The release of Digimedia's annual Top 100 Digital Agencies Report arrives at a fascinating crossroads for the enterprise landscape. For years, digital transformation was treated as a high-speed race to amass platforms; today, it is a deliberate exercise in precision and execution. Notably published entirely in English this year to showcase Belgian digital excellence to a global audience, the 2026 report delivers far more than a simple ranking. It serves as a benchmark for where the market is heading.As proud contributors included in this year's report, the team at Sigli has had a front-row seat to these shifting dynamics. Rather than viewing these findings as just macro-level data points, forward-thinking organizations see them as a practical blueprint for corporate evolution. Here is what the latest Belgian and international market signals tell us about where the industry is heading—and, more importantly, how business leaders can turn these insights into better, highly localized decisions.A Market Moving from Ambition to ExecutionThe era of speculative technology spending is officially behind us. If previous years were defined by a mad rush to adopt the newest tools, often driven by industry buzz, 2026 is the year of radical selectivity.The Top 100 Report highlights a stark stabilization in corporate tech strategies amidst market consolidation and shifting business models. Leaders are no longer asking, "What can this technology do?" but rather, "What will this technology do for our specific bottom line?" The market shift is entirely outcome-driven.Innovation for the sake of innovation has been replaced by a rigorous focus on deployment, scalability, and measurable efficiency. The companies winning in this environment are those narrowing their focus to a few high-impact initiatives, choosing to perfect execution rather than diluting resources across a dozen experimental proofs-of-concept.Why Insight Alone is Not EnoughEvery year, thousands of executives download industry reports, highlight key trends, and agree with the overarching conclusions. Yet, a persistent bottleneck remains: the friction gap between knowing market trends and turning them into internal action.Identifying a trend at a macro level is relatively simple; re-engineering a legacy business process to capitalize on it within your own walls is remarkably difficult. The Top 100 findings reveal that while 2026 organizations have no shortage of data, they frequently lack the internal connective tissue required to execute. Transformation initiatives rarely fail because the overarching vision is wrong; they fail because the path from a report to daily operations is blocked by misaligned stakeholders, talent shortages, fragmented data systems, and the lack of a structured, low-risk delivery plan.The Themes Business Leaders Should Watch in 2026To cut through the noise, the report points toward a few foundational, highly practical themes that will separate market leaders from the laggards this year:From Generative Hype to Autonomous AI Agents: The conversation has officially shifted away from basic chat interfaces that merely generate text. The focus is now on specialized AI agents built for real-world operations, such as intent-based triage in customer support, predictive analytics, and automated operational workflows.Data Engineering Over Data Accumulation: Amassing vast quantities of data is no longer a competitive advantage; having clean, unified, and compliant data is. This year, the priority is building robust data pipelines and visualization models that teams can actually rely on for daily decision-making under modern regulatory environments.Purpose-Built Business Process Automation (BPA): Replacing manual, repetitive tasks with intelligent automation has shifted from a luxury to an operational necessity. Connecting disparate backend systems to ensure outcomes are consistent, predictable, and fully auditable is a top priority for ops leaders.From Report Findings to Business DecisionsData without a structured roadmap is just background noise. To prevent the Top 100 Report from becoming shelfware, organizations must use a systematic framework to convert macro insights into micro execution:Discovery & Alignment: Before committing budget or writing code, bring cross-functional stakeholders together. Validate your assumptions on the ground and map the report's trends directly onto your existing organizational pain points.Prioritization via MVPs: Avoid the temptation to boil the ocean. Select one or two core operational challenges, build a focused Minimum Viable Product (MVP) or Proof of Concept (PoC), and validate its business value quickly.Roadmap Planning: Scale systematically. Once an MVP proves its worth, lay down a flexible roadmap that allows your technology infrastructure and your team’s internal capabilities to mature in tandem.Turning Market Signals into Practical TransformationAt Sigli, our philosophy has always been anchored in a straightforward truth: technology is only as valuable as the real-world business problems it solves. The 2026 Top 100 Report provides the market coordinates, but your specific operational reality must dictate the vehicle you build."The future belongs to companies that can bridge the gap between technical complexity and operational simplicity. Our role in this market ecosystem is to ground these macro trends in what actually works on the factory floor, the office dashboard, and within local engineering teams."True digital transformation isn't about chasing every single signal in the market; it's about executing the right ones with absolute precision.Want to dive deeper into the data? You can explore the official findings and download the preview edition directly at the Digimedia Top 100 Digital Agencies Report landing page.Ready to translate these market insights into a tailored strategy for your business? Contact the Sigli team today to set up a practical discovery consultation.

Sigli, a European software development and technology partner, has announced a strategicpartnership with OVHcloud, Europe’s leading cloud provider.As an OVHcloud implementation partner, Sigli will help mid-sized and enterprise organisations adopt sovereign cloud solutions with greater confidence. The partnership brings together OVHcloud’s European cloud infrastructure and Sigli’s experience in discovery, architecture, migration, software delivery, data, automation, and AI implementation.Together, the companies aim to support European businesses that need more control oversensitive data, compliance, cloud architecture, and long-term technology investments.“Sigli brings the practical implementation expertise thatmany organisations need when moving from cloud strategy to real cloud adoption.By combining OVHcloud’s trusted European infrastructure with Sigli’s discovery,architecture, and delivery capabilities, we can help businesses make moreconfident cloud decisions where data sovereignty, compliance, and performancematter most.”— Guido Laout, Sales Director, Northern Europe Cluster, OVHcloudFor many organisations, choosing cloud infrastructure is only one part of the challenge. Regulated and data-sensitive businesses also need to understand which workloads to move, how to reduce migration risk, how to integrate existing systems, and how to ensure that cloud decisions support performance, compliance, and business value.Sigli will support organisations across the full cloud adoption lifecycle, including discovery workshops, workload assessment, architecture design, migration planning, implementation, integration, and post-launch optimisation.“Many businesses know they need more controlover their cloud decisions, especially when sensitive data, regulation, and AIare involved. But infrastructure alone is not enough. The real value comes frommaking the right architectural choices, validating risks early, and turningcloud strategy into a working system. That is where Sigli’s role as animplementation partner becomes important.”— Mike Baleika, CTO at SigliThe partnership is especially relevant for organisations in regulated and data-sensitive industries, where cloud decisions increasingly need to balance sovereignty, security, compliance, scalability, and practical implementation.First joint initiative: webinar on cloud decisions forsensitive dataAs the first jointinitiative under the partnership, Sigli and OVHcloud will co-host a strategicwebinar on Thursday, 18 June 2026.Titled “Clouddecisions for sensitive data: how to reduce risk and stay in control,” thesession is designed for C-level executives, IT directors, and digital leadersin the Benelux and UK markets who need a clear framework for managing sensitivebusiness data in a changing global environment.The webinar willcombine Sigli’s practical perspective on implementation, migration strategy,and business logic with OVHcloud’s expertise in trusted infrastructure andEuropean data sovereignty.Topics willinclude:· Strategic workload placement· AI deployment in protected environments· DORA and GDPR compliance considerations· How secure infrastructure can become acompetitive advantageTo register, visit Eventbrite.About SigliSigli is a European software development and technology partner helping mid-sized and enterprise organisations de-risk and deliver software, data, automation, cloud, and AI projects. Since 2015, the company has delivered 150+ project implementations for 30 clients. Sigli combines discovery, architecture, and full-cycle delivery to turn complex technology decisions into working solutions that create measurable business value.Learn more at www.sigli.com.About OVHcloudOVHcloud is a global cloud player and the leading European cloud provider operating over500,000 servers within 46 data centers across 4 continents to reach 1.6 million customers in over 140 countries. Spearheading a trusted cloud and pioneering a sustainable cloud with the best performance-price ratio, the Group has been leveraging for over 20 years an integrated model that guarantees total control of its value chain: from the design of its servers to the construction and management of its data centers, including the orchestration of its fiber-optic network. This unique approach enables OVHcloud to independently cover all the uses of its customers so they can seize the benefits of an environmentally conscious model with a frugal use of resources and a carbon footprint reaching the best ratios in the industry. OVHcloud now offers customers the latest-generation solutions combining performance, predictable pricing, and complete data sovereignty to support their unfettered growth. Learn more at www.ovhcloud.com.Media ContactMaryia MisharavaSigli PR TeamEmail: maryia.misharava@sigli.comPhone: +32 480 2051 99Website: www.sigli.com

Most Western assumptions about China are stuck in the past. We still see a low-cost factory of copycats. However, that’s a dangerous mistake. To separate myth from reality, Sigli’s CBDO Max Golikov invited Pascal Coppens onto the Innovantage podcast.Pascal is a sinologist and tech entrepreneur who has spent almost forty years bridging the gap between Silicon Valley and China. He breaks down how a survival-first mindset is now driving China to lead the global race in innovation.In 1988, Pascal Coppens started studying Chinese at the university in Ghent, Belgium. What began as an academic interest quickly became a lifelong focus. But his fascination with Asia started even earlier. As a child, he practiced martial arts, including kung fu and karate. That early exposure sparked a deep curiosity about Eastern culture.By 1996, Pascal moved to China to continue his studies. It was a turning point. He quickly realized that language alone would not set him apart. He expanded into engineering and business, later completing an MBA. This combination would define his career.From telecom to Silicon Valley and backPascal entered the tech industry in 1999, working for Alcatel, a major telecommunications company at the time. His career soon took him across borders and industries.In the early 2000s, he became part of a company acquired by a US firm. This led him to Silicon Valley, where he spent several years during a critical period of global tech expansion.By 2005, he returned to China. There, he built and ran his own business for over a decade. In total, he spent nearly 20 years working outside Belgium, most of it in China.In 2017, he moved back to Europe to launch another software venture. After returning, Pascal noticed a gap. Many people in Europe had a limited or outdated understanding of China.He began sharing his experiences through talks and presentations. Demand grew quickly. Today, he writes and speaks extensively about China, helping businesses and policymakers better understand its rapid transformation.Shift in Chinese InnovationAccording to Pascal, China’s innovation story has changed dramatically over the past two decades.In the early 2000s, China was still learning from the West. But around 2015, a shift began. The “Made in China 2025” strategy marked a new phase. China no longer wanted to follow. It aimed to lead.Chinese innovation does not mirror Silicon Valley. It operates under different pressures. As competition is intense, survival is often the primary driver. Companies must move fast or disappear. Such an environment explains why sectors like electric vehicles and AI are advancing so quickly in China.China is not just competing with