Discussions about the progress made in the AI world today are very often accompanied by assumptions that, quite soon, it will become more feasible to hire AI instead of real people. At first glance, it may seem that such concerns are indeed well-grounded. But is that the case? To talk about this and explore the real capabilities of AI agents, Max Golikov, the Innovantage podcast host and Sigli’s CBDO, invited Frank Sondors to the studio.Frank’s career started in big tech at Google. This allowed him to take a closer look at the power of machine learning, not just in advertising but also as a tool to drive business growth.Later, he worked at different companies, focusing on big data and AI. When he joined a company called Whatagraph, he began to question traditional approaches to scaling sales teams. At that time, the mindset was “growth at all costs”. This was often achieved by hiring more salespeople. But Frank saw this as flawed. Most sales teams suffer from high attrition, and only a small percentage of specialists deliver meaningful results. In his experience, for every 10 people that you hire, only one is a natural salesperson. Another 2 or 3 can be trained, while the remaining majority lack the motivation or even don’t fit for the role.This insight led him to co-found Salesforge, a platform designed to help companies build a sales pipeline with minimal headcount. The company uses big data and AI to automate repetitive sales tasks. By embedding agentic capabilities into the software, Salesforge enables businesses to rely less on average performers and empower their top salespeople to bring significantly higher output.AI agents: Easy explanationA lot of businesses today aim to hire so-called “autonomous humans”. These are individuals who can fully own a part of the business without constant oversight. For example, salespeople are expected to manage their pipelines, close deals, and solve problems independently. In such a situation, leadership can provide guidance instead of supervision.This concept parallels how AI agents function. Like autonomous employees, AI agents are given a specific goal and the context for achieving it. In a sales use case, an AI agent can get a task to reply to a prospect using information such as a sales playbook, pricing, or FAQs. Its objective is to move the conversation forward to a micro-conversion, like booking a meeting.Unlike traditional chatbots that rely on predefined scripts, AI agents can reason through incoming replies and use the available context to respond intelligently and dynamically. This increases the likelihood of getting the desired outcome.AI agents for sales teamsFrank believes that when it comes to integrating AI agents into sales teams, there is no one-size-fits-all solution. The ideal setup should always depend on the company’s structure and sales strategy.In large organizations with 50 or more salespeople, human representatives usually focus on high-value enterprise accounts where deal sizes and sales cycles justify the investment. However, working with smaller accounts, for example, in the SMB segment, often isn’t cost-effective due to the lower return per deal.This is where AI agents excel. They can be deployed to handle outreach via different channels, including email or LinkedIn. They can engage SMBs with tailored messaging to book meetings, visit a product page, sign up on the platform, etc.By assigning AI agents to lower-priority or high-volume segments, businesses can maximize efficiency. They free human reps to concentrate more on strategic deals.Frank also mentioned another key use case for AI agents. Many early-stage startups, with fewer than 10 employees, struggle with prospecting. Quite often, it happens because the founders are overwhelmed with product development and customer management.According to Frank, a startup’s survival is based on two things: building a great product and selling it effectively. If pipeline generation falls by the wayside, there are serious growth risks.Founders have several options:They can do the prospecting themselves (if they have the time and skills).They can hire an agency (it is a rather expensive approach).They can turn to AI agents (In this case, they can simply configure an AI agent within their sales software, which will handle outreach autonomously).Are there any limitations of AI agents?Despite the inspiring examples that showcase the potential of AI agents, there are a bunch of downsides that businesses should be aware of.One of the biggest pitfalls isn’t the technology itself. It is the context in which it is used. For example, many smaller companies approach Salesforge looking to scale outreach. But the problem is that at this time, they haven’t even achieved product-market fit. In those cases, it doesn’t matter whether outreach is done by humans, agencies, or AI. All the efforts will be bound to fail.The second thing is that even with product-market fit, companies may lack channel fit. For instance, not every customer will respond well to cold outreach via email or LinkedIn. If a company has chosen the wrong acquisition channels, AI agents won’t magically fix this problem.Frank compares AI agents to Google Ads. You invest in it to generate conversions. But if it doesn’t perform, you churn. At Salesforge, the team is actively learning where AI agents work best. They consider different industries, deal sizes, and other variables to create a full picture.Frank also mentioned some other challenges with AI agents. For example, when you task AI with writing emails, it often fails to create messages that feel genuinely human. Depending on how the agent is built and orchestrated, the output can look as obviously AI-generated. For many customers, it may be a red flag today.Crafting AI-generated emails that look like human-written ones requires significant effort. It involves careful prompting, rich contextual data, and smart engineering behind the scenes. Tools like n8n or Make.com can help automate workflows, but if the end result feels robotic, it reduces the chances of getting a response.Four pillars of success in salesAccording to Frank, success in modern sales depends on what he calls the core four pillars.Pillar 1. Email deliverabilityThe first and most overlooked factor is whether your emails are reaching inboxes. Deliverability, or getting messages into the primary inbox, not spam, is foundational. Great sales outreach starts with using software that consistently ensures this. It doesn’t matter how strong your targeting or messaging is if no one sees your emails.Pillar 2. Email infrastructureThe second pillar involves the email infrastructure, which includes software and hardware components that impact deliverability and sending reputation. Frank stresses that a well-configured infrastructure improves overall email success.Pillar 3. Message itselfThe third pillar is where AI agents are currently making the biggest impact. It’s email copy.Preparing a high-quality, personalized email often takes a human around 15 minutes. It involves researching the prospect on LinkedIn, checking their company website, and finding relevant angles for personalization. Despite that effort, the harsh reality is that 90% of emails go unanswered. Quite often, it happens because timing is off or the recipient simply isn’t in-market.That’s why AI agents can be so powerful. They can combine two essential datasets:Seller data (what your company does, the problem it solves, value props, pricing, and the cost of inaction);Buyer data (publicly available information about the prospect, such as their role, industry, behavior, or company context).By merging these, AI can generate emails that are highly tailored. Such emails can be written in the recipient’s native language, which can dramatically increase response rates. For example, sending outreach in French to prospects in France can double reply rates. But there is a catch. When people respond, they usually expect the conversation to continue in French. So you need to have a French-speaking sales rep ready to continue the communication, or you will lose trust.Pillar 4. TargetingNo matter how good your emails are or how well they are delivered, if you are reaching out to the wrong people, you are wasting time and money.Historically, SDRs or marketers worked with large lists pulled from databases and manually qualified leads. It is an extremely time-consuming and error-prone process. Now, AI agents are starting to take on this task more effectively.Frank said that AI is better than humans at list qualification for one simple reason. The error rates are lower. AI can process thousands of leads using consistent logic, flagging which contacts match your ICP and which don’t.Where AI agents excelThe efficiency of AI agents greatly depends on the deal size and the sales cycle length. For instance, for high-value, long-cycle enterprise deals, the use of co-pilots that assist humans is far more realistic than full automation. Enterprise sales are still deeply relationship-driven, and the risk of an AI making an error remains a sticking point. The larger the stakes, the less businesses are willing to fully delegate tasks to AI.However, in low-ticket deals with short sales cycles, AI agents shine. In this space, autonomy and speed greatly matter. Businesses can deploy AI agents to run scalable outreach at high volumes.Despite the current limitations, AI agents already provide impressive performance for small-account outreach. Frank sees reply rates of 2-2.5%, with 10-20% of those replies being positive. Even with high processing costs, the return on investment justifies scaling these agents for such tasks.Speaking about the future, Frank mentioned the value of agentic swarms (not standalone agents, but interconnected teams of AI agents, each handling different parts of the sales process). Businesses should view such swarms of AI agents as digital SDR teams. One agent can handle list building, another crafts outreach, another manages follow-ups, and another coordinates calendar scheduling.Future of AI agents and humans in businessAI agents are already transforming how companies recruit, build processes, and optimize operations. It raises a big question: Will humans still remain in the business in the future?Every business has repetitive tasks, and smart companies are trying to automate those tasks using AI agents. As Frank shared, at Salesforge, they started aggressively doing this a couple of months ago by implementing n8n, an advanced workflow automation tool. It helps to build complex flows, trigger AI agents, write code, and manage operations without increasing headcount.That’s the goal of such efforts: to scale output without scaling team size. Salesforge plans to create 1,000 n8n flows by the end of 2025, which means about 10-20 new flows per week. Everyone at Salesforge contributes to this by identifying repetitive tasks they want to eliminate.This automation-first mindset also shapes how they approach hiring. Frank explained that when they face a business problem, they ask the following questions.Can we solve it with an off-the-shelf AI agent?If not, can we build a custom automation in n8n?If that fails, can we bring in an agency or consultant?If not, only then they we consider hiring a person.They have chosen such an approach because today hiring is slow, expensive, and competitive, especially in tech. The role of an "American mindset"Frank also mentioned the importance of what he calls the American mindset. It is based on staying ahead of the competition by improving efficiency, cutting unnecessary headcount, and constantly optimizing operations. In his opinion, businesses that fail to innovate or streamline simply let others outperform them.Some industries haven’t changed in 20 or 30 years, and Frank finds that deeply concerning. Meanwhile, countries like the US and China continue to evolve at enormous speed. If European businesses don’t adopt the same urgency, foreign players will enter the market and show how it should have been done.Experimentation in businessAccording to Frank, experimentation is a big reason why many companies are super successful. In his words, great businesses are built on the principle of improving by 1% every day. But that kind of consistent progress doesn’t happen by accident. It comes from deliberate testing and learning.He emphasized that without experimentation (it can be A/B testing, trying new features, or optimizing internal processes), businesses can’t grow. A good example here is Google. While its homepage may seem static, the company is constantly running thousands of experiments, even shifting a button by a single pixel to see if it drives better results. These micro-optimizations, powered by vast traffic and rapid testing, are part of why Google stays ahead.Continuous optimization for attention and valueIn today’s crowded digital landscape, dominating attention isn’t a matter of chance. It is the result of relentless optimization. Whether a business is B2B or B2C, Frank believes it must become focused on intersecting with their potential customers where they spend their time. For most professional audiences, that means LinkedIn.But what content will be the most valuable for them? This varies by audience. That’s why it is essential to experiment. Frank himself regularly tests different content types to measure engagement and refine his approach. Optimization isn’t just about the content itself, but about learning what resonates. It requires daily posting, ideally once or even twice a day, from Monday to Friday. While this level of effort is resource-intensive, it’s justified given the organic reach and brand authority it can generate. At present, Frank doesn’t see AI agents as capable of producing the nuance or authenticity required for this kind of content strategy. However, that could change in the future.He also noted the emergence of AI-generated avatars, fake but highly realistic video personas that are already being used in some marketing campaigns. As AI moves from text to voice and video, Frank sees 2025 as a turning point in the “video phase”. This year, we will increasingly encounter synthetic content that will look quite realistic. Therefore, businesses must be even more thoughtful and strategic in how they capture attention and build trust.While most of this work still relies on what Frank calls the “human puff”, he sees a growing role for AI agents in the near future. His company’s head of YouTube has developed a tool that analyzes video assets and helps repackage them for LinkedIn in ways designed to boost engagement. The tool recommends post formats, hashtags, which companies or individuals to tag, and other elements that maximize visibility and performance.This hybrid approach, powered by human-led creativity and AI-driven optimization, can be viewed as the future of content strategy. AI agents are not ready to replace humans in storytelling, but they are increasingly used in guiding what types of content are likely to succeed.Based on what we’ve heard from the experts who visited the Innovantage podcast studio earlier, this hybrid formula is quite applicable to many domains today. AI is becoming more mature and advanced, but a human touch is still a must.To learn more about the power of technologies in the business world, don’t miss the next podcast episodes.

