable—what used to take me 40 hours of manual research and proposal drafting now takes 12 hours, allowing me to serve three times as many nonprofit clients while maintaining quality. My advice for organizations considering AI integration: start with clearly defined, repetitive tasks that have measurable outcomes, not creative strategy work.
The AI excels at pattern recognition and data synthesis, but human insight remains essential for relationship building and mission alignment. I’ve helped clients secure over $8 million in additional funding since implementing this system because we can now customize proposals with unprecedented precision. The key is viewing AI as an amplifier of human expertise, not a replacement for authentic storytelling and genuine community impact. That’s how impactful grants fuel mission success.
Wayne Lowry, CEO, Scale By SEO
ChatGPT Halves Content Production Timeline
A specific game-changer for us was integrating ChatGPT into our content production pipeline. We used to brief writers, wait 2-3 days for a draft, then go through rounds of edits. Now, we generate first drafts with AI, tailored using prompt templates tied to each client’s tone and SEO targets. Our editors fine-tune from there.
This shift slashed our content creation timeline by nearly 50%. It also allowed us to take on more projects without adding headcount—directly impacting revenue per staff hour.
The big lesson: don’t chase flashy tools. We started small—one format, one team. Then we refined prompts, built internal templates, and layered in brand voice guides. That structure made scaling smoother.
Focus on real friction points. If AI can take the first 70% of a task off your plate, it’s probably worth testing. Just don’t skip the human review—speed is only valuable when paired with accuracy.
Eugene Leow Zhao Wei, Director, Marketing Agency Singapore
AI Media Monitoring Delivers Strategic PR Snapshots
When our PR team was drowning in daily news alerts, I piloted an AI-driven media monitoring platform that used natural-language processing to automatically tag, summarize, and score every mention of our brand and key competitors. Instead of spending four hours each morning combing through dozens of alerts, the system delivered a single, one-page “PR Snapshot” by 8 a.m., complete with sentiment breakdowns and priority flags for any potential crises. I spent the first week tweaking its keyword filters and teaching it our internal scoring rubric, but by week two it was nailing 95% of what I’d have manually highlighted—and freeing up my mornings for strategy work instead of busywork.
Seeing that time savings, I’ve learned that clear objectives and human training are critical for any AI rollout. My advice: start with the one task your team hates most, define measurable success criteria (like “reduce alert-triage time by 50%”), and invest time upfront teaching the AI your vocabulary and priorities. Always keep a human in the loop for the first month to validate its outputs, then expand its scope once you trust its accuracy. That formula turned a headache-filled chore into a daily strategic briefing—and it’s a repeatable model for any business looking to plug AI into its workflows.
Tony Ragan, President, Absolute Pest Management
AI Matching Cuts Grant Research Time 70%
We implemented AI-powered grant opportunity matching at ERI Grants, transforming how we identify funding prospects for nonprofit clients. This system analyzes foundation priorities, past giving patterns, and proposal requirements, reducing research time by 70% while increasing match quality dramatically. The efficiency gain allowed us to focus more energy on crafting compelling narratives rather than endless database searches. My advice: start small with one specific process, measure results rigorously, and ensure your team understands the ‘why’ behind the change. AI works best when it amplifies human expertise rather than replacing it—think of it as your research assistant, not your replacement. The key is maintaining that personal touch funders expect while leveraging technology to identify the perfect partnership opportunities. That’s how impactful grants fuel mission success.
Ydette Macaraeg, Part-time Marketing Coordinator, ERI Grants
