Today, Sigli devotes considerable efforts to helping individuals and companies discover the value of cutting-edge technologies, including artificial intelligence, and their influence on business processes. One of the most recent educational initiatives was a free webinar for SMEs called “The AI Profit Toolkit”. The key goal of the webinar hosted by Sigli’s CBDO Max Golikov was to provide the audience with practical recommendations on how to adopt AI in a practical, budget-conscious way. In this article, we will share the key insights on how to launch AI pilots that create a measurable impact without serious risks. What business value can AI create? The impact of AI can be broken into several categories. The largest portion (57%) of AI’s business value comes from boosting productivity.AI often helps automate daily tasks, reducing manual workload. Everyday routine automation accumulates 17% of the general business value. A share of 16% represents customer experience enhancements. 10% is related to other use cases.Is AI the best technology to use today? The golden rule number one that every business owner and decision-maker should learn is that AI isn’t required everywhere. It’s a powerful technology, but you shouldn’t use it unless you really need it. Instead, you also need to consider other tools. In many cases, their implementation can help you save money and, at the same time, achieve much better efficiency. Before you apply AI, you should have a very good understanding of why you need to do it. How can I estimate the profitability of any AI project? One of the most reliable ways to do it is to apply an AI value matrix. It helps prioritize projects based on technological feasibility and business value. The group of technological factors that should be evaluated covers data quality, technological complexity, as well as the availability of skills and people. Business factors include business goal alignment, sponsor support, and measurable KPIs (like the decrease of time or resources needed for performing this or that task). The project that will get the highest score in the overall results is likely to succeed and bring the most significant impact. How to analyze technological complexity? When you are considering the implementation of a particular AI tool, you need to understand how much time and what resources will be required to achieve the level when this solution starts bringing real value. There are several core questions that you need to ask yourself: Is there a similar native feature in your current SaaS/ERP? (Today, a lot of systems offer their own AI functionality and it is possible that you won’t need to introduce your custom tool instead). Does a managed API already exist? Are there any latency or throughput constraints? Is it secure to implement this solution? Is a security review needed? Those solutions that require minimal effort from your side (like low-code components or plag-and-play SaaS) will get the highest score according to the AI value matrix. What does business goal alignment show? This parameter will help you evaluate the link between the AI use case under consideration and a strategic or P&L (profit and loss) objective. Thanks to it, you will be able to move your focus from overhyped tech trends that don’t have any relation with your existing bottlenecks to mission-critical initiatives. How can I understand that one AI pilot is better than the other one? To answer this question, let’s turn to one of the real-life cases that the Sigli team worked on. The client was a mid-sized Dutch EdTech platform that has 175K active learners on its online courses. The main problem that the company wanted to solve was the reduction of time and resources that were required for user support services. Tutors used to spend 18% of their paid hours answering FAQs, which cost the company more than €1 million per year. Our experts had different project ideas under consideration. Some of them were an LLM tutor-assist bot, a drop-out risk predictor, and an adaptive learning path recommender. As you can see from the matrix below, the first idea won. But why did it happen? To launch such a bot, the company already had a rich set of labeled data (more than 22K historical chat transcripts tagged by topic). There were no specific difficulties in integrating the solution with the company’s platform. It was possible to apply clear success metrics (response time and tutor time). And last but not least, the project received strong support from a single sponsor (it was the Head of Learner Success who owned the tutor budget and worked with retention KPIs). As a result, the successfully implemented tool helped cut the median first-time response time by 84% from 8h to 1.3h and free 0.25 FTE (full-time equivalent) per tutor. How to make sure that my AI implementation will be a successful one? To maximize the ROI of your AI project, you need to focus on the key elements: Quick-win quadrant on the AI value matrix. You should select projects that score at least 7+ on Technical Feasibility and 8+ on Business Value. These are the ideal quick wins. Clean and accessible data. High-quality, well-organized data significantly reduces preparation time and helps avoid repeated model re-training. One primary KPI. It is vital to choose a use case with a clear metric that is already tracked. This makes "before vs. after" comparisons simple, and you can eliminate the need for extra instrumentation or debate. Single budget owner as a sponsor. Having one accountable stakeholder enables fast approvals, resource access, and sustained momentum. Measurable business deliverables. You should make sure that your AI project produces tangible, financial results. The goal is to prove that AI delivers profit, not just innovation for its own sake. Final word Artificial intelligence can help you overcome a lot of existing bottlenecks and greatly boost your business growth. But all this is possible only when AI is applied deliberately, with the right expectations and a strong business case. As you can see, the most successful AI initiatives are not driven by hype or fear of missing out. They are driven by clearly defined goals and measurable outcomes. At Sigli, we are always ready to support your business at any stage of your AI journey: from strategy development and pilot selection to full-scale implementation. Don’t hesitate to contact us and learn more about how to turn the potential of AI into real business value.

