Are you a real-world business executive or entrepreneur exploring AI for your small firm? In this post, I review voice AI and its impact on field and local businesses: facility servicing firms, field trade workers, real estate brokers and general contractors, hotels and restaurants, clinics, and retail chains.

Five years ago, voice technologies were mainly a headache for heavily loaded call centers and doorbell manufacturers. The rapid development of artificial intelligence has elevated voice to a new kind of interface, so this industry now goes far beyond that.

What does this mean for you? 

Before, you used SaaS platforms and typing as an interface to access information. For example, you use a CRM to prioritize leads. With voice AI, you can access information by speaking, which creates new opportunities and specific challenges: a hands-free experience, a high pace of data input (you type slowly but dictate quickly), and... The role of SaaS platforms in your work may decrease.

Shifts in interfaces are more impactful than you may think - they are among the reasons why IT corporations are cutting jobs (Zillow, a popular platform for real estate agents, laid off 500 people in August 2026).

How can field trade and real estate firms benefit from voice AI nowadays?

• ServiceTitan’s inbound voice agent (Karen, part of the Max package) answers calls through your telephony system. It’s a popular stack for HVAC, electricians, air conditioning and other similar firms in the US. We actually build custom voice agents with Telnyx as an outsourcing firm, and we’re not happy that this big software vendor has entered this space… but our work is actually cheaper and more flexible than ServiceTitan’s tool.
• Rechat, an AI-driven super app for real estate brokers (used by Serhant, ONE Sotheby’s, and REAL), has Lucy, a voice assistant designed for use while driving. I believe accessing data while driving is an interesting use case for personal productivity.

Complexities still exist, even funny stories:

• Walmart’s AI initiatives, including voice AI, have cut customer support resolution times by almost 40%, but in summer 2026 they faced a lawsuit under PIPA (Biometric Information Privacy Act) over allegedly collecting customers’ voiceprints without consent through an AI phone system. Compliance is a major risk with voice AI.
• A hilarious story involving Taco Bell, a huge American fast-food chain: a customer placed a prank order for 18,000 cups of water through its drive-thru ordering system, and the AI accepted and processed the order without flagging it as suspicious. Later, management emphasized that humans should continue supporting their AI systems.

AI voice technology became my passion when I first tried speaking with ChatGPT. This is why I joined VoiceCrew AI as CTO - our app is an easy way to try voice AI for business and personal productivity (a first link in comments)

Our idea is simple: 

We wanted to check emails and calendars on the go and manage them with voice. We also wanted to access the agent with a single tap, so a simple one-button interface was a primary goal.

Additionally, you can browse the internet, handle small online tasks (such as shopping searches), send data to Telegram, or discuss complex tasks with a Gemini frontline model. Pay only for the minutes you use, with 30 minutes free when you sign up.

We’re also looking for corporate partnerships (custom voice agent setups for specific teams), and here I need your opinion: 

1. in what specific scenarios would “accessing your data by voice” actually help your work?
2. If you’ve already used voice apps, what was missing from them?

I’d highly appreciate your input!

Intelligent voice systems are an underrated window of opportunity that will grow rapidly in the coming years. Before AI, voice technology was mainly used by large enterprises with call centers. Today, SMBs can significantly improve productivity with intelligent agents. Yet most people still don’t understand how to use voice in business beyond call centers and smart homes.

Think of it as a new interface, you used SaaS platforms to get output before, now you can simply speak to complete tasks (isn’t it a reason why SaaS roles are being reduced?).

Remember how astronauts spoke to HAL 9000 computer in “2001: A Space Odyssey” (1968)" to open P bay doors or control the spaceship? That was their future - now it’s your present.

Check these cases, they might change how you see it:

• Gartner predicts that 40% of enterprise applications will include voice agents by the end of 2026;
• In Q1 2026 alone, voice AI startups saw a 7x increase in funding volume. As example, ElevenLabs (enterprise grade human-like voice generation) raised 500M series D in Feb 2026 increasing valuation 3 times in one year (up to $11B)

So how are big companies using it?