In today's digital-first world, trust isn’t an afterthought — it’s a critical requirement. Data breaches, project failures, and inconsistent delivery have made businesses more cautious than ever about who they work with. At Sigli, we believe that trust should be built into the very core of our operations — not just our technology stack, but our team.That’s why, as of March 3rd, 2025, every new hire at Sigli will undergo a mandatory background check, powered by our new partnership with Certn.Why background checks matter in techIn many software and AI development companies, background verification is either sporadic or absent altogether. But as a partner entrusted with sensitive data, mission-critical systems, and long-term strategic initiatives, we believe clients deserve full confidence — not just in our code, but in the people behind it.Why Sigli chose CertnCertn is a globally recognized leader in background screening solutions, designed for speed, compliance, and scalability. Unlike traditional providers that rely on fragmented databases and slow turnaround times, Certn leverages AI and direct integrations with thousands of global data sources to deliver background checks that are:Fast – Most reports are completed within minutes, not days.Global – Certn covers 200+ countries and territories, enabling seamless checks for distributed and remote-first teams.Compliant – Built to comply with international standards like GDPR, SOC 2, and FCRA.Candidate-friendly – Designed to respect privacy and provide transparency during the process.By integrating Certn into our hiring flow, we ensure that our growth doesn’t compromise our standards — and that every team member is vetted with the same care, no matter where they’re located.What this means for our clientsFor our clients, this partnership represents more than a new policy. It’s a message: when you work with Sigli, you’re working with a team you can count on — not just for technical excellence, but for integrity.Sigli's clients trust us with more than just code. They trust us with business continuity, innovation pipelines, customer data, and strategic outcomes. Knowing that each Sigli team member has passed a robust, standardized verification process adds another layer of assurance — one that matters more than ever in today’s landscape.Raising the industry standardIn many ways, background checks have been overlooked in the tech sector — seen as a corporate necessity only for certain roles or industries. At Sigli, we believe that’s due for a change. High-trust partnerships require high-trust teams. And high-trust teams begin with verified people.This isn’t just a checkbox for Sigli — it’s a conscious choice to lead by example. We hope it sets a new benchmark for what clients expect, and for how companies approach growth responsibly.The bottom lineSigli is not just building AI products. Sigli is building a company where trust is engineered into every detail — from the way we code, to the way we hire.Thanks to our partnership with Certn, we’re confident we can scale that trust as we grow. Line by line. Hire by hire.

Small and mid-sized businesses often see the value of AI but struggle with knowing where to start. That’s why Sigli is hosting a free online webinar — designed specifically to help SMEs adopt AI in a practical, budget-conscious way.What You’ll Learn“The AI Profit Toolkit: Benchmarks, Playbooks, Quick Wins” is a 60-minute session packed with real-world insights, including:Realistic benchmarks from recent UK/EU use casesA six-pillar AI readiness checklistFive quick-win AI pilot ideas you can launch nowA playbook to scale from pilot to real business resultsAccess to a downloadable toolkit + full session recordingWhether you're a company owner, director, or operations lead, this session will help you take your first AI steps confidently — and profitably.“We see many businesses sitting on the AI sidelines because they’re unsure where to start,” says Max Golikov, Chief Business Development Officer at Sigli and featured webinar speaker. “This session is about showing them how to move forward with confidence and business logic.”Event Details🗓️ Date: June 4, 2025🕑 Time: 14:00 CEST / 13:00 BST📍 Location: Online (link provided upon registration)🎟️ Cost: Free👉 Reserve your spot now

Artificial intelligence is gradually transforming customer experiences and often replaces human agents in different industries. eCommerce is one of the brightest examples. But does it really mean that interaction will be fully digitalized one day? Or is there still space for human communication? These are some of the questions that the Innovantage podcast host and Sigli’s CBDO Max Golikov prepared for his new guest. In the latest podcast episode, Max had an insightful conversation with Tomer Azenkot. After starting his career in tech and product management about 20 years ago and then moving into sales leadership in enterprise software, Tomer joined Vee24 as its CEO in 2022. Vee24 is a SaaS platform that helps businesses elevate customer experience on their websites by incorporating video chat and other chat tools as part of the customer journey. Currently, the company is mostly focused on eCommerce businesses.AI chatbots and human interaction in digital commerceOver the past two years, the rise of AI chatbots and especially widely known tools like ChatGPT has significantly impacted the digital commerce landscape. A key topic of discussion today is how these tools influence the customer journey and whether they compromise the human element of the online experience.AI brings efficiency. But it can’t always replace the value of human interaction. As Tomer explained, at Vee24, they always try to identify moments within the customer journey where a personal touch is still essential. In these cases, relying solely on bots can lead to frustration. Many users still prefer to reach a real person when AI falls short.To support his words, Tomer provided a couple of real-life examples.AI is highly effective for straightforward customer needs. For instance, it is exceptionally good at checking an order status. In such cases, customers appreciate quick, automated responses. Human assistance is not needed.However, complex or emotionally driven tasks, such as choosing jewelry for your wife, require human connection. In such situations, it is still worth speaking with a knowledgeable salesperson who can offer personalized advice and even showcase products over high-definition video. This is where Vee24 entered the game by bringing the in-store experience online through real-time, human-led video interactions.According to Tomer, 90-95% of customer journeys can be handled efficiently by AI. However, the remaining scenarios, where trust, emotion, and nuanced guidance matter, still demand a human touch. Many retailers have heavily relied on AI by applying it in the wrong contexts. As a result, this has led to poor customer experiences and lost business. The key is striking the right balance between automation and human engagement.The right AI-human balanceA good example of effective AI-human integration comes from Jordan’s Furniture, a regional furniture chain in the northeastern US. They have structured their website to direct interactions based on the nature and value of the customer journey.AI chatbots handle the majority of routine, transactional tasks, like checking order status or initiating returns. They do it very efficiently. However, there is also an option to transfer the conversation to a human agent if needed. This keeps operational costs low while ensuring customer needs are met quickly.When people are looking for high-value pieces of furniture, the site connects them with a live salesperson in-store. These experts guide customers through the showroom via live video, offer personalized advice, and often successfully upsell related items, replicating the in-store experience online.Why video isn’t widely used in digital commerce todayThe limited adoption of video in online retail highlights a significant opportunity, and Vee24 is actively addressing it at the current moment. While video shopping experiences are more common in markets like the UK, where several furniture retailers already offer live video consultations, the US market has been slower in adopting this approach.One key reason is cost. Many US retailers have prioritized AI solutions to cut operational costs, often at the expense of customer experience. Hiring human agents, especially for video, is believed to be pretty expensive compared to implementing automated bots.However, data from Vee24’s customers shows that the investment pays off. Live video consultations significantly boost conversion rates and increase average order value by two to four times. For high-consideration purchases, especially in sectors like furniture, the return on investment justifies the added human involvement. Tomer shared that one of the questions that he has for Vee24’s prospective clients is “What happens when a customer walks into your physical store?”. In most cases, staff are available to guide, advise, and enhance the shopping experience. But online, many retailers expect customers to navigate complex purchases fully on their own.This disconnect is especially striking in sectors like furniture, where high-touch sales are the norm in-store. Despite the fact that eCommerce often generates a significant share of sales, retailers frequently underinvest in online human support.Tech shift in businessA decade ago, business communication relied almost entirely on audio conference bridges. Long meetings involved dialing in, entering codes, and trying to figure out who had just joined. Now, that feels outdated. Video calls have become the norm, especially following the shift driven by the COVID-19 pandemic. The same evolution is beginning to unfold in e-commerce. While text-based chat remains the default on most websites in 2025, it may soon feel just as outdated as those old phone bridges. Video is no longer a cutting-edge technology. This technology is already mature and widely accessible.What’s innovative today is not the video itself, but how platforms can integrate it into intelligent, human-centric customer journeys. That’s where the value is created.Is AI really needed for enhancing customer experiences?Tomer believes that AI adoption is far more than just hype. AI can be integrated in many practical value-driven ways. For example, AI-powered chatbots and tools can be used for sentiment analysis that helps improve customer interactions by learning from past conversations. He also mentioned that Vee24’s engineering team relies on AI to write code more efficiently, debug faster, and optimize development workflows.As it is said, in the short term, the impact of technologies is often overvalued. But in the long term, it is undervalued. Therefore, the key is using AI intentionally, not just to follow trends, but to solve real problems.The best way to introduce new technology is through structured experimentation. For example, this can be A/B testing on a website or piloting a new internal tool. By setting clear metrics and measuring outcomes, businesses can make a well-informed conclusion on whether it will be feasible to invest in the scaling of these initiatives. AI adoption: Failures and successesTomer shared that in some early chatbot deployments, results didn’t meet their initial requirements. However, the team made the required adjustments and achieved the desired improvements. The balance between AI and human agents is another area where ongoing tuning based on real-world data is essential. A recent internal success story illustrates this well. While preparing to launch its new website, Vee24 tested a generative AI website builder. For just $20 a month, the tool, driven by natural language prompts, produced an 80-90% complete site within hours. The ease of making edits, like resizing a logo with a single prompt, and the speed of iteration were eye-opening. Though not suited for complex or highly regulated sites, the solution exceeded expectations and showcased AI’s value for the right use case.Practical AI in retail: No silver bullets, just smart integrationQuick fixes through AI are largely a myth, especially in traditional retail. Many of the companies Vee24 works with are over a century old and are naturally cautious about change. These organizations often value human interaction