At ThirdSpace BUZZ we're using AI to assist in a slew of creative tasks via generative AI interfaces and different prompts that have been built to achieve expected results. We use it in our strategy sessions as well as our creative process to rapidly ideate, draft ideas, and construction newsletters and content that drive value. We've been able to generate revenue for projects, gain awareness about the brand, and even solicit RFPs for our services with the assistance of AI.
While I cannot share specific client solutions without their consent, I can share how AI is driving value and increasing profit margin at Miami Cyber. A recent use case I leverage is accelerating go-to-market operations and sales velocity. Traditionally, my sales cycle consisted of an introduction meeting (30 mins), discovery (1-2 hours), and pitch/review (90 minutes). This meant a prospect going through an entire sales cycle would consume about 4 hours of labor. I found that this discovery was still not deep enough, and the time to perform due diligence varied widely. I created a 20-question quiz that takes the prospect about 10 minutes to complete and gathers a strong understanding of maturity across 5 domains that I find important to the forward adoption of AI and emerging technology in their business. This quiz has a backend value matrix that generates a maturity score across the 5 domains and an overall assessment for the organization. This data is sent via the quiz platform to a node editor where an agent trained in interpreting the data ingests the unique disposition of the company that has completed the quiz. The agent is engineered to perform research, assess organizational readiness, and provide a structured output that is then adopted into a vibe-coded template we created. Within minutes of completing the quiz, our team and the company that submitted the quiz receive a customized readiness report for their unique disposition, showing deep due diligence at a rapid pace—something that can only be done by AI—accelerating a sales cycle discovery process that would have taken hours down to minutes. Each quiz taken is estimated to save the company upwards of $1,000 per prospect in pre-sales discovery and engineering.
I was probably one of the earliest adopters of AI mainly because I've always been entrepreneurial especially around technology-powered solutions. So in the past, each time I wanted to build a tech business, I had to spend expensively to get a developer who would usually fail at translating my vision the way I saw it. And when I brought in a co-founder, my input/value was usually devalued because I was the non-technical one! AI gave me a new lease of life! Now I build and exercise my creativity at a 100%! My latest/current business is a Registered Nurse-only Job Aggregator platform called IntelliResume Health. I've built the entire thing from the first line with AI in less than a month! AI has enabled me to solve problems and remain lean. I'd be excited to share how non-technical founders who love to have 100% control in their solutions can easily leverage AI to scale their solutions.
How Small Companies Are Profiting From AI: An AI Coach's Perspective I am an AI coach. I can see one clear thing. The companies that get good results are not always the biggest or the ones with a lot of money. They get success because they make smart choices. They work with clear plans and stay focused. The Success Pattern Foundation First: You need to start with your data before you use AI. Make sure the data is clean and that you can trust it. Choose one thing to focus on. It should be something that will have a big effect. Pilot Small: Start with one or two things that feel hard. Make clear goals that you can see. Bring your team in early. AI gives people more power. It does not take their place. Scale Strategically: First, let people see what you get back from what you put in. Then, start to grow. Build the skills of your team. Help them learn more. Do not only trust vendors. Critical Lessons What Works: To get good results, use AI with the tools you already have. Focus on real outcomes, and not on unclear goals. Biggest Pitfalls: Not all companies that say they use AI will give you what you need. You should ask for prices that match how much you use the service. A lot of businesses learn that the cost can get much higher as time goes on. It can also be tough to get people who know a lot about AI. So, rather than hiring more people, work with coaches who can help you. My Core Advice Some businesses do not move forward because they do nothing. It is not always because they choose wrong. The buzz around new trends can keep them from acting. This is why some get left behind. Begin with one task. Look at the results. See what you get. Learn from that. Then, make your work better. Your strength is in how you plan and use smart ideas. A good team can help you make it work. You can learn new things. Then you can use what you know and do something with it. Start with a small job that looks at the part of your company that takes the most time. This can help you see what works best for your business.