the West. It is building its own ecosystem, with its own rules and priorities. Meanwhile, Europe struggles with slower decision-making and fragmented strategy. This makes it harder to respond at the same pace.Why China can feel more capitalist than Silicon ValleyChina’s economy is full of contradictions. That is exactly what makes it hard to understand from the outside.This country often feels more capitalistic than Silicon Valley. At the ground level, the market is intense. People are driven to compete and make money. The energy comes from the bottom up.At the same time, China shows strong social features. There is a clear effort to spread growth across society and avoid leaving people behind. Wealth is not meant to stay only at the top.This creates a paradox. On the one hand, there is fierce competition and ambition. On the other hand, there is protection and control.In practice, this balance works. For generations, people have been taught to work hard, improve their lives, and support their families. But the system does not allow chaos to grow unchecked. The government plays a key role here. It does not remove competition. Instead, it manages the pressure created by it.This is where many Western views fall short. Chinese entrepreneurs are not limited. They operate in a system where intense freedom at the bottom is balanced by control at the top.Why our perception of China is outdatedPascal highlighted that much of what people believe about China is no longer accurate. Some of it was never fully true. But a lot of it is simply outdated.One reason is distance. During the pandemic, travel to China became difficult. Since then, visibility has remained limited. China is also less direct in promoting its achievements. As a result, many still rely on old assumptions.China is still seen as a low-cost manufacturing hub, driven by cheap labor and poor-quality products. The reality is more complex. China now operates across the full spectrum of quality. Low-end products still exist, especially on mass-market platforms. But at the same time, Chinese companies produce some of the highest-quality goods in the world.Another common belief is that China mainly copies the West. That may have been partly true in the past. Today, it is far less so. Chinese companies are now major innovators. They compete not just with global players, but with each other. This internal competition has pushed the need for stronger intellectual property protection as well.A key driver behind this shift is the rise of China’s middle class. With hundreds of millions of consumers demanding better products, companies have been forced to improve. Innovation is no longer optional. It is expected.There is also a misunderstanding of how China’s system works. Many see it as purely top-down, shaped by government plans and long-term strategies. But that is only part of the picture. The government may guide the direction, but the momentum comes from the market itself.How to survive China’s brutal marketChina is one of the most competitive markets in the world. Success is possible, but it requires a different mindset.Government support can help, but it comes with trade-offs. Subsidies and incentives may offer an advantage, yet they often bring restrictions. For this reason, many companies think carefully before accepting them. It is never a free benefit.In the past, Western companies had a clear edge. Ten or twenty years ago, China welcomed them with strong incentives, like tax breaks, cheaper land, and preferential treatment. In some cases, they even had advantages over local firms.That era is over. Today, the playing field is more balanced. Through joint ventures and partnerships, foreign companies can access similar conditions as Chinese businesses. The legal and business environment has matured.But that does not make things easy. The biggest challenge is understanding the market. Without local knowledge, language skills, and a willingness to adapt, foreign companies struggle. What works elsewhere rarely works in China without changes.In some sectors, the barriers are even higher. Software and social media are among the toughest. Regulations are strict, competition is intense, and scaling is difficult. Many global tech companies have faced this reality and failed to gain traction.How entrepreneurs can learn from China’s innovationEntrepreneurs do not need to move to China or learn Chinese to succeed. But understanding the environment makes a big difference.Language helps, as it builds relationships. And in China, relationships matter. Strong networks make business easier. Still, it is not a requirement.What matters most is knowing how the market works.In China, the moment a new idea appears, dozens of competitors follow. Not because they copy, but because they see opportunity.The challenge is not demand. China has plenty of consumers. The challenge is the sheer number of competitors chasing the same market.That pressure shapes better entrepreneurs. Those who can survive in China can compete anywhere.Survival before innovation: The Chinese MVP modelIn Europe, startups often begin with an idea. They aim to build something new, solve a problem, and stand out. Innovation comes first, and growth follows.In China, the order is reversed.Companies start with the simplest possible product, not just a minimum viable product, but the absolute minimum needed to get a first customer. Speed matters more than perfection.From day one, the focus is on sales and market feedback. Products are launched quickly and improved in real time. Instead of creating new markets, companies respond to existing demand.Only after this initial battle, when fewer players remain, does true innovation begin. At that stage, companies refine their products, differentiate, and expand into new markets.Reputation also plays a different role. In many Western markets, quality must be strong from the start. In China, early imperfections are accepted. If a company fails early, its reputation does not matter anyway.Is the Chinese model better than the Western one?Both models can work. The Western approach, which includes building a strong idea, securing funding, and scaling, can be very effective, especially in stable markets with fewer competitors and industries that require time and precision. But in fast-moving environments, the Chinese model often performs better.When competition is intense and customer needs change quickly, speed matters more than perfection. Markets like consumer tech evolve rapidly. In these conditions, the Chinese approach has a clear advantage.Are Chinese cars actually high quality?As Pascal explained, the idea that Chinese cars are low quality no longer reflects reality. Today, Chinese manufacturers cover the full range. There are still cheaper models, but there are also premium vehicles that compete with top global brands. Take companies like BYD. They offer everything from affordable electric cars to high-end models. The gap between entry-level and premium is wide, but that is true for any global brand.Chinese companies can now match international standards across most categories. In the past, they competed mainly on price. Today, they compete on quality, features, and innovation.Another shift is happening behind the scenes.Much of the innovation is no longer just consumer-facing. It is happening in the supply chain. A large share of modern car components (batteries, software systems, electronics) comes from Chinese companies.In many cases, even non-Chinese car brands rely on Chinese technology inside their vehicles.Do monopolies exist in China?China has large, dominant companies. But true monopolies are rarely allowed to exist unchecked.The government plays an active role in this. Like Europe, China has anti-monopoly regulations. In recent years, authorities stepped in more aggressively to limit the power of big tech and protect smaller players.Companies like Alibaba dominate large parts of the domestic market. Others, such as CATL, hold major global positions. In some sectors, Chinese firms lead the world.These companies operate under closer oversight. If they grow too powerful or risk harming competition, the government intervenes. The goal is to keep the market dynamic and prevent smaller companies from being squeezed out.Another key point is timing. Many Chinese giants expanded globally later than their Western counterparts. But that is now changing. Companies like BYD are entering international markets and competing directly with established European and American brands.Policies such as tariffs or delays in the transition to electric vehicles aim to protect local industries. But they may also slow down competition. In the long run, this can be risky.Real Lesson in Chinese AgilityChina’s market moves fast. Prices drop quickly. Entire industries shift in short cycles. In this environment, rigid strategies fail. What works instead is agility.It means adjusting quickly, working with partners, responding to new conditions without delay, as well as accepting a level of chaos as normal.In many ways, Chinese companies are used to this. They operate in uncertainty every day. Over time, that builds resilience.Why Europe struggles to make choicesOne of Europe’s biggest challenges is decision-making. Unlike China or the US, Europe struggles to prioritize and pick winners in strategic industries.In China, the government invests in ecosystems rather than individual companies. They decide which sectors (like green energy, EVs, or batteries) are critical, then build partnerships and momentum around them.Europe has done similar things historically. Airbus is a classic example: different countries contributed components to build a powerful aviation industry. Belgium and Switzerland excelled in biotech and chemicals by fostering specialized clusters. But today, European policy often tries to be fair to everyone. It spreads resources thin and helps companies that might not survive on their own. This approach dilutes impact. Europe often fails to strengthen ecosystems in a decisive way. Another difference lies in funding structures. Chinese support often comes as loans tied to performance, encouraging companies to build value. In Europe, grants are more common. Sometimes they go to firms that already have significant resources, favoring the most bureaucratically savvy rather than the most innovative.What strong leadership looks likeEuropean leaders are entrepreneurial and willing to take risks, but the organizational culture often slows execution. In China, speed and flexibility are built into the system. Competitors can emerge overnight, and market positions are constantly challenged.A striking example is Temu. Within a decade, it challenged Alibaba’s dominance by rethinking distribution, cutting middlemen, lowering prices, and engaging younger consumers. Huawei illustrates this mindset on a global scale. Even after being blocked from US markets and having restricted access to chips, the company survived and adapted. It expanded into energy, EVs, airports, and chip production, constantly searching for the next opportunity.Why is there no AI race?The popular idea of an AI race between the US and China is misleading. In reality, the two countries are approaching AI very differently.The US is focused on AGI (artificial general intelligence), which presupposes creating AI as intelligent as humans. The country sees the competition as a race to be first. Efforts center on massive computing power, advanced chips, cloud infrastructure, and top talent. It’s about winning a theoretical summit rather than widespread adoption.China, by contrast, emphasizes practical application. Companies embed AI across industries, such as manufacturing, healthcare, materials, and pharmaceuticals. They focus on scalable adoption. Many tools are open source and affordable. They democratize AI so businesses can implement it quickly.This approach allows China to set global standards in applied AI. While the US chases AGI, China is embedding AI everywhere, shaping real-world practices and expectations.Trust is also a factor. Chinese businesses and consumers are generally more willing to embrace technology than their Western counterparts. Europe’s cautious approach slows adoption, limiting its ability to compete.As one of the world’s largest AI developers, China should be part of global debates on governance, safety, and standards.Why open-source AI is a game-changerIn China, the focus isn’t just on inventing AI technology. It’s on building applications that solve real problems. Proprietary breakthroughs are valuable, but the real advantage lies in how AI is applied. By making AI open-source, China has created a highly competitive market where hundreds of models compete and improve rapidly.Open source fosters collaboration. Developers can adapt and enhance models. As a result, better applications can push improvements to the models, which in turn enable even stronger applications. Chinese AI now matches, or even surpasses, US benchmarks in many areas. These results prove that openness doesn’t mean falling behind.As both Max and Pascal agreed, in AI, widespread access and rapid iteration can be more valuable than keeping technology proprietary. The market itself drives innovation at an extraordinary pace.Want to learn more about tech and innovation? The Innovantage podcast will give you a clear look at the trends that actually matter. Don’t miss our next episodes. 

