There isn’t a one-size-fits-all approach to building a startup amid rapid advances in AI and increasing competition in the business environment. Every project has its own approach and unique story of success. However, it is always crucial to draw on the experience of other businesses when shaping your own unique strategy.In the latest episode of the Innovantage podcast, its host and Sigli’s CBDO, Max Golikov, speaks with Jerome Cloetens, co-founder and CEO of Palau, about what it takes to survive and grow today.Palau operates in the area of ESG and sustainability reporting, where regulation and enterprise expectations evolve faster than most early-stage companies can adapt. Jerome also shared his vision of the trade-offs between bootstrapping and fundraising, the discipline required to sell to enterprises, and why enterprise sales can become a strong competitive edge for a startup.How AI-driven Sustainability Reporting for Startups Can Address Climate and Compliance Challenges: The Story Behind PalauThe Palau project was built to solve a problem that closely mirrors financial reporting, but in the domain of climate and sustainability. Large corporations today face growing pressure from banks, investors, and regulators to be fully transparent about their environmental impact. Palau addresses this challenge with an AI-driven reporting platform. The system helps companies collect sustainability data, standardize it, and transform it into the specific formats required by regulators and investors. As a result, it helps reduce manual work and turn complex ESG data into structured reports.Climate and sustainability have lost political momentum in the US following recent regulatory shifts. However, global demand continues to rise. Europe, Asia, Brazil, and parts of the US, such as California, are introducing stricter reporting requirements. As many San Francisco-based startups pivot away from climate-focused solutions, Palau has the possibility to benefit from a temporary competitive window.Palau was founded by a four-person team. One co-founder was Jerome’s cousin. Another founder, who is now the CTO, brought deep technical expertise and built the core platform. The fourth person has experience in working directly with complex sustainability compliance requirements at a large European firm. Thanks to this, she can contribute regulatory and domain expertise. The initial version of the product was built to solve real problems that she faced in her role.As for the right timing for the project, Jerome believes that it is more about execution, not about perfect conditions only.The team adopted a fast-moving approach. They launched small experiments, tested ideas quickly, and relied heavily on market feedback. Bootstrapping vs. Raising Capital: Funding Strategies for AI-driven Sustainability Reporting StartupsJerome initially chose to bootstrap Palau. This allowed the team to experiment efficiently during the first year and, at the same time, maintain tight focus and cost discipline.The strategy shifted once Palau reached 15 to 20 enterprise clients, including companies generating more than $300 million in annual revenue. At that point, speed became a constraint. Hiring engineers purely from operating cash flow was possible but slow. This increased the risk of losing momentum in a fast-moving AI market.External capital provided a financial buffer and enabled faster execution without abandoning a lean operating mindset.Today, engineering remains the company’s core investment. There is no dedicated marketing or sales team yet. Jerome personally handled the first 20 customers and plans to onboard up to 100 before scaling sales. According to him, maintaining direct relationships with customers at early stages is crucial as it helps understand their needs and shape the product.Building Efficient Teams for AI-driven Sustainability Reporting Startups: The Role of AI and Human ExpertiseJerome views AI as a powerful accelerator for engineering. But it is still not a replacement for human expertise (at least at the current moment). Lower-level tasks are increasingly supported by AI tools. However, core engineering work requires experienced developers.At the same time, in his conversation with Max, he highlighted the advantage of small, focused teams. Even without AI, lean teams can outperform larger organizations as they move faster and make clearer decisions. AI strengthens this dynamic by allowing teams to stay small while increasing output.Lessons from Early Startup Decisions: How AI-driven Sustainability Reporting for Startups Drives Focused GrowthLooking back, Jerome would largely follow the same path. As he was a first-time founder, mistakes were inevitable. But starting with a single, real customer proved to be the right approach. The team built a focused solution for one enterprise where a co-founder worked. It means that the product already solved a real-life problem before attempting to scale. That initial success created a repeatable foundation for acquiring additional customers. And it’s much more efficient than raising capital around an untested idea.The early strategy was simple: talk to customers and build a product. There was no emphasis on pitch decks or long-term forecasts. Daily work revolved around customer calls and shipping code. This approach accelerated learning and product-market fit.Financial control was another lasting lesson. Bootstrapping forced strict visibility into the runway and recurring revenue. Even now, after raising external capital, that mindset remains at Palau. Winning Enterprise Clients Through Community: Leveraging AI-driven Sustainability Reporting for StartupsEnterprise sales are typically slow and complex, as such businesses are often resistant to change. Decision cycles are long, while stakeholders are fragmented. Moreover, new vendors often face high credibility barriers. Despite this, Palau set an ambitious target of serving 300 enterprise clients by the end of 2026.The core insight was a widespread knowledge gap. Most large companies were facing climate and sustainability reporting requirements for the first time. They had limited internal expertise around risk assessments, transition planning, and regulatory disclosure. Instead of leading with a product pitch, the Palau team first built a practitioner community where in-house sustainability leaders and consultants could exchange knowledge and learn from one another.That community became a trust engine and a distribution channel. The role of partner and community reputation in accelerating enterprise sales can’t be underestimated. Without established reputations, closing a single client can take six months of meetings, pilots, and internal approvals. With community partners who have worked with these companies for years, Palau can reduce that timeline to just a week.Community-led Growth as a Go-to-Market Strategy for AI-driven Sustainability Reporting StartupsThe community exists first as a learning environment, not a sales funnel. One of the key goals of its creation was to show sustainability teams that with automation, they can work faster and take on more ambitious initiatives. This bottom-up approach contrasts with the dominant top-down enterprise sales model. But it has proven more effective in building trust within complex organizations.Enterprise adoption is typically preceded by a 30-day pilot. During this period, the team gathers rapid feedback, iterates quickly, and adjusts direction where needed. Community members are not always direct buyers. But their sustained participation builds familiarity and credibility, and, as a result, makes future enterprise conversations significantly easier to initiate.Leveraging Enterprise Feedback for Product Evolution in AI-driven Sustainability Reporting StartupsDirect feedback from early users led to meaningful changes in product scope and positioning. The team initially targeted smaller companies but quickly identified a larger need among enterprises that generate $200 million to $300 million in annual revenue. These organizations faced the highest regulatory and investor pressure. That’s why they require more robust reporting capabilities and governance controls.Initially, the Palau company lacked the full enterprise-grade IT security and compliance frameworks expected by large corporations. Early sales cycles were slow due to security reviews and risk assessments. Nevertheless, this situation demonstrated the necessity of investment in cybersecurity and access controls to meet standard enterprise requirements.Meanwhile, sustainability reporting remains highly customized, as each enterprise interprets regulatory and stakeholder demands differently. That’s why flexibility is a must.Adapting Strategy for Global Enterprise Markets with AI-driven Sustainability Reporting for StartupsAs Jerome explained, early attempts to scale across industries highlighted a structural challenge. A single team couldn’t hold deep domain expertise for every regional and sector-specific regulatory regime. Instead of building all AI workflows internally, Jerome’s team introduced a protocol that allows external experts to design their own AI-driven workflows with a set of provided tools and frameworks.This approach enabled rapid expansion into different markets, where local consulting partners with established credibility could tailor solutions for various industries, such as the pharmaceuticals. The same model now supports clients across semiconductors, manufacturing, construction, and other sectors, and reduces dependence on a single vertical.How products can change communities and transform enterprise workflowsThe platform has reshaped how consulting partners and enterprises handle sustainability reporting. Manual, low-level tasks (document review, gap assessments, data validation, etc.) are now largely automated. As a result, human experts can focus on higher-value, strategic work. This shift greatly supports highly ambitious project scopes.AI plays a central role as it can process vast amounts of regulatory and corporate documents, identify compliance gaps, and flag issues for expert review. Consultants then need to verify the AI’s output, while subject-matter experts provide final recommendations. Non-financial Data as a Strategic Asset in AI-driven Sustainability Reporting for StartupsNon-financial data (climate, water usage, social metrics, etc.) is becoming increasingly critical for enterprise decision-making. Financial reports guide planning and forecasting. Meanwhile, structured environmental and social data allows companies, insurers, and investors to assess risks and make informed strategic choices.For instance, large corporations like Nike already provide transparent reporting on carbon footprints and resource consumption. This information enables stakeholders to evaluate impact and identify gaps. Governments