deeply and are not quick to adopt new technology.Some businesses may initially see AI as a “silver bullet” and be ready to apply it everywhere. However, for Vee24, the real challenge is typically the opposite. The company needs to overcome resistance to change and encourage retailers to shift their mindsets. Unlike tech companies that may chase innovation aggressively, retail businesses move more slowly. They require thoughtful, well-integrated solutions rather than instant transformations.Why is it so?Tomer provided a couple of examples from his practice. Watches of Switzerland, a major retailer of luxury watches, has modernized its customer service approach over the past five years. Initially relying solely on phone and email support, the company now has dedicated video agents in both London and the US to offer live consultations online.These video interactions mirror the boutique experience by delivering personalized, high-touch service to digital shoppers. Given the high value of their products, which can exceed $100,000, the company prioritizes human interaction over automation.This approach contrasts sharply with mass-market retailers like Amazon, where AI-driven, efficiency-first models dominate.Another example that Tomer mentioned was a women’s bra company. The company was struggling with a return rate exceeding 50% and was exploring the use of live human support to complement its existing AI solution. Despite having an advanced sizing app that helps customers find the right fit using their phones, the business still faced significant returns often due to user error during the sizing process.Recognizing the limitations of AI, the company wanted to add expert human guidance to the online shopping experience. The goal wasn’t just to improve customer experience or achieve higher sales. The most important task was to reduce costly returns that threatened business sustainability.A common issue in implementing AI is the misconception that it’s a quick fix or a magical solution that will solve all problems without human input. Many businesses mistakenly assume that once AI is introduced, it will instantly deliver perfect results without the need for fine-tuning or oversight.In reality, AI may handle the first 80% of a task quickly and successfully. For example, it can generate a website in hours. However, refining that last 20% to meet specific needs often requires significant additional work.Value of human connection in salesSuccess in AI projects and modern sales is driven by a mix of clear expectations, smart implementation, strong processes, the right people, and reliable partners. AI should be seen as a tool to support (not replace) the human touch.At the heart of business success lies customer experience. People don’t just buy products. They buy from people they trust and like. A great experience builds loyalty. Quite often, it becomes even more significant than price or speed.AI plays an increasingly large role in customer journeys. However, according to Tomer, truly memorable experiences are still mainly human. No one says: “The AI experience was so great, I’m definitely coming back because of it.” Brands that prioritize meaningful human connection will always stand out.Rise of one-person unicornsWhile discussing the power of AI in the modern business world, Max also asked Tomer whether he really believes that it is possible to build a successful project valued at $1 billion without having a full-time team.Tomer explained that if the idea is compelling enough, such a scenario can become a reality. At Vee24, they have significantly reduced full-time staff and leaned into outsourcing across various functions like sales and support. Despite being external, these team members feel like part of the company.Vee24 has shut down physical offices and the entire team operates almost entirely remotely. This flexibility has helped the company cut costs and scale seamlessly. While it’s not literally a one-person business, even with outsourced talent, the structure allows it to set ambitious revenue goals.Today, there can be different approaches to building a successful company. You can hire a big team of more than 300 employees, or you can rely on international outsourcing and automation. They both have their advantages and limitations, which every funder should carefully consider.Future of customer interactionTomer predicts that businesses will become significantly smarter in their use of AI, will start deploying it more effectively, and will understand when not to use it at all. Today, many negative AI interactions stem from misuse or poor implementation. In the future, companies are expected to adopt a more strategic approach.AI is projected to become a standard component of digital customer experiences, particularly in backend operations. This includes data analysis, reporting, and customer behavior evaluation. Namely, in these areas, AI demonstrates its outstanding performance.Another growing tech trend, according to Tomer, is the use of video. He anticipates an increase in video consultations and interactions over the next five years.Tomer mentioned that one of the most overlooked technologies in today’s retail space is appointment scheduling. While it is a cornerstone of productivity in the tech and business world, its potential in commerce hasn’t been fully realized yet. In business settings, nearly everything runs through a calendar. But when it comes to our personal lives, especially how we interact with retailers, scheduling is rarely part of the process.This case reveals a significant opportunity. For example, buying a diamond isn’t something most people do impulsively. It’s a considered purchase. Scheduling a consultation and seeing it seamlessly added to your calendar, just like any important meeting, brings a sense of clarity and professionalism to the experience.Though the retail world continues to digitize, the most successful businesses will be those that blend the speed and efficiency of AI with personalized human interaction. AI has proven its power in handling routine tasks and streamlining operations. But when it comes to building trust, guiding emotional purchases, or offering expert advice, human connection remains irreplaceable.Technology is advancing fast, but the heart of great commerce is still human. That’s the main insight that Tomer shared with Max and with the entire audience of the Innovantage podcast. Want to get more insights from business experts and tech leaders? New podcast episodes will be available soon.

Today, it may seem that AI is discussed everywhere. Probably, this statement is really not far from reality. Artificial intelligence and its transforming power are an extremely popular topic. The Innovantage podcast hosted by Sigli’s CBDO Max Golikov, also makes its contribution to raising public awareness about the potential of this technology. In the new episode, Max decided to concentrate on the real-world impact of AI on the business world.To talk about this, he invited Maxime Vermeir, Senior Director of AI Strategy at ABBYY, who agreed to share valuable insights with the podcast’s audience.Maxime is one of those experts who “were studying AI before AI was cool”. Over the years, the industry has changed a lot, and he has seen its evolution from many angles. In his career, he worked with enterprise tech, SaaS solutions, and mobile apps, and even launched a startup before the startup scene became mainstream.These days, his focus is sharply on artificial intelligence. Now, he helps people understand and harness it in meaningful, productive ways. AI: Is the hype real?AI is a very trendy technology. But what is behind this hype? Maxime acknowledged that hype has always been a part of the tech industry from Web 2.0 to the cloud. AI is no different in that regard.Artificial intelligence has entered the mainstream, sparking both excitement and concern. On the one hand, more people are becoming curious and educated about AI. This opens up new opportunities for businesses. On the other hand, this surge in attention often leads to inflated expectations and sometimes misconceptions. For example, there are people who still worry that robots may take over their jobs.What stands out here is how rapidly the AI hype cycle is compressing. Unlike the slow and chaotic early days of cloud adoption, the AI space is maturing much faster. Regulations are coming in quickly. Companies are looking for transparency and ask AI vendors to provide them with detailed information about security protocols and the way the technology is built.For Maxime, that’s a sign of real progress. The industry is beginning to get rid of the “one-size-fits-all” approach and is moving toward a more thoughtful, pragmatic perception. The hype may still be there. But also, there is a growing sense of responsibility. Practical value of AI solutionsHe also emphasized the importance of practical value over hype. For instance, in some cases, it can be simply not feasible at all to build your own large language model, which can be a very expensive and time-consuming project. Instead, it can make more sense to rely on much simpler, classic technologies like regular expressions.There is real innovation happening in AI, and powerful tools are emerging. But not every problem needs a big solution. It takes time for people to understand that the “next big thing” isn’t always the right choice. It is much more important to match the right tool to the right use case, with a clear understanding of both their capabilities and limitations.As the hype around LLMs exploded, many rushed to apply them to document data extraction and even made that one of their first use cases. Maxime found this to be not the best idea, given the technology’s broader creative potential. This led to a widespread belief that LLMs could fully replace established solutions like Intelligent Document Processing (IDP), a mature technology used by enterprises for over 20 years.At one point, an analytics firm even declared that “IDP is dead” and compared it with RPA (Robotic Process Automation), which was overshadowed by newer tools.But today, the narrative has changed.As Maxime noted, businesses have realized that throwing PDFs into LLMs often leads to a lot of issues, like hallucinations, context limitations, and inaccuracies. This is especially problematic when extracting critical data. The consequences of such mistakes can be quite tangible and costly.With decades of experience in document processing, ABBYY has always taken a different approach. Long before the LLM boom, they were already leveraging transformer-based models tailored to particular tasks, such as document segmentation or value extraction. The key is combining technologies within a purpose-built platform, designed specifically to deliver reliable results in real-world business environments. This approach can bring true value to businesses.AI's creative potentialMaxime highlighted that there are several kinds of AI that can serve different goals. For example, generative AI shouldn’t be the first choice for tasks like data extraction. Instead, it would make sense to rely on more traditional, proven methods like machine learning, convolutional networks, NLP, and transformer-based models. All of them have been around long before ChatGPT entered the game.For example, ABBYY also integrates large language models into its platform, but with a clear purpose. They are used to deal with more complex, probabilistic use cases where generative AI can truly add value. When used appropriately, it creates a so-called “1+1 = 3” effect, where multiple technologies are used together to deliver better outcomes than any single one could do when applied alone.Solving real-world problems always requires a combination of AI methods, each chosen in the right context. As Maxime mentioned, while GenAI might not be a go-to option for document processing, it has great creative power. It makes such tools highly helpful when it is necessary to overcome the blank page problem in writing and content creation.Maxime explained that it is possible to transform generative AI into a powerful creative partner for your personal and business workflows. While AI doesn’t always perfectly understand users’ intents, its ability to offer thoughtful, predictive responses can help to refine and evolve your own ideas.He also sees generative AI as a good social equalizer in