Today, there is no doubt that AI has enormous potential and the power to transform many industries. However, to truly understand its impact, it helps to look at real-life examples in specific sectors. In this episode of the Innovantage podcast, hosted by Sigli’s CBDO Max Golikov, we can take a closer look at how AI is reshaping the legal profession.Max invited Aku Sorainen, Founder and Senior Partner at Sorainen and Chairman at Crespect, to his studio to speak about the challenges and opportunities AI brings to law firms. They discussed how technology is changing the way lawyers work and why automation and critical thinking are becoming essential skills for legal professionals today.Aku is from Finland and studied at the university in the early 90s. At that time, the Baltic countries had just regained independence. These countries were close to Finland, yet very little was known about them. In his studies, Aku chose to focus on their business laws. He wrote his thesis on the legal systems of all three Baltic states. Large Finnish companies supported his work, as they also lacked knowledge about these markets.Focus on business law across the BalticsAfter he finished his thesis, those companies began contacting him. That’s when he saw the business potential. He moved to Estonia and helped open an office for a Finnish law firm. Two years later, he started his own firm, Sorainen.At the start, he had little experience in running a law firm. Managing an international firm was even more new to him. Still, the firm expanded quickly. It opened in Tallinn in 1995, then in Latvia, and later in Lithuania. By 1999, it was already active in three countries.The firm was a true startup. There were no legacy systems and little operational experience. So his team needed to build everything from scratch. They created an ISO-certified quality system and developed their own legal practice software.The goal of this solution was simple. At Sorainen, they wanted to see what each of the company’s offices was doing and to implement software that would support their processes.This approach became the foundation of their work for the next 25 years.Aku’s company is a pure business law firm. They advise clients across all business sectors. In recent years, their work has expanded.They have built a growing corporate crime practice. However, everything they do still links back to business law. They do not handle traditional criminal cases. Their focus may seem narrow, but it is deliberate. From the start, they followed two guiding principles.The first was to cover all three Baltic states. This came from Aku’s academic work. Many international clients saw the Baltics as one single market and often overlooked the cultural and language differences. Still, the idea of one region shaped the firm’s strategy.The second principle was to run the business in a Finnish way. This meant being structured, practical, and consistent in how they worked.Doing business the Finnish wayFor Aku, the Finnish way comes from his upbringing. It is based on simple values.Be honest. Be direct. Stay open. Be curious.And think like an entrepreneur. These values shaped how he works and leads.Sorainen operates as one partnership. Today, it has 51 partners. Profits are shared across the whole firm, not by office or country. Nevertheless, full integration is not easy. Many people still think in terms of their own country. At the same time, most of the work is similar across the Baltics. Around 90% is standard business law. It includes contracts and common transaction practices. Though only about 10% is local law, this small part often feels bigger than it really is.Where the firm is highly integrated is in its internal operations. Business services are fully shared. Systems work across all offices. T is led from Estonia. The COO is based in Lithuania. Risk and compliance sit in Latvia. Revenue management is also in Latvia. HR is managed from Estonia. This setup supports one unified firm, despite the fact that offices are based in different countries.Law & tech: Building systems that actually workIt’s interesting to mention that Aku’s firm has no headquarters. It operates as a flat organization. Talent is hired across the whole region, not tied to one country. If they need someone, they find the best person and build the role around them.Mewanhile, technology has been part of Sorainen from the very beginning. Early on, they realized they needed a central system to run the business. Almost by accident, they chose a CRM tool, which came from a small company next door. It seemed useful, so they adopted it.Then something important happened. One of their lawyers was also passionate about tech. He started coding in his free time and helped to build a full practice management system that became the backbone of the firm.Years later, that system started feeling outdated. The company’s management decided not to build again and started looking for the best tools on the market. But the result seemed surprising. None of the systems fit their needs. Most were built around financial management. Their own system was different. It focused on the client journey first. Financials were just the end result.This was a key insight. They realized they had created something unique. So they chose to rebuild their system from scratch. This time, it was cloud-based. The system worked well across multiple countries. Seeing success, they started thinking bigger. Could this system work for other law firms as well?They decided to turn it into a product. The first cloud version was built on a low-code platform. But it was too expensive and not scalable. So once again, they rebuilt everything from scratch.It took two to three more years. It has been a long journey. But it shows one thing clearly. Good legal work is not enough today. Strong systems and the right use of technology make the real difference.Lawyers and AI: Hype vs realityLaw firms have always been conservative. Many firms followed the same approach for decades and stayed highly profitable. That made change difficult. As some say in the UK, it is hard to tell millionaires that their business model is broken.AI has started to shift this mindset. Over the past year and a half, even the most traditional firms have begun to pay attention. Many are now testing new tools and trying to understand their value.Today, most firms already use tools like Microsoft Copilot. Many lawyers also use ChatGPT, sometimes privately, sometimes through secure business versions. On top of that, new legal AI platforms like Harvey and Legora have gained attention. These tools are designed to analyze legal documents. This is where AI works best right now. Many firms use it for research and document review. At first, there was a lot of excitement about drafting. You could press a button and get a perfect contract or memo.In reality, it is not that easy. AI can produce a lot of text, but the quality is often inconsistent. Some lawyers relied on it too much. In a few cases in the US and the UK, lawyers submitted AI-generated content without proper review, which led to fines and court issues.Recently, usage patterns have started to change. More companies now use AI for analysis and research. At the same time, they use it less for drafting than before.One key problem is data quality. Many law firms have large databases, but the data is unstructured. For example, one firm may have tens of millions of documents. But only a small portion is truly useful. Over time, systems allowed too many versions and duplicates. The issue is no longer access to data, but finding the right data. This is where real value lies today: not in generating text, but in organizing and structuring data so AI can actually work effectively.Why automation is the keyOne proven way to structure data is document automation. This is not new, as it has existed for over 25 years. But many firms still overlook it.The idea is simple. You gather all your firm’s knowledge into one master template that holds best practices and experience. From there, you adapt it to each specific case.Automation makes this process efficient. Lawyers answer questions, tick boxes, and make choices. Each step guides the next. In the end, you get a solid first draft.But without structure, AI cannot deliver quality. First, you need order and clean data. That takes time, effort, and discipline.At Sorainen, they have automated hundreds of templates. The most common documents are already systematized. This allows lawyers to produce high-quality documents quickly.However, one challenge remains. This approach can improve efficiency, but not necessarily revenue. Do AI tools replace junior lawyers?Aku shared that he personally relies on tools like ChatGPT, Copilot, and Gemini on a daily basis. But their usage is different. For quick answers, he turns to Gemini. For deeper work, he uses ChatGPT. He has even adjusted the settings so the tool challenges his prompts and asks follow-up questions. This helps him think more clearly and get better results.This shift has influenced how he works with junior lawyers. He now relies less on them for basic research. Tasks that once required junior support can now be done faster with AI.But this does not mean juniors are no longer needed. It changes what is expected from them.There are several key issues. The first is critical thinking. AI can produce answers, but not always correct ones. Sometimes it creates false or misleading information. If juniors accept these outputs without checking, it becomes a serious problem. So the ability to question and verify information is now essential. Some people naturally have this skill. Others struggle with it. AI makes this difference more visible. The second issue is training. In many fields, constant training is normal. Athletes train every day. Special forces train constantly. But lawyers often do not. They focus on work, not practice. In the US, some law firms even build mock courtrooms inside their offices. They run practice trials before real cases. This kind of training is still rare in Europe.Lawyers value independence. They prefer freedom and flexibility. Many resist mandatory training, as they do not like being told how or when to learn.With AI changing the profession, training becomes even more important. Lawyers need to learn how to use these tools properly. AI vs. mediocre lawyersToday, Aku believes the future will leave less room for mediocre lawyers. Routine tasks are disappearing. There is less need for people who only review documents or draft simple texts.But he highlighted his belief that AI wouldn’t replace lawyers entirely. He compared it to what happened with Google. When people first started searching for medical advice online, many thought doctors would become obsolete. That did not happen.People still use Google for health questions. Now they also use AI for more detailed answers. Nevertheless, doctors are still essential.He sees a similar pattern in law. Legal work is often rule-based, which makes it suitable for technology. Large amounts of legal data can be stored and processed. But that does not remove the need for human judgment.AI can’t replace responsibilityIn their conversation with Aku, Max points out another important aspect of using AI. Artificial intelligence can draft a document, but who is responsible if it’s wrong? If it references cases that don’t exist, someone will face the consequences. AI cannot take responsibility. Lawyers and firms still must.Responsibility becomes even more critical as the stakes rise. For small matters, a person might experiment with AI. But when the risk grows and reaches €100,000 or more, trusting AI alone becomes too dangerous.That’s one more argument that proves that AI should be perceived as a tool, not a replacement. It can make work faster and more efficient. It can handle routine or boring tasks, freeing lawyers to focus on what truly matters.Ethical application of AIAnother big question is trust. Can law firms rely on AI with confidential client data? Using AI may risk feeding sensitive information into a general large language model, potentially breaching confidentiality. This concern is not unique to law, as it affects many other industries.Ethics also extends to copyright. For example, some companies have sued AI developers for training models on