and pension funds can use these datasets to screen partners and make policy decisions more quickly.Right now, this standardized non-financial data may seem to be a niche sustainability concern. But in the future, it will transform into a core tool for financial and operational decision-making.The Myth of Sustainable Businesses: Integrating AI-driven Sustainability Reporting for Startups into Profitable GrowthJerome noted that sustainability is not about certificates, activism, or greenwashing. It is backed by the idea of making data-driven decisions that respect planetary boundaries and, at the same time, allow running a profitable business. The real goal is to integrate non-financial data into everyday enterprise decision-making and ensure compliance, risk management, and operational efficiency.Here, the balance is important. Sustainability should not compromise business growth. Measuring Real Impact: How AI-driven Sustainability Reporting for Startups Drives Meaningful ChangeFor Jerome, success isn’t measured by the number of clients onboarded superficially. As he explained, it’s much more important to have companies that actively use the platform to advance their sustainability transition. Many organizations start with compliance just to check boxes to satisfy stakeholders. But the real value emerges when enterprises leverage the data to drive meaningful change and uncover business opportunities.The platform focuses on supporting companies willing to take action. It helps them track progress toward long-term targets such as Europe’s 90% emissions reduction goal over the next two decades. Navigating Regulation in Sustainability with AI-driven Sustainability Reporting for StartupsEuropean climate regulations are evolving, but clarity remains a key challenge. Companies often want to comply voluntarily. But they need to keep processes lean and transparent and avoid bureaucratic hurdles. California’s climate laws are a good example of how localized enforcement can outpace federal action.Many guidelines in Europe are unclear and written in legalistic formats. As a result, companies need to invest significant effort just to understand compliance requirements. Palau addresses this issue by using AI to rephrase regulatory text into readable, actionable guidance.The Palau team also encourages regulators to join its community, learn from its members, share feedback, and iterate on guidelines. The Toughest Challenge for a Founder: Overcoming Market Shifts with AI-driven Sustainability Reporting for StartupsThough Palau is growing quickly today, its journey was not without pitfalls. January of 2025 was the most difficult period for the team. A sudden regulatory change disrupted their prior business model, and a loan repayment left the startup deeply in the red.The team faced low morale. The market shift compounded the pressure. To survive, they quickly pivoted to a protocol-driven approach that empowered experts to manage AI workflows. Intensive work over six weeks helped secure new clients and stabilize cash flow, which brought the company to a positive financial position.Motivation from Client Impact: How AI-driven Sustainability Reporting for Startups Fuels Business GrowthDaily engagement with prospects and clients provides ongoing encouragement. AI tools capture key insights from client interactions. This information allows the founders to see the product’s real-world impact. Securing deals with large, bureaucratic enterprises is challenging. However, it offers a unique sense of accomplishment that fuels continued momentum.Palau’s largest client generates around $10 billion annually and proposed a five-year contract. Jerome resisted signing long-term deals in the first year, as the product is expected to improve significantly over the next few years. At the moment, he views such contracts as premature. The solution should evolve before committing to extended agreements.Agentic Browsers and the Next Wave of Disruption: Impact on AI-driven Sustainability Reporting for StartupsAccording to Jerome, AI will reshape how startups are built and how software is used. While we still can observe the hype cycles and risky bets in the space, the overall trajectory is unstoppable. Ignoring the speed and momentum of AI progress, especially agent-based systems, is a strategic mistake.For startups that rely on third-party AI models instead of owning them, the risk of being replaced is real. Palau’s response is to stay model-agnostic and focus on what others can’t easily replicate. In its case, it is a strong community, proprietary workflows, prompts, and domain-specific data. Today, agentic browsers are among the major breakthroughs. These AI agents can autonomously move across platforms, log into systems, and complete complex tasks end to end. The use cases of such agents can go far beyond standard business applications. For instance, Jerome shared his personal example where an AI agent completed an entire online scuba diving certification exam on his behalf.With such solutions that can act across tools and websites, the way users interact with software is fundamentally changing. Products are not isolated anymore. They are becoming part of a broader workflow across the internet.AI Disruption and Market Hype: Navigating the Risks and Opportunities for AI-driven Sustainability Reporting StartupsAt the end of their conversation, Max and Jerome talked about the transformative potential of AI. The technology has clear societal benefits and a range of important real-world applications. For example, AI-driven tools for education and dyslexia can make a tangible difference.At the same time, the overvaluation and hype surrounding AI companies look worrying. The recent $5 trillion valuation of Nvidia is a good illustration of how market logic can diverge from reality. Financial and strategic decisions around AI are often driven by cycles of irrational enthusiasm, instead of coherent analysis. This brings new risks to the market.Critical thinking and rational evaluations are a must to leverage the technology responsibly.Want to learn more about emerging technologies and the changes they lead to? Here’s what Max Golikov will discuss with his new guests in the next episodes of the Innovantage podcast. Don’t miss them!

Most “digital transformation” efforts don’t fail because the technology is wrong. They fail because the organisation never builds a shared way of working around that technology, so the change fades as soon as the project team moves on.That’s exactly why Sigli created a digital transformation guide designed for leaders who want transformation that actually sticks. It’s built around actionable insights, real case studies, and a proven roadmap for adaptive digital transformation.This article explains what “lasting change” really means, what a practical digital transformation guide should help you do, and how to use Sigli’s guide to turn strategy into execution.Why Digital Transformation Still Stalls in Real CompaniesDigital transformation often starts with the right intentions: modernise systems, improve productivity, become data-driven, use AI and automation wisely. But in practice, many initiatives get stuck in familiar traps: tool-first decisions, siloed ownership, unclear priorities, and a lack of measurable outcomes.Sigli’s perspective is simple: digital transformation isn’t just about introducing new technology, it’s about fostering a mindset that embraces ongoing change and adapts to new challenges. When that mindset isn’t present, organisations revert to old habits. New tools become “extra work.” Teams build workarounds. Leaders lose confidence in the data. Adoption becomes a constant push.That’s why the most useful starting point isn’t “Which platform should we buy?” but “How do we align people, processes, and tech so the change survives real life?”What a Good Digital Transformation Guide Should Actually DoA digital transformation guide shouldn’t read like a trend report. It should help you make decisions and move. In a practical sense, it should do three things well.First, it should help you align leadership around a shared definition of success. Not vague ambition, clear outcomes you can observe and measure.Second, it should give you a roadmap that fits real constraints: limited time, limited capacity, competing priorities, and the need to show progress without rewriting everything at once.Third, it should help you build transformation capability inside the organisation: the culture, habits, and governance that keep the change going after the initial momentum.Sigli’s guide is positioned exactly for that: a practical guide to leading digital transformation focused on aligning people, processes, and technology for transformation that works.What’s Inside Sigli’s Digital Transformation GuideSigli’s Digital Transformation: A Guide to Lasting Change brings together expert perspectives and a roadmap you can apply with your leadership team. It explicitly focuses on practical implementation and building a future-focused mindset, not just adopting tools.The guide also features insights from several “top industry experts,” including leaders and practitioners connected to Sigli, such as a Flemish Government official, Estonia’s Government CTO, and a professor from Vlerick Business School.If you’re trying to drive change in a mid-market environment, where progress matters more than perfection, this combination is valuable: strategic clarity, real-world lessons, and a roadmap that is meant to be used, not admired.How to Use This Digital Transformation Guide in the Real WorldIf you download a guide and it sits unread in someone’s inbox, it won’t change anything. The fastest way to turn a digital transformation guide into action is to use it as a working document with your leadership team.Start by using it to create shared language: what “transformation” means in your context, what outcomes matter most, and what will not be prioritised right now. Then translate that into a roadmap with clear ownership: who drives which part of the change, how decisions get made, and how progress will be tracked.This matters because transformation is not a one-time event. As Sigli’s own blog content emphasises, long-term success depends on how well your culture adapts, how leaders support the change, and whether continuous learning is built into daily work.Download the Digital Transformation Guide From SigliIf you’re leading change and want a clearer roadmap that connects strategy to execution, download Sigli’s digital transformation guide here.Reading is a great start. Turning it into a focused plan is where results happen.If you want, book a call with Sigli and we’ll help you translate the guide into a practical first step, typically by mapping one critical workflow, agreeing on measurable outcomes, and shaping an execution plan your team can actually sustain.