I'd describe our approach to AI adoption at Summit Search Group as open but cautious. We've experimented with the technology and explored potential ways it could improve our operations, but we're in no rush to implement it for any core functions, and are always mindful of concerns over accuracy and bias with the AI tools that we do adopt. While not every AI implementation we've tried has proven to be beneficial in the long-term, we have found two very helpful uses for it that I wanted to highlight. On the new services side, we've recently added AI-generated market intelligence briefs as a premium client product that is delivered at the start of each retained search, and can also be purchased as a stand-alone product. Essentially, we've trained a generative AI to synthesize current labor market data with historical placement outcomes, salary benchmarks, and regional talent availability. This information is then turned into an executive brief in plain language with a data appendix customized to the client's location and niche. We've found this not only brings more value to our clients and helps position us as a strategic advisor, but is also very useful in setting realistic expectations early. For our internal operations, our most beneficial use of AI thus far has been creating a firm-wide recruiting copilot. This is essentially a customized AI assistant that's trained on our past placement data and candidate/client information, as well as market and industry data. Recruiters within the company can use it in multiple ways. The most helpful has been expanding cross-office insights. When a recruiter is working on a search, they can use the copilot to access talent pools across offices, potentially surfacing candidates that would have been overlooked from just searching within their city or region. It can also be used to draft customized outreach tailored to the client or candidate's sector, or to generate interview frameworks tailored to the industry and role level. Using this tool improves our efficiency and helps us maintain consistency across offices, while also helping us to provide the best possible service to our clients.
Small and medium-sized companies already have a structural advantage when it comes to AI adoption. Unlike large organizations, SMBs can change processes quickly, test small automations, and see results almost immediately. In large enterprises, rolling out AI across teams or departments often takes so long that the underlying models and tools evolve multiple times before implementation is complete. In my own work, I use AI primarily to support repetitive, structured tasks rather than to automate entire operations. I rely on no-code tools like Make.com and increasingly n8n, combined with custom GPTs in ChatGPT for proposal creation and data structuring. For reporting, I use my own datasets with NotebookLM and Gemini Canvas, connected through Google Colab. Before introducing these workflows, preparing a report for one client took about 1 hour and 20 minutes. Today, it takes roughly 35 minutes. Across 13 clients per month, this saves nearly 10 hours monthly, allowing me to scale output and stay competitive on pricing without reducing quality. The biggest risk is over-trusting AI and confusing confident responses with correctness. There is already discussion around "AI-induced psychosis," where decision-makers begin to treat chatbots as validation engines that constantly agree with their assumptions and oversimplify complex realities. This can lead to flawed strategic decisions and a false belief that entire businesses can be automated end to end. For SMBs, the safest and most effective approach is partial automation: using AI to remove friction and save time, while keeping human judgment firmly in control.
To begin with, AI helped us identify areas where we were losing money: 1. We operate in the international market, and often our holidays and weekends do not coincide with those of our clients. AI helped identify these overpayments, and we restructured the entire system. In addition, AI forecasts for the entire year how much compensation for overtime is expected for the company as a whole and for each engineer, depending on the project. 2. One of the company's benefits is paying for employees' sports activities. HR collected data on all employee benefits, and AI indicated that only about 50% of employees actually use this benefit; for the rest, we were simply paying money without any benefit. We changed the benefit structure and solved the problem. 3. After AI analyzed our planned salary review system, we prepared a document on a new Salary Review system and working with grades. AI gave us suggestions that we ultimately discussed and used to form a new concept. It describes how grade, results, assessment, and salary decisions are now linked. Indeed, much depends on the completeness and accuracy of the data. Because the result is based on their analysis, small inaccuracies can ultimately show, for example, 16% instead of the actual 7%. In the first stage, we double-checked everything manually, identified bottlenecks, and corrected them. For instance, in engineer payments, it wasn't entirely correct to simply take the payment amount; we needed to take into account sick leave, vacations, and unpaid leave days. This required further development, and as a result, we now have an almost perfect AI planner.
We leveraged AI to predict which prospects are most likely to convert. That insight drives sharper sales prioritization. Our outreach success rate improved dramatically. The technology transformed guesswork into predictable revenue. The profit outcome was immediate in closed deals. Sales teams now focus only on high-probability leads. That increased efficiency strengthened both top and bottom lines. It's the simplest use of AI with the fastest business payoff.