their photo libraries. Similar debates exist in art. AI generates work based on millions of existing pieces. However, it’s vital to remember that historically, artists also studied others’ work to develop their style. The clear answers are still hard to define.A more direct ethical concern arises in legal practice. What if someone used AI to figure out how to mislead a judge without being caught? That crosses a personal and professional line. Clients are using AI tooClients are using AI more than ever, and it changes how law firms interact with them. The expectation for more work at lower cost, seen since the 2008 financial crisis, hasn’t disappeared. AI doesn’t change that. Instead, such tech tools may even reinforce it.Sometimes clients run the firm’s legal documents through their own AI systems before sending feedback. Often, the comments are unhelpful.AI can also complicate communication. For example, it may generate long emails, while lawyers prefer concise messages. The recipient may then need another AI tool just to reduce it back to a one-liner. It’s a funny cycle and not very energy-efficient.Will law firms become tech companies?As Aku explained, in the future, some law firms may start to resemble tech companies, especially those handling routine, lower-value work. Technology allows these firms to scale without adding more lawyers.Other firms, dealing with complex cases, may need fewer junior lawyers and more senior experts. The focus shifts to experienced lawyers, while routine tasks are automated. The number of partners could increase, but the structure of the firm may change.At Sorainen, they are using AI in a different way. Instead of just automating documents, they focus on capturing soft operational and client data. This includes lessons learned, client expectations, debriefing notes, and CRM information.Once this data is structured and put into AI tools, the firm gains powerful insights. Lawyers can better understand their client base, spot opportunities, and prioritize work. AI highlights emerging leads that might otherwise go unnoticed.But with all these benefits, AI doesn’t replace lawyers. Instead, it amplifies their ability to understand clients and unlock business potential. It allows law firms to work smarter, not just faster.Curious to learn how AI and other emerging technologies are transforming different industries? That’s exactly what the Innovantage podcast offers. Don’t miss the upcoming episodes!

Choosing a technology partner often feels like a risk decision. For many mid-market leaders, the logic seems obvious: a bigger vendor must be the safer choice. A well-known brand, a large delivery organization, and a long list of enterprise clients can create a sense of security before the project even begins.And sometimes, that logic is right. Large software development companies and global consulting firms can be the right choice for global rollouts, massive transformation programs, and projects that genuinely require hundreds of specialists across multiple regions. But bigger is not always safer.For many mid-market companies, the hidden cost of hiring a large vendor is not only the invoice. It is the loss of flexibility, speed, and direct access to the people who actually make technical decisions. The best vendor is not the biggest one. It is the one that fits the project’s pace, complexity, and decision-making needs.The comfort of a big name can hide the real delivery riskA strong brand reduces perceived risk. That is part of its value. When a company chooses a large technology vendor, leadership can feel reassured. The vendor has case studies, references, sales teams, frameworks, and polished delivery processes. Internally, the decision is easier to justify.But the risk does not disappear just because the vendor is large. It often moves somewhere else. It moves into long onboarding cycles and expensive discovery phases before anything practical happens. It moves into rigid scopes that are difficult to change and slow responses when priorities shift.It moves into change requests that make every adjustment more expensive and distance between the senior people who sold the project and the delivery team doing the work.For mid-market companies, this distance can be costly. A project may not need the weight of a global consulting structure. It may need a senior team that can quickly understand the business context, challenge assumptions, make technical decisions, and move from discussion to delivery without months of overhead.The hidden cost is often speedMid-market leaders usually do not have unlimited time. They may need to show results before the next budget cycle. They may need to validate a new product idea, modernize a system, connect fragmented tools, or prove that a business case is worth further investment.In that context, a six-month vendor onboarding process can become a problem in itself. The company is not only paying for delivery. It is also paying for the time spent waiting for delivery to begin. This is where smaller senior teams can win. Not because they are small by default, but because they can often work closer to the problem. They can bring solution architects, senior engineers, delivery managers, tech leads, and decision-makers into the conversation earlier. They can test simpler alternatives. They can adjust scope when new information appears. They can scale the team up or down without turning every change into a formal negotiation. Speed is not about rushing. It is about reducing the distance between a business question and a technical decision.Senior access changes the quality of the workOne of the biggest differences between large-vendor delivery and smaller senior teams is access. In many large-vendor setups, senior experts are visible during the sales process. They appear in discovery calls, proposal presentations, and steering meetings. But once the project starts, the day-to-day work may be handled by more junior teams, while senior people remain several layers away from the actual decisions.That model can work for large, stable, well-defined programs. But it can be frustrating when the project is complex, evolving, or uncertain. Mid-market projects often need senior thinking close to the work. They need people who can ask: Is this the right architecture? Is this scope still valid? Is there a simpler path? Should we build, integrate, automate, or redesign the process first?These are not only technical questions. They affect cost, timing, maintainability, and business value. When senior experts stay close to delivery, decisions become faster and more grounded. There is less translation between account management, architecture, engineering, and business stakeholders. There is less risk that the original intent gets diluted as it moves through layers of communication. For complex projects, proximity matters.Flexibility is not a nice-to-haveIn technology projects, priorities change. A business stakeholder sees a new opportunity. A system limitation appears. A regulatory concern changes the scope. A proof of concept reveals that one feature matters more than another. A simpler integration becomes more valuable than the original custom build.Good delivery depends on how quickly the team can respond.Large vendors often bring strong structure, but structure can become rigidity when the project needs adaptation. Scope changes may require formal approvals, new estimates, revised contracts, and expensive change requests. By the time the change is approved, the business context may have already moved again. Smaller teams can often adapt faster. They can test a simpler approach before committing to a large build. They can change technical direction when evidence supports it. They can reassign senior attention where the risk is highest. They can move from consulting to custom software development without treating every adjustment as a separate engagement.This does not mean smaller teams should be unstructured. Smaller does not mean informal, chaotic, or less accountable. The best smaller vendors combine structure with responsiveness. They document decisions, manage scope, communicate clearly, and keep delivery disciplined. The difference is that their structure supports progress instead of slowing it down.When bigger vendors are the right choiceThe point is not that big vendors are bad. They are often the right choice when the scale of the project truly requires them. If a company is running a global rollout across many regions, coordinating hundreds of specialists, or managing a massive transformation program with complex procurement and governance requirements, a large vendor may be exactly what the situation demands.Big vendors can bring global coverage, deep benches, standardized delivery models, and the ability to support extremely large programs over long periods of time. The question is not whether large vendors have value. The question is whether that value matches the specific project. A mid-market company does not always need the largest possible delivery machine. Sometimes it needs a focused senior team that can diagnose the problem, challenge the assumptions, and build the right solution without unnecessary overhead.When smaller teams winSmaller senior teams tend to be strongest when the project requires speed, flexibility, direct access, and practical problem-solving. They are often a good fit when a company needs to:Validate an idea through a proof of concept or MVP.Build custom software around specific business processes.Integrate systems that do not work well together.Modernize workflows without launching a massive transformation program.Move from a vague business challenge to a clear technical roadmap.Respond quickly when priorities change.Get senior architectural input without adding unnecessary layers.This is especially relevant for mid-market companies that are large enough to have complex systems, but not large enough to absorb enterprise-level delivery overhead without consequences. They need partners who understand complexity but do not automatically add more of it.A practical example: focused teams can still deliver enterprise-grade workA smaller vendor does not mean a smaller standard of delivery.One example from Sigli’s work is a comprehensive Salesforce implementation for a medium enterprise in North America. The project involved integration with external systems, custom development, workflow automation, business intelligence services, DevOps, and scalable architecture for future digital transformation initiatives. Sigli assigned two Salesforce developers who worked alongside the client’s solution architect and integration developer, supporting analysis, architecture, implementation, stabilization, and ongoing support. The results included unified data, improved visibility into customer lifecycles, reduced communication delays, automated workflows, and better SLA compliance. This case is not about replacing a large vendor in every situation. It shows something more useful: a focused team can deliver structured, complex, business-critical work when the fit is right. The client did not only need more people. They needed the right people close to the problem. That is the difference mid-market leaders should pay attention to.Discovery should reveal the right vendor modelDiscovery is not only about defining scope. It should also help leadership understand what kind of partner the project actually needs. Some projects need enterprise-scale delivery. Others need a compact senior team that can move quickly, clarify the problem, and build a practical path forward. The mistake is deciding this too early, based only on brand recognition or perceived safety.A good discovery process should answer questions such as:How much ambiguity is still in the project?How quickly does the business need to see results?How often are priorities likely to change?Does the project require hundreds of specialists, or a focused senior team?Is the biggest risk scale, or is it clarity?Does the company need a delivery machine, or direct access to decision-makers?These questions help prevent a common mismatch: hiring a heavyweight vendor for a project that actually needs senior proximity and flexibility.A simple framework for choosing the right partnerChoose a large vendor when the project depends on scale. That may include global rollout, multi-region coordination, massive transformation programs, complex procurement structures, or delivery requiring hundreds of specialists over a long period of time.Choose a smaller senior team when the project depends on proximity. That may include proof of concept development, MVP delivery, integration work, custom software development, technical discovery, changing priorities, unclear requirements, or situations where direct access to solution architects and senior engineers will make the difference.Choose based on the shape of the problem, not the size of the logo. A well-known brand can reduce perceived risk, but the wrong delivery model can increase practical risk. The safest choice is the partner whose structure matches the way the project actually needs to move.Bigger brings scale. Smaller brings proximity.For mid-market leaders, vendor choice should not be a reflex. A big name can be reassuring, but reassurance is not the same as delivery fit. The real question is whether the vendor can move at the speed of the business, adapt when priorities change, and keep senior expertise close to the work.Sometimes, the right answer is a large global firm. But sometimes, the better answer is a smaller, senior team that can work directly with your business, challenge assumptions, simplify the path, and deliver without unnecessary overhead.At Sigli, we help companies approach consulting and custom software development as one connected process: understand the problem, define the practical path, and build what the business actually needs.If your project feels stuck in vendor complexity, or you are unsure what kind of delivery model fits your next initiative, discovery is the right place to start.Bigger vendors can bring scale. But smaller teams often bring proximity, and proximity is what complex projects need.