On the Innovantage podcast, its host and Sigli’s CBDO Max Golikov and his guests often talk about technology from a business perspective. In this episode, the focus is shifted to the role of technology in political and social life. What are the risks of online anonymity? And how can it harm democracy?To find answers to these questions, Max invited Belgian politician and former State Secretary for Digitalization Mathieu Michel to join him for a conversation.Today, Mathieu is a member of the Belgian parliament and focuses on innovation and emerging technologies, including blockchain, artificial intelligence, and cybersecurity. His work centers on how these technologies shape democracy, economic development, and the daily lives of citizens.As Mathieu explained, his core mission is to ensure that technological progress is grounded in trust. According to him, innovation can create positive opportunities only when citizens have confidence in the systems behind it. And politics plays a critical role in establishing this trust and guiding technology toward the public good.Today, Mathieu is one of the leading voices behind Europe’s digital identity initiatives, such as eIDAS 2.0 and the Declaration of Leuven. He emphasized that digital tools must reinforce democratic values. However, at the same time, technology carries risks, and society should be ready to face them. Role of technologyMathieu views technology as a tool to improve human life. Being a liberal and an optimist, he believes deeply in people’s ability to create positive change. What motivates him is humanity’s constant capacity to invent new solutions that make life easier and better.Today’s major challenges, like climate, mobility, healthcare, and others, require forward momentum. However, when societies face uncertainty, many instinctively look backward.Though emerging technologies inevitably raise hard questions and introduce new risks, according to Mathieu, that is not a reason to slow down. It is a reason to stay creative and find solutions.The EU AI Act and its impact on technologyCurrently, we can observe the growing influence of artificial intelligence on democratic systems.Europe began discussing AI regulation four years ago. But the AI Act took effect only three years later. This illustrates how slowly governance moves compared to technological progress. Nevertheless, as Mathieu stressed, regulation is essential for establishing trust. But it must not become a barrier to innovation.Policymakers need a deeper understanding of emerging technologies and a more proactive mindset. Instead of slowing innovation, governments should guide and support it. As a result, AI will be able to develop in such a way that strengthens society and expands opportunities.Europe’s habit of limiting technologyEurope often responds to emerging technologies with excessive caution. Though the AI Act’s risk-based approach has its value, the final framework imposes more restrictions than necessary. According to Mathieu, Europe underestimates the global competitive landscape, particularly against regions moving faster on AI development.Overregulation could leave Europe technologically behind. The solution is not to suppress or fear new tools, but to guide their deployment responsibly. While some regulations prohibit innovation, it’s important to support it, especially on issues like misinformation and online harm.There are proposals that resemble “ministries of truth,” where authorities decide what is true or false online. Such initiatives could undermine democratic principles and even lead to dystopian scenarios.Fake news: Real threatFighting misinformation starts with strengthening people’s ability to think critically. And education here is central. Citizens need practical tools that will help them verify identity and understand who they are interacting with online. As a result, they will be able to apply the same judgment they use in the physical world.The core problem is that the online environment was built differently from the real world. Anonymity and unclear accountability are among the core characteristics of the online space. Reintroducing basic trust mechanisms would help people navigate AI-generated content, fake news, harassment, and social platforms more safely.As philosopher Hannah Arendt stated, without facts, genuine opinion becomes impossible. Fake news eliminates the possibility to create shared reality for all citizens, which weakens the foundation of democratic life. Freedom and responsibility in the digital ageMathieu noted that digital identity must balance two core pillars of human existence: freedom and responsibility. In the physical world, individuals exercise their freedom with the understanding that their actions have consequences for others. The introduction of the internet enabled access to information but also weakened accountability.When people operate anonymously, responsibility often disappears. This leads to harassment, misinformation, and harmful behavior online. At the same time, our real-world interactions rely on knowing who we engage with. Freedom of expression remains fundamental, but users must have the ability to evaluate who is speaking and whether they have relevant expertise. Today, there are a lot of examples of fake videos produced by AI. Some of them look quite realistic. This signals a shift to a world where digital content is false by default. It means that individuals now need to verify authenticity, instead of just assuming it.Verifying who we areIt is necessary to build efficient methods to bring responsibility back into the digital world. The first step is to understand how responsibility works in the real world.In real life, as citizens, we are accountable to our country and to society. If we commit a crime or break rules, we are responsible because our actions are tied to our citizenship.How do we reconnect responsibility in the digital world? The answer is simple: we need to know people who are online. This doesn’t mean everyone must be identifiable all the time, but when we want accountability, digital identities must be linked to real people.Technological standards make this possible. The EU regulation called eIDAS provides standards for applications that verify digital identities. This ensures that online, the person you are interacting with is real and accountable. For example, you can sign a digital contract knowing the other party is a real person, or verify that an article or research is signed by an actual expert.This approach doesn’t prevent people from speaking freely online. It simply allows verification when needed. Governments and private platforms can adopt these standards. For instance, Belgium has introduced a digital wallet, MyGov, where verified digital identities can be used across multiple applications.In Europe, the Declaration of Leuven standardized three types of online profiles:Verified profilesVerifiable profiles (like in blockchain-based systems)Anonymous profilesBeing anonymous is still allowed. But platforms can choose to apply these standards to differentiate profiles. For example, on Facebook, you could filter content to see only verified users or choose to include anonymous users. The experience changes completely when you can tell who is behind a post, message, or email.This also applies to AI and digital content. You could sign an algorithm, an AI output, or even a photo, to guarantee that you are responsible for it. Identity vs. profileIt’s important to distinguish between a profile and an identity. When you are on PlayStation, Facebook, or any forum, you are using a profile. A profile doesn’t make you responsible. It is just a representation of yourself online. You can be anyone you want.An identity, on the other hand, links you to responsibility. It allows others to verify who you are and keep you accountable for your actions. An identity doesn’t prevent anonymity entirely. But if you want to be responsible, there must be a way to trace actions back to you.When people know their actions are tied to a real identity, it can reduce aggression and help restore social norms from the real world into the digital space.Digital acceleration and its impact on democracyIn the real world, it took centuries to balance freedom of speech, social control, and responsibility. Laws and social norms evolved slowly over time.Digital technology, however, accelerated this process dramatically. The internet’s early anonymity temporarily removed personal accountability, which boosted unrestricted freedom of expression. While this allowed new voices to be heard, it also fueled polarization and misinformation.We can see the effects in modern politics, for example, in the United States, where digital dynamics have amplified divisions and challenged shared truths. The rules of the strongest or loudest voices dominate online discourse. If unchecked and not managed properly, it could threaten the foundations of democracy, as such a situation undermines trust and accountability.Democracies won’t be saved by a single action or the push of a button. They require ongoing reflection and effort.Bringing responsibility back into the digital world is key to slowing polarization. This isn’t just a problem in the United States. It is also visible in Italy, France, Belgium, and other countries. People often prefer black-and-white thinking as they don’t want to consider the nuances and shades of gray.Identity verification for privacy and securityAccording to Mathieu, identity verification tools should not just impose obligations. They should open new opportunities for people. The goal is to know who people are interacting with online and leave the choice of using verification to the individual. For instance, a platform like Facebook could offer verification, and users could decide whether to participate.Verified identities can improve online interactions and strengthen communities. Nevertheless, anonymity also has value, as it preserves freedom of speech when necessary. People might choose to be verified in some situations and anonymous in others. Flexibility is key.In certain cases, partial verification may be required, such as confirming a user is over 18 to access age-restricted content. These tools can provide security and accountability without limiting personal freedom.Building trust in digital identityMany citizens have some concerns about sharing personal data online. They may have fears of misuse, data leaks, or the implications for vulnerable groups, like immigrants. These concerns are valid and must be addressed carefully.The solution lies in building trust through secure, standardized identity verification. The eIDAS framework establishes technical standards for authenticating identity while prioritizing privacy by default. It is designed not to control people. Instead, it is aimed at confirming facts about them, such as age, citizenship, or other credentials. As a result, online interactions can be trusted.Trust is the foundation of digital innovation. People may trust different institutions, governments, banks, or companies like Google. But what matters is that they trust something.In Belgium, Mathieu led the MyData project. This platform allows citizens to see who accessed their data and for what purpose. This transparency is crucial for building confidence in how personal information is handled.Rethinking trust in social media platformsToday, all major platforms that Europeans use are American. But can people trust such platforms with their data? There is no simple answer. What matters is introducing digital tools that enhance user responsibility and experience.If platforms like Facebook choose not to adopt such tools, new European services that respect accountability and provide better experiences for users could emerge. Mathieu mentioned examples like Twitter’s transition to X, where changes in policy pushed many users to leave. Similar dynamics could occur across Facebook, LinkedIn, Instagram, or Snapchat. By enabling users to choose how they are identified, Europe could foster new social media models that prioritize responsibility and trust.Why startups should invest in digital identityDigital identity regulation creates significant opportunities for innovation. Startups can develop secure, verifiable channels and even physical devices to store and manage digital identities.Many users avoid online markets simply because they don’t trust the system. If startups introduce reliable authentication tools, they can unlock a huge, untapped market.The eIDAS standards allow any company to build solutions that help users verify their identities. This could range from social media applications to secure email plugins that guarantee the sender’s identity.It would be a missed opportunity if only tech giants like Google, Facebook, or Microsoft dominated this space. Startups have the potential to lead the way and create innovative tools for the digital world.European values as a business opportunityAs Mathieu highlighted, Europe’s shared values can become a foundation for new business models. Many people prefer to engage with services that respect European principles, such as human-centered technology and personal data ownership.In the US, data often belongs to companies. In China, the government owns it. In Europe, it should belong to the people. This perspective opens opportunities for European companies to offer alternatives where users control their own data.How regulation can fuel startup growthAccording to Mathieu, the economy and startups should thrive through ideas and private investment, not government intervention. The government’s role is to create a positive environment and rules that unlock the potential of startups.Good regulation is more effective than incentives alone. Europe’s regulations, like GDPR and NIS2, protect citizens and human-centered values. But they can be heavy and challenging for startups. Lighter, well-designed regulations would help startups innovate while safeguarding essential principles.However, the main investment challenge in Europe isn’t for startups or early-stage scale-ups. In this region, companies transitioning to a larger scale have the biggest issues. At this stage, securing funds becomes difficult, and many promising European startups end up relocating to the United States.This isn’t a problem of subsidies. It’s a lack of investors willing to commit at this level. Europe nurtures seeds of innovation, but the gap in late-stage investment drives its most promising companies abroad. As a result, European success is often measured by exits to American companies.Investments in the future of technologies Ten years ago, companies like OpenAI and Nvidia were small or unknown. But they managed to become global leaders because they are ready to innovate. European startups can achieve the same, but only if they focus on the next generation of technologies.Blockchain can be a good example here. While some hesitate to adopt it due to financial risk or uncertainty about its future relevance, due to a lack of experimentation, Europe will always lag behind. The same applies to other emerging fields like quantum computing.Digital literacy: A cornerstone of democracyRapid digitization has outpaced society’s ability to adapt. Now, it is vital to address this gap in education, not just for young people, but for everyone.The impact of AI has grown very quickly, which makes it essential to help people navigate these technologies for themselves. Mathieu compared digital literacy to swimming. Just as physics behaves differently in water than on land, actions in the digital world have unique consequences that require understanding.Today, Belgium has a program on digital citizenship. The country also cooperates with the Council of Europe to create educational initiatives that can be very helpful. They focus on essential skills, like managing identity, passwords, and privacy online. Thanks to them, individuals can engage responsibly and freely. As Mathieu explained, digital identity and secure standards are not about restricting freedom. They are designed to restore the balance between freedom and responsibility that allowed democratic societies to flourish before the internet existed.Want to learn more about the future of technologies and the new challenges that they bring? Don’t miss the next episodes of the Innovantage podcast.