This is Chris Valero from MHP Sales Manager. We are providers of sales and marketing solutions for mobile home park owners and operators. Although our operations are backed by a team of MHP marketing specialists, we also make use of AI to deliver scalable results for our customers. How MHP Sales Manager is Profiting From AI We've developed an AI-powered mobile home-trained agent that works 24/7 to respond to interested prospects' inquiries in seconds. This AI agent then pre-qualifies prospects based on their interactions and builds a clean list of leads ready to schedule a home tour. This way, we ensure that our sales reps focus only on the prospects that are in the consideration stage of the sales funnel. This tool, along with a team of MHP marketing experts, has helped us close over 180 mobile homes in the last year for our customers. Thanks, Chris Valero CEO | BluePaperclip https://www.mhpsalesmanager.com/
A major way our small agency has used AI is to develop incredibly detailed standard operating procedures for our services. As a small agency, most processes live in my business partner's and my heads. This has helped us internally keep track of all the steps involved in our services and will help us delegate process steps to new team members as we grow our team. We essentially brain-dump thoughts and half-formed explanations into a message prompt and ask AI to organize the information into clear step-by-step SOPs. One of the most valuable techniques has been asking, "What questions would you have if given this task?" because this helps identify areas that need further elaboration or clarification. These are always living documents, so as the programs we use receive updates, we can further rely on AI to update the steps when changes occur. I cannot begin to describe how much time this has saved us!
I am using AI a lot in my small business, primarily for marketing tasks. We are using the paid version of ChatGPT which allows us to create custom GPTs for repeatable workflows. Here are the benefits that we have managed to achieve: 1. We saved $10k per year as we don't have to outsource Linkedin marketing anymore. We created a custom GPT that turns out blog posts into a series of shorter Linkedin posts. The paid version of ChatGPT is really good at generating images for our posts as well. We also implemented a Linkedin outreach automation that integrates with ChatGPT. ChatGPT reads the prospect profile and uses the information from it to personalize the first line of our message. 2. We saved $15k by creating a custom GPT that writes our blog post articles that actually rank on the first page of Google. We trained this GPT on our existing articles that rank well on Google already. We taught it to mirror the writing style, section structure, etc. Now we load our blog post content plan into chatGPT and it generates an article that requires some quick edits. The main way to optimize the results of your AI model is to give it more context. In an ideal world I would load all of my customer reviews and all the content from my website to my custom GPTs. I want them eventually to select what case studies to write about and what reviews to mention in the articles instead of me.
We built Aeon Hire because we got tired of watching companies hire based on gut feelings and prayer. It's an AI-powered platform that does smart resume analysis, generates customized interview questions, transcribes conversations in real time, and surfaces predictive fit scoring so you're not guessing anymore. Our recruiters used it internally for years before we started licensing it to clients. That being said, AI isn't magic. I'll keep saying this over and over until it sinks in. It's an accelerant. When you have a weak foundation, you'll continue to get garbage output. The small companies I see winning aren't chasing every shiny tool. They're picking one real problem, like candidate screening taking too long or losing track of feedback across interviews, solving it with AI, measuring whether it actually moved the needle, and then building from there. Start small. Solve real problems. That's the playbook.