The episodes of the Innovantage podcast hosted by Sigli’s CBDO Max Golikov cover a wide range of topics, including technology, business, and the role of digitalization. In the spotlight of the new episode are not just technologies, but also people. How can business leaders successfully implement innovations without facing resistance from their teams?Thiago de Faria, Senior Solutions Architect at AWS and a recognized Team Topologies expert, shared his perspective on this and many other important questions.Thiago has spent the past decade at the intersection of data, distributed systems, and organizational dynamics. Through years of hands-on experience, he came to understand that success isn’t just about algorithms or tools. It’s about communication and how teams work together.This insight led him to pursue leadership roles, including such positions as CTO, director, and team lead. He later joined AWS, where he led startup solutions architect teams, working closely with early-stage startups. After a period of freelancing, Thiago returned to AWS. Now, he focuses on enterprise modernization. His goal is to help organizations realize that technology is rarely the real challenge in their transition. They need to pay more attention to everything else around it. “It is about people, and it has always been.”For Thiago, building sustainable businesses goes far beyond technology. It is about people and communication. Success depends on understanding human behavior, managing egos, and being kind.He doesn’t believe in the top-down leadership model and the "do it because I'm the boss" mindset in the modern world. Today’s teams are motivated by more than just money or fear. The traditional stick-and-carrot approach no longer works. Instead, leaders must tap into what truly drives people and apply that insight to how technology and organizations evolve.Team Topologies as a framework for structuring teamsThiago mentioned Team Topologies as a powerful framework for structuring teams and improving how they collaborate. It was developed and described by Matthew Skelton and Manuel Pais. The framework is based on the ideas of DevOps, Agile, Lean, Deming’s principles, and the Theory of Constraints. Its core goal is to enable fast flow from idea to production, while still meeting security, compliance, and feedback needs.Overplanning or overcommunicating can also be dangerous for team efficiency. Team Topologies introduces a shared language to design team interactions intentionally. Without this structure, teams often drown in context, which can result in cognitive overload and a loss of focus.In Team Topologies, the team is treated as the smallest meaningful unit. And the key idea behind that is the fact that sustainable outcomes come from well-structured, collaborative teams rather than separate heroes. Team Topologies isn’t a rigid framework or a call for company-wide reorganization. Instead, it starts with identifying value streams and understanding how work actually flows through the organization.How to implement significant cultural changes within teamsThiago emphasized that meaningful cultural change within teams is neither strictly top-down nor bottom-up. It requires a combined approach. Such changes take time. It can be quarters or sometimes even years. The key is to create sufficient awareness of the broader context, the processes in place, and the value stream itself. By determining bottlenecks and reducing handovers, organizations can begin shifting toward a more collaborative and efficient culture.This cultural shift is deeply tied to technical practices rooted in DevOps: continuous integration, continuous deployment, fast feedback loops, trunk-based development, and robust testing. But it’s not just about better tooling. It’s about bridging the gap between business and technology.One of the biggest mindset shifts is moving away from a factory-style model where tech teams wait for perfect requirements before building. Instead, developers must become more curious about the business and more engaged with customer needs. Collaboration shouldn't be sporadic, and it shouldn’t be handed off via tickets or rigid requirements. It should be ongoing. The core challenge lies in bridging the gap between existing infrastructure and organizational culture. It can’t be imposed top-down through mandates or principles alone.The real test of culture comes during crises like missed deadlines, outages, or security issues. In such situations, nobody wants to take responsibility for that because responsibility can be really painful.Often, it is not about people avoiding responsibility, but about misalignments and overloaded teams that make real ownership nearly impossible.That’s where the principle of fast flow becomes crucial. To avoid such situations, it is necessary to reduce cognitive load, streamline knowledge requirements, and minimize distractions. This will allow teams to focus on real ownership and deliver value more effectively.Psychological safety is a must for cultural changesAccording to Thiago, there is no one-size-fits-all model for implementing cultural changes. One of the biggest challenges is building psychological safety, which is a prerequisite for any meaningful