Business app creation without coding in Benelux has moved from a “nice productivity hack” to a serious operating model for many SMEs. Teams can ship workflow apps quickly, automate approvals, and reduce manual work, often without waiting for IT capacity. The catch is that as these apps start processing employee and customer data, the organization inherits GDPR, security, and continuity responsibilities that informal builds are rarely designed to meet.Why Business App Creation Without Coding in Benelux Is Booming and Getting RiskyAcross the Netherlands, Belgium, and Luxembourg, SMEs are under constant pressure to digitize processes without adding large engineering teams. No-code platforms fit that reality: they shorten delivery cycles, make iteration easier, and let domain experts translate requirements directly into working tools.Risk enters because adoption tends to be organic. A small internal app becomes widely used, more fields get added, integrations appear, and suddenly you have a business-critical system that was never designed with role-based access, retention, logging, or clear accountability. In the Benelux market, this matters even more because GDPR expectations are operationally mature and enterprise customers often ask SMEs to demonstrate vendor and security due diligence.From Quick Wins to Compliance Headaches: The Dark Side of No-CodeThe “dark side” is rarely dramatic at first. It looks like success: more teams adopt the app, more data moves into it, and the app becomes the default place to work. Then the questions start, usually after a customer questionnaire, an internal audit request, or a data subject deletion request.Typical failure patterns tend to cluster into a few themes:App sprawl and unclear ownership (nobody can confidently say who owns which app or who approves changes)Sensitive data creeping in (personal data copied for convenience, then retained indefinitely)Access drift (too many admins, broad sharing links, offboarding gaps)Vendor/DPA blind spots (tools adopted quickly without processor terms and sub-processor clarity)New silos (each app becomes its own dataset, harming reporting and future AI initiatives)The core problem is not no-code itself. It is the absence of a repeatable operating model that matches how quickly business app creation without coding in Benelux can scale.Introducing Sigli’s No-Code Governance & Compliance Framework for Benelux SMEsSigli’s framework is built to preserve speed while introducing proportionate controls. It is not “enterprise bureaucracy for SMEs.” Instead, it standardises a few essentials such as visibility, risk-based approvals, GDPR-by-design defaults, production hardening for critical apps, and monitoring. This way, teams can keep building without creating avoidable exposure.Think of it as a set of reusable patterns. Builders get clear guidance and templates. Leadership gets accountability, auditability, and predictable risk management. IT and security get fewer surprises.Step 1: Map What You Already Have: App & Data InventoryYou cannot govern what you cannot see. The first step is an inventory that is quick to assemble but consistent enough to be actionable. Each app should have a named business owner, the platform it runs on, its user audience, and a short statement of purpose. From there, you add what matters most for risk: the types of data processed and the integrations that move data in and out.A simple Green / Amber / Red risk label works well for SMEs:Green: low-risk internal productivity appsAmber: apps processing personal data with limited scopeRed: business-critical apps, sensitive categories, or broad sharing/integration footprintsThis label becomes the engine for approvals and hardening later.Step 2: A GDPR-by-Design Blueprint for No-Code in BeneluxFor business app creation without coding in the Benelux, GDPR-by-design must be practical. The aim is to make the compliant approach the easiest approach by embedding good defaults into templates: minimal data collection, clear purpose, retention rules, controlled sharing, and deletion workflows.Allowed vs. forbidden data in citizen-built appsInstead of forcing every team into legal interpretation, classify data in plain language.Generally acceptable data is what you truly need to run the workflow: business contact details used for routing, operational status fields, and basic internal references.Restricted data — such as employee performance context, absence details, customer financial identifiers, or identity-document-related information—should trigger review and stronger safeguards.Typically off-limits (unless formally approved with robust controls) includes special category data and any form of high-impact profiling or automated decision-making that materially affects individuals.This approach keeps innovation moving while preventing the most common mistake: placing highly sensitive data into apps that were never designed for it.Data minimisation, retention defaults, and vendor/DPA checklistData minimisation is easiest when it is structural. Templates should encourage builders to collect only what the process needs and to avoid open-ended free-text fields where sensitive information tends to appear.Retention should be standardised as well: if nothing is configured, data often lives forever, which is both risky and unnecessary.Vendor due diligence can be lightweight without being optional. A short DPA and vendor checklist should confirm (at minimum) processor terms, sub-processor transparency, deletion/export procedures, breach notification expectations, and practical support routes. This is especially important when apps rely on multiple connectors and automation tools, because your data footprint can expand quickly.Step 3: Hardening Critical No-Code Apps: From Prototype to Production-GradeThe moment a no-code app becomes critical—supporting revenue operations, customer delivery, finance controls, or HR workflows—it needs production-grade discipline. Hardening is the transition from “useful tool” to “managed system,” and it reduces both security incidents and business disruption.A compact hardening checklist is usually sufficient for SMEs:SSO and MFA where supported, to centralise identity and reduce account sprawlLeast-privilege roles (viewer/editor/admin) with periodic admin reviewSecure secrets handling (vault/secure variables, never stored as fields)Audit logging enabled so access and changes are traceableControlled exports and integrations to reduce uncontrolled data replicationDeletion and retention enforcement that is tested, not assumedIf downtime or data loss would materially impact operations, this checklist should be non-negotiable.Step 4: Central Data Hub—Stopping No-Code from Creating New SilosNo-code can unintentionally fragment data. When each team builds an app with its own version of “customer,” “order,” or “employee,” reporting becomes inconsistent and confidence erodes. Over time, this becomes a major blocker for analytics, and especially for AI, which depends on reliable, well-defined data.A central data hub approach reduces this risk by establishing systems of record and encouraging apps to reference authoritative data rather than copying it. In practice, that means consistent identifiers, standard entity definitions, and controlled interfaces for reading and writing data. The result is fewer silos, stronger reporting, and a cleaner foundation for future automation and AI readiness.Step 5: Governance Guardrails That Don’t Kill InnovationGovernance fails when it treats every app like a high-risk system. SMEs need a fast lane for low-risk solutions and deeper review only when it is justified. A risk-based operating model makes that possible while improving visibility.App catalogue, traffic-light approvals, standard roles, short playbook trainingAn app catalogue creates a single source of truth: what exists, who owns it, what data it touches, and whether it is business-critical.From there, a traffic-light approval model keeps teams moving:Green apps follow pre-approved patternsAmber apps complete a short reviewRed apps require formal security/privacy inputTo make this work in real life, roles must be explicit. At minimum, someone owns the business purpose and risk, someone administers the platform controls, and reviewers exist for higher-risk cases.Finally, training should be brief and practical, focused on data classification, retention, access control, and safe integration patterns, so adoption is high.Step 6: Continuous Monitoring and Support: Staying Benelux-Compliant Over TimeEven well-built apps drift. People change roles, access accumulates, integrations expand, and new data fields appear. Continuous monitoring prevents “compliance decay” by making changes visible and reviewable.Use monitoring where it adds the most value:Periodic access reviews for critical apps (especially admins and external sharing)Change awareness when new fields, exports, or integrations affect data sensitivityVendor/DPA touchpoints aligned with renewals and major platform changesA clear support route so citizen developers can ask before shipping risky patternsThis keeps governance lightweight while ensuring it remains real.Turning No-Code Chaos into a Governed, AI-Ready App EcosystemWhen you combine visibility (inventory), safe defaults (GDPR-by-design), targeted hardening (for critical apps), and disciplined data patterns (central hub), business app creation without coding in Benelux becomes scalable. You reduce surprises, improve audit readiness, and raise the overall quality of your data landscape. Just as importantly, you keep delivery speed high because builders reuse proven templates instead of reinventing decisions and controls each time.How Sigli Helps Benelux SMEs Scale No-Code Safely and ConfidentlySigli helps SMEs across the Benelux operationalise no-code safely without killing momentum. That typically includes establishing the app and data inventory, implementing GDPR-by-design templates and review paths, hardening business-critical apps, designing central data patterns that reduce silos, and setting up monitoring routines that keep compliance stable over time.The practical outcome is consistent: faster delivery with fewer risks, clearer accountability, and a stronger foundation for analytics and AI initiatives.Ready to put governance in place without slowing delivery? Book a call with Sigli to assess your current no-code landscape and get a practical, risk-based plan for scaling safely.