I'm a multi-time sales leader who now works as an Operating Advisor for Private Equity. There, I get to see how dozens of sales organizations across our portfolio are using AI, and I've just finished a research project evaluating 158 AI tools for sales to separate what's hype and what creates real results as part of a book coming in April (The AI Handbook for Sales Professionals). One of the best uses I've seen from AI in small businesses is to scale the knowledge of the founder/CEO for new team members. They create a private GPT that they give a prompt like "I'm writing a sales playbook for my growing team. Your job is to interview me to extract key customer stories, industry trends, and other information they'll find relevant" - and then answer ongoing interview questions over time (text-to-speech is a great way to get it out of their head and into the machine). Within about three weeks, GPTs have drafted really strong, usable playbooks and training materials - and can then be made avialable to selelrs to answer questions from the vantage point/in the voice of the founder. A key challenge for small businesses is that their talented CEO/founder can only scale so far - and the business eventually gets too big to have them part of every customer interaction or sales pitch. Using AI in this way helps small businesses overcome that hurdle. More at www.jdmillerphd.com/aihandbook
As AI developers, we're seeing a shift in practice: small and medium-sized organizations begin to use more and more AI not for purposes of "automation," but for maximizing profit. I'll name just a few examples. 1. Unprofitable customers identification. Through analyzing transaction data, customer service interaction data, return data, and LTV, AI can provide the ability to model a data set that defines the behavior of "cost-consuming" customers. This gives companies a headstart in changing service routing, limiting discounts, or offering other scenarios. 2. Finding funnel bottlenecks. With dozens of channels, hundreds of touchpoints, and thousands of micro-events, AI models can look for possible nonlinear dependencies that traditional systems cannot. For example, "People who began the onboarding process on a mobile device but didn't view the video had a 84% dropout rate". This data point isn't visible to the naked eye or standard BI systems, but it does provide a clear opportunity for retention improvement. 3. Shaping product priorities. Rather than collecting customer feedback manually, companies can use LLMs to review and analyze customer feedback, customer support chats, customer reviews and user sessions. The pain points can be aggregated and segmented based on the meaning, not a particular word. This means that businesses can create products that are actually needed by the market and not because someone decides to do so. 4. Developing and verifying hypotheses. LLMs help make faster and better-informed decisions and develop MVP flows without the necessity of hiring the whole team (copywriters, researchers, designers). However, all of this works if you have two things: historical data and a clear success metric. Without those two components in place, the biggest danger to your progress is that the AI may be churning out reports that are only attractive in looks and not really bringing any progress or profits to the table.
This AI angle is something I am passionate about as a solopreneur so I wanted to reach out and introduce myself. I was just this week in the final stages of a Business Insider feature on a very similar topic when they hit an editorial pivot, so very excited that your opportunity came up right now. I founded Kyoto Botanicals, a Colorado-based wellness brand, and I handle every facet of the business myself: strategy, product development and sourcing, operations, ecommerce, wholesale, marketing, customer relations, design and creative, you name it. We have 0 employees and AI is allowing me to scale in ways that never were possible before. My company operates in an extremely competitive category and AI has fundamentally changed not only what I do, but what I can even imagine I can do. I believe deeply in brands controlling their own messaging and content, and AI has allowed me to dramatically scale my vision quickly without wasting time and money and losing intellectual control and authenticity with outside agencies. Creating at scale: with the help of AI, I can take all of my ideas for long-form educational content, video content, and marketing materials and quickly generate drafts and outlines for each which I can then write in my own voice. This helps me create significantly more content on a weekly basis than otherwise would be possible since I am so much more organized. SEO: this is the biggest win so far with AI. The ability to iterate and teach myself an entirely new skill in 3 months has been eye opening. The ability to ask questions and create in depth conversations around SEO has allowed me to attain depths levels of expertise that would never have been possible with self help books and forums. Marketing automation: My automation in Klaviyo has become extremely streamlined with AI helping me identify gaps and create perfect automation streams to fill those gaps to create a top level marketing stack without a single agency involved. Over the last 4 months I have completely rebuilt the brand from the ground up, launching multiple new marketing channels, massively improving SEO visibility and structure, and expanding retail distribution, all with a meager AI monthly subscription cost. This has been a hands on experience where I have made some massive mistakes along the way that I can share, but I learned from them quickly to come out the other side stronger as business owner with a stronger company and a better AI understanding.
Sam Gupta here. I am the CEO of an independent digital transformation strategy consulting firm, with a focus on AI readiness initiatives, generally augmented by our internal AI service capabilities. In our experience, the challenges are real. Even internally, we are yet to find a truly commercial opportunity beyond pilots. The success cases we have seen generally involve buying pre-built AI capabilities through an enterprise software vendor. Speaking from our background in software development processes for over 20 years, we find that while generating millions of lines of code overnight is possible, calibrating, governing, and optimizing these agents requires more effort than traditional software development. The current agents are also only capable of extremely focused activities, without causing hallucinations or producing misleading results. In the enterprise context, finding these isolated use cases requires substantial expertise that most SMBs lack unless they have a very mature IT and software product development organization. With the exception of SaaS or tech-savvy companies, this level of maturity is rare. This process is similar to screenwriting for movies. The quality of the script determines whether these AI agents will produce commercial results. When these capabilities are procured through a credible enterprise software vendor and implemented by a qualified team, we have seen direct business results in customer service, where agents can field initial inquiries and have the option to jump in when conversations become complex or take longer. These workflows were traditionally handled by offshore call centers (at least for lower margin products such as eCommerce). Whether AI agents would be more profitable, in these cases, than offshore resources would depend on the cost of deploying and maintaining them. But this is perhaps the most common case for enterprise scenarios where we have seen a direct profitability boost for companies. Other cases where profitability might not be directly attributable include use cases such as analyzing and suggesting improved planning numbers that can be fed into transactional or processing systems to reduce inventory costs or improve cash positions. We have seen agentic capabilities in production across use cases, ranging from predictive maintenance to synthesizing product feedback to building new software features with some indirect profitability.