creativity. In the past, expressing creative ideas often required technical skills or specific tools. Such things were serious barriers for some people. But now, with intuitive AI interfaces, users can simply explain what they want and get results.This shift is unlocking creative potential for a much bigger group. More people can now bring their ideas to life, regardless of background or skills. How do executives generally approach AI tools?Today, many executives approach AI tools with very high expectations. After implementing a solution, they want to see a quick, transformative impact. But the reality is more complex.As AI becomes more mainstream and visible in everyday media, there is a perception that it can do everything. This leads some decision-makers to underestimate the effort and resources required to implement it effectively. While generative AI demos are undeniably impressive, turning that potential into practical value is much more challenging.Maxime pointed out that while creative applications of AI are usually quite simple to understand and leverage, enterprise use cases are a different story. Business processes involve collaboration, integration across systems, and clear ROI expectations. The true impact lies in automating end-to-end processes, not just speeding up separate tasks. Executives need to understand their processes first, identify the real bottlenecks, and only then choose the right technology to improve them.How to measure the success or failure of implementing AI solutions?The wrong way to implement AI is to focus only on speeding up individual steps or reducing human input without understanding the full workflow. While those changes may show short-term gains, they often create new issues in other stages.Success in AI adoption should be measured by improvements in the overall process. That’s where process intelligence plays a key role. It allows you to visualize, test, and predict the impact of changes across the entire workflow. Without that insight, automation alone won’t deliver meaningful results.Time often becomes a key metric in measuring AI success. However, it shouldn’t be the only one. For example, automating an insurance claim process too quickly with the wrong tech, like an LLM, may initially seem efficient but could lead to costly errors and rework. A better measure of success includes fewer mistakes, faster and more accurate outcomes, and compliance with regulations like the EU AI Act. AI regulation: Does it really help?Maxime strongly believes that the real challenge with AI isn’t the technology itself, but how we implement and use it. He views the EU AI Act as a necessary step in regulating this technology, although it could move faster. The Act doesn’t hinder innovation. It ensures transparency and accountability, which are essential for building trust in AI systems.This regulatory framework helps to clarify AI’s role and prevents its misuse. As a result, businesses can stay compliant and avoid a lot of risks.Organizations can either self-assess or work with third parties to ensure compliance with transparency requirements to promote a more responsible use of AI in the business space.AI vs traditional technologies: Balance requiredThe reality is that AI is being promoted in exaggerated ways, similar to how RPA is now being rebranded as "agent technology" to attract more market attention. Nevertheless, when selecting AI tools, enterprise executives should focus on real outcomes such as cost savings or process improvements backed by customer testimonials.Today, there are a lot of vendors that name well-known technologies like GPT or cloud services behind their solutions. At the same time, they don’t provide insight into any unique features and approaches that determine the value and differentiation of their tools.For businesses, it is vital to balance old and new technologies. While a well-established technology introduced over 30 years ago offers reliability and a proven track record, the downside is that it may lack flexibility or fail to address newer, evolving needs. It’s essential to combine different technologies strategically and create solutions that deliver real value. AI should be implemented thoughtfully, not because it is one of the latest trends or buzzwords, but because it can change processes for the better.AI agents and the future of workAccording to Maxime, AI agents are currently both a hype and a useful technology. While Salesforce has contributed to their promotion, there is potential for AI agents to address issues that RPA couldn’t. For example, they can automate complex processes and enable broader access to technology. AI agents are designed for practical automation, and they can orchestrate existing tools without human intervention. The reason for optimism is that more people now understand the importance of process intelligence. This awareness is increasing, making organizations more cautious and focused on properly implementing AI to avoid failure.It’s interesting to mention that 80% of AI projects today still fail. But of course, people do not want to be this 80%, they want to be the lucky 20%.How to reduce the chance of failure? Maxime mentioned that AI can only be effective if you provide it with the knowledge of your organization. Here, it is necessary to focus on the data about how your business operates and the information locked in your documents. Additionally, you need to clearly explain how your processes work. That is where process intelligence can help. By providing a blueprint of your current workflows and what changes will lead to the desired ROI, you can guide AI to deliver results. Maxime stated that AI could be the next big step in enterprise automation. People are tired of dealing with countless APIs and bypass services. They want to have integrated platforms. It is still a bit early for agent frameworks to be fully enterprise-ready, but this can happen quite soon. Will AI agents take people’s jobs?Every major tech shift brings both job creation and job displacement. It absolutely doesn’t mean that designers or artists are no longer needed just because AI can generate visuals. There will always be two groups: those who resist change, fearing it will replace them, and those who embrace it, using it to amplify their skills. AI is just another tool. It doesn’t replace expertise, it enhances it. Just like a paintbrush doesn’t make someone a painter, AI doesn’t make someone an artist without the underlying skill.New roles, like prompt engineers, are already emerging. At the same time, some repetitive tasks are being phased out. It’s the natural cycle of innovation. Those who adapt, learn, and evolve will continue to find new opportunities.According to Maxime, this technology shift is no different from those before it. When RPA was introduced, people were afraid that automation would replace their manual tasks. And it did. But it also opened the door to more valuable work.AI can perform some very tangible, very clear tasks. For example, if somebody is bad at writing emails, AI can help this person to do it at an average level.But for experts, the value lies in how well they guide the tool. The quality of the input directly determines the quality of the output. “Garbage in, garbage out” still works. If you provide vague prompts, you will get generic results. But if you take the time to explain structure, context, and intent, the output can match the quality you aim for. AI adoption: Practical adviceAt the end of their conversation, Max Golikov asked his podcast guest to share practical recommendations for AI leaders and founders.Maxime said that they must start by clearly defining the problem they are trying to solve. One of the most common mistakes is adopting technology simply for the sake of innovation, especially during intense hype cycles, which are becoming more frequent and extreme.Whether building a new product or implementing AI within a business, the core principle remains the same: clarity of purpose is critical. For enterprise leaders in particular, a deep understanding of their existing processes is non-negotiable. Without that insight, applying AI effectively is nearly impossible.With clear goals and process visibility, leaders can choose the right tools with precision. This will help them avoid the common pitfalls, like using overly complex solutions for simple problems. ‍Identifying the proper use of emerging technology is one of the things you can learn from the episodes of the Innovantage podcast. Don’t miss the next episode to stay up to date with all the latest trends in the business world.

How to build a data culture in the AI-powered environment In today’s world, data is more than just a byproduct of business operations. It’s a strategic asset. A lot of organizations invest in data tools and technologies. Nevertheless, the real challenge lies in creating a culture where data is understood, trusted, and consistently used to drive decisions at every level. How to do it? That’s one of the key questions that Max Golikov, the Innovantage podcast host and CBDO at Sigli, discussed with his guest Dr. Carsten Bange. Dr. Bange is the founder and CEO of BARC and an expert market analyst for data analytics and AI. For over 25 years, he has focused on evaluating software vendors and technologies, helping organizations make informed decisions based on market trends, strengths, and weaknesses of various solutions. While BARC began as a technology advisory firm, its scope quickly expanded. Today, the company also supports clients with data and AI strategy, organizational development, and data governance. All this is a key foundation for successful analytics initiatives. A major area of Dr. Bange’s work is data culture, which is the human side of data-driven transformation. As he emphasized, even the most advanced technologies can fail without the right mindset, skills, and engagement from people. Lack of adoption is often not a technical issue, but a cultural one. To promote this idea, he launched The Data Culture Podcast. He interviews practitioners and leaders who share their experiences in building a strong data culture within their organizations. What is data culture? As Carsten explained, data culture refers to the unwritten rules that shape how organizations work with data. It encompasses the values, beliefs, and behaviors that support the effective and ethical use of data across the organization. At its core, data culture defines how people think about and interact with data, how they use it, and for what purposes. Besides that, it determines how organizations leverage data to drive decision-making, process improvement, and innovation. Data culture eats data strategy for breakfast One of the most common questions organizations face is: “How to build a strong data culture?”