A HubSpot to Pipedrive data migration is usually planned around moving contacts and deals. But the real risk for Benelux sales teams is losing the conversation history that explains what actually happened in the pipeline: emails, calls, meetings, notes, and the files attached to them. When that history is missing or no longer linked correctly, operational reporting dashboards stop being trustworthy. Trends break on the migration date, response-time reporting becomes meaningless, and sales cycle analytics suddenly looks better or worse for the wrong reasons.Why Conversation History Matters for Operational Reporting Dashboards BeneluxConversation history matters because most operational dashboards are built on activity data and timestamps. If you track time-to-first-response, number of touchpoints per stage, stage velocity, or handover quality between SDRs and Account Executives across the Netherlands and Belgium, you rely on a clean trail of activities connected to the right people, organisations, and deals. Once those links are gone, you don’t just lose context for the team. You lose the ability to report accurately and make decisions based on real performance.The Risk: What Gets Lost When You Move from HubSpot to PipedriveThe biggest mistake companies make when moving from HubSpot to Pipedrive is treating conversation history like a nice-to-have. In practice, it’s the hardest part to migrate because it’s not only data, it’s data with relationships. A single email might need to appear on a contact record and on a deal record. A meeting might involve multiple contacts. A note might have an attachment that should remain accessible later. If you only export the core records via CSV and import them into Pipedrive, you can end up with a CRM that looks complete at first glance, while the timeline of work is gone or disconnected.Step 1 – Scope the Migration: Which Conversations Must Survive the Move?Start by defining what you actually need to preserve. Keeping everything forever sounds safe, but it often creates cost and complexity without improving reporting. A practical approach is to decide which activity types must survive—emails, calls, meetings, tasks, notes—and what time window is required to keep dashboards and management reporting consistent. For many Benelux SMEs, keeping the last 12–24 months fully intact is enough to preserve the operational view, while older history can be archived externally if needed.Step 2 – Clean Up HubSpot Associations Before Touching Any DataBefore you export anything, fix broken links and messy associations in HubSpot. Conversation history only stays meaningful if it stays attached to the right objects. Duplicated contacts, deals linked to the wrong company, and activities logged only on a contact but not the deal are common problems. If you move that mess as-is, you will import confusion into Pipedrive, and you won’t be able to reconstruct accurate timelines later.Step 3 – API vs CSV: Choosing the Right Export Method for Reliable HistoryCSV exports are useful for static CRM objects such as contacts, companies, and deals. But conversation history often requires API-based extraction to be complete and reliably linked. If you care about preserving email and activity timelines, you need an export that captures the activity content, timestamps, and the associations that connect each activity to the correct contact, deal, and company. This is where many migrations silently lose fidelity because the export method simply doesn’t include all the relationships you need to rebuild the story.Step 4 – Build a Staging Layer: One Place to Reconstruct Deals, Contacts and ActivitiesOnce you have the data out of HubSpot, treat the migration like an engineering project, not a file upload. The safest approach is a staging layer: one place where you store raw exports, normalize them into a consistent structure (people, organisations, deals, activities, files), and build mapping between HubSpot IDs and the new Pipedrive IDs. That mapping is what lets you import conversation history into Pipedrive and still connect each email, call, meeting, or note to the right deal and the right person.Step 5 – Import into Pipedrive with Full Links: Deals, People, Organisations and ActivitiesImporting into Pipedrive should follow a simple logic: create the objects first, then bring in the timeline. Organisations and people come first, then deals linked to them, and only then activities and notes, so the links can be created correctly. If you import activities too early, you’ll either lose the linkages or spend time manually repairing them after go-live.Step 6 – Don’t Forget Files and Attachments: Contracts, Proposals and Key DocumentsFiles and attachments deserve special attention. Contracts, proposals, and key documents are often attached inside CRM timelines rather than stored in a separate document system. If you forget these, your team will feel it immediately after go-live—especially when they need to reference a signed agreement, confirm what version of a proposal was sent, or review key details in a handover.Step 7 – Quality Assurance: How to Prove Your Sales History Is IntactQuality assurance is where you prove the migration worked. Don’t rely on a couple of spot checks. Compare activity counts for a sample set of deals, verify that activities appear in the right timelines, and check that reporting still behaves as expected. If your dashboard previously showed average time-to-first-response or average stage duration, those numbers should not suddenly change because of the migration. If they do, the data may be present but the links or timestamps are not preserved in a way your reporting can use.How Preserved Conversation History Strengthens Operational Reporting Dashboards in the BeneluxWhen conversation history is migrated and linked correctly, Benelux operational dashboards stay reliable. You retain continuity in trend reporting, analyze pipeline velocity and conversion by segment, and track SLAs and team performance without the migration cliff that makes year-over-year comparisons impossible. More importantly, your sales team keeps the context they need to run deals and hand over accounts smoothly.How Sigli Can Help: A Practical HubSpot → Pipedrive Migration Checklist for Benelux SMEsSigli can help you run a HubSpot to Pipedrive data migration with reporting continuity as a primary requirement, not an afterthought. In practice, that means scoping the history that matters, cleaning up HubSpot associations before extraction, choosing the right export method for activities, using a staging layer to reconstruct links, importing into Pipedrive in the correct order, and validating the outcome with measurable QA checks tied to your dashboards. If your team relies on operational reporting for pipeline management across the Benelux, preserving conversation history is not optional. It’s the difference between a clean cutover and a CRM reset.

User training and onboarding UK is becoming a decisive capability for SMEs adopting AI faster than they are building the practical skills to use it safely, consistently, and profitably. That mismatch is widening the AI skills gap in ways that show up in everyday work: uneven quality, compliance anxiety, duplicated effort, and teams reverting to old habits when outputs feel unreliable. The competitive advantage is not simply “having AI,” but onboarding people to apply it well in real workflows, with clear standards and verification. This article explains what the AI skills gap looks like on the ground inside UK SMEs, why traditional training often underdelivers, and how in-context coaching can turn ad hoc experimentation into confident, measurable AI adoption.Why User Training and Onboarding in the UK Matters More Than Ever in the Age of AIAI is no longer confined to specialist teams. It is becoming a universal layer across drafting, summarising, analysis, and decision support, touching customer communication, proposals, internal documentation, reporting, and workflow automation. In SMEs, where people are generalists and capacity is tight, inconsistent AI usage creates operational volatility. One employee may accelerate work responsibly; another may produce confident-looking but inaccurate outputs that create rework or risk.This is why user training and onboarding now sits at the intersection of productivity and governance. When teams are taught to provide high-quality inputs, follow role-based boundaries, and validate outputs before they are relied upon, AI becomes a stabilising force. When they are not, AI becomes a source of noise, risk, and distrust.User Training and Onboarding UK: What’s Really Happening Inside SMEs TodayIn many UK SMEs, AI adoption begins organically. A few early adopters find value, and usage spreads informally. That informal spread is the first warning sign: it produces fast uptake but inconsistent practice because prompts, habits, and “rules” travel without context.What typically emerges is a patchwork of usage styles. Some people use AI for ideation, some for drafting, some for summarising meetings, and some for ad hoc analysis. Leaders then encounter two competing realities: apparent productivity gains in pockets, and an increase in quality control effort elsewhere. The organisation may feel “more active” but not necessarily “more effective.”A common pattern looks like this:A tool is introduced (or quietly adopted by individuals).A few high performers get real wins and share prompts informally.Quality becomes uneven as others copy prompts without understanding the underlying structure.Managers begin rewriting or double-checking more work than before.Risk concerns surface late (often after a near-miss), and adoption becomes hesitant or fragmented.This isn’t a motivation issue. It is an onboarding design issue: SMEs often train “AI awareness” rather than onboarding “AI-in-your-workflow.”The AI Skills Gap in UK SMEs: Everyday Symptoms You Can’t IgnoreThe AI skills gap is best diagnosed through operational symptoms. You will rarely see it described as “skills” internally; you will see it as friction, rework, and inconsistency.Typical symptoms include:Prompt roulette: success depends on who is asking and what they happen to type.Polished but thin output: content reads well but lacks substance, specificity, or correct context.Rework loops: drafts bounce between stakeholders because nobody trusts the first pass.Inconsistent tone and decisions: different employees produce different “company voices” and different levels of caution.Risk paralysis vs. risky overuse: some avoid AI entirely; others use it without boundaries.Shadow usage: unofficial tools or personal accounts appear because “approved” ways of working aren’t clear.If these patterns are visible, AI is not being onboarded as a capability. It is being “tried” as a tool.Real-Life Scenes from the Frontline: