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.

Many founders think that grants are just about paperwork. Nevertheless, in reality, they are more about positioning and execution. In this episode of the Innovantage podcast, its host and Sigli’s CBDO, Max Golikov, speaks with Jonathan Spruytte, Grants & Incentives Senior Manager at KPMG Belgium. With prior experience at EY and a research background from Ghent University, Jonathan helps organizations secure Belgian and EU funding through a structured grant strategy and writing.He has a rich background in tech and academic spheres. During his PhD studies in computer science, Jonathan explored the impact of European legislation on technology. His research included feasibility studies on initiatives like EU-wide roaming and emerging technologies such as 5G on trains. So he focused on both technical performance and economic consequences.Initially, Jonathan considered an academic career. But later, he transitioned into grant writing, which now allows him to apply his analytical skills to a variety of industries and technologies. What consultancies actually doConsultancies offer a wide range of services to companies of all sizes, from multinationals to small startups. Firms like the Big Four are best known for audit, tax, and legal services. But their expertise spans far beyond those areas. They aim to help companies innovate and remain competitive in the market. This can involve anything from strategic advice and operational optimization to specialized projects like grant writing.Within consultancies like KPMG, there are specialized technical teams with deep expertise across a range of technologies. In Belgium, for example, these teams often work with platforms like Microsoft and O2. And they help companies implement existing solutions effectively. They can efficiently guide clients in selecting the right technologies to address specific business challenges.Consultancies do not typically engage in fundamental research or in product development. They focus on bringing proven technological solutions to companies and tailoring them to practical needs. How grant writing worksGrant writing at consultancies involves far more than simply filling out forms. The process begins with understanding the client. It is vital to listen to the company’s planned activities, identify challenges, and map how grant support can help overcome them. The next step is the actual grant preparation. It includes selecting the appropriate funding program, gathering necessary information, drafting the application, and submitting it. Beyond submission, consultancies also assist with reporting and ongoing compliance.Consultancies work with companies of all sizes. Smaller teams and startups benefit from close collaboration with founders. Meanwhile, larger companies, including multinationals, typically receive assistance in specialized areas or niche topics.Are you actually grant-ready?To determine whether a company is ready for grants, it is necessary to understand its near-term plans (typically over the next six to twenty-four months). The focus is on identifying key challenges the company faces and assessing whether grants can help overcome them.Of course, it is also possible to start by looking at available grants and then seek projects to match them. This approach often leads companies to pursue opportunities outside their core focus. However, it is a risky strategy for startups that need to stay aligned with their trajectory. Larger companies may have more flexibility. But startups must prioritize their own strategic goals.Grant readiness also depends on the company’s needs and context. For example, in research and development, a company should consider whether it has specific projects planned in the coming years and whether external funding is necessary. If a company has recently raised substantial capital, the time and effort required for grant applications may not be justified.Aligning innovation and grantsThe relationship between innovation and funding can go both ways.For regional grants, the approach is usually company-driven: businesses identify their challenges and seek grants that can help overcome them. In these cases, innovation typically comes first. At the same time, grants serve as a tool to support existing plans and projects.For European-level funding, the dynamic is often reversed. Programs set by the European Commission define priority areas (for instance, cybersecurity, defense technologies, or sustainability initiatives) and provide grants to encourage companies to consider these strategic goals. Where your grant strategy should startThe development of the grant strategy usually begins with initial discussions to clarify objectives. Then, there should be interactive workshops or interviews that are required for collecting the detailed information needed for an application submission. The first meeting usually focuses on understanding the company at a high level (its origins, goals, and market context). Subsequent meetings dive deeper into specific aspects of the business. One session explores the commercial side (pricing, market strategy, and sales approach), while another focuses on the technical side, detailing what the company intends to develop and how it plans to do so.After these meetings, the grant team begins drafting the proposal. During this phase, the company may receive periodic status updates.Once a draft is nearly complete (around 90% or 95% finished), founders review it in detail and can provide feedback on content or emphasis. This helps ensure that the final document accurately represents the company’s vision and strategy. In total, the founder’s time commitment is usually around 20 hours. Meanwhile, the consultancy often invests ten times that effort behind the scenes to produce a submission-ready proposal.For typical development projects, the writing phase takes about three months on average. Moreover, you should bear in mind that the evaluation phase adds a similar amount of time. From start to finish, the full process generally requires five to six months.Working with experienced consultants significantly increases success rates. These rates often reach even 95% when projects are handled on a success-fee basis, where payment is contingent on grant approval. Companies that try to write grants themselves frequently face longer timelines (up to nine months) and lower chances of success.Real grant success storiesSuccessful grant projects often combine innovative ideas with the right mix of skills and execution capabilities. Some prominent examples include:The use of computer vision in robotic surgery to automate training procedures;Early-stage detection of biomarkers in ophthalmology;Smart soccer shoes that monitor field conditions;Systems for tracking the shelf life of potatoes, and others.Each of these projects showcased unique innovation, but their success depended equally on the founders’ ability to bring the idea to market.Grants are most effective when the project team has the necessary expertise and connections. For instance, a robotic surgery project requires not only IT knowledge but also access to medical expertise and hospital networks. When projects