transformation. If even one person on a team doesn’t feel safe, the team as a whole isn’t truly safe.Psychological safety starts with trust among teammates, across roles, and with leadership. Trust isn’t built through blind agreement. It’s built through transparency.For Thiago, a practical way to foster trust is to surface assumptions and clearly explain decisions. People don’t have to agree with every call, but they should understand the rationale behind it. Disagreement is fine when it is followed by commitment and free of blame if things go wrong.Platform groupsOne of the most impactful ideas to emerge from Team Topologies is the concept of platform groups. They are responsible for building and maintaining internal platforms, tools, services, and building blocks that reduce the cognitive load for product-focused teams.Thiago explained that teams that are directly delivering customer value are often overwhelmed. They are expected to handle everything: databases, deployment pipelines, infrastructure, testing frameworks, compliance, programming patterns, and business context. That’s an unrealistic cognitive burden, and it’s often why these teams default to focusing only on the technical layer.Platform groups solve this by offering clear, reusable paths, or prebuilt ways to deploy services, manage infrastructure, or handle CI/CD. Their main goal is to streamline delivery by eliminating unnecessary friction.However, many companies misapply this concept. They form a single overloaded “platform team” tasked with managing everything, from CI/CD and data infrastructure to Git workflows. As a result, such teams become a bottleneck themselves. That’s why the shift to true platform groups is important. Here, it is essential to keep in mind that they should be purpose-driven, focused units with clear boundaries, allowing for scale without burnout.Thiago also highlighted another team pattern from Team Topologies. It is enabling teams. They unite cross-functional experts, such as architects or systems specialists, who embed temporarily with other teams to unblock problems, offer guidance, and enable better practices before moving on. Companies should think of them as internal consultants focused on capability-building, not control.Transformative impact of cloud computingCloud computing introduced a profound shift in the technology landscape.The first big transformation it enabled was accessibility. Cloud computing removed the barrier to entry. It turned infrastructure into a utility that is available on demand based on the pay-as-you-go principle. It enabled startups and solo entrepreneurs to bring ideas to life without the need to secure bank loans just to spin up their first server.But the second wave of transformation came with serverless technologies (or, as Thiago calls it, “serviceful” computing). Instead of managing servers or configuring infrastructure, teams can now focus almost entirely on solving business problems. These new patterns allowed developers to work faster, experiment more freely, and scale effortlessly. This approach closely aligns with the principles behind Team Topologies.Thiago admitted that this shift was the biggest he had seen in his career before the AI transformation that we can observe today.However, he emphasized that not everything belongs in the cloud. There are workloads that make a lot of sense to keep on-prem. This is especially relevant for companies with decades of investment, expertise, and operational maturity around legacy systems.The real challenge of cloud transformation isn’t just technical, it’s human. Telling someone their years of expertise with data centers or custom infrastructure are no longer needed can trigger fear and resistance. That’s why change management becomes essential.Cloud-first approach: Is it always a good idea?Many companies today embrace a “cloud-first” approach. But, as Thiago noted, it’s often cloud-first only until compliance or cost gets in the way. The problems typically begin when companies attempt a “big bang” migration and try to rebuild or replatform everything at once. Thiago recollected cases where highly competent teams are tasked with rebuilding existing systems from scratch on the cloud, but team members didn’t have enough experience in cloud-native patterns.What comes next is often a “lift and shift” migration. It means that applications are moved to the cloud using the same designs and operational assumptions that worked on-premise. As you can understand, this method can result in multiple issues.Sometimes a lift and shift makes sense (for example, when delaying migration would incur hardware costs or lease renewals). But that should be the exception, not the rule. Instead, Thiago advised a more incremental, wave-based approach that includes team enablement and intentional architectural planning.The key to successful cloud-first transitions again lies in psychological safety. Companies should help people understand why the transition is happening and show how their existing knowledge can evolve in a cloud context.From the cloud back to on-premise solutionsToday, there are a lot of talks about cloud repatriation, which presupposes moving workloads back to on-prem. However, Thiago