When User Training and Onboarding in the UK Goes WrongThe fastest way to understand the gap is to look at frontline scenes where the cost is real.A customer service agent drafts a response using AI. It is fast and polite, but it misses key context from earlier messages, fails to reference the company’s policy correctly, and creates the impression that the customer’s experience hasn’t been read. The complaint escalates, not because AI was used, but because there was no trained habit of context-checking and verification.A sales manager uses AI to accelerate a proposal under deadline pressure. The document is compelling, but it borrows outdated pricing language and implies delivery timelines that the operations team cannot support. The client spots inconsistencies later, trust erodes, and the team scrambles to correct what should never have been committed.An ops lead asks AI to “write an SOP” from scratch. The result looks professional but doesn’t match how the work actually happens, so nobody follows it. The business ends up with documentation theatre — more pages, less reliability.These are not edge cases. They are predictable outcomes of onboarding that does not teach the “how” of AI use inside real workflows.Why Traditional Workshops Don’t Work: The Limits of One-Off AI Training for UK TeamsTraditional workshops tend to underperform because they focus on capability awareness rather than capability execution. People leave knowing what AI can do, but not knowing what they should do in their specific role, with their specific systems, under their specific constraints.One-off training also fails to create durable behaviour change. AI competence is built through repetition: writing better inputs, setting boundaries, validating outputs, and learning when not to use AI. That is a practice loop, not a single event.Workshops can be useful as an introduction, but they should not be mistaken for onboarding. In SMEs, the impact comes from training that is embedded into work and reinforced over time.In-Context Coaching: A New Model for User Training and Onboarding in UK SMEsIn-context coaching shifts training from “learning about AI” to “learning with AI while doing real work.” It works particularly well for SMEs because it is lightweight, specific, and creates immediate operational benefit.A practical coaching model usually includes these steps:Select high-frequency workflows where speed and quality matter (e.g., first-response customer emails, proposal outlines, meeting follow-ups, internal SOPs, reporting commentary).Define what “good” looks like for each workflow (tone, completeness, factual accuracy, escalation rules).Create reusable patterns (prompt templates, input checklists, output formats, verification routines).Coach in short cycles (15–30 minutes) using live work and immediate feedback.Update patterns continuously based on what fails in practice, not what sounds good in theory.The value is not the “perfect prompt.” The value is operational consistency: people learn the same approach, apply it repeatedly, and improve it together.From Copy-Paste Chaos to Confident AI Use: Practical Examples from Typical UK WorkflowsIn customer service, the shift is from “paste the customer message and hope” to a consistent first-response method. The agent learns to summarise the situation, highlight relevant policy points, draft a response in the company’s tone, and then validate that the draft contains no invented facts. The result is faster replies that reduce escalation risk because the workflow forces the right checks.In sales, strong onboarding reduces the temptation to let AI generate entire proposals unchecked. AI is used to create a structured outline, produce a first-pass executive summary, and surface clarifying questions. Commercial terms, pricing, and delivery commitments remain controlled through approved references and human confirmation. That keeps the speed benefit while reducing the risk of overpromising.In operations, AI becomes a powerful way to turn messy notes into standard operating procedures that people will actually follow. The key is that SMEs train staff to feed AI what it needs, real steps, exceptions, definitions of “done”—and to review outputs with process owners who understand edge cases. That combination creates documentation that is both readable and operationally accurate.In finance and reporting, the biggest leap often comes from using AI for narrative clarity rather than unverified analysis. When teams provide confirmed drivers and metrics, AI can produce sharper commentary, risk summaries, and action framing. This reduces the burden of writing while protecting analytical integrity.How to Measure the Impact of Better User Training and Onboarding in UK OrganisationsMeasurement should be simple enough that an SME will actually keep doing it. The most useful approach is to pick a small number of workflows and track changes in time, quality, and risk.Time is the easiest starting point. If first drafts take less time to produce and require fewer revisions, adoption is working. Quality can be captured through a basic acceptance signal: how often does a draft go through with minimal edits, and how often does it require manager rewrites? Risk and governance can be tracked through reduction in avoidable incidents, improved use of approved tools, and fewer “shadow” processes.A straightforward measurement set for each workflow typically includes:Productivity: time to first draft or time to resolution.Quality: first-pass acceptance rate or reduction in escalations/corrections.Governance: fewer policy breaches and less unofficial tool usage.You do not need perfect attribution. You need directional proof that onboarding is reducing rework and increasing consistency.First Steps for UK SME Leaders: Closing the AI Skills Gap Through Smarter User Training and OnboardingFor most SMEs, progress comes from focus and clarity rather than big programmes. The most effective first steps are:Choose 3–5 workflows where AI can help and where inconsistency is costly.Set clear boundaries on what information can be used with AI tools and what must never be used.Create role-based templates employees can reuse immediately (inputs, output format, verification routine).Run short in-context coaching sessions weekly for a month using real work and capturing learnings.Measure before and after using simple indicators that leaders care about: speed, quality, and risk.When user training and onboarding UK is done this way, the AI skills gap shrinks in a visible, operational manner: fewer rewrites, fewer errors, faster cycle times, and a workforce that can use AI confidently within the reality of UK SME operations.

In the Innovantage podcast, we have often discussed technology and its role in business, but we have never talked directly about market fit. What are investors looking for today? And how do they determine whether a project is set up for success? To find the answers to these questions, podcast host and Sigli’s CBDO Max Golikov, invited Gytenis Galkis, Partner at Superhero Capital, to his studio.Gytenis is a venture capitalist with over a decade of experience investing across the Baltic and Nordic startup ecosystems. Over the years, he has reviewed hundreds of startups annually and made direct and fund-based investments in nearly 100 tech companies. He works with pre-seed and seed-stage companies, which provides him with a comprehensive picture of the market trends. Thanks to this, he has a deep understanding of the patterns and traits that separate high-potential teams from the rest. And he agreed to share his expert insights with the audience of the Innovantage podcast.Filtering 600 startups a year: Is it possible?To find the projects that align with the fund’s investment strategy, Gytenis and his team review approximately 600 startups every year. Over a decade, this process has amounted to thousands of startup evaluations, including both direct reviews and indirect exposure through events and pitch sessions.Gytenis explained that filtering such a high volume is essential to identify the few startups with true potential. When it comes to early-stage investments, traditional product-market fit is very difficult to assess. That’s why he prefers evaluating so-called founder-market fit. It means that in the first turn, he looks at the entrepreneur’s skills and vision of the market opportunities, as every project starts with a founder.Nevertheless, even with a very careful selection, outcomes are often uncertain. But identifying strong founder-market fit increases the likelihood of success. Product-market fit vs. founder-market fitIs there a huge difference between the approaches to assessing product-market and founder-market fit? According to Gytenis, product-market fit occurs when a company clearly defines its value proposition, and customers understand and actively adopt the product. In such a way, a product is moving beyond early adopters to broader market awareness. Here, he named OpenAI as an example. Mainstream recognition came only several years after initial development. Product-market fit was achieved after a row of iterative improvements.Meanwhile, founder-market fit refers to the alignment between the founding team’s skills, experience, and the market they aim to serve. Gytenis noted that teams with relevant industry knowledge or unique insights are always better positioned to succeed. Bill Gates can be named as a good example of a founder who determines the future success of his company. His early exposure to one of the first computers gave him a competitive advantage that later enabled software innovation.Startup success rarely comes overnight. Very often, a team needs to accumulate sufficient experience and insight over years (or even decades) to address a particular market need effectively and build a sustainable venture.The role of experience (and founder’s age) in startup successAge and experience provide founders with an advantage in building successful startups. More mature entrepreneurs typically understand fundamental business operations, possess relevant industry contacts, and have experience in such basic processes as hiring, training, and scaling teams. These skills allow them to methodically grow companies and navigate challenges effectively.That’s why, according to statistics, there are more successful entrepreneurs among those who started businesses in their 40s than among people who try to launch their projects in their 20s and 30s.Of course, younger founders can also succeed, especially when they bring unique insights and the ability to learn rapidly. Such cases are quite rare, but they still exist and demonstrate that experience is not an absolute requirement for startup success. Timing and insight in startup evaluationWhen communicating with founders, Gytenis always pays attention to whether they can