look good on paper but don’t workNot every project that appears promising at first glance will succeed. Experienced grant consultants can detect whether a company truly understands its problem or is only pursuing a great idea without full context. Early conversations often reveal whether the project team has validated the problem with potential clients and assembled the right expertise.Consultants ask detailed, sometimes challenging questions to understand the company and its project thoroughly. Building trust is essential. Founders must feel comfortable sharing sensitive details and responding to in-depth inquiries. This personal dimension also impacts grant success during evaluation. Companies presenting their projects to evaluators must communicate convincingly, particularly for high-risk European grants where pitching skills can determine whether a project advances.As Jonathan noted, companies that consistently secure grants and achieve success typically share four key traits:Innovation. They have a strong idea that stands out and has the potential to make a meaningful impact.Market viability. They can sell and scale the idea effectively. The product or service must meet a real market need.Expertise. They have the right team with the necessary technical skills and domain knowledge to execute the project successfully.Financial readiness. While grants provide support, additional funding is often needed to develop products, enter the market, and sustain growth.Why outsource grant writingOn the one hand, it may seem that grant writing is a simple task, and practically everyone who likes writing can successfully cope with it. However, this task has its peculiarities that require a particular approach.Grants come with complex regulations, specific criteria, and nuanced requirements. Experienced consultants know these rules, can navigate open questions efficiently, and provide guidance based on daily practice.Moreover, writing a strong grant application is time-consuming. Thanks to outsourcing, founders can pay more attention to their core business activities.Human factors in grant writingAccording to Jonathan, a successful grant writing team is always strengthened by the curiosity and problem-solving mindset of its members. Experts often come from diverse backgrounds, including technical fields, economics, business, and others.Effective grant writing involves taking difficult concepts, analyzing them, and translating them into understandable content for a broader audience. Writing skills are essential. But the same is true about the ability to grasp a wide range of topics. At the same time, we shouldn’t ignore the importance of understanding the economics of a project and presenting a strong business case.Many team members come from academia. However, the work is distinct from typical academic writing and requires the skill to communicate technical ideas in a way that convinces evaluators. AI in the grant workflowArtificial intelligence has become a practical tool in the grant writing process. Nevertheless, it can’t be viewed as a replacement for human expertise. AI helps streamline workflow and improve efficiency. This allows consultants to deliver high-quality proposals faster while keeping costs manageable.Now, it is primarily used as a process tool. It assists in structuring information, identifying key objectives, and analyzing technical challenges. AI doesn’t simply generate text for proposals. It can perform the role of a discussion partner as it helps the team scope problems and clarify the storyline for the grant. Jonathan explained that at their organization, AI-generated drafts are never used without review. Every piece is rewritten and refined by the team. Typically, each proposal is reviewed by at least two team members.But acceleration of the grant writing process is not the only use case where AI can be present in this field.In recent years, many clients have started bringing AI-generated documentation. While some of these documents provide useful starting points, they often lack the specificity and clarity that are required for a successful proposal.This can make initial meetings less productive, as the team needs to invest more time and effort to uncover what is actually planned.There are also situations where AI-generated content can misrepresent the project. For example, a client might submit a draft with functionalities or objectives that were suggested by AI, but they may differ from the company’s intentions.Hidden dangers of AI proposalsEven with AI-generated content, human judgment remains essential. Consultants can often detect when a document was prepared by AI. Startups rarely produce multi-page responses on their own. Long documents often indicate AI assistance.AI can’t replace the nuanced understanding of a client’s goals and uncertainties. The ultimate responsibility for accuracy lies with the company and its founders. Grant consultants translate a problem into a proposal. But they rely heavily on the client’s expertise, particularly in specialized fields. For instance, consultants may understand IT or general processes, but in sectors like food production, the team must trust the founder’s knowledge of industrial practices.This reliance creates a potential risk. AI-generated or even client-prepared content may include subtle inaccuracies. Consultants verify information whenever possible, but some details can still slip through. The challenge is compounded in innovative projects. By their nature, these projects tackle new problems. Because there is no precedent, evaluating feasibility is inherently uncertain. Is AI right for every project?AI is increasingly part of grant-related projects, but it is rarely the core innovation. Very often, it is a tool to solve a specific problem. Jonathan noted that over the past few years, the focus has shifted from developing AI models from scratch to applying existing architectures in practical use cases. The real innovation often lies in how companies structure and manage data, integrate AI into workflows, and design solutions that leverage these tools effectively. The AI model itself is just one component of a broader system.Meanwhile, as AI automates certain tasks, companies still need skilled personnel to manage models, ensure data quality, and intervene when issues arise. Future AI-driven workflows are likely to combine automation with human-in-the-loop systems. This will create new roles to manage and interpret AI outputs. Innovation trends to watchInnovation is thriving across almost every sector, from medical technology and biosciences to food, logistics, and manufacturing. According to Jonathan, no industry appears to be slowing down, and the influx of AI-driven solutions is accelerating the pace.New problems and new opportunities build a good ground for startups and established companies. We can observe the constant emergence of novel challenges. This ensures that new roles, products, and projects continue to be created.Curious to learn more? Stay tuned for more insights from industry experts. Don’t miss the next episodes of the Innovantage podcast, where we continue to explore innovation and the trends shaping the future of technology and business.