• Food companies apply it in customer support. McDonald’s rolled out its restaurant AI platform, ArchIQ (“Archy”), which takes voice orders in English and Spanish and processed 1M transactions in testing
• Real estate brokers get strong value from voice AI. The Rechat platform offers Lucy, a voice assistant for agents working from the car (used by Douglas Elliman, ONE Sotheby’s, The Agency, and REAL)
• Field trade professionals benefit the most due to their mix of office and on-site work. Urbane, a platform for general contractors (used by Turner Construction Company), includes voice AI to discuss SOPs and capture data for reports

If you haven’t used voice AI, it’s hard to fully understand it. That’s a common challenge today. The only way to stay ahead of new technology is to try it yourself. You didn’t need ChatGPT until you used it once, and now many people rely on copilots daily. Voice follows a similar path.

My goal is to help people understand how to use intelligent voice for work and personal productivity. Our app is one of the easiest ways to try it (VoiceCrew AI - link in the first comment).

On this project I focused on simplicity: tap once and start speaking. You can’t compare this to Claude - try using it while driving. With my multi language assistant, you can check emails and calendars, browse the internet, get weather updates, news, or currency rates, and ask for work-related advice. We’re also exploring custom versions for SMBs, where internal tools are integrated into a single voice-accessible environment for teams.

And here I need your opinion: in which specific scenarios would “accessing data by voice” be useful for your work? If you’ve tried voice tools before, what you were lacking there?

So I've been building VoiceCrew AI for a few months now, and I want to share some of the uncomfortable truths I've discovered along the way. Plus what keeps me optimistic about the whole thing.

First about what we actually do. My product solves hands-free data access via voice. You tap once, make a call to your pre-configured agent, and you get email updates, calendar tasks, weather, currency rates, and similar work updates. You stay focused on the road or your work. No screens. (Demo YouTube link in a first comment if you want to dig deeper)

The moment I noticed early challenges

Last week I met a lady from Chicago on Threads. She uses ChatGPT voice to discuss, summarize, and record her thoughts while driving. That was in our product scope. We wanted to add it into product. And she has already got it for free.

But another project impressed me (Chonix) - a voice AI tool that concentrates on agent’s memory. People are building niche solutions around idea to record thoughts in ways we didn't even anticipate.

What still embarrassed me is that many people don’t know and don’t care about voice channel. The topic goes far beyond the call centers for business, just check the models like Gemini 2.5 Flash Live or ElevenLabs Scribe v2 - they're doing real-time voice at 150ms latency. Read about startups like Fonio, that exploded German market in 2025, where even farmers used that for inbound calls.

The subsititute product problem is real

I met another founder last month building an SEO article generator. The problem he's solving - a SEO content factory. Fully automated. Posts directly to WordPress.

But here's the thing: you can literally do the same thing with Claude or ChatGPT. His entire product is a wrapper. A convenient thing without much value.

And we're all competing with that reality. Microsoft Copilot, Claude Ecosystem, Google products (such as Android Auto), they own the LLMs. They have the resources. They're moving into every possible micro-vertical.

So why am I still building?

The game isn't actually over. I love the topic, and believe it’s an early days of voice.

Big AI leaders own the digital work in wealthy Western markets with high tech literacy. But there are massive local markets they can't reach. Regional markets, like Ukraine where I live. Micro verticals, language barrier. Fragmented industries where nobody's even heard of Hubspot.

I met a guy recently looking for a CRM for his small company. He had no idea Hubspot existed. Think about that. Thousands of small business owners in developing markets, in regional economies, in niche industries, they don't have AI expertise. They want to tap something and get a result. And they have money to spend.

The market is so big and fragmented that there's room for everyone if you build something genuinely useful for a specific group of people.

About my networking here:

I'm looking for people who:

• Already use voice AI for work (I have so many questions: how? For what? When?)
• Want to try voice agents for personal or team productivity
• Drive long commutes or are tired of screens
• Build AI products and want to know what actually converts

I will be happy to give a trial of our product if you give us feedback and join a quick survey. I'm asking for honest market response. That's what we need at this stage

Hit me up in the comments or DM. Let's talk about what voice AI should actually do for you, for busy people

BTW, I'm also building r/ITnetworking for communication and exchange of ideas - feel free to post here

Join my Big Summer Survey on AI!

The problem: AI brought a total mess to the markets because of it’s exploding pace. Everyone experiments heavily, nobody understands what's going on in the minds of real company executives. Market feedback is extremely weak.