clearly explain why their team is uniquely positioned to solve a problem and why the timing is right. YouTube and Uber were among those companies that came to the market when they were needed, which enabled their success. In these cases, founders were able to detect the missing pieces in the ecosystem and leveraged emerging technologies to create scalable solutions.Gytenis explained that evaluating market fit is both art and science. Many founders often overestimate demand and rely heavily on marketing without deep technology. Others assume that a great product alone will drive adoption. At the same time, some teams possess strong technological capabilities but lack go-to-market understanding. As a result, though their ideas may be good from a tech perspective, they simply don’t align with the market realities.Consensus vs. non-consensus investingGytenis and Max also discussed the balance between consensus and non-consensus investing in venture capital. Highly hyped deals are often expensive and competitive, similar to public stock markets. At the same time, non-consensus opportunities are riskier, but they can generate outsized returns. In this context, it’s worth mentioning Airbnb. Most investors initially rejected this startup. But ultimately, it delivered exceptional outcomes.Early-stage investing has some similarities with poker. Initial investments are modest, which allows the investor to monitor whether a company shows signs of product-market fitIf the startup progresses, additional funding follows through subsequent rounds. If not, the position is cut. Such a staged approach helps manage risk and still maintain the potential for high returns.Speaking about efficient investing, Gytenis highlighted that access to deals is critical. VCs with broader networks can evaluate and select more opportunities. This gives them a better chance to identify undervalued startups. Investment size, stage, and risk tolerance all influence portfolio strategy. While some funds favor high-conviction bets, others, like Superhero Capital, prioritize diversification across many early-stage companies to have more chances for higher yields.Why great teams still fail without market demandThe interplay between founders and market opportunity is a must for a startup to succeed. As Gytenis explained, even the best teams struggle if the market is not ready or the product does not address a pressing need. He used the analogy of vitamins versus painkillers. We always forget to take vitamins, but we never forget to take a painkiller if we have pain. So if you find the product that can address a big market pain, you will buy it. That’s why products that solve urgent problems naturally attract customers. However, desirable but non-essential offerings often fail to gain traction. In venture capital, success often comes when an “A” team meets an “A” market, which means that both the founder’s capabilities and the market’s demand align.Companies like Tesla and Better Place can illustrate the importance of timing and market readiness. Despite significant funding and an innovative battery-swapping model, Better Place failed. It happened because the mass market was unprepared for widespread EV adoption. Tesla chose another approach. It targeted affluent early adopters and then gradually scaled to broader markets over a decade.It’s also important to define the right way to access the market. Even with a strong product, founders must understand distribution, early adopters, and positioning. Otherwise, their projects will easily fail. Historical examples like MySpace, Kodak, and Nokia demonstrate that early technological insight alone is insufficient without market alignment and the patience to grow in step with adoption.Balancing risk and reward in early-stage investingEarly-stage investing is a balance between founder-market fit and risk-return assessment. At Superhero Capital, the team first evaluates whether a startup’s founders have the right experience and insight to succeed in their market. Once that alignment is confirmed, they assess the risk-reward profile. It means that they consider potential ownership stake and the likelihood of generating meaningful returns for the fund.When assessing a startup’s risk-reward profile, Gytenis considers whether an investment can realistically return the entire fund within 7–10 years. For a €50M fund making around 30 early-stage investments, he expects most to fail or only return the initial capital. Given this, the top five must generate the full returns.This is why ownership size and valuation discipline matter: buying only 1% of a company requires a multi-billion-euro exit (and that is an unlikely outcome at the early stage). At the same time, acquiring 15% or 20% at a reasonable price can deliver a full-fund return even with a nearly €250 million exit, which is far more achievable.While unicorns are desirable, the fund aims for several “mid-sized” outcomes (investments returning between €100 million and €500 million) that cumulatively meet the fund’s target returns. This disciplined approach balances exposure across multiple bets, because it is still crucial to bear in mind that many early-stage investments will either break even or fail.Learning from missed opportunitiesSpeaking about investments, it also makes sense to touch on the topic of missed opportunities. As well as any other investors, Gytenis has experience of this kind. He recalled passing on investments due to personal conviction or competing priorities. These cases provide valuable lessons in evaluating founders and market potential.Gytenis shared that early-stage decisions often involve not only data-driven analysis but also subjective judgment, which can be quite dangerous for decision-making. Factors such as founder drive, insight, and market understanding may be difficult to quantify but crucial for success. That’s why documenting decisions and learning from both successful and missed investments helps improve evaluation processes over time.While many early-stage investments may fail, systematic reflection on what worked, what didn’t, and why can enhance long-term venture performance.How preparedness meets luck in entrepreneurshipDoes luck play an important role in entrepreneurial success? Definitely yes, but not only. Preparedness also makes a huge contribution. Gytenis mentioned an example of a young startup entering a rapidly growing market. Initially, it was rejected by investors. But later, the company secured a contract with a major unicorn customer, which became a milestone in its business journey and opened new horizons for it.Such an opportunity could be seen as luck. However, Gytenis noted that the founders’ preparation for offering a competitive system and effectively executing the tender was critical. The contract enabled the startup to scale rapidly, growing 70-fold within two years, and attract additional customers.Luck alone is insufficient. Success comes when consistent effort, market understanding, and operational readiness intersect with favorable circumstances. Founders must be ready to act decisively when luck strikes.Practical tips for foundersGytenis advised founders to focus on market insight and anticipate how the world will evolve. Successful startups often emerge from bets on future trends and technological shifts. For instance, Netflix was among those companies that capitalized on changes in internet speed and content consumption. Founders must develop a unique perspective on how markets and technologies will change, even if the outcome is uncertain.The importance of complementary founding teams shouldn’t be underestimated as well. Solo founders can succeed, but they must be able to hire talented individuals who can fill the existing gaps in their knowledge or skills.Ideal teams often form through prior collaboration on projects or professional relationships where complementary skills (like marketing or technology) are already proven. This shared experience and understanding of each other’s strengths, together with a clear market insight, provides a stronger foundation for building successful ventures.Baltic startup ecosystemIn their conversation, Max also asked Gytenis to share his opinion about the current state of the Baltic startup ecosystem. This ecosystem is relatively small, but it is gradually growing, and each country is working in its own niche. Government initiatives (such as Lithuania’s 2017 program to become a regional hub) have delivered tangible results and already attracted international companies to establish local operations there.Strong GDP growth and rising purchasing power further support the ecosystem’s development. Nevertheless, talent acquisition remains the primary constraint. Attracting skilled professionals (especially from abroad) and retaining them is a challenging task due to cultural and geographic factors.According to Gytenis, education and high-quality job opportunities are key levers for building the talent pool. It’s crucial to understand that salaries in the region are lower than in Western Europe. However, infrastructure and lifestyle quality make it an attractive location for startups. Relocation to Silicon Valley can be a necessary step for later-stage funding, but pre-seed and early-stage ventures can succeed by focusing on building a global product locally and securing regional investor validation.Trust and relationships remain central to venture investment in the Baltics. Face-to-face interaction helps investors gauge founder credibility and commitment. Long-term collaboration always relies on mutual confidence and shared vision for growth.Humans in the Age of AIAlready today, we can see how powerful computers can be in optimizing various tasks, like deal-making or data analysis. Nevertheless, Gytenis believes that humans will continue to engage in activities, but it’s highly likely that they will do it for enjoyment and personal fulfillment. Even if fully autonomous vehicles dominate, some people will still choose to drive classic cars for pleasure.Gytenis assumed that business will also retain a human dimension, particularly in areas like relationship building and judgment-based decision-making. Despite the efficiency of AI, markets are not purely logical. Human behavior, sentiment, and psychological biases will continue to shape outcomes. FOMO, trends, and other emotional drivers ensure that human participation remains essential, even in a highly automated future.Nowadays, a lot of experts agree that the future will be hybrid: AI will ensure efficiency and scale, while humans will focus on creativity and meaningful engagement in business and life. This sounds like a quite optimistic forecast.Want to learn more about the present and future of technologies in the business world? That’s what you can find in the episodes of the Innovantage podcast. Don’t miss the next one!