. Dr. Bange highlighted that “data culture eats data strategy for breakfast” as even the best data strategies fail without the right behaviors and ways of thinking. Implementing data culture is not about issuing a directive. Culture can’t be turned on or off. It must be influenced through ongoing, targeted efforts. Carsten shared his framework that organizations need to tackle when they want to improve data culture. This framework includes 6 areas. Data literacy Enhancing data literacy is often the starting point. Upskilling employees, increasing their confidence and competence with data, as well as fostering a shared understanding of its value are foundational steps. Data access Data culture depends on data access. In many organizations, it is limited either due to technical constraints or restrictive access rights. There are two models of data access. The first one is “need-to-know”. It presupposes that access must be requested and approved. The second one is “right-to-know”. It lets data be open by default unless it’s sensitive, like HR or personal information. The latter fosters trust, openness, and initiative. People have access to data and they can use it to bring benefits to their organizations. Data communication According to Carsten, communication plays a vital role in reaching people and shaping their behavior around data. To build a strong data culture, leadership must consistently communicate the strategic value of data to their employees. They should show how data aligns with business goals and supports competitive advantage. It’s also worth sharing success stories and role models. Real examples of how data has driven results, like increasing revenue or gaining new customers, can motivate others to change their attitude toward the data they have. Data strategy A successful data strategy must be based on the existing data culture. Ambitious plans for enterprise-wide AI or advanced analytics are unrealistic if employees lack the tools, skills, or access to data. Too often, strategies are overly technical. They are focused on architecture or infrastructure. But they neglect the people who must use those tools. Data culture should be an integral part of any data strategy to ensure alignment with organizational reality and to support real execution. Data leadership Strong leadership is critical to fostering a data-driven culture. While grassroots efforts are valuable, they reach a limit without top-down support. Senior leaders must actively promote data initiatives and model the behaviors they want to see. Carsten pointed out that the biggest blockers are often in middle management, where key resource and access decisions are made. If middle managers withhold support, it can slow down cultural progress. Data governance This component is about balance. Too little governance leads to chaos. Too much creates fear and resistance. Overly strict rules or legal-heavy processes can discourage people from working with data at all. Effective data governance should enable data use, not restrict it. It should guide employees, support data quality, and create clarity without driving anxiety. In a positive data culture, governance is seen as a help, not a hurdle. Who benefits the most from the data culture? According to Carsten, company size is not the deciding factor when it comes to benefiting from a strong data culture. That’s a conclusion that he has made after years of working with a wide range of organizations and interviewing nearly 150 guests on The Data Culture Podcast. Of course, large organizations often have more resources to work with data. For example, they can form dedicated data culture teams. Such teams may focus solely on promoting data literacy, leading internal communication efforts, and organizing events like annual award ceremonies celebrating successful data projects. This structured approach allows them to scale data culture initiatives across the enterprise. Smaller companies may not have formal teams but they can still adopt the same principles. While the scale and execution differ, the core concepts and framework remain fully applicable. What companies succeed in building a data culture In his discussion with Max, Carsten mentioned a strong link between overall company culture and the success of data culture initiatives. For example, organizations moving toward data products tend to succeed when their company culture already promotes collaboration, openness, and knowledge sharing. In contrast, organizations with siloed, disjoined cultures often struggle with such approaches. Among forerunners in a data culture, Carsten named Merck, a global pharmaceutical leader, that has a dedicated data-focused team. Before the first pan-European Data Culture Summit, Bange conducted a study to identify organizations actively investing in data culture roles. The research, based on LinkedIn data, revealed that: Large enterprises are more likely to assign formal roles for data culture. Europe leads globally in adopting these roles, with the UK and Germany at the forefront. In Europe, the rise of data culture roles started in around 2021, while in the US, it began 2 years later. South America is showing rapid growth and is even outpacing North America in the number of data culture-related roles. The financial services sector, including banking and insurance, is currently the most active in data culture, accounting for over half of all identified roles. This is likely due to the industry’s data-heavy nature and strong regulatory requirements for data governance and quality. Data governance in decentralized data landscapes Effective data governance must align with an organization’s structure and operational reality. "One-size-fits-all" models don’t work. Your model must be adapted based on whether the company is centralized or decentralized. Many organizations today are moving toward decentralization of structure and data ownership. This reflects a long-term trend in data and analytics: shifting responsibility and ownership closer to business units. This is also often accompanied by decentralizing platforms, tools, and access. Such a shift challenges traditional ideas of centralizing all data in one place. The once-dominant data warehouse approache, which aimed to consolidate all data centrally, is no longer practical for many organizations. The growth in data volumes, the rise of real-time IoT data, and increasing complexity make it difficult (and sometimes even impossible) to bring all data together in a single location. Instead, modern data architectures often follow distributed models, such as data fabric, which help to maintain a coherent framework for interoperability and governance. What drives data decentralization? According to Dr. Bange, the engine behind decentralization in data and analytics is the need to scale data usage across the organization. To create a strong data culture, companies need to empower more people to actively work with data and analytics tools. Centralized models often create bottlenecks either in data access or in the limited availability of central data teams. In many cases, central data teams are overwhelmed and can’t fully support the growing demand for analytics. As a result, business units need to decide whether they should wait or take the initiative themselves. Decentralization becomes a logical step here. Thanks to this approach, teams can access and integrate their own data, build local data capabilities, and act autonomously. One major benefit of decentralization is proximity to domain knowledge. Domain expertise is critical for building meaningful analytics or AI models. Being closer to the actual business processes allows teams to identify relevant use cases, involve stakeholders early, and ensure real-world adoption. This is especially important when transitioning from pilot AI projects to enterprise-scale deployment. The main challenges at this stage are often organizational, not technical. Scaling AI and analytics requires changing workflows and embedding new tools into existing processes. All these issues can be addressed faster when data teams are integrated within the business units. Benefits of the hybrid approach However, entire decentralization is often not the best choice. Here is when hybrid models come into play. Hybrid models offer the most practical and scalable path forward for organizations navigating data governance. Dr. Bange explained that this approach strikes a balance between central oversight and decentralized autonomy. It means that it can adapt to organizational complexity while enabling growth. There are two key reasons to centralize certain aspects of governance. First of all, some topics are too critical to leave to individual teams. Regulatory compliance, such as GDPR, is a prime example. Instead of having dozens of teams interpret and apply these rules independently, centralized governance ensures consistency and reduces risk. Secondly, limited expertise in emerging areas like AI often requires a centralized starting point. Over time, as capabilities mature, these roles can be gradually decentralized and central units are shifted to supporting roles, like education and community-building. At the same time, organizational diversity plays a crucial role. Within the same enterprise, different departments or regions can be at vastly different levels of data maturity. Some may have strong internal teams, platforms, and domain expertise. Others may rely heavily on centralized support and shared services. A hybrid approach acknowledges such differences. It allows flexible service models, where units can choose what they handle independently and what they consume from central teams. AI’s influence on data culture and governance The rise of AI has significantly shifted the conversation around data in organizations. What was once a specialized concern for data teams has now reached the boardroom. Executive leadership increasingly recognizes that AI requires high-quality, well-governed data to deliver real value. This understanding has reinforced the need for robust data governance practices. As companies aim to expand their AI capabilities, they must also address long-standing challenges around data quality and accessibility. The roles of data and AI literacy are equally important across the organization. Just as with broader data culture efforts, successful AI adoption requires behavioral and mindset shifts. Employees must understand what AI is, how to use it, and feel empowered to experiment with it. Access not only to quality data but also to AI tools and infrastructure remains crucial. Making AI capabilities widely available within the organization democratizes innovation but also increases the importance of governance frameworks to guide ethical and compliant usage. The introduction of the European AI Act underscores this point. While some organizations view it as restrictive, others see its value in providing clarity. With it, companies have received a stable framework within which they can build and scale AI-powered vs. traditional approach to data When it comes to becoming data-driven, organizations face a common dilemma: should they fully rely on large language models and hope that AI is smart enough to help them work with data, or should they take a more conservative route and focus first on cleaning and organizing their data? Dr. Bange believes the real challenge is doing both at the same time. AI often acts as a trigger for companies to finally take a closer look at their data. Poor quality, outdated models, and years of underinvestment in data infrastructure are typical issues. Ideally, organizations would fix their data first and then build AI use cases on top. But that approach isn’t realistic in a fast-moving environment. Nobody wants to hear that leveraging AI requires two years and several million euros just to clean the data. According to Carsten, it could be sensible to opt for a more pragmatic approach: find use cases where AI can deliver early value while simultaneously improving the data foundation. Such projects can demonstrate the potential of AI. They also provide time to make the necessary long-term investments in data quality. Challenges of data culture and AI implementation There are two major blind spots for organizations trying to implement data culture. The first one is the human element. Amid the excitement around new AI models and technological advancements, companies often don’t notice the central role of people. As AI automates more tasks, the need for human oversight and engagement becomes even more critical. Building a strong data culture isn’t just about tools. It is also about collaboration, and continuous learning. The second blind spot is underestimating the speed of technological change. Many organizations lack a clear grasp of how rapidly AI is evolving. This can make them slow to adapt or experiment. As a result, they may fall behind their more agile competitors that embrace AI-driven automation and innovation more quickly. Practical advice for technology leaders At the end of their talk, Max asked Carsten to share recommendations on how to start building a data culture at an organization. The first tip was quite simple: just to start. Too many organizations hesitate or overthink the process. However, taking action is vital. He also recommended using a structured framework, such as his own model with six key areas that influence data culture. This framework helps organizations assess where they currently stand and identify which aspects need the most attention. Dr. Bange also mentioned two areas that are often underestimated at the beginning of the journey: data access and data communication. Many companies don’t realize their importance until they are already a year or two into the process. And this can become a serious obstacle for them. Want to get more expert insights into how to boost your business growth in the data-driven world? New Innovantage podcast episodes will shed light on this! Stay tuned!