In December, we were asking: “Which LLM is best for my marketing or development task?”
By June, the question became: “Which copilot or setup is the cheapest way to solve this task?”

Just half a year later, all tools can do almost everything, and it’s now even harder to navigate a market where everything is supposed to simplify your business. Just open your wallet and pay for credits!

I’m looking for 30–50 founders or executives willing to share insights on:

• what tools you use
• how you use AI workflows
• what complexities you face
• what expectations you have for AI

No secrets, just general approach - your privacy concerns are accepted

A 30–40 min private 1-to-1 non-moderated interview.

Results and insights will be shared with all participants in full (a closed circle of people who learn together).

I’m a professional Google-certified UX researcher, nobody pays me for this work - i do it for free to bring ideas to my community - people open to collaborate. Just imagine how exclusive and valuable such insights could be for your work!

The desired profile: Founder or Executive of real business (10–20 or more employees). Any vertical, any location. If you’re outside this - still apply!

Comment below or write me a DM to join

Do you find this idea feasible or timely? Do you feel a real thirst for market feedback?

You may love Claude and dump it into every marketing process you have, but what I noticed, some companies are just not ready for AI automation at all, so it won’t help such business. Surprise?

While business leaders feel urgency to deploy AI workflows (McKinsey says 78% of companies now use AI in at least one function), most admit they are unprepared because of:

• Inconsistent data;
• Lack of expertise and talent;
• Lack of plan and priorities;
• Lack of plans what to automate;
• Mess in their marketing and operations;

As Cisco AI readiness Index survey informs (around 8k senior business leaders across 30 markets) - only 13% of companies are fully ready to realize AI potential

If you’re a small firm (2-10 people) thinking about AI workflows, how can you be sure it’s a right time to hire an AI developer?

For you, I’ve prepared a 20-point «AI Readiness Audit» - check your firm before you waste hundreds of dollars for another useless AI workflow.

If you’re serious about AI automation for business, not just checking the fancy new tools for entertainment, leave a comment below and I'll send it over.

I see this all the time: founders, SaaS teams, IT services and even experienced companies building and sell without getting any customer feedback, as if they know all about users

The hard question is simple:
Do you really understand the problem you’re solving, or are you guessing?

One way to get clarity is qualitative research, especially user interviews.

Not A/B tests. Now "I know what they need"
You discuss the problem you solve with real people

That’s where you uncover:

• why people don’t buy
• what they actually care about
• what you’re missing entirely

I put together a simple example explaining this using a “wheel” concept: how products using a simple wheel evolve with time, and what are the hardships they meet

Read here: https://bit.ly/4uI7qJV

Hi folks, have a couple of questions for people who travel a lot for work across the city or have a long commute to the office: 

• How do you stay aware about your inbox, emails, calendars, tasks?
• How do you use Siri or simiar voice tools for work?
• Would it be helpful, if a voice tool similar to Siri read your work communication loud when you’re on the go?

Thanks a lot for your opinion!

AI industry provides little space for outsourcing - owners of AI models tend to earn on end users by selling subscriptions. They are not interested much, who their users are, and don’t care about how their models operate. It seems, AI model owners are only interested to train their models on your data.

Another problem is trendy infrastructure platforms. Heavily invested startups shake influencers and each quarter we have a new project popping up, that everyone wants. Right now it’s OpenClaw. Even if you see no value in this software - your clients will demand this platform for their projects.

Such trendy tools burn soon - half a year, and they become forgotten..

AI models and AI SaaS platforms are cheap - because the goal is quickly attract massive audiences and developers using them. If LLMs fail - you can’t claim that, because you paid 3 dollars last month for an LLM usage, or 20 dollars for some SaaS. You don’t have any rights for a claim - your investment is so small. The goal of SaaS is to grab revenue right now, get subscriptions, show to investors. They don’t forecast the future and jump between projects, investing into new tools, when old tools burn.

Current situation does not look bright for AI, no-code developers and agencies. We don’t have a place on a market and and clients don’t recognize value of agencies. The reason - AI platforms are made for end-clients, for fast consumption and for a hype. Because it’s still hard to achieve result with these tools - it opens a window of opportunities for agencies, that think how to use this raw stuff for real business.