The most valuable consulting moments often feel uncomfortable at first. Not because the consultant is trying to prove a point, not because the client is wrong, and not because disagreement itself has value.They feel uncomfortable because good consulting exposes the gap between what a business wants to build and what it is actually ready to support. For executives, this gap is not theoretical. It affects budgets, timelines, technical risk, data security, internal alignment, and the long-term usefulness of the solution being developed.That is why the best consulting relationships are not built on automatic agreement. They are built on healthy tension. A good consulting partner should not simply confirm the original brief, accept every assumption, and start execution as quickly as possible. Sometimes, their most important responsibility is to pause the conversation and say: “This may not be the right problem to solve yet.” Or, more specifically: “This is not an AI problem yet.”Agreement is easy, accountability is harderThere is a version of consulting that feels comfortable in the beginning and expensive later. The client explains the idea. The vendor nods. The scope is accepted. The proposal is prepared. Development starts. Everyone feels aligned because no one has challenged the assumptions behind the project.Then, months later, the real issues appear. The data is not ready. The process is unclear. The priorities are conflicting. The expected timeline was unrealistic. The required level of security was underestimated. The requested feature does not solve the real business problem.At that point, the project does not fail because the team could not build. It fails because the wrong thing was allowed to move forward without enough scrutiny. This is why “yes” can be dangerous in consulting. A partner who agrees too quickly may not be reducing risk. They may simply be postponing it.The client request is not always the real problemExecutives often come to a consulting partner with a solution already in mind.They may ask for a SaaS feature, an AI assistant, a recommendation engine, a data platform, a portal, or an automation layer. The request is usually logical from their perspective. It is connected to a business goal, a funding opportunity, a competitive pressure, or an internal transformation agenda. But a requested solution is not the same as a diagnosed problem. This distinction matters.A company may ask for AI because it wants to appear innovative but the real issue may be fragmented data. A team may ask for automation because work is slow but the real issue may be an undocumented process. A business may ask for a new platform because it wants to scale but the real issue may be unclear ownership, weak governance, or legacy constraints.In one of our previous articles, we discussed why AI often fails when it is treated as a shortcut for broken foundations. AI can multiply business value, but it can also multiply confusion if the underlying processes, data, and systems are not mature enough.The same principle applies to consulting more broadly. Before building the requested solution, a strong partner should ask whether that solution is still the most responsible path.Healthy tension protects the investmentChallenging the client does not mean creating conflict. It means protecting the business from investing in the wrong direction.Healthy tension looks like respectful disagreement. It is when a consultant questions the requested solution, the client’s readiness, the decision-making process, or the assumptions behind the timeline, not to slow the project down, but to prevent avoidable cost, risk, and rework.This is especially important when AI is involved. AI can make a project more attractive on paper. It can also make it more expensive, harder to secure, harder to audit, and more difficult to explain. If the business logic is unclear or the data is sensitive, AI may introduce risks that a simpler solution would avoid.These risks are not only technical. They are executive risks.Personal data needs to be protected.Security expectations need to be realistic.Auditability and traceability need to be designed from the start.Intellectual property needs to be handled carefully.The cost and timeline need to reflect the actual complexity of the solution.If these questions are ignored early, they do not disappear. They return later as budget overruns, compliance concerns, delayed delivery, or a product that cannot be safely scaled.That is why good consultants challenge. They are not trying to win an argument. They are trying to protect the outcome.A Sigli example: when the requested solution was not the safest pathA client came to Sigli with a clear business goal: use a grant opportunity to create a recommendation portal as a SaaS feature on their cloud.At first glance, the idea looked like a product development challenge. The client had a vision, a funding context, and a desired feature. The expectation was that consulting support would help accelerate execution. But once we looked deeper, the situation was more complex.There was confusion in the internal processes. Priorities were not fully aligned. The expected deadlines did not reflect the real level of execution effort. The client also had an overly optimistic view of what had already been implemented. In other words, the risk was not only whether the recommendation portal could be built.The risk was that the client would invest in building it before the business, technical, and operational foundations were ready. There were also important concerns around personal data, security, auditability, traceability, and intellectual property. Adding AI into the solution too early could have increased both cost and delivery time without solving the core problem.In that situation, the responsible answer was not simply: “Yes, we can build it.”The responsible answer was to challenge the path. Not by dismissing the client’s ambition. Not by making the project sound impossible. But by showing where the assumptions did not match the reality of implementation.The goal was not to convince the client through abstract arguments. The goal was to solve the actual problem. That distinction matters.Good consulting is not about being right in the room. It is about helping the client make a better decision before too much money, time, and reputation are committed to the wrong one.Challenge is not arroganceOf course, there is a difference between a consultant who challenges constructively and one who simply complicates the project. Executives can feel this difference quickly.A strong consultant brings clarity. A weak one hides behind complexity. A strong consultant explains the “why” behind their recommendation. They want the client to understand the trade-offs, risks, and alternatives clearly enough to make an informed decision.A complicator does the opposite. They use complexity as a shield. They make the problem feel bigger so their role feels more necessary. The same distinction appears in how consultants respond to feedback. A strong consultant is adaptable. If new information appears, they adjust. If constraints change, they reconsider. They can stand their ground without becoming rigid. A weak consultant mistakes stubbornness for expertise. They keep defending the same recommendation even when the facts change. This is why the best consultants aim for minimum viable complexity. They do not add layers because they can. They look for the simplest possible path to the desired result. They search for the 20% of effort that can create 80% of the value. They are not afraid to tell a client that a massive project may be unnecessary.Discovery is where healthy tension belongsThe best time to challenge a project is before development begins. That is why discovery should never be treated as a formality. It is not a box to tick before “real work” starts. It is where the most important consulting work often happens.Discovery is where assumptions are tested.Risks are made visible.Priorities are clarified.Technical feasibility is examined.Business readiness is assessed.The simplest viable path is identified.This is also where uncomfortable questions are cheapest.It is much better to discover during a workshop that the data is not ready than to discover it after months of development. It is much better to question the need for AI before building the architecture around it. It is much better to reduce the scope early than to rescue a bloated project later. For executives, this is the real value of discovery. It does not delay progress. It protects progress.What executives should expect from a consulting partnerExecutives should not evaluate consultants only by how quickly they agree. Fast agreement can feel efficient, but it is not always a sign of competence. Sometimes, it means the consultant has not looked deeply enough. Sometimes, it means they are optimizing for the sale rather than the result.A stronger signal is the quality of the questions a consultant asks.Do they challenge the problem statement?Do they ask what business outcome the solution is meant to create?Do they examine readiness before proposing technology?Do they make risks understandable?Do they explain trade-offs clearly?Do they propose a better path when they say no?Do they reduce complexity instead of adding to it?These are the behaviors that create trust. Not blind agreement. Not technical theatre, not endless discovery for its own sake. Real trust comes from knowing that your consulting partner is willing to protect the outcome, even when that requires a difficult conversation.Good consulting makes the decision sharperClients do not need consultants who agree with everything. They need partners who can improve the quality of the decision.Sometimes that means confirming the original idea. Sometimes it means reshaping it. Sometimes it means replacing an AI ambition with a simpler, safer, more valuable solution. Sometimes it means saying: not yet.The point is not to challenge for the sake of challenging. The point is to make sure the business does not confuse movement with progress.At Sigli, this is why we treat discovery as a critical part of the work. It gives both sides the space to test assumptions, discuss risks, and find the most practical path before serious investment begins. Because in consulting, agreement may make the first meeting easier. But healthy tension makes the final outcome stronger. The most valuable consulting moments often feel uncomfortable at first.