clarified that, in practice, he rarely sees this happening at scale. More frequently, he can observe companies that have never completed their cloud transition in the first place. These organizations may have adopted a “cloud-first” mindset years ago, only to realize later that some workloads were better left on-prem, or that not all systems needed to move.According to him, it’s vital to understand that not everything needs to be in the cloud.But today, cloud providers are prepared even for scenarios that require local infrastructure. Quite often, they offer hybrid options. For instance, Outposts by AWS bring AWS-managed infrastructure into the customer’s data center, still connected to the broader AWS ecosystem. It means that businesses can maintain full control locally, but the rest of their systems can still run in the cloud.At the same time, he also highlighted that it’s a myth that running LLMs on-prem is automatically more secure. If you are calling a third-party AI endpoint with no guarantees, that’s one thing. But platforms like AWS Bedrock give you private, VPC-based endpoints where no one else can access your data.Development of cloud computingAccording to Thiago, the 80/20 rule is a good one to describe what is happening in the modern IT infrastructure. 80% of workloads can be handled by broadly available, standardized solutions, while 20% will always require specialized, often bespoke approaches.He explained that platforms like AWS have matured to a point where the majority of business needs can be met using higher-level, off-the-shelf services. The extensive partner ecosystem has enabled businesses to build powerful platforms on top of AWS, without having to reinvent the wheel.Most businesses no longer need to create their own data platforms from scratch. There are already high-level solutions that help them avoid most of the complexity.However, many large enterprises still run highly customized legacy systems, often built on mainframes, and in some cases written in outdated languages with hundreds of thousands of lines of code. These systems are not easy to modernize. But they may be too critical to simply discard.Thiago explained that the middle layer, which is the part between front-end experiences and the legacy back ends, has already been undergoing modernization for years. What is left now is the hardest part: modernizing the base layer. It can be a real challenge, especially when companies face a knowledge drain after original developers retire.That’s where AI and ML come into play.AWS, for instance, provides tools like AWS Q Transform for mainframe apps. It leverages AI to analyze and explain complex legacy codebases, making them easier to understand and refactor. Integration of AI and ML into existing systemsThe explosion of interest in generative AI and large language models has captured global attention. Nevertheless, Thiago cautioned against abandoning the foundations of traditional ML, which continue to deliver significant value across industries.In the conversation with Max, Thiago urged organizations not to overlook the decades of progress in statistical learning, which have become overshadowed in the post-ChatGPT era. Since the launch of ChatGPT that happened in November 2022, much of the industry’s focus has shifted disproportionately toward LLMs and generative models, often at the expense of simpler and more efficient ML solutions.Thiago compared today’s LLMs with a battalion of interns. Modern LLMs are capable of generating content, conducting research, and providing ideas, but they are inherently biased and often lacking in precision or authority.“They speak with confidence, like white Reddit males who think they’re always right,” Thiago joked.Hallucinations, inconsistency, and lack of source traceability are among the main issues related to the mass use of large language models. Thiago views this as a call to action for better guardrails, source attribution, and AI literacy.Tip for business leadersMax also asked Thiago to share advice for leaders who want to implement AI solutions and build resilient technical infrastructures.“Be empathetic and be kind. That is the most important thing that I can tell people. Everything else will follow from it,” Thiago said.With all the changes that they can bring, technologies are just tools, people are the drivers of transformation. Leaders must resist the urge to chase innovation for innovation’s sake. Instead, they should focus on enabling teams, simplifying processes, and creating environments where individuals feel safe, valued, and heard. This is the main conclusion that can be made from this insightful conversation.Want to learn more about the world of business and technology? New Innovantage episodes will be available soon.‍