Every episode of the Innovantage podcast offers a new perspective on different business aspects and the role of technologies in them. This time, Max Golikov, the podcast host and the CBDO at Sigli, invited Agu Aarna to talk about tech due diligence and the impact of AI on the investment landscape. Agu is a Co-Founder and Partner at Intium Tech, a tech advisory firm specializing in helping large companies and private equity funds buy and sell tech businesses. Over more than 20 years of his professional journey, he has accumulated experience in such spheres as development, architecture, and executive leadership. All this helped him to get a good understanding of how the tech world works. Seven years ago, he transitioned into consulting, helping businesses with acquisitions, carve-outs, and value creation. In 2021, he co-founded Intium Tech. With Intium, Agu and his team wanted to create a standardized approach to assessing technology, similar to what exists in other sectors. They recognized the need to describe technology in a clear, structured way for investors and business leaders. As they developed their system, they realized it could be integrated into software. This led to the creation of their own platform, which enables more efficient analysis of acquisition targets. How technology affects business In his dialogue with Max, Agu emphasized the complexity of technology’s impact on business. A minor technical detail can have significant business implications. That’s why assessing its true effect is crucial. Blindly following best practices is not the best approach. The focus should be on understanding their relevance to a company’s goals. For example, if a company doesn’t run unit tests, it’s not just about missing a best practice. First of all, it should raise questions about the quality of its solutions, leadership, and overall strategy. It’s necessary to find out why it is so. According to Agu, the key lies in finding a balance and understanding both the business’s ambitions and how technology can support them. This dynamic relationship between business goals and technology is what he finds most important. Challenges in tech due diligence Tech due diligence (TDD), which is one of the core aspects that Agu’s firm is focused on, is a detailed examination of a company’s technology infrastructure, products, and processes, typically conducted before a merger, acquisition, or investment. As Agu highlighted, the approach to such analysis has evolved significantly over the years. In the 2000s, it was viewed as a “nice to have” process. It presupposed that a couple of tech experts would assess a company’s technology, often resulting in a laundry list of issues based on their own expertise. This approach lacked a comprehensive view of the business impact. By the 2010s, tech due diligence had become more professional. It already could offer a broader perspective on leadership, architecture, and infrastructure. However, the analysis still lacked a focus on the actual business impact of these issues. In the 2020s, the focus shifted to understanding the business impact of technology and analyzing companies from this perspective. However, inconsistencies in reporting remained a challenge. Different experts can emphasize different aspects, which leads to varying results. Such an issue highlighted the need for a standardized approach. How to make TDD more efficient today Agu believes that to solve this, the industry needs more consistent, high-quality analyses. This could be achieved by leveraging software instead of relying on people-driven processes. This shift toward software-powered solutions, like the one developed by Intium, aims to provide a more scalable and smooth approach to tech due diligence. When discussing tech due diligence, Agu also highlighted two key aspects to focus on. First, it’s crucial to educate clients that tech due diligence is more than just a code or architecture review. Technology is the engine that powers a company, but just like a car, it needs to be steered in the right direction. Evaluating technology requires understanding its context within the business, not just identifying flaws in infrastructure or architecture. Equally important are the people managing the technology and the processes that connect them. Inefficiencies here can quickly undermine technical strength. The second key aspect is taking a comprehensive 360-degree view of the company. Concentrating on only one part of the technology or business won’t provide the full picture. Without this broader perspective, risks and crucial elements to make the deal successful might be overlooked. Moreover, Agu identifies several key risks in tech due diligence that can lead to failed deals: One major risk is when technology is presented as a core asset but doesn’t live up to expectations. Another risk is technical debt and architecture. If the debt requires too much effort to manage or fix, it can cause a deal to fall apart. A third risk is insufficient preparation for the sales process by the target company. When private equity firms are considering mature companies, a lack of proper preparation can reveal too many unknowns, making the deal seem too risky. A well-conducted TDD not only helps determine whether to buy this or that company but also provides information to negotiate the price, impose conditions in the purchase agreement, and even structure earnout plans. Key factors investors should pay attention to It is believed that when you are investing in tech businesses, technology always remains the key factor to evaluate. However, this is not always true. Agu explained that in early-stage investments like seed, pre-seed, and Series A or B, technology is often secondary (as at such stages there is hardly any tech at all). What investors are looking at are the ideas and leadership. Investors should focus on exploring whether the leadership team understands the technology they are working with. Here, the key task is assessing the leadership’s technical acumen to ensure they can build and execute on their vision. As companies move into the growth phase, product-market fit is already established. It means that technology becomes crucial. Scaling the technology to support growth is a different challenge from proving a market problem. This makes tech due diligence more important at this stage. In private equity, where mature companies are involved, technology is already a significant factor. Agu stressed the importance of being transparent and truthful when communicating with investors. If a company misrepresents its technology or misleads investors, it can result in the collapse of the entire deal. AI wrapper companies: Good or bad? While talking about tech innovations, Max mentioned the growing number of so-called AI wrapper companies. They build user-friendly interfaces or apps on top of existing AI technologies, often providing a simpler or more tailored experience for end-users. Instead of developing their own AI models or deep technologies, these companies focus on wrapping AI capabilities into practical solutions. They interact directly with users and often become "sticky" due to people’s habits. Agu believes there is nothing wrong with establishing a wrapper company. In fact, being a wrapper company can be even more important than being a deep tech innovator like OpenAI. He pointed out that AI wrapper companies need to work in some specialized areas like prompt engineering, which may not require deep tech knowledge but still involve particular skills. These companies must know how to effectively augment prompts and optimize user interaction. He also noted that developing and hosting AI can be expensive, adding another layer of complexity for companies in this space. According to Agu, building your own AI is not impossible. However, convincing investors that the team has the expertise to do it is challenging as AI can be very technical. When evaluating an AI company, it is crucial to determine if AI is truly the right tool for the indicated problem. For example, traditional mathematical or statistical models may work as well as AI in some cases, and using AI unnecessarily could signal a lack of understanding of the problem. However, in competitive markets, simply being a wrapper around AI isn't enough. Teams behind such projects must specialize in and understand how AI works. This is also necessary to choose whether they will use off-the-shelf solutions or develop their own models. Privacy is another major concern, particularly in regions like Europe, where data protection is strict. In some cases, companies opt to develop their own AI in order to avoid privacy issues with third-party systems. Impact of AI regulation and privacy laws AI regulation and privacy laws, such as GDPR, have sparked significant debate. Nevertheless, over time, they have proven to be pretty manageable and even beneficial. For instance, GDPR served as a template for other laws like the CCPA in California and the UK’s data protection frameworks. These regulations were initially seen as hurdles but now they are generally accepted as necessary for privacy protection. There is a concern that regulation can stifle innovation. This can happen not necessarily due to any created barriers, but due to the lack of input from business and tech representatives during the drafting process. A more collaborative approach that includes industry experts can make the regulations much more balanced and practical. Regulations are important for protecting personal data. It is crucial to remember that not all market players have good intentions. Without regulation, the misuse of personal data, especially in AI training, could lead to manipulation on a massive scale. Proper regulation ensures that the technology benefits society without being exploited. Policies serve as a tool to raise awareness and guide behavior. They are like a friendly reminder to look both ways before crossing the street, providing useful information that helps keep us safe. When viewed in this light, regulations aren’t obstacles but safeguards that help us navigate potential risks. As AI and technology continue to connect us more deeply, establishing ground rules becomes essential. These rules will help define what data can be used and under what circumstances, ensuring that people are not overwhelmed by the complexities of these technologies. With proper guidelines, people can better understand and trust the systems in place. This clarity is vital for preventing confusion and misuse as the tech landscape evolves. Future of AI for investors These days, there are a lot of talks about the role of AI in different industries and domains. That’s why Max couldn’t help but ask Agu to share his vision of the role of AI in tech due diligence. AI is already being used by investors, particularly in early-stage analysis. Today investment firms leverage AI to gather data on potential companies, analyze it, and automate certain tasks. For example, AI can notify investors when a company becomes more lucrative to drive further investigation. Investors can also use advanced tools like ChatGPT to ask AI for advice about companies. AI plays a significant role in the early stages of investing, and its use extends to later stages and new purposes. However, relying entirely on artificial intelligence without expertise can be risky. If you input a company’s documents into AI, like OpenAI’s ChatGPT, and ask for a summary of the top issues, the technology may provide a polished response that seems accurate but could be misleading. This is because AI sometimes hallucinates and fills in gaps with logical but incorrect information, leading to wrong conclusions. This can be especially problematic for non-experts who might be misled by the polished language. AI is particularly useful in summarizing large amounts of data. But it should always serve as a tool to support expert analysis, not replace it. The key is using AI’s output as an input to the expert’s thinking while controlling that AI doesn’t miss important details. This approach allows for more accurate and reliable results. AI has made significant progress in assisting with due diligence. However, it is still not at the point where it can fully conduct the process on its own. Connecting AI findings to the investment thesis and business impact remains a significant challenge. While AI can provide valuable insights, human expertise is required to make sense of AI-generated data in a meaningful way. In the future, AI may gradually take over more tasks, with humans focusing on areas where AI struggles. However, a key challenge will be ensuring that AI systems continue to evolve. They need constant feedback to stay updated with new information, trends, and market shifts. Without this ongoing learning, AI may become outdated and far less helpful. Investment opportunities and trends in the tech market While talking about the current investment opportunities, Agu noted that in recent years, many specialized startups have emerged. What makes them successful is their focus on niche products that effectively solve specific market problems. According to Agu, today a lot of private equity accounts are sitting on a significant amount of dry powder, which means that there is capital ready for immediate investment. This situation suggests that a period of consolidation may be on the horizon, where smaller companies are acquired and merged into larger corporations. This trend is likely to create opportunities for venture capital and growth equity investors who have supported these niche companies. In particular, AI wrapper companies, if they solve a real problem and maintain strong customer relationships, are well-positioned in this environment. In conclusion, Agu agreed with the common opinion that AI is here to stay. It is expected that this domain will become increasingly efficient over time. We will likely see the emergence of more advanced AI use cases and implementations. However, all of these AI systems will still require resources to operate. Therefore, anything that powers AI is likely to remain essential moving forward, which is a quite expected trend. And if you want to learn more about the current and future trends in the business world, the Innovantage podcast is exactly what you need. The next episodes will be available soon (moreover, don’t forget to verify whether you haven’t missed the previous ones)!