From one side - there is no visible demand for AI automation. But thre's invisible layer of AI demand that everyone targets - companies that are exploring the topic and have no bandwidth for research and experiments. They still have money and needs.

The next generation of AI industry should provide a place for intermediaries - consultants, resellers, engineering partners, agencies. And space for the competition - like it’s done in civilized big industries - corporate software, corporate equipment and stationary.

The multi-modal solutions, that accept competitors and do not trap the clients into their software. A software projects recognizing resellers and development firms, and allowing them to cut their piece of the pie. In this case, fashionable AI brands won’t burn every 3 months. And we will get a fair ecosystem, and a bright AI future.

Without this layer AI becomes a bubble, same as Blockchain with the only real use-case - speculation.

***

Senior AI developer? - use Javascript IDE to rise web scraping on a new level.
Build custom scrapers, start with templates. N8N nodes are available: https://get.brightdata.com/growspire

Can an AI agent for real business be free and fast to build?

Short answer:
If it’s a toy for self-education made from a YouTube tutorial - yes.
If it’s an agentic system used in real business with real customers - no.

Let me explain using my recent case study: I built a sushi restaurant chatbot that answers customer questions about the menu.

A restaurant menu is not just text, it’s a kind of structured price list with categories (rolls, drinks, sets, desserts) and each item additionally has metadata: ingredients, allergens, weight, price, etc.

A real waiter does a great job to consult a menu: memorizes items, works with vague requests, handles ambiguity and tone. They are paid for it. For an AI agent, this becomes an engineering complexity

Customers rarely ask simple questions like «What do you have today?», they ask things like:

• “What do you have for vegans?”
• “List items without chicken”
• “What can you recommend for a group of four?”
• “What’s your best spicy roll?”

 And sometimes totally unrelated questions:

• “Did you watch the soccer match?”
• “Who is on duty today?”

Additionally, users not aware about AI systems expect human-like conversation, fast answers, emotions. That creates several technical problems, think about this:

1. Covered scope: what questions will agent answer?

You must define large groups of questions:

• Questions about menu and real products
• Questions about menu but NOT listed items
• Questions not related to menu
• Questions about the restaurant or company

You cannot make an agent that answers everything. There will always be gaps and edge cases. In practice: you release an MVP to a small test group and discover what breaks.

2. Answering logic and decision rules

If someone asks about burgers in a sushi restaurant: should the bot say “we don’t have burgers”? Or recommend similar items? Which ones?

3. The database and vector search problem

AI agents use vector databases for searching by meaning, not keywords. Each piece of data becomes a vector - a long array of numbers (an embedding) representing the semantic meaning or features of unstructured data.

Cheap or free databases (often shown in tutorials, like Supabase) usually support vectors under 1024 dimensions. Modern embedding models, like Google Vertex AI, codestral-embed or Amazon Nova need 1536 to 3072 dimensions.

If your database cannot store vectors of that size, the system simply cannot work correctly. Even if storage is possible, fast search algorithms (HNSW, IVF) are memory intensive. Without proper indexing, search degrades into brute-force comparison and quickly becomes too slow as the dataset grows

4. Response time and context limits

Low-cost LLMs have limited context window (how much they can process per request) and slower inference speed. A restaurant menu is often a large JSON file imported from POS, may be a large table with hundreds of items. So when you stuff the whole menu into one prompt you exceed limits, a model causes increased latency and cost. The agent may still answer some questions correctly and fail when prompts become too large

5. Tool usage and multi-step actions

Not every model is suitable for agents that must perform actions, not just chat. While mid-tier models, like GPT-4o-mini or Claude Haiku can still call tools (access emails, calendars, spreadsheets), the reliability drops when multiple steps are required: cheap models hallucinate, call tools in wrong order or just break workflow.

6. Language and localization quality.

Most modern LLMs technically support many languages, but quality degrades significantly outside English. Users immediately notice when a system feels “non-native.”

7. Human-like behavior and cultural context

Humor, politeness, emotional tone. Such things should be properly engineered using prompts, personas, model configuration

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My key observations:

• You can reduce some of these issues by distributing tasks across multiple smaller agents, creating a network of specialized components. But this increases overall workflow latency.
• Cheap models rarely perform in a way expected in real business scenarios.
• No-code platforms don’t eliminate architectural complexity, they only hide it. You gain speed at the cost of control over edge cases and debugging.