In the episodes of the Innovantage podcast, its host and Sigli’s CBDO, Max Golikov, usually invites guests to talk about technology and its impact on business. But the topic of the latest episode will resonate with a much broader audience. This time, the focus is on the art and the mastery of pitching to investors. Let’s be honest, when teenagers want to go out late for a Friday evening, they also need to make some kind of a pitch in front of their parents.To dive into this and entrepreneurship in and of itself, Max joined the podcast Robin De Cock, Professor of Entrepreneurship at Antwerp Management School.Robin has spent nearly 20 years helping students and business owners develop their ideas. Over time, he noticed that even strong ideas often fail during investor pitches due to poor presentation, which can be frustrating after months of work. This encouraged him to start teaching people how to effectively pitch and sell their ideas. Based on this desire and inspired by the growing body of academic research on pitching, he decided to write his book “Mastering the Pitch”.Evolving perception of entrepreneurshipFor a long time, entrepreneurs were seen as visionaries who could almost predict the future. However, over the past 10 to 15 years, this perception has shifted, especially with the rise of methods like the lean startup.Today’s entrepreneurs focus more on testing hypotheses, experimenting, and validating ideas with the market in iterative cycles to reduce risk. While vision and risk-taking are still important, there is now a stronger emphasis on evidence-based approaches.What is Robin’s book about?Robin’s book “Mastering the Pitch” is aimed at helping people improve the way they present their ideas. His main goal behind writing this book was to ensure that strong, impactful ideas don’t go unnoticed simply because they were poorly presented. He wanted to support entrepreneurs with insights that are not only practical but also grounded in scientific research. Unlike many other books on pitching, which are often based on the author’s personal experience, Robin’s approach brings together data from different sources. The book is built on two key pillars. First, Robin translated complex findings from academic research on pitching into clear insights for a broader audience.Second, he conducted interviews with entrepreneurs and investors across Europe and the US to understand how academic insights align with real-world practice. In his book, Robin also addressed several myths about pitching, starting with the idea that there is a magical formula for success. He compared pitching to dating: what works with one investor might not resonate with somebody else. Another common misconception is the overemphasis on slides. While a visual component is important, Robin explained that successful pitching involves much more. It should also be powered by passion, energy, tone of voice, body language, and team dynamics. Nonverbal elements often play a larger role than words alone in convincing others. Surprising insights from pitching to investors researchOne of the most surprising findings Robin discovered in his research is just how quickly people form impressions. Studies show that within just 150 milliseconds, an audience begins to form an opinion of a speaker. After 30 minutes, people will have a lasting impression of you. This highlights the importance of being yourself from the very start.Robin emphasized that when you are trying to impress investors, pretending to be someone else is not the best way to do it. Perhaps you will be working with these people for years, therefore, honesty and consistency from the very early stages are essential. According to one study, people tend to fall into certain “boxes” or behavior patterns during a pitch. One box is called the pushover. It means that people often agree to change even core aspects of their idea to please investors. Nevertheless, standing firm on your core vision while staying open to constructive feedback is crucial for building long-term trust and credibility.The impact of cultural background on pitching to investorsCultural background plays a significant role in how pitches are delivered and evaluated. Much of the research was conducted in the US. In this country, pitching tends to be bold, direct, and focused on world-changing ideas. In contrast, European investors often look for detailed explanations, early evidence, and proof of concept.In Asia, the approach is more indirect and relationship-based. Trust must be established before business discussions can move forward, and the process is usually more hierarchical. Decisions often take longer as pitches need to pass through multiple levels of approval. Storytelling in pitchesAccording to Robin, the role of storytelling in effective pitching can’t be underestimated. Research shows that stories are 22 times more memorable than facts. Stories capture attention, make messages stick, and help audiences connect emotionally with the idea.Instead of simply listing key elements like the problem, solution, and business model, Robin encourages entrepreneurs to weave these into a narrative. For example, sharing a personal experience that led to discovering a broader market problem can make a pitch far more engaging. But crafting the story is only 50% of the challenge. The other half is delivering it in a compelling and captivating way. Use of humor: Is it a good idea?Humor can be a powerful tool in a pitch. In settings where multiple pitches happen in one day, a well-placed joke can help you stand out. Self-deprecating humor, in particular, can enhance authenticity and build trust.However, there’s a balance to