Every startup founder wants their business to achieve success. But does every startup founder have the required traits that will lead their business to success? This was one of the questions that Max Golikov, the Innovantage host and Sigli’s CBDO, addressed to his podcast guest Mike Sigal.Mike is an expert with over 35 years of experience as both a founder and investor, who is now a founder of Sigal Ventures, a Venture Partner at GPO Fund, MiddleGame Ventures, and Pella Ventures, and serves on the Investment Committee for SC Ventures. During his professional journey, he has seen the peculiarities of the entrepreneurship world from different perspectives. In his conversation with Max, they also discussed the current state of the fintech market, the challenges of the VC industry, and the value of resilience in the business space.Entrepreneurship is a force for goodOver the years, Mike founded or co-founded eight startups, varying from graphic arts, cloud databases, analyst firms, software, and fintech, to a nonprofit. His journey included raising venture capital, experiencing both successes and failures, and serving as an executive at a company through an IPO. He has been through the entire startup to exit journey multiple times. Between startups, Mike consulted for mid-to-large corporations, which led him to work with SWIFT. There, he helped bridge the gap between startups and global banks, creating a competition that introduced fintech unicorns like Wise and Revolut to the industry.Among his other career milestones, he was also invited to join 500 Startups (now known as 500 Global) as an Entrepreneur in Residence. In this role, he helped them to build their fintech acceleration program and became a General Partner of their Fintech Fund.The COVID lockdown became a turning point for Mike. By 2019, he already knew that being a VC wasn’t what he loved most while working directly with founders was.Before the pandemic, his role meant constant travel. His tasks and responsibilities included keynoting conferences, meeting investors, connecting with entrepreneurs, and exploring startup ecosystems worldwide. But when the world shut down, that part of the job disappeared.Mike was forced to slow down and reflect. At that time, he realized what truly mattered: helping others. With decades of experience, he decided to shift his focus from investing to coaching founders and fund managers.According to Mike, entrepreneurship is a force for good. Supporting those building the future became his most fulfilling work.How not to take the wrong pathMike believes both entrepreneurship and venture capital require thinking in long-term cycles, which are often 10 years or more. A single VC fund takes years to raise and another 7 or 10 years to run. As for a venture-backed startup, it typically needs the same timeline to get to liquidity.According to Mike, before diving in, future founders and investors should ask themselves whether they truly love the journey enough to commit a decade (or even more) to it. Success in either path isn’t about the next quarter or year but about embracing those long cycles.For Mike, the way to stay balanced in a professional life is to focus on what brings daily joy.Mike emphasizes the importance of regular self-reflection as a discipline, whether daily, weekly, or monthly. He compares it to customer development, but in this case, you are the product. The process involves asking:Where am I trying to go?What am I learning?What new questions are surfacing as I grow?Just like talking to customers reveals insights, reflecting on your own journey helps uncover where the real value lies. Mike believes this practice builds both confidence and clarity, not just for yourself but for anyone you mentor.Only we ourselves are responsible for our own growth. If we are not controlling our own personal and professional development, then who is?What constitutes a good founder?Mike decided to explore what traits VCs look for in founders and turned to artificial intelligence to help him. He asked ChatGPT to focus specifically on insights from seasoned investors who have backed unicorns. He formulated four key questions:What founder traits do VCs value most and why?What traits make the biggest difference in entrepreneurial success?What early-stage behavioral or psychological signals indicate potential? What tools can help surface those traits?What are the red flags?The exercise highlighted five top traits VCs commonly seek:Visionary leadership. It is the ability to see some versions of the future and inspire others. ChatGPT offered Elon Musk and Steve Jobs as examples of founders with this trait.Exceptional execution. This is a skill of turning vision into reality. Jeff Bezos is a person who has such a skill.Resilience and grit. These traits presuppose the ability to push through setbacks. Here, ChatGPT named Brian Chesky of Airbnb.Deep market insight and domain expertise. They are crucial for disrupting industries. Melanie Perkins of Canva was mentioned here.Adaptability and fast learning. These traits demonstrate your skill of being agile and pivoting quickly when needed. According to ChatGPT, Stewart Butterfield of Slack possesses these traits.Just for fun, Mike also gathered common red flags that cause VCs to pass on deals. He shared them in a room full of VCs and founders and asked them to raise their hands if they had ever rejected a deal for each reason. Every hand went up each time. Here are some of the positions included in the list:Unbalanced teams (all technical or all business team members);Frequent staff turnover;Founders lacking self-awareness;Romantic relationships between co-founders;Founders working on too many projects;Founders with narcissistic tendencies.While discussing this topic, Mike mentioned the research from Defiance Capital, which studied 2,018 unicorn founders in the US and Europe from 2013 to 2023. The findings revealed three common drivers behind unicorn founders’ success:No plan B. These founders were all-in. There was no safety net or fallback. For them, failure wasn’t an option.A chip on the shoulder. They had something to prove, whether to themselves, the world, or both.Unlimited self-belief. They truly believed they could make it happen, no matter the obstacles.According to Mike, these traits often separated unicorn founders from the rest. And namely, they can also be mentioned in the context of another, even broader notion. It is resilience.The power of resilienceWhile talking about resilience and its role in business, Mike mentioned Hummingbird Ventures, one of Europe’s top-performing venture funds. This fund is known for its unique thesis: they invest primarily in founders who are neurodiverse or trauma survivors. This approach is based on the belief that these people see the world differently and possess exceptional resilience.Founders who have overcome extreme challenges (it could be growing up in war-torn regions or rising from refugee camps) often develop the inner strength needed to navigate the tough journey of building a company. Hummingbird sees that experience as a competitive advantage.Learning from failureWhen it comes to failure, its role (and the value of lessons learned) shouldn’t be underestimated.Mike shared a personal story about his fear of public speaking. Early in his career, after his startup was acquired, he found himself a senior executive leading product and technology through an IPO. At his first major organizational meeting, surrounded by lawyers, bankers, and other executives, he froze.At that moment, he realized that he could easily let his team down because of his fear. That became a turning point. From then on, he committed to improving his communication skills, especially in high-stakes settings.Such failures could be very painful. But they often become a catalyst for growth, especially in corporate environments where failure is less tolerated than in the startup world.You can’t control how quickly the market or the world around you changes. That’s a given. The real question is: how fast and how efficiently can you learn? If you, as an individual, a team, or a company, can learn faster and cheaper than the others, your chances of winning go way up.What truly makes the difference is your ability to learn from mistakes. If you can minimize the cost of those mistakes while maximizing the speed of learning, you naturally start moving faster than everyone else. And that’s where the edge comes from.The fintech industry todayIn 2021, the global financial services industry represented about $12.5 trillion in market capitalization. Out of that, only around 2% was fintech. Projections suggest that by 2030, the industry will grow to $22 trillion. But fintech will still only make up about 7% of that total.There’s still a long way to go before financial services are truly transformed by modern technology. Yes, many financial institutions already use technology. However, a lot of solutions are 40 or 50 years old, built on legacy systems that weren’t designed for the digital age.It’s also worth noting that financial services remain one of the most profitable industries on the planet, with gross margins of 18%. That translates to roughly $2.3 trillion in annual profit up for grabs. This is an enormous opportunity for entrepreneurs and investors.If we take some comparatively simple things like retail and small business savings accounts or sending money internationally through platforms like Revolut and Wise, a lot has been already done.Emerging trends in the fintech worldWhat is coming next in the fintech space is much harder but also much more interesting. Technologies like AI, embedded finance, and finally, a clearer global regulatory framework are maturing and could reshape the industry.AI in fintechIn the context of using emerging technologies in fintech, Mike mentioned the findings of the Bank of America research. The research revealed that over the last 20 years productivity across S&P 500 companies skyrocketed. Specifically, the number of employees required to generate $1 million in revenue dropped from about nine people to just over one.And that was even before generative AI tools like ChatGPT became widely accessible.Just imagine how many people global financial institutions employ and then think about the productivity gains that AI could unlock. It can give you a hint of the scale of change that might be coming.TokenizationAnother concept that, according to Mike, looks quite promising is tokenization.When considering tokenization, it is important to set aside speculative crypto. This isn’t about meme coins or hype-driven tokens. Instead, the focus is on real-world assets, like buildings, infrastructure, and commodities.Today, there are an estimated $475 trillion or even more in real-world assets globally. The vast majority of this is still managed through paper-based processes and PDF documents.Digitizing these assets and automating their management could dramatically improve efficiency. Furthermore, tokenization would allow these assets to be fractionalized into much smaller pieces, enabling access to investment opportunities that were previously out of reach for most people.For example, people in Sub-Saharan Africa could invest in a fractional share of Apple or own a small piece of a revenue-generating office building in London.If regulated by strong, modern frameworks, tokenization could unlock a more inclusive and efficient global financial system, where access to high-quality assets is democratized on a global scale.Embedded financeThe idea of embedded finance is something that Mike really likes, particularly in terms of its potential to drive growth in emerging markets. However, he believes that the path to growth in such regions doesn’t solely lie in increasing venture capital investments. While many may advocate for more VC funding, he thinks, the true opportunity lies in deploying more debt into emerging markets.At present, major institutions like the World Bank, IFC, Goldman Sachs, and others are limited to operating with large-scale debt investments, typically in the billions of dollars. This is largely due to the high costs associated with underwriting and the profitability goals these organizations are trying to achieve.The challenge, according to him, is that these large institutions are constrained by the size and scale of their debt, which doesn’t always meet the needs of smaller, more localized markets. At the same time, these markets could greatly benefit from more accessible, tailored financial solutions.Embedded finance could act as a bridge to solve this issue, offering scalable, more adaptable solutions to drive growth without being confined by traditional financial models.VC cycles and key startup challengesWhen it comes to corporate VCs, there are several things they need to be mindful of when looking at the market, founders, and potential unicorns. One of the biggest challenges they face is the timeline mismatch between startups and corporations.Startups often operate on rapid timelines, moving quickly to develop products, secure investments, and scale. For example, a startup may be able to code and pitch a product in a matter of weeks or months. However, corporations typically work on annual or quarterly cycles. As a result, it becomes much more challenging for them to move at the same pace.This timeline mismatch becomes especially evident when a corporate VC is looking to make a major technology investment. The process within a corporation can take a significant amount of time (perhaps 18 months) to make an investment decision, another 18 months for procurement, and another 18 months for deployment. But even within the first 18 months, a startup may not survive due to lack of funding or any other factors.Corporations, on the other hand, often expect to see a return on investment within a couple of quarters or a year. However, early-stage startups typically require a 7 to 10-year horizon before they can generate liquidity.This disconnect between the timelines and expectations of startups and corporations creates significant challenges for both sides. To successfully collaborate with startups, corporate VCs need to recognize these challenges and adjust their expectations. This will help to avoid misunderstandings and missed opportunities.As you can see, there is still a long way to building an ideal environment in this space. Nevertheless, it is precisely such challenges that forge founders and help them reach new heights.The tech world and the startup ecosystem are highly dynamic. Therefore, the ability to adapt and learn from mistakes in the shortest possible time remains one of the most important priorities on the path to success.If you want to know more about what is happening in the tech industry and understand the trends shaping its future, don’t miss the upcoming podcast episodes, where Max Golikov and his guests will continue sharing inspiring insights.