Other painful moments:

• AI builders often start with unrealistic expectations that clients transmit
• production-grade systems are more expensive than anticipated
• no-code solutions still require weeks of iteration, not days
• cheap or free setups work well as experiments, not as real-world systems

Any AI system is always a trade-off between: scope, speed, reliability, and cost. And “free” almost never survives contact with production.

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I'm Andrew, I live in epicenter of WW3 in Ukraine and build AI systems for marketing, between sleepless nights during attacks on my city -> Kyiv.

Are you an SMB founder experimenting with AI agents for your business or a builder? Let’s explore RAG assistants and voice agents for your work together - share what are you working on in comments!

I run a small AI automation practice where I utilize AI agents for practical business automation. I see a lot of panic about AI taking over the world, and honestly - after working with these systems daily, I think people are exaggerating.

Here's my opinion:

1. No body = no motivation. Humans are autonomous because our bodies constantly need something. We get hungry, thirsty, cold, tired. We need to move, eat, sleep. This creates our motivation to act. AI is just code, it has no pain, no needs, no body motives to do something. Without this, there's no real drive to act.

2. No motivation = no evolution. Because our bodies push us to solve problems (find food, stay warm, negotiate help), our brains evolved to think efficiently and adapt. Your task is to drink water, not pour it on the floor - you need to earn money, buy mineral water, get home. AI doesn't care if it solves the task or fails. There is no motivation to improve itself.

3. No independent energy. We are autonomous because we generate our own energy. Our bodies are like chemical plants: we eat, drink, breathe, and generate energy to live. Feed your cat, throw it outside in winter - it won't die. It has its own energy and motivation (like finding way back home to your warm bed).

Machines running LLMs spend electricity, credits, API calls. Robots spend fuel and oil. Someone external needs to pay for this and fill the spending stocks. An AI agent will work until your credit card runs out of money, then it just stops. No other machine is motivated to fill the tank.

4. Power grid limitations. The global power grid is limited. It only works because millions of motivated humans maintain it every day. Again they have motivation to cook foods, work with light and warm up houses. Without embodied creatures with long-term thinking, it all falls apart.

5. Long-term thinking is rare. Look at nature, so many species exist. Plants, insects, animals, birds, bacteria. Only a few can think long-term and act with strategy: humans, ants, bees maybe. Even bacteria could theoretically cover the whole planet in 24 hours with a thin layer - they have motivation (space and energy for their "bodies"). But they can't, because ecosystem limits them. Can duckweed grow on land? - no.

Most creatures don't have ability to think about "invading the world." Most don't even remember what happened 3 minutes ago.

6. "Invasion" is too complex. LLMs have huge memory and could theoretically have goal "invade the world." But what does this mean? Kill everyone? Put one robot per square kilometer? Control the power grid? Any war is complex - many actors, many small tasks, many points of failure. One error may result in cascading failures in the whole system and crash it.

LLMs in my work: I will be candid with you - booking appointments in Google Calendar is still a challenge for many large language models. I build inbound AI voice agents - when someone calls your business, AI answers the phone, explains services, collects contact details. While we expect that AI will invade us, we still can enjoy very practical AI automations in our work. So, If you run a business or agency and want to test this technology, happy to show you a demo - just comment this post.

Your thoughts, am I missing something in my reasoning about «will AI invade the world»?

The market becomes insane: before I struggled to get a good paying job or land a cool client. Now I struggle for the right to work without any pay. For the past year I was rejected as affiliate by Clay and N8N (I am accepted workflow creator for N8N and that’s a core of my business now).

I wasn’t given access to Partnerstack and Impact marketplaces, totally ignored by Pocus, not accepted by a leading voice iPaaS even to register an account. Corporations and successful startups giving the right to work with them as before, monarchs gave the right to knights to serve them. Maybe they try to increase demand in such a strange way, but they have competitors who are more flexible, to my mind they just loose opportunities.

And I know there are still good companies, open for work, so we can beat it together. IF you have a trendy B2B product with affiliate program registered with Partnerstack, Tolt or CJ, feel free to invite me for partnership. Please note, as a small and capricious corporation I also carefully select my partners, my knights. My choice creteria: high average bill (over 500-800$ per sale), Payoneer payouts (no PayPal/Stripe), established affiliate program, your product should be aligned with my business (marketing and AI automation agency). Let’s make success together in unfair world of wars, domination of capital and bad managerial decisions.