strike. A little humor can enhance your message, but too much can shift focus away from your idea and make the pitch feel unprofessional. The key is to keep humor spontaneous and natural. It could be perfect to use it at the beginning to break the ice.The effectiveness of humor also depends on your personality and the audience. If humor fits your style and aligns with the general tone, it can work well. However, forced, inappropriate, and excessive jokes can damage credibility. Common pitching pitfallsOne of the most common causes of pitch failure is technical glitches, especially with live demos. Robin mentioned the infamous Surface tablet pitch where Microsoft executives struggled with a malfunctioning device. Given this, Robin recommended always testing tech thoroughly and having a plan B.Another example of failures is Steve Ballmer’s overly enthusiastic pitch, which became more entertaining than convincing. This situation is one more proof that balance is a must.Cornerstones of investor relationship buildingA common misconception is that a 10-minute pitch will immediately secure funding. In reality, a pitch is just the starting point of a longer relationship-building process with investors. The goal isn’t just to impress them during your short speech, but to open a conversation that leads to follow-up discussions and eventual trust. Ideally, you will be able to reach a stage where investors approach you because your pitch, press coverage, or buzz around your idea makes them curious.Tailoring your pitch format to the context is crucial. At networking events, a clear and compelling one-minute or even two-sentence pitch can spark meaningful conversations. If you struggle to summarize your startup in a few lines, it may indicate you haven’t yet clarified your core idea. Meanwhile, a full 10-minute pitch should still leave room for dialogue and relationship-building, not just persuasion.Trust and honesty are essential. Trying to hide flaws or challenges can backfire. Investors are experienced and will uncover the truth eventually. Finally, doing your homework on investors is key. Each investor has a specific focus, budget range, and strategic interests. When you understand this, you can tailor your pitch to align with their goals. Moreover, some successful founders also keep investors in the loop with regular updates. This ongoing communication builds familiarity and trust, increasing the likelihood of future investment when the timing is right.The myth of the perfect first pitchOne of the most persistent myths about pitching is the belief that you only get one shot and that it has to be perfect. In reality, very few successful entrepreneurs deliver a flawless pitch the first time. Pitching, like any skill, improves with practice, iteration, and feedback.Take The Beatles, for example: they played over 1,200 live shows before landing their first record deal. Similarly, Jeff Bezos held around 60 investor meetings before raising Amazon’s first million dollars.Pitches evolve. The more you present your idea, the more you learn. With time, you can better realize where people lose interest, which questions come up repeatedly, and what truly resonates. That feedback loop is vital. If multiple investors point to the same weakness, it’s a signal to adapt your message or your business model.Failure is just a part of the process. You can’t succeed without failing along the way.Robin explained that entrepreneurship is rarely a smooth ride. It is more like a roller coaster filled with highs and lows. The way you interpret those ups and downs can make all the difference.If the goal is purely to make money as quickly as possible, the pressure can become overwhelming. Every setback feels like a crisis. However, if the journey is seen as a learning process or a chance to grow, experiment, and improve, then failures become valuable lessons rather than crushing defeats.This perspective applies equally to pitching. Viewing a pitch as a “make-or-break” moment only increases the pressure and anxiety. But if pitching is approached as an opportunity to learn, get feedback, and refine your message, it becomes part of a growth process. The stakes are still high, but the mindset is healthier and more sustainable.How to manage stressManaging stress before and during a pitch is also crucial. Science shows that how we perceive stress plays a major role in how it affects us. If you view stress as a sign that your body is preparing to perform, it can actually enhance your performance. But if you see stress as a threat or a sign of impending failure, it can quickly become debilitating.There are many personal strategies to manage stress, including physical activity, breathing techniques, or even small rituals.Besides that, preparation remains the most powerful antidote to stress. The more prepared you are, the more confident you will feel. But preparation alone isn’t enough. Practicing on stage, in front of an audience, is essential to becoming a great pitcher.AI in pitching to investors: Assistant, not replacementThe current AI hype has certainly influenced pitching, but it can’t replace the human element. As long as humans are making investment decisions, connection remains highly