The technology sector has greatly evolved over the last decade, becoming more diverse and more open to specialists with different skills. But a persistent gap remains: women still make up a small minority of tech leadership. Can the situation change in the near future? To find an answer to this question, Max Golikov, Sigli’s CBDO and the host of the Innovantage podcast, invited Diana Gold to his studio. Diana is CTO and Head of Digitalization and Technology at Gijos, Partnership Associate Professor at Vilnius University, and PhD candidate at ISM University of Management and Economics. And her impressive experience has helped her develop a broad perspective on the tech industry. Diana’s road to CTODiana’s journey into IT began with her choice of a university program. She was strong in mathematics and interested in technology. So she enrolled in Management Information Systems at Vilnius University.During her studies, she began working as an IT analyst at Siemens. She helped develop a new system by preparing technical specifications and conducting user training. Later, she spent a year in Sweden pursuing a master’s degree in ICT Entrepreneurship.After returning, she joined IBM as an SAP consultant. Following certification training in India, she worked on international projects across Scandinavia, particularly in Sweden and Finland. Over more than eight years at IBM, she gained extensive consulting experience. But she realized that the role required constant travel, which was challenging as she had young children.Looking for opportunities closer to home, she joined Telia in Lithuania as a team lead for the SAP team. At the time, the company was undergoing a major transformation program and was rebuilding its system architecture and migrating legacy systems. Eventually, she transitioned from leading a team of around 30 people to becoming a delivery manager responsible for standardizing project delivery across the entire IT organization. Later, organizational changes within the group led her to take on the role of Country CIO.She was then invited to join the top management team as Chief Digital and Data Officer. The role placed her in close collaboration with leaders from marketing, HR, legal, and other functions. At this position, Diana needed to translate complex technological concepts into strategic decisions for the broader organization.Today, as CTO at Gijos, she continues to expand her expertise. Gijos is an energy company primarily focused on providing district heating, while also operating in the broader energy sector. The company is involved in electricity balancing and is actively developing a range of innovative projects. One of the most notable initiatives is the construction of a hydrogen production facility in Lithuania. Tech in the energy sectorAs CTO, Diana oversees the company’s entire technology landscape, including servers, cloud infrastructure, and networks, as well as advanced technologies like artificial intelligence. She leads a technology team of nearly 50 people, which is relatively large for a company of this size. Current initiatives include replacing the billing system and upgrading key platforms such as self-service solutions, asset management, and finance systems. At the same time, her team is responsible for maintaining existing systems and services (service desk operations, end-user devices, and everyday IT infrastructure).Another major part of her role involves preparing the organization for upcoming regulatory changes, including the NIS2 Directive. This European regulation will impose stricter cybersecurity and infrastructure requirements on critical sectors. It means that companies will need to meet comprehensive standards for how their networks, infrastructure, and applications are managed and secured. Organizations are expected to comply by the first quarter of 2027. Regulation and innovationDiana believes that digitalization plays a central role in the company’s long-term strategy. Technology is not just a support function but a key driver of progress and transformation.While new regulations introduce strict compliance requirements, she sees them less as a burden and more as an opportunity. In her view, such regulations force organizations to address technology hygiene. These are the foundational elements of infrastructure, systems, and data management that are often overlooked in favor of more visible innovation projects.Many companies struggle with outdated infrastructure and poor data quality. These issues make it difficult to implement advanced technologies.The new directive provides a rare chance to prioritize these fundamentals. By upgrading infrastructure, improving system architecture, and resolving legacy issues, organizations can build a much stronger technological base.Digital transformation: Don't try to invent a bicycleOrganizations do not need to reinvent processes that already exist. That’s one of the main lessons that Diana has learned in her professional journey. Many well-established frameworks and best practices are available. But the real challenge is to choose the ones that best fit a company’s needs.During a large transformation program earlier in her career, her team initially attempted to design their own methodology for managing collaboration among more than 100 people. After several unsuccessful attempts, they turned to an existing framework. It immediately improved coordination and outcomes. Today, there are tools like large language models that can help identify relevant frameworks. When teams share the same rules, priorities, and roadmap, collaboration becomes significantly easier. Everyone understands the direction of the project and how decisions are made. All this reduces confusion and unnecessary escalation.Cultural change in digital transformationAccording to Diana, digital transformation is not only about technology. While organizations often focus on new systems and architectures, the more important task is to change how people work together. Transformation affects how teams collaborate, how priorities are set, and how decisions are made. In many cases, shaping this cultural shift takes even longer than implementing the processes or technologies themselves.For that reason, building the right organizational culture is a critical part of any transformation effort.Diana explained that successful change usually begins with a critical mass. It is a group of people who genuinely believe in the transformation and are willing to experiment with new approaches. There is no need to change the entire organization at once. Instead, it is often more effective to start with a smaller initiative that demonstrates real results. When teams can see tangible benefits in practice, the impact is far more convincing than explanations alone.However, cultural change cannot succeed without strong leadership support. Introducing new roles and working methods requires alignment from senior management. Leaders need to understand the transformation and actively champion it across the organization.Advice for driving cultural changeLeading cultural change begins with people. No transformation can be driven alone. It requires a committed team that believes in the direction and is willing to move forward together. Diana said that building such a team takes time. Nevertheless, it is the most important foundation for meaningful change.What is necessary for efficient changes?It is vital to start small. Instead of attempting a large-scale transformation immediately, leaders should begin with a pilot initiative. Another critical factor is strong communication. Leaders need the ability to clearly explain their ideas and persuade others.Equally important is belief in the change itself. Only leaders’ confidence and persistence can push the effort forward.Over time, Diana realized that technology leaders must develop a form of internal sales. Promoting ideas inside an organization is very similar to selling. Instead of external customers, the audience is internal stakeholders.This ability to sell ideas is not limited to business environments. It also plays a key role in education. As a Partnership Associate Professor at Vilnius University, Diana sees teaching as another form of communication and persuasion.In the classroom, educators must engage students. Lecturers need to present knowledge in a way that keeps them interested and motivated to participate. In this sense, teaching also involves selling ideas and knowledge.Diana first considered teaching after gaining extensive industry experience. She designed her first course entirely from scratch. The positive feedback from students and the energy of working with young people greatly inspired her to pursue a PhD at ISM University of Management and Economics.Family and career: Planning and disciplineBalancing a career with personal responsibilities requires strong planning and discipline. With multiple professional roles, Diana relies on structured time management to stay organized.Her approach involves planning well in advance. Instead of leaving tasks until the last moment, she reviews upcoming weeks or months to anticipate deadlines and prepare early. This helps reduce last-minute stress and ensures that important responsibilities are handled with the necessary focus.Prioritization is another key part of her approach. She carefully evaluates which tasks require immediate attention and which can be postponed. As she said, it is sometimes necessary to decide “which battles can be lost” in order to concentrate on what matters most.Even with careful planning, maintaining multiple roles can become challenging. Recently, she decided to step back from teaching at Vilnius University because balancing it alongside her full-time work and doctoral studies had become too demanding. Women in techWhen Diana started her career at IBM, workplace conversations around gender were very different from today. She mentioned an internal employee survey that asked women whether they used so-called female traits to influence colleagues (for example, pretending not to understand something to encourage others to explain it). Such a question would be difficult to imagine in a modern corporate survey.While the technology sector has become less male-dominated over time, progress is still limited. Diana’s doctoral research at ISM University of Management and Economics explores whether artificial intelligence could influence diversity in technology leadership.Early research shows that interest in technology careers among women remains relatively low. Around 26% of girls say they aspire to work in technology. But only about 14% to 20% eventually pursue such careers in Western countries. Even among those who enter the field, some leave during hiring or early career stages due to stereotypes in recruitment processes.The numbers shrink further at leadership levels. Decisions about promotions can still be affected by biases. Some women choose not to pursue management roles due to concerns about work-life balance or the pressure associated with leadership positions. Diana can analyze the situation firsthand through her involvement in CIO.LT, an association of IT leaders in Lithuania where female members remain a small minority. On one hand, the rise of AI tools, along with no-code and low-code technologies, may shift the focus from purely technical skills to broader capabilities such as analytical thinking, problem-solving, and strategy. This shift could make technology careers more accessible to a wider group of people.On the other hand, AI systems trained on historical hiring data can unintentionally reinforce existing biases. If past data reflects male-dominated hiring patterns, AI-driven recruitment tools may replicate those patterns unless the data is carefully audited and corrected.Despite these challenges, Diana is optimistic. She believes AI will primarily automate repetitive tasks rather than replace human