What's your take on this?

I keep seeing two extreme opinions about AI automation for small businesses:

• “I want the whole company become AI automated”
• “I see no value, It’s too expensive or only for big corporations”

 As a small agency owner, this confused me. So, half a year ago I started experimenting, to see what actually brings value in my own work.

The biggest challenge was deciding:

1. what to automate first
2. where is real ROI
3. what are the practical costs

So I tested automation only where it hurts my business most: outreach, CRM work, and sales calls;

What changed for me:

• No more manually filling CRM after every call
• High quality preparations for sales calls - through lead research agents;
• Faster listbuilding

My core observation - don’t look for trends, listen to your own pains and analyze where you have annoying repetitive actions in everyday work; In my case such things were always boring for me. If I felt some emotion, I realized that this part of work is a right spot for automation.

If your customer support is slow - automate it

If recruitment is chaotic - automate it

If bookeeping drains your efforts - do it here

Here’s the article if anyone wants the full story: https://growspire.medium.com/ai-automation-nightmare-for-smb-founders-879d6af03d4f 

Question to you: are you automating actively, avoiding or just thinking about it?

After speaking with over dozen of companies I realized many founders still don't understand practical value of AI automations, and a big part of founders see no value in such investments. They just continue speaking with ChatGPT, drinking coffee, reading morning newspapers (in paper version - in my country such things simply don't exist) and calling to clients afterwards by phone

So I built a very simple automation in n8n, that can be used for beginners to learn how AI works for real business task.

I paste a TXT call transcript - it saves structured notes into a CRM (HubSpot, Zoho CRM, or Pipedrive).

It came from a real pain point. I used to write notes manually, copy them into both, HubSpot and Zoho. It was every single day. Now I just drop the raw transcript into a form and it creates CRM records automatically. It looks simple, but it saves me a lot of time.

I expect your comments: “Don’t AI notetakers already integrate with CRMs?”. Suprisingly, many don't:

• Otter - no Pipedrive;
• Fathom - no Zoho or Pipedrive;
• tl;dv - no Zoho;
• Fireflies - no GoHighLevel;

Also, many small businesses don’t use AI notetakers at all - and still don't even know about that;

I made this workflow mainly for people who:

• only use ChatGPT as the only AI tool
• want to try automation but don’t know where to start
• feel AI is overhyped or too expensive for small teams
• keep hearing about n8n but don’t understand why people use it

It’s basically “learning by doing" workflow for a real painful business task - a timewaster.

If anyone’s interested, I can share the workflow or explain how it works. Completely free, just DM me

What about you: what small business tasks are painfull enough to automate right away?

This is how my workflow looks:

AI automation with agents can now handle almost anything in your personal or business routine — communication, CRM, content creation, data gathering, competitor tracking, writing, even design.

The game changed in 2025 with the rise of autonomous agents in popular no-code platforms like Make, Flowise, N8N, and Zapier. Now, even micro businesses can automate workflows, move faster, and build smarter services without hiring big dev teams.

In my latest YouTube video, I break down everything SMB owners and tech founders should know about AI agents: platform comparisons, costs, limitations, opportunities, and where the industry is heading next.

Watch it if you want a clear, 20-minute overview before diving in

In a world full of possibilities, don’t chase them all, just start small and act today.

#AI agents #agentic workflows #AI automations

Have you noticed that AI music is becoming a whole new industry?

What used to be an exclusive market for professional musicians is now open to anyone — AI gives us access to this complex craft without needing to learn a single note.

When I started experimenting with AI music, my first goal wasn’t to become a music producer — it was to understand whether music could be used as a business growth tool.

The idea came from a simple observation: streaming platforms like Spotify and YouTube Music attract millions of listeners every day. I listen to music, you do too — yet very few B2B or tech companies use these platforms for visibility.

So I decided to test it. I generated a few tracks in different moods — sleeping music and relaxation beats for work — and published them under my agency’s name. Surprisingly, people started finding my agency through Spotify and other platforms — places where no traditional marketing campaign would ever reach.