SaaS customer success UK is how you help customers get real results from your product so renewals, expansion and referrals become predictable – not accidental. It is the shift from “we sold a licence” to “we own an outcome together”, and it is the difference between a fragile subscription business and a resilient growth engine.This matters more than ever. UK SaaS companies are operating in a crowded market, with rising acquisition costs and increasingly cautious buyers. New deals are harder to win, procurement cycles are longer, and finance teams are asking tough questions about every line item. In this environment, the companies that win are the ones that keep and grow the customers they already have.In this article, we will walk through a practical way to think about SaaS customer success UK: the realities of the market, the most common pain points for SMEs, the roles and processes you actually need, and the metrics that matter. Then we will give you a practical checklist you can use to design your own SaaS customer success UK strategy, content and service page, plus a 30-day action plan to get started.The Reality of SaaS Customer Success UK TodayThe UK SaaS market has matured. There are more products than ever competing for the same budget, and most buyers already have a stack of tools in place. Recurring revenue sounds stable on paper, but in reality it can be fragile: one quarter of unexpected churn or a batch of downsells can wipe out a big portion of your new ARR.For a long time, SaaS customer success was treated as “support with a nicer name”. A few onboarding calls, some ad hoc training sessions, maybe a quarterly check-in – and then everyone hopes for renewal. That approach no longer works. Customers expect you to be proactive, outcome-focused and aligned with their internal pressures.Modern SaaS customer success UK teams tend to work toward three big goals. First, reduce churn by making sure customers get to value quickly and stay engaged. Second, increase expansion and Net Revenue Retention (NRR) by spotting growth opportunities inside existing accounts. And third, turn customers into advocates who are willing to provide testimonials, referrals and participate in case studies that fuel your go-to-market.Common SaaS Customer Success UK Pain Points for SMEsMany UK SaaS SMEs know customer success is important, but day-to-day reality often looks messy and reactive. The symptoms tend to repeat themselves across different companies and products.Churn that feels randomCustomers look happy, then suddenly go quiet. Emails slow down, usage drops a little, and by the time someone notices, there is a cancellation email with vague reasons like “budget cuts” or “moving in a different direction”. Without clear health signals or an early warning system, churn feels random – and leadership starts to believe nothing can be done.Weak product adoption and low engagementEven when sales cycles go well, the energy often collapses after onboarding. Logins drop dramatically after the first month. Only one champion really uses the product, and the wider team never gets past the basics. Key features that drive real value remain untouched, and renewal conversations become a debate about price instead of a conversation about impact.Overloaded team stuck in reactive modeIn many SaaS SMEs, “customer success” is a catch-all label. The same people handle tickets, chase invoices, join every training call, prepare custom reports and get pulled into last-minute sales demos. They want to be strategic, but they spend most of their week firefighting, doing unpaid consultancy and answering the same questions over and over.No clear link between CS activity and revenueOn top of all this, it can be difficult to prove the impact of customer success to leadership or investors. The team is busy, customers like them, but there is no clean way to show how specific activities drive renewals, expansion or NRR. That makes it hard to argue for more resources or to prioritise CS initiatives over short-term sales targets.Foundations of SaaS Customer Success UK – Roles, Processes, MetricsTo move from “busy and reactive” to “predictable and strategic”, you need a few solid foundations. For UK SaaS SMEs, this does not mean building a huge department; it means being deliberate about roles, processes and metrics.Key roles in a right-sized UK SaaS CS functionAt the core, you have your Customer Success Manager. The CSM is responsible for understanding customer goals, designing success plans, coordinating onboarding and driving ongoing adoption. They are the main strategic point of contact for the customer.For heavier products – for example, those involving integrations, data migration or change management – an Onboarding or Implementation Specialist is extremely valuable. This role focuses on the initial setup, technical configuration and project-style delivery so that time-to-value is kept under control.Finally, even in a small team, it helps to have someone thinking about Customer Success Operations or Digital CS, even if that is part of another role. This person looks after playbooks, automations, data and tooling: email sequences, in-app journeys, health scores and reporting. They make sure your CSMs are not reinventing the wheel for every account.Core processes across the customer lifecycleStrong SaaS customer success UK programmes are built around a few repeatable processes across the customer lifecycle. Onboarding is the first. You should have a clear journey for the first 30, 60 and 90 days that outlines responsibilities, milestones and success criteria for both your team and the customer.Adoption is the second. This is where you actively drive usage of the features that matter most for your customers’ outcomes. Regular value check-ins, training sessions, in-app prompts and targeted content all play a role here.Then come QBRs or strategic reviews. Whether you call them Quarterly Business Reviews or something more appropriate to your cadence, these meetings are where you connect product usage to business results, re-align on goals and discuss future opportunities.Finally, you need renewal and expansion playbooks. That means having structured steps for how you run renewal conversations, what you do when risk signals appear and how you identify and pursue expansion opportunities such as additional seats, modules or regions.Essential metrics for SaaS customer success UKThe right metrics bring all of this together. At the top level, you need to track both logo churn and revenue churn, as well as Net Revenue Retention. These metrics tell you if you are keeping customers, and whether your existing base is shrinking or expanding financially.You also need leading indicators. Time-to-value and onboarding completion rates show how effectively new customers are getting started. Product usage metrics and health scores help you spot accounts that are thriving or struggling long before renewals come up.If your CS team does not know these numbers, you are flying blind. Even simple dashboards built from your product analytics and billing data can transform how you prioritise your time and conversations.Modern Challenges in SaaS Customer Success UK (and How to Handle Them)Once the foundations are in place, most UK SaaS SMEs face a new set of challenges: how to scale without burning out the team, how to balance human touch with automation and how to ensure customer success stays tightly connected to the product roadmap.Doing more with less – scaling with digital and automationNo SME can afford to run high-touch, bespoke programmes for every customer. Scaling SaaS customer success UK usually means introducing more digital and automated elements without losing the human element where it matters.Email sequences that guide customers through key milestones, in-app product tours and tips, recorded webinars and self-serve help centres can all offload repetitive work. AI can help summarise call notes, extract action items, flag risk signals in conversations and personalise outreach based on usage or segment.The goal is not to remove people but to make sure their time is spent on high-value, strategic conversations instead of manual follow-ups and repetitive tasks.Balancing high-touch vs tech-touch in UK SMEsA simple but powerful move is to segment your customers by ARR and complexity, then choose touch models accordingly. Your largest, most complex accounts may need a dedicated CSM, regular strategic sessions and tailored success plans. Mid-market or lower-ARR customers might be better served with a mix of digital journeys and periodic group sessions or office hours.The important thing is to be intentional. Decide what “high-touch”, “low-touch” and “tech-touch” look like in your world, document what each segment gets, and make sure the experience still feels coherent and valuable.Closing the loop with productCustomer success is also a rich source of insight for your product team. Every day, CSMs hear about friction points, missing features and emerging use cases. If that information stays in individual inboxes or call notes, you lose a competitive advantage.Establish a simple cadence between CS and Product: for example, a monthly review where key patterns are shared, top issues are prioritised and upcoming roadmap items are discussed. Use a common template to capture feedback and impact. This turns customer voices into structured input rather than noise, and shows customers their feedback genuinely influences the product.Real-World SaaS Customer Success UK in Action – Learning From Case StudiesTheory is useful, but real examples are what usually change minds. When you look at successful SaaS customer success UK programmes in the wild, certain patterns show up again and again, not only in pure SaaS companies but also in modernisation, data and automation projects.Strong onboarding and implementation are usually the foundation. Complex implementations – such as legacy system upgrades or new data platforms – succeed when there is a clear plan, shared milestones and ongoing collaboration between provider and client. That is customer success thinking in action, even if the label is never used.Long-term engagements often show how continuous improvement and automation increase value over time. Instead of a one-off project, the relationship evolves into an ongoing partnership where each iteration brings more efficiency, better insight or new capabilities.There are also many examples where external teams effectively augment internal product or engineering teams in UK and EU product companies, helping them execute faster while maintaining ownership of the product. That model resonates strongly with how many SaaS customer success functions want to operate: as an extension of the customer’s team, not just a vendor.If you want to see how structured implementation, automation and long-term partnership look in practice, explore our customer stories on the Sigli case studies page – from legacy system upgrades to data platforms and AI-powered tools.As you read, look for patterns: how onboarding is structured, how value is tracked over time and how collaboration evolves from project to partnership. These stories can act as a mirror for your own SaaS customer success UK journey and highlight where your approach is already strong and where you may need to invest.A Practical SaaS Customer Success UK Checklist for Your Own Content and Service PageYour website is often the first real contact point where prospects try to understand whether you take customer success seriously. You can use the following lens as a checklist when designing or refreshing your SaaS customer success UK content and service page.Start with the hero. In one clear sentence, define SaaS customer success UK in your own words and make sure you mention retention and expansion explicitly. A visitor should immediately understand that you care about outcomes, not just features.Give the context. Anchor your message in the UK SaaS market reality. Explain that acquisition is getting more expensive, churn is a real risk and that customer success is strategic, not just a support function. This helps decision-makers justify investing time and budget in CS.Reflect your audience’s problems. Speak directly to pains like unpredictable churn, weak adoption and overloaded teams. When readers see their reality described clearly, they are more likely to trust your proposed approach.Show the substance. Do not stop at nice-sounding principles. Describe the roles, processes and metrics that make your customer success model real. Explain how CSMs work with customers day-to-day, what your onboarding journey looks like and how you track value.Address modern challenges. Demonstrate that you are not stuck in 2015. Talk about how you use automation, digital CS and product alignment to scale, and how you balance high-touch engagement with efficient tech-touch at different customer segments.Provide proof. Link to case studies and share specific outcomes where possible: improvements in adoption, reductions in churn, uplift in NRR or expansion. If you do not have your own stories yet, start by looking at patterns on our Sigli case studies page and think about how similar principles might apply to your product.End with a clear call to action. Offer a low-friction next step such as a discovery call, a success audit, a benchmark review or a playbook download. Make it obvious what a curious prospect should do if they recognise themselves in your content.How to Get Started With SaaS Customer Success UK in 30 DaysYou do not have to rebuild your entire operating model to make progress. You can make meaningful changes to your SaaS customer success UK approach in a month, especially in an SME environment where decisions can be taken quickly.In week one, map your customer journey and define your basic metrics. Write down the steps from contract signature to renewal, and identify where customers typically stall. At the same time, make sure you can calculate logo churn, revenue churn and Net Revenue Retention, and that you have at least a rough view of product usage by account.In week two, create a simple success plan template and outline a 30/60/90-day onboarding journey. The success plan captures customer goals, key milestones and responsibilities on both sides. The onboarding journey lays out the touchpoints, content and outcomes you expect in the first three months.In week three, define one or two basic health signals and a simple risk review cadence. For example, you might combine usage trends, support volume and sentiment into a health score. Then schedule a weekly or bi-weekly review where the team looks at at-risk accounts, agrees on actions and tracks outcomes.In week four, update your SaaS customer success UK website page using the checklist above. Refine the hero message, add context about the UK market, describe your processes and metrics, and link to relevant case studies. Make sure there is at least one clear CTA that invites visitors to take the next step with you.If you are an SME leader in the UK running a SaaS business, you do not need a 20-person CS team. You need a clear strategy, a realistic operating model and a way to communicate it to customers, investors and your own team. The sooner you make customer success intentional, the sooner your existing customer base can become a genuine growth engine.One easy way to move forward is to book a 30-minute SaaS customer success UK assessment call. Share your current churn, ARR and team setup, and we will help you identify the three most important priorities for your situation: whether that is tightening onboarding, improving adoption or building the right digital layer.If you prefer to start more quietly, begin by reviewing patterns in our case studies and mapping them to your own customer journey. As you recognise similar challenges and solutions, you will see more clearly what your next step in SaaS customer success UK should be.