roles. This will allow professionals to focus more on creative and analytical work.Moreover, diversity in teams leads to better products. When teams include people of different genders, ages, and cultural backgrounds, they bring a wider range of perspectives and can make products more successful.Invisible barrier for women in techThe greatest barrier for women in technology is rooted in mindset. The challenge often begins early, with family expectations, schooling, and even university guidance shaping how girls perceive their potential in technical fields.In the workplace, barriers are less pronounced. For instance, when Diana joined Gijos, her team had only five women out of 46 members. Today, that number has grown to 14.To overcome invisible barriers, a conscious effort to counteract biases is a must. Encouragement plays a critical role. Girls and young women need to hear that technical fields are accessible to them and that they do not need to master everything from the start. The technology sector offers a wide range of roles, so there is a fit for many different skills and interests.How to get more women into techReaching young people at schools and universities is key to sparking interest and showing the diverse opportunities within the field.Hackathons, coding workshops, and career talks are effective ways to inspire girls and demonstrate what is possible in tech. One example in Lithuania is the Empowering Girls program. It introduces school students to technology and shares real-life career experiences. For women already in the workforce or looking to reskill, programs like Women in Tech provide structured support to transition into technology roles. These initiatives have successfully helped hundreds or even thousands of women gain skills and confidence to enter IT. AI will change everythingDiana believes that artificial intelligence is already transforming the way we work and it will continue to reshape the future. She notes that even 30 and 50 years ago, many tasks were highly unproductive. However, modern systems have become essential for business operations. Similarly, AI is no longer optional. Organizations that fail to adopt it risk falling behind.Technology itself is a tool. That’s why the outcomes depend entirely on how it is used. When applied thoughtfully, AI can drive better decisions and generate positive societal impact. At the same time, its misuse, such as relying on biased or incomplete data, can lead to poor results.Diana observes two schools of thought about AI’s impact on the workforce. One predicts widespread job losses and social disruption. Meanwhile, the other sees an opportunity for unprecedented productivity and innovation.She aligns with the more optimistic view. According to her, AI, when implemented responsibly, enables people and organizations to accomplish far more than they could previously.Artificial intelligence can become a transformative force across every sector (public, private, and education alike). The cultural differences between the public and private sectors are smaller than expected. Both must collaborate and adapt to achieve meaningful results for society.When it comes to education, it’s important to understand that students are already using AI tools, such as large language models, in their projects. It makes no sense to restrict access. Instead, educators must embrace these technologies and teach students how to use them responsibly. This includes validating results and distinguishing between accurate and misleading outputs.Advice for women in techThe tech field is broad. There are opportunities for a wide range of talents and personalities. Analytical individuals can thrive in roles like data analytics or IT analysis. Holistic thinkers may focus on architecture, strategy, and vision. At the same time, collaborative personalities can excel as scrum masters or team coordinators. In short, there is a place for everyone in tech.Diana also noted that coding, once seen as a high barrier, is increasingly a commoditized skill. Today, anyone can build a minimum viable product in minutes, test it, and iterate quickly. This fail-fast approach allows innovators to experiment without needing years of deep programming expertise. Professional developers remain essential. But their work is becoming smarter and more productive thanks to modern tools.Both Max and Diana agreed that now is an ideal time for women to enter technology. With curiosity and the willingness to learn, everyone can leverage their unique skills to create meaningful impact in a rapidly evolving field.Interested in discovering more about the present and the future of the tech space? That’s what you can learn from the next episodes of the Innovantage podcasts. Don’t miss them!‍

AI ambition is everywhere. It shows up in leadership meetings, innovation roadmaps, strategy decks, and urgent conversations about market pressure. It sounds like progress. It feels like momentum. And in many organizations, it creates a strong belief that the business is ready to move.But that is often where the confusion begins. Many companies are not struggling because they lack interest in AI. They are struggling because they confuse AI ambition with AI readiness. That difference matters more than most leadership teams expect. Ambition helps start the conversation. Readiness determines whether that conversation can turn into business value.At Sigli, we often see companies approach AI with strong intent but limited clarity around what actually needs to happen first. The result is familiar: a promising initiative starts as an AI discussion, but quickly reveals process issues, ownership gaps, integration complexity, or data limitations that need attention before any implementation should begin. That does not mean the idea was wrong. It means the organization was ambitious before it was ready.What is the difference between AI ambition and AI readiness?It is the belief that AI matters, the pressure to innovate, the urgency to act, and the sense that the business should be doing something now. Ambition is not a bad thing. In fact, without it, most organizations would delay meaningful change. But ambition is not the same as readiness. AI readiness is the practical ability to make AI useful.It means the organization has the right conditions in place to turn an idea into an outcome. That includes a clear business objective, a process mature enough to improve, usable data, realistic scope, clear ownership, and an environment where adoption is actually possible.A simple way to think about it is this:AI ambition says: we want to use AI.AI readiness says: we know where AI can create value, what it depends on, and what needs to happen first.That is the gap many companies underestimate.Why companies confuse AI readiness with AI ambitionThis confusion is easy to make because ambition is visible and readiness is not. Leadership teams can align quickly around broad goals like innovation, efficiency, or competitive advantage. But readiness lives at a different level. It requires more operational questions. It asks whether the business is truly prepared to support the initiative it wants to launch. There are a few reasons this confusion keeps happening.AI is often discussed at strategy level before execution reality is examinedAt leadership level, AI is usually framed as a growth, efficiency, or transformation opportunity. That is natural. But when the conversation stays too high-level for too long, the business may start making decisions before the underlying conditions have been tested.Market pressure creates urgency before clarityWhen competitors are talking about AI, boards are asking questions, and teams are bringing forward ideas, businesses feel pressure to move. That urgency can make early action feel like maturity, even when the foundations are still unclear.The market rewards solution-first thinkingA lot of AI conversations start with what technology can do rather than what business problem needs solving. That leads companies to ask where they can apply AI before they ask whether AI is the right answer at all.Operational blockers stay hidden until laterProcess instability, poor data quality, weak ownership, unclear scope, and system complexity often do not become visible until implementation starts. By then, the cost of confusion is already rising.What happens when AI ambition outruns AI readinessWhen ambition moves faster than readiness, AI projects tend to become heavier, slower, and more expensive than expected. Sometimes the use case is not properly defined. The idea sounds good, but the expected value is vague. Sometimes the process underneath the use case is inconsistent or poorly understood. Instead of improving the workflow, the project exposes that the workflow itself was never ready. Sometimes the data exists, but not in a usable way. Sometimes integration complexity is underestimated. And sometimes AI is being applied to a problem that standard automation or process redesign could solve more simply.This is when hidden costs begin to build. Timelines stretch. Scope becomes unstable. More stakeholders get involved. Confidence drops. The initiative becomes harder to govern, harder to adopt, and harder to justify. In those situations, the problem is usually not a lack of ambition. It is a lack of readiness.Why AI readiness assessment matters before implementationA readiness assessment helps leadership teams start in the right place. Instead of asking, “How do we launch an AI initiative?” it asks a more useful set of questions:What are we actually trying to improve?What is slowing the business down today?Would AI solve the problem directly, or are we dealing with a process issue first?What conditions need to be true for AI to create value here?What should happen before implementation starts?This is not about slowing things down for the sake of caution. It is about reducing wasted effort, avoiding avoidable complexity, and making smarter decisions earlier. A readiness assessment mindset gives leadership teams a more realistic view of where AI can help, where it cannot, and what the right next step should be.Most AI projects are also process questionsOne of the biggest misconceptions in AI strategy is the idea that every valuable AI discussion should end in an AI implementation. In reality, many of the most useful AI conversations uncover something more fundamental. The real issue may be process design, data quality, systems integration, or a lack of ownership.That does not mean the initiative failed. It means the business is finally looking at the right problem. In fact, one of the clearest signs of maturity is the willingness to step back and say: this is not ready for AI yet, or this is not really an AI problem at all. That is where a lot of value begins.A practical example: when AI was not the right answerA client wanted to automate invoice import and reconciliation for accounting in SAP ERP. The business goal was straightforward: improve speed and reliability by removing manual effort from a repetitive workflow.At first glance, it could have been framed as an AI opportunity but it was not an AI problem. It was a standard automation problem. Trying to solve it with AI would likely have increased implementation cost and extended execution time without creating better business value. So the right move was not to turn it into an AI project. The right move was to solve the process need directly. This is what AI readiness thinking looks like in practice. It does not begin by forcing AI into the solution. It begins by asking what the smartest path to the business outcome actually is.What AI readiness actually looks likeFor leadership teams, AI readiness is a combination of conditions that make implementation realistic. Here are the main areas worth assessing before moving forward.Business value clarityA business should be able to explain the problem it wants to solve in clear