valuable. Pitching through avatars or fully AI-generated videos may deliver a polished message, but it lacks personal connection, which is critical. Investors want to evaluate you, your passion, your credibility, and your commitment. You can’t just sell an idea. You need to show who they will be working with.AI is best used as a co-pilot. It can:Help craft compelling slides;Speed up research;Improve storytelling structure;Offer suggestions on clarity or tone;Simulate investor feedback or likely Q&A questions.For instance, AI can critique your pitch from an investor’s perspective, helping spot missing elements or test how your story holds up under scrutiny. While not all feedback will be useful, it can highlight blind spots or spark new thinking.AI can also help founders prepare for the Q&A session by generating possible questions. Pitching and Q&A: Key tipsAfter the adrenaline rush of a pitch, many entrepreneurs make the common mistake of answering questions before fully hearing them. It’s important to listen carefully to the entire question before responding.During Q&A, avoid getting defensive or attacking the questioner. Investors always want to see that you are open to feedback and able to handle criticism professionally.A useful strategy is to prepare backup slides and “go-to” messages. They should include key points you want to reinforce throughout the Q&A.Structure of a pitchA pitch should have a clear beginning and end, and both are crucial. It’s best to start by connecting with the audience. You can share a personal story, a striking use case, or a key number that highlights the problem. You shouldn’t jump straight to the solution. Instead, it is recommended to focus on why the problem matters.The problem-solution fit must be clear and simple. If the audience doesn’t understand this early on, the rest of the pitch won’t land. This is the backbone of your presentation.To succeed, you need to close with a strong summary of your company, the problem you are solving, and your solution, or finish with your mission. A memorable opening and closing make your pitch much more powerful.Pitch mantra: Keep it short and powerfulInstead of long mission statements, every startup should have a mantra. It is a short, sharp phrase (even just three words) that captures the essence of the business. It helps founders distill their core purpose. It clarifies your thinking and gives others a clear, memorable takeaway.You can use it to open or close your pitch for a strong impression.Talking too much vs. too little in a pitchBoth extremes can hurt a pitch. But in practice, founders are more often guilty of talking too much. It’s rare to see a pitch where too little is said. More commonly, there’s information overload.But humans have a limited capacity to process information quickly. A pitch should be clear, focused, and paced. The perfect pitch in one wordMax asked Robin to describe the perfect pitch in just one word. And teh answer was: passion.Passion shows the founder’s drive and commitment. These are crucial qualities for surviving the highs and lows of entrepreneurship. But passion alone isn’t enough.Equally important is evidence. It is proof that the idea works and that the business case makes sense. A perfect pitch combines both passion and proof.Examples of great pitchesAccording to Robin, there are many strong examples today, especially from female entrepreneurs. One standout was Jasmine Tagesson, founder of Hormona, who delivered a compelling pitch at Slush 2021. Within the first 10-20 seconds, she clearly articulated the problem and created an immediate connection with the audience. It was concise, impactful, and emotionally resonant.A more iconic example is Steve Jobs during the launch of the iPod. His pitch had excellent pacing. He sped up to build excitement and slowed down to emphasize key points. He also delivered a strong competitive analysis, clearly showing the shortcomings of existing products and positioning the iPod as the superior solution. What entrepreneurs should know before a pitchAt the end of their discussion, Robin provided some advice for those who are preparing to make a pitch:Show energy, drive, and genuine belief in your idea.Be well-prepared, know your numbers, and demonstrate that you’re serious and committed.Build a match with investors. Be collaborative and easy to work with. Listen to feedback.In addition, Robin shared a useful framework. It is the four Ps of pitching:Profile. You need to explain who you are and why you are pitching this.Plan. It includes the structure, flow, and logic of your pitch.Proof. You should share evidence, market validation, and data to support your claims.Performance. How you deliver your pitch also matters. Pitching isn’t just a startup ritual. It’s a universal skill that applies to anyone trying to convince others of an idea, a project, or a vision. Whether you are pitching to investors, partners, or even your parents, the same principles will work. You need to be honest, open, and well-prepared.Want to get more actionable insights from business experts and tech leaders? New episodes of the Innovantage podcast will be available soon. Don’t miss them!

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!