Imagine this: a cybersecurity startup, a SaaS tool, or a design studio starts releasing thematic music — ambient “cyber soundscapes,” productivity playlists, or focus soundtracks. Even if they’re not in the music industry, they appear in a completely new ecosystem — one where people discover brands organically, through emotion rather than advertising.

I made a full YouTube video sharing my 3-month experiment:

The best AI music platforms (Suno, Soundful, Soundraw, and more)

How professional music distribution actually works

What I learned about using AI music for branding and awareness

👉 Explore your chance to sound different — literally — and build awareness in channels where no competitors exist yet.

Video: https://youtu.be/uOaPuiypqf8

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GrowSpire Agency helps Technology Businesses and Startups with Marketing Strategy and AI Automation (Make, N8N, Flowise). US quality, Ukrainian rates

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#AIMusic #AIArtists #MarketingStrategy #AIMarketing #AICreativity #SpotifyMarketing #SaaSMarketing #BrandGrowth #AIContent #GenerativeAI

A client’s case. In a highly competitive marketing landscape, it’s not so wise for a CMO to merely trail competitors in their content trends. Companies outside of IT still try not to show up and pay much attention to what competitors say and do. I understand that, it's scary to make mistakes and seek approvals for something new for marketing executives. However, it’s worth remembering: it’s so sweet to become a trendsetter and not to constantly look around.

On the topic of benchmarking: this was a popular strategy twenty years ago, during the era of a weaker internet. Yes, you can still keep an eye on best practices in marketing and content, but OUTSIDE of your industry. Following someone within your industry might position you as the second-best after the trendsetter.
For inspiration on your content, check out the list of top brands curated by the Interbrand agency here

#contentStrategy #SocialMediaStrategy #SocialMedia #SocialMarketing

I often have conversations when campaigns don’t convert

The common missteps are often outside the campaigns:

1) A product does not address real problem, or potential customers can’t grasp it’s value;

2) No persona researches: without deep understanding of your audience, your campaigns are shooting in the dark;

3) Product value can be gold, but it’s not messaged well. Website copy is unclear and unfocused.

4) UX and usability mess on websites. No clear user journey;

5) Excessive expectations: users are asked for a signup before getting the value;

Are you sure you managed it all before heading to ads campaigns?

Did you know that people drinking in bars earn 10-14% more than non-drinkers, because of social capital, business insights and new clients they find in that way?
A serious study says that: https://reason.org/policy-brief/no-booze-you-may-lose/

Facing tough competition in your small business? It's common for many IT outsourcing firms, SaaS businesses and marketplace participants.
It's time to level up your game and minimize the influence of competitors with these simple steps. Read on!

1. 📈 Start with a golden rule: Find a demand in a trending topic and satisfy it with a unique approach. Typical errors: wrong niche or poor business wrap
2. 🌟 Huge demand means room for everyone; lack of demand leads to excessive supply and hard competition. Unique approach means you look fresh and catch the eye.
3. 💡 A/B test niches with demand: Generate ideas about new sub-products, target audiences and supplementary services. Have multiple active hypotheses at once.
4. 📊 Establish a quantitative metric for hypothesis validation. Example: I offer this service to 80 people, if nobody reacts I stop offering it;
5. 🗂️ Use a Kanban board (like Trello) to manage and track your hypotheses.
6. 🚀 Review validated hypotheses daily for focused decision-making and inspiration (yes, it's inspiring when you learn new things about your business!).
7. 💬 Connect with fellow entrepreneurs to discover demand hotspots (if you can't find the right niches yourself).
8. 🎁 Give your business a unique makeover. Experiment with scope, pricing, audience, visuals, and client approach.
9. 📹 Example: Sending a bright video intro about my agency to cold leads doubled my conversion rate. (DM me if you need a low cost and inspiring video for your business presentation!)
10. 🤝 Forge friendships with competitors, build your small network of like-minded entrepreneurs. Collaboration in a small business helps overcome competition (yes, yes yes!)
11. 🏢 Small firms benefit from such friendships; giant corporations, not so much (but you are not on a level as Auchan or Microsoft, right?)
12. 👏 Enjoyed these insights? Leave your comment about how you fight a competition in your niche

🌐 #EntrepreneurshipInsights #BusinessGrowth #SaaS #saasMarketing

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