How can tech startups survive today? How to find a good idea that will rock the market? Who can help you to guide your team if you have a limited budget?To discuss these topics, Innovantage podcast host Max Golikov, who is also the CBDO at Sigli, invited Laimonas Sutkus to join him in his studio. Laimonas is a person with robust expertise in helping businesses launch their projects and manage tech teams in such highly competitive fields as AI, fintech, health tech and others.In his career, he has gained experience as a software developer, tech advisor, CTO, and fractional CTO, working with businesses at different stages of their development. In this episode of the Innovantage podcast, Laimonas spoke not only about his professional path and the peculiarities of the tech industry landscape today but also shared valuable insights and practical recommendations for startup founders.Being a Fractional CTO: What does it mean?Laimonas began his fractional career in early 2024. As he admitted, before that he even hadn’t known that such roles exist nowadays. According to him, he discovered the concept by chance through a LinkedIn post from another fractional CTO. This inspired him to explore the field.A fractional CTO operates as a hands-on consultant and provides technical leadership to companies that don’t require a full-time CTO. This role is particularly beneficial for non-technical businesses like marketing agencies and small pharma companies, as well as early-stage tech startups. Such teams may not need a full-time executive but they still require expert guidance to avoid common pitfalls.Unlike a traditional CTO, a fractional CTO is available on a part-time basis. It can be a few hours per day or even just a few hours per week.What is important to highlight here is that this person is not a third-party consultant. This specialist is a full-scale team member, despite the limited hours that he or she devotes to your business per week.This expert helps businesses navigate technical challenges, streamline processes, and make informed decisions.The fractional model extends beyond CTOs to other executive roles, such as fractional CMOs and CFOs. And all these roles follow the same principle. These professionals provide their strategic expertise without being full-time employees.For a little bit less than a year, Laimonas worked as a fractional CTO. Nevertheless, now he has a full-time job. And here are the key pros and cons of a fractional role that he defined.Advantages of being a fractional executiveAmong the benefits, Laimonas highlighted the flexibility and security that come with a fractional career. Fractional employees can choose their projects and work with multiple clients.Moreover, this approach helps to reduce financial risk. If you lose one or two clients, it doesn’t mean that you will lose all your income at once.In other words, a fractional executive operates as a one-person business and can maintain great autonomy.Disadvantages of being a fractional executiveHowever, this independence also comes with challenges. Fractional professionals must handle not just their core expertise but also a wide range of other tasks, including sales, marketing, and client acquisition. All these activities are traditionally managed by entire departments in a business.As a result, Laimonas shared that a significant portion of his time was spent on prospecting, lead generation, and outreach rather than on his actual technical work.For specialists like fractional CMOs, CFOs, or CTOs, the ideal scenario is to focus solely on their expertise. In Laimonas’ case, his passion lies in technology, not in sales or marketing. Constant business development efforts could be very draining and that’s the key disadvantage of this career path.How the AI landscape is changingAs artificial intelligence remains one of the most widely discussed topics today, Max and Laimonas also couldn’t omit it in their conversation.Laimonas joined the AI space long before this technology became mainstream. He has been building AI-based products since 2014.Over the years of his work, he witnessed how AI development has changed with the emergence of large language models like ChatGPT. Previously, AI required hands-on data science, machine learning experimentation, and model deployment. Today, AI is more accessible. Developers can integrate it into products with simple API calls, avoiding the need for complex model training. This shift has allowed businesses to incorporate AI quickly and transform non-AI products into AI-powered solutions sometimes in a matter of hours.According to Laimonas, earlier many startups approached AI as a standalone product rather than a tool. Laimonas mentioned Rabbit R1 and AI Pin as examples. These are gadgets designed to function as AI-powered assistants. Nevertheless, they failed. It happened because they lacked a strong foundational business model.Today, it has become obvious: AI is not a product in itself but a feature that can enhance existing solutions.Laimonas believed that in the future AI will continue to be a powerful tool for gaining a competitive advantage. However, success will depend on integrating AI into solid business ideas. It will work much better than just relying on AI as the core offering.AI market realitiesAccording to the article published by Sequoia, one of the biggest VC firms, the vast amount of capital poured into AI-based solutions now requires an additional $500–600 billion in revenue across these companies for investments to break even. At the moment, it’s difficult to say whether this target is achievable or not. However, it brightly highlights the significant financial pressure on the AI sector.Laimonas mentioned that the gap between business profitability and AI investments exists not only for startups but also for major players like Google, Meta, and Microsoft. These tech giants lead AI development today because only they can afford the immense costs of training large-scale models. Such efforts often require tens or even hundreds of millions of dollars.Despite such a market situation, investors remain optimistic. This can be seen in the steady growth of the S&P 500 index, which tracks the stock performance of 500 of the largest companies listed on the US stock exchanges. However, here we can observe a notable concentration on the so-called “Magnificent Seven”. Seven major tech firms (Microsoft, Meta, Tesla, Amazon, Apple, NVIDIA, and Alphabet) make up nearly 30%-35% of the index.The last time when such a concentration was observed was in the dot-com bubble era.AI: Is it just another bubble?Laimonas sees obvious similarities between the current AI hype and the early 2000s internet boom. The internet was also a revolutionary technology. It went through a speculative bubble that eventually crashed before stabilizing into long-term growth.Could this happen to AI as well? The expert believes AI is following a similar trajectory. There was an initial boom. Now we can expect a likely correction that will ultimately result in a lasting impact.According to Laimonas, AI is definitely a very good technology. Nevertheless, it is already being weaponized. Deepfake videos of world leaders, AI-generated propaganda, and automated disinformation campaigns are becoming widespread. Large language models, when integrated into social media platforms, further amplify misinformation. That’s why it’s also worth taking into account this “darker” side of AI while analyzing its role in our society.The value of feedback for startup foundersLaimonas emphasized that one of the most important lessons for new founders is accepting that their initial ideas can be flawed. In the beginning, a startup’s vision is rarely perfect, and founders must be willing to refine it. Instead of treating an idea as something sacred, they should focus on building a minimum viable product (MVP), testing it, and gathering feedback.The reality is that most early concepts will fail. However, failure is part of the process. Founders must continuously iterate. This should include seeking feedback, adjusting the product, and repeating the cycle. All this should be done again and again until product-market fit is achieved. The key is to remain adaptable and recognize when something gains traction.However, not all feedback is equally valuable. Some users may explicitly state why they don’t like a product. For example, they may explain that they stopped using a product because the price is too high or because it doesn’t address some of their needs. That’s a very helpful type of feedback.Nevertheless, more often, the feedback is implicit: users simply don’t engage. In such cases, founders must investigate why it has happened. This requires reaching out to former or inactive users, analyzing usage patterns, and identifying the reasons behind low adoption.Deep, specific feedback is crucial to making the necessary improvements that lead to success.Why full-stack for early startups?In early-stage startups, achieving product-market fit requires rapid iteration cycles. The faster a startup can implement and test changes, the higher its chances of success will be. The chosen technology plays a crucial role in this process. It can either accelerate development or become a bottleneck. It is the responsibility of a technical co-founder, fractional CTO, or experienced consultant to ensure the right technological choices are made to support fast iteration.Traditionally, technical teams are structured with dedicated backend developers, frontend developers, QA specialists, and sometimes mobile engineers. While this model worked well in the past, it is often too slow for modern startups that need a competitive edge.As a response to such market needs, full-stack frameworks and technologies have started gaining popularity. They integrate multiple aspects of development into a single streamlined system.Frameworks like Next.js and Vercel provide infrastructure, frontend, and backend capabilities in one codebase. As a result, they enable faster deployment and iteration. However, these technologies come with some pitfalls, such as vendor lock-in. To fully unlock Next.js’s benefits, software developers often need to use Vercel, which can be costly and restrictive.Other frameworks, such as Remix, offer an alternative approach. For instance, Remix allows developers to write frontend and backend logic within the same file. This might seem disorganized at first. However, following strong design principles can result in a well-structured and efficient system.A single full-stack developer in such a case can often outperform a traditional five-person team consisting of separate frontend, backend, and QA engineers. The key advantage lies in eliminating communication overhead and reducing knowledge gaps. In other words, one developer can deliver all features without dependencies on other specialists.This shift toward full-stack development, combined with AI-assisted coding tools, significantly shortens iteration cycles. Features that previously took a full day to implement can now be developed in a fraction of the time.For startups aiming to stay agile and efficient, prioritizing generalist developers, who can build entire features independently, is more effective than hiring narrow specialists. Specialization should come later when the team grows to a size where dedicated roles in infrastructure, frontend, backend, and QA become necessary. Initially, focusing on generalists ensures maximum speed, flexibility, and resource efficiency.Balancing the concentration on today and tomorrowStartups must strike a balance between focusing on immediate survival and planning for the future. While long-term vision is important, over-prioritizing future scalability at the expense of present execution can be fatal. If resources are not managed well and iteration cycles are too slow, a startup risks running out of cash before it ever reaches the future it envisions.The priority should always be profitability and survival.Scalability issues, expansion challenges, and the need for team specialization are all positive problems. They signal that the business is working, clients are coming in, and revenue is growing. Growth problems indicate success, whereas failure to manage short-term sustainability can lead to an early shutdown.Some kind of uncertainty is an inherent part of the tech industry. Tech teams constantly need to solve scalability problems. While the nature of these problems evolves, the challenge itself never disappears. Mature IT leaders and software developers must recognize this uncertainty and design solutions, architectures, and infrastructures that accommodate future changes.A well-structured codebase should reflect the uncertainties of the business. It must be flexible enough to adapt to different directions as the company expands. Designing with adaptability in mind ensures that as business needs shift, the technology can keep up without requiring a complete overhaul.Where to find the right mentorship?For early startups, it is also vital to have people who will professionally guide them at least at the initial stages of their development.While many mentorship services are available online, they often lack a very important element. This key element is trust. It is difficult to assess a mentor’s true experience, expertise, and quality of services without firsthand knowledge.Instead of relying solely on external help from the internet, startup founders should first turn to their personal networks, including friends, former colleagues, business partners, and industry acquaintances. These trusted connections can either offer direct guidance or introduce founders to experienced professionals within their networks.Human connections are invaluable. In the startup world, relationships often open doors to mentorship, partnerships, and new opportunities that wouldn’t be accessible otherwise. Entrepreneurs should prioritize building and maintaining strong professional relationships, as these connections often prove more beneficial than any formal mentorship services.The journey of building a tech startup is filled with challenges: from finding the right idea to managing scalability. According to Laimonas Sutkus, flexibility and readiness for iterations are among the key components that can drive a tech startup to success.Want to learn more about technologies and their role in the business world? Don’t miss the next episodes of the Innovantage podcast where its host Max will welcome new experts in his studio.