Small businesses have begun to convert AI from hype into reality. About 68% of business owners are already using AI, with an additional 9% planning to start using it this year (source: https://www.foxbusiness.com/economy/small-business-ai-adoption-jumps-68-owners-plan-significant-workforce-growth-2025). At EVhype, AI helped us analyze and organize user feedback and data regarding charger performance. Instead of weeks, we identified and processed hundreds of inputs in a matter of days. As a result, we were able to reduce duplicate support tickets by 27%, and prioritize our product support and staffing. With AI chatbots, we are able to save customer support from low-level tasks, and reallocate their time to more valuable tasks. AI chatbots also improve customer experience as support is able to respond to more challenging questions. In surveys of small and medium-sized businesses, 89% of respondents use AI to improve efficiency (source: https://icic.org/research/small-business-entrepreneurship/ai-in-business/). Business owners reported a decrease in time for customer response, saving 20+ hours, and their workflows were automated. Those hours can now be reinvested into the business. Start small, and focus on support, analytics, and employee feedback. These combinations provide fast and practical results.
We are using AirOps for AI content refreshes of our old blog posts. It's very organized and keeps our brand voice and tone in place when we scale. Of course, every content workflow is improved - like content briefs, keyword research, headings structure, etc. We are saving easily 2-3 hours per one blog post of 2-3K words.
Small business AI adoption has happened much quicker than most people thought it would, and it's producing real results. As a virtual event agency, we've embedded AI in many aspects of our business including, but not limited to: 1) scheduling/logistics, 2) managing vendors, and 3) predicting risk using historical data and current event parameter inputs to produce optimal schedules and resource assignments. AI is doing interesting things in marketing too. We use AI to create personalized audience targeting by analyzing previous event data, social media and web behavior. AI also assists in creating blogs based on topics determined through analysis, copy optimization and SEO. AI powered networking within virtual events helps attendees find the right people to network with and therefore increases attendee engagement. Finally, one of the greatest uses of AI post event is in providing analysis of how attendees engaged during the event, and their attendance patterns/levels of interaction. This analysis creates actionable insights that allow us to tailor follow up campaigns and improve future event experiences. To successfully embed AI in your business, you need to start by identifying specific areas that cause you pain versus attempting to embed AI across your entire organization at one time.
We built our own AI tool to convert customer support data into insights. Most AI tools handle scripted responses well. However, they struggle with integration and understanding behavior — an area where support can add real business value. Instead of asking AI to answer customer questions more quickly, we taught it to organize what customers are really asking. We use AI to tag and group requests across channels, so product and support teams can notice recurring issues, gaps in the knowledge base, and areas of product friction. We show those patterns on a dashboard, allowing teams to see when issues spike, which customer segments are affected, and what typically triggers follow-up requests. Compared to manual analysis, this provides teams with 2 to 3 times more insights, often weeks earlier, and helps reduce repeat inquiries by 10 to 30%. That said, this process isn't easy. AI isn't something you set up once and forget about. It requires a lot of time and resources: models need training, review, and context updates. Even when AI identifies the right signals, people still have to determine what matters and what actions to take next. Today, the question isn't whether to use AI. The real question is where to apply it first. I recommend starting by using AI to understand customer demand at scale — repetitive issues, documentation gaps, and product friction. Without this, you're just guessing. To make a proper decision on what to automate and what should remain untouched, you need real data and big picture.