operational terms. Is the goal to reduce cost, save time, lower risk, improve decision-making, increase reliability, or create growth? If the value is vague, the use case is usually still too early.Process maturityIf the underlying workflow is unstable, inconsistent, or poorly understood, AI will not fix that on its own. In many cases, process clarity is the real prerequisite for useful AI.Data readinessHaving data is not the same as having usable data. Leadership teams need to know whether the data required for the use case is accessible, reliable, relevant, and fit for the intended purpose.Systems and integration realityA promising use case still has to work in the real environment. That means understanding what systems the solution must connect to and whether implementation is realistic within the current stack.Ownership and governanceA serious initiative needs clear ownership. Who owns the problem? Who owns the implementation direction? Who owns the outcome after launch? If accountability is diffuse, delivery becomes difficult to manage.Change and adoption readinessEven a technically sound solution fails if teams cannot absorb it. The organization needs enough operational capacity to adopt new workflows, trust the output, and support the solution after launch.Scope and sequencingA company may be broadly interested in AI and still be starting in the wrong place. Readiness includes knowing whether the next step should be discovery, prioritization, process redesign, a focused pilot, or implementation.Not being ready for AI is not a failureThis is one of the most important points for leadership teams. Finding out that the business is not ready for AI yet is not bad news, it is useful clarity. It means the organization has avoided pushing investment into the wrong solution too early. It means the next move can be chosen more intelligently.Sometimes that next step is implementation. Sometimes it is process mapping. Sometimes it is data cleanup. Sometimes it is a readiness assessment that creates a more realistic path forward. The key point is that “not ready yet” is often a better outcome than moving ahead with false confidence.How leadership teams should assess where they standThe most useful question is not whether your business is interested in AI. The real question is whether your business is ready to use it well.That means assessing:whether the business objective is clearwhether the process is mature enoughwhether the data is usablewhether the systems support the use casewhether ownership is definedwhether the organization can adopt what gets builtwhether the scope is realistic and well sequencedThis is exactly why a structured AI readiness checklist can be so valuable. It helps leadership teams separate intention from execution reality and identify what needs attention before budget, time, and energy are committed in the wrong place.AI readiness comes before AI impactThe companies that succeed with AI are are the ones with the clearest understanding of their readiness.They know where AI can create value and where process work has to come first.And they know that the smartest way to move forward is not always to build immediately, but to assess honestly.If your leadership team is exploring AI and wants a clearer view of what is realistic, what is blocking progress, and what the right next step should be, start with readiness.Not sure whether your business is ready for AI?We help leadership teams assess process maturity, data readiness, implementation fit, and the right next step before they commit to the wrong solution.Book a readiness call

A leadership team feels the acute pressure of "missing the boat." A budget window opens, or perhaps a strategic grant becomes available. A vendor arrives with a polished deck, a series of impressive demos, and a high-velocity proposal. Suddenly, the entire conversation jumps straight to the finish line: Large Language Models (LLMs), custom prototypes, and aggressive six-month timelines.But for the modern executive, this is exactly the wrong place to start.At Sigli, we have observed that the most successful AI strategies do not begin with technology. They begin with a diagnostic inquiry. For CIOs, CTOs, and enterprise leaders, AI should not start as a "solution" to buy. It should start as a forcing function, a strategic provocation that clarifies business problems, tests legacy assumptions, and exposes what actually needs to evolve within the organization's DNA.Sometimes that evolution requires a generative model. Sometimes it requires a fundamental restructuring of a data pipeline. Identifying the difference isn't a failure of the AI initiative; it is the definition of fiduciary responsibility and strategic progress.The Inverse Logic of the AI Sales PitchThe traditional enterprise sales cycle is built on a "Problem-Solution" framework. The vendor identifies a pain point and offers a tool to fix it. However, AI is not a traditional tool like a CRM or an ERP. It is a probabilistic engine that thrives on high-quality data and clearly defined logic, two things many enterprises lack in their legacy processes.When we frame AI as a "solution" before the problem is fully understood, we fall into the Inverse Logic Trap. Instead of asking, “What is fundamentally slowing our growth?” the focus becomes, “How do we force AI into this specific workflow?”This framing creates an opening for "False Momentum." False momentum feels like progress because workshops are being scheduled, internal newsletters are announcing "AI task forces," and roadmaps are being drawn. But if the underlying business outcome remains vague, you aren't accelerating; you’re just failing at a higher frequency.As Sigli’s leadership often notes, the biggest limitation of AI today isn't the technical capability of the models, it's uncertainty of outcomes. Without a diagnostic phase, "inexpensive" consulting or rapid prototyping becomes the most expensive line item on the ledger. It creates the impression of movement while delaying the structural clarity required to achieve a real Return on Investment (ROI).Case Study: When AI Becomes a Diagnostic LensTo understand how AI acts as a conversation starter, we can look at Sigli’s work with one of the UK’s most prominent property data platforms.On paper, the project was a classic AI "solution" play: “Implement advanced Machine Learning to enrich property data and power new predictive features for users.” It was a high-value, high-visibility goal. But once the diagnostic conversation began, the team didn't just look at models; they looked at the "machinery" of the business.The "AI project" acted as a lens that revealed four deeper operational truths that a standard "solution" vendor would have ignored:Data Readiness vs. Model Sophistication: The team discovered that the AI couldn't function without dozens of new, robust data pipelines. The real value was found in the movement and cleaning of data, not just the intelligence of the model.Infrastructure Realities: Strict confidentiality and data sovereignty requirements meant that a "standard" cloud AI approach was non-viable. The conversation shifted to a complex on-premise deployment strategy that protected the client's core assets.Operational Debt: The project exposed a significant lack of documentation and several layers of complex legacy datasets. These had to be resolved before any "intelligent" layer could sit on top of them.The Performance Paradox: The diagnostic phase revealed that existing models were actually hindering the user experience because they were too slow. The "solution" wasn't more AI; it was more efficient AI integrated into a high-performance architecture.By treating AI as a conversation starter rather than a plug-and-play solution, the organization didn't just build a feature; they built a hardened infrastructure that made insights repeatable and features shippable.The Three Pillars of a Diagnostic ConversationWhen an executive shifts from "buying a solution" to "starting a conversation," the diagnostic framework should center on three key pillars:1. Knowledge LiquidityWhere is vital institutional knowledge trapped? Often, AI is pitched to "replace" human effort, but its higher value lies in making trapped knowledge liquid. If your best underwriters or engineers leave, does their logic leave with them? A diagnostic AI conversation asks how we can use technology to codify and distribute that expertise across the firm.2. Judgment ConsistencyIn many enterprises, the "problem" isn't speed; it’s variance. If three different managers look at the same data and make three different decisions, the business is inefficient. AI is a tool for reducing variance. The conversation should not be "How do we automate the decision?" but "Where is our human decision-making wildly inconsistent, and why?"3. Material Impact (The P&L Test)Executives must ruthlessly ask: “If we fixed this one thing with AI, what would actually change on the P&L?” If the answer is a marginal gain in "efficiency" that doesn't lead to increased throughput or reduced cost, the project is likely "Innovation Theater."Speed vs. Strategy: Why "Slow is Smooth"In the military, there is a saying: "Slow is smooth, and smooth is fast." This applies perfectly to AI implementation.A good partner doesn't amplify the illusion of a quick fix. They help dismantle it. In the property data case mentioned earlier, Sigli’s process prioritized Research, Pipeline Development, and Sequential Integration. This approach prioritized "Data Readiness" over "AI Novelty."This often means slowing the sales process down to improve the eventual decision. It means asking the uncomfortable questions that define a project's success before a single line of code is written.Is the data biased?Is the process we are automating actually logical?Who "owns" the output of the AI once the consultants leave?Without these answers, speed is a liability. This is why many projects that begin as “AI initiatives” eventually turn into something else, perhaps a master data management project or a workflow automation overhaul. That shift is not a sign that the AI idea was "wrong", it is a sign that the first conversation finally became honest.The Litmus Test for PartnersFor executives, the question isn’t whether a vendor "does AI." In 2026, every vendor "does AI." The real question is: How do they behave when the original AI idea begins to weaken under scrutiny?Do they protect the narrative or the outcome? A "solution" vendor will fight to keep the AI buzzword in the project scope to justify the price tag. A "partner" will steer you toward the most executable step, even if that step is less glamorous.Do they treat discovery as overhead? If a vendor wants to skip the diagnostic phase and move straight to "building," they are treating discovery as a cost to be minimized rather than essential risk management.Do they focus on Constraints or Capabilities? Immature AI conversations focus on what the tech can do. Mature executive conversations focus on what the organization cannot yet do, and why.The Value of the "Narrower" StepEnterprise value is not created by novelty. It is created when technology fits the business well enough to be operationalized, adopted, and trusted by the people on the front lines.The companies winning the AI race are not necessarily those who moved first. They are the ones who used the AI conversation to find their real constraints. They understand that AI is a diagnostic tool that exposes weak process logic, vague ownership, and poor data discipline.One of the healthiest signs in a high-level AI conversation is the willingness to leave the room with a narrower, less glamorous, but more executable next step. Stop treating AI as a purchase decision. Treat it as a strategic inquiry. Judge your partners not by how quickly they can sell you the answer, but by how deeply they help you define the problem. That is where the real ROI begins.