In the high-stakes world of enterprise delivery, agreement is frequently mistaken for competence. When a CEO or a Board greenlights a major transformation initiative, they are often met with a chorus of "yes" from prospective vendors. On the surface, this feels like momentum. It feels like a partnership built on speed and alignment.However, some of the most expensive project failures in corporate history began with a vendor saying yes too quickly.The easiest partner to buy from is rarely the safest partner to trust. For the CEO, the real risk isn’t just a project that runs over budget; it is the strategic distraction of spending executive capital and organizational energy on a solution that doesn't actually solve the problem. To navigate this, leaders must distinguish between two very different types of partners: the Order-Taker and the Advisor.The Trap of the "Yes-First" VendorIn a competitive market, vendors are incentivized to reduce friction. Their goal is to win the brief, and the fastest way to win a brief is to validate the client’s requested solution without question. This "Order-Taker" model optimizes for approval and short-term speed.The problem? Complex business problems are rarely solved by the first solution that comes to mind. When a vendor accepts a flawed brief just to get started, they aren't being helpful, they are merely deferring the inevitable friction. The hidden costs of this compliance: integration bottlenecks, scope creep, and technical debt will eventually surface, usually mid-delivery when the budget is already committed.Defining the Difference: Order-Taker vs. AdvisorThe distinction between these two roles is operational, not just philosophical. Here is how they compare across key delivery behaviors:The Order-TakerAccepts the brief as written: They take the initial request at face value without questioning the logic.Avoids early friction: They prioritize maintaining "momentum" and keeping the client happy in the short term.Optimizes for compliance: Their primary goal is to get approval and move quickly to the billing phase.Delivers a product: They will build exactly what was asked for, even if it is fundamentally the wrong solution for the business.The AdvisorPressure-tests assumptions: They dig into the "why" behind the request to ensure the foundation is solid.Focuses on outcomes: They ask what business result actually matters most, rather than focusing solely on the tool.Optimizes for value: They prioritize fit, feasibility, and long-term commercial success over easy agreement.Protects the client: They have the courage to prevent a wasteful build, even if it means narrowing the project scope.An Advisor understands that their job is not to deliver the requested solution; their job is to solve the business problem. Sometimes, that requires the courage to tell a client they are headed down the wrong path.Why “No” Is the Ultimate Form of ServiceSaying "no" does not mean blocking progress. In an advisory-led model, "no" is a tool used to strip away complexity and focus on outcomes. It usually manifests in four critical ways:No to the wrong technology: Not every problem requires the latest trend. Choosing a hammer for a screw is a waste of capital.No to unnecessary complexity: The more complex a solution, the higher the maintenance, governance, and stakeholder burden.No to hidden future costs: A flashy proposal often hides a mountain of implementation debt. An Advisor surfaces these trade-offs early.No to solving the wrong problem: Often, the request targets a symptom rather than the root cause.Case in Point: When "AI" Isn't the AnswerConsider a recent engagement involving a request to automate invoice import and reconciliation within an SAP ERP system. The goal was commercially sound: increase speed, reduce manual error, and improve reliability in accounting operations.In the current climate, it would have been easy to frame this as a "Generative AI" initiative. Doing so would have likely secured a larger budget and more internal "hype." However, upon analysis, it became clear this was not an AI problem. It was a standard automation and integration problem.By refusing to force an AI narrative, we protected the client from:Inflated solution costs associated with unnecessary LLM tokens or specialized infrastructure.Increased execution time caused by training models for a deterministic task.Unnecessary risk in a process (financial reconciliation) where 100% accuracy is non-negotiable.The right service was not to win an "AI brief." It was to provide a grounded, commercially sensible path to the business outcome.The Reality of the "AI Project"We are currently seeing a market-wide pattern: a surprising number of "AI projects" are actually discovery exercises in disguise. They uncover process gaps, fragmented data foundations, and integration bottlenecks.When a partner tells you that your AI ambitions are premature because your data architecture can't support them, that isn't a failure of vision. That is high-level advisory work. It is far better to spend $50k on a discovery phase that says "not yet" than $5M on a failed implementation that says "we should have known."Why This Matters to the CEOFor a CEO, the value of a partner who says "no" is found in capital preservation and focus.Strategic Clarity: You need partners who can distinguish between strategic value and "technical theatre."Confidence in Judgment: When a partner finally says "yes" to a project, you know that "yes" has been earned through rigorous challenge, not just a desire to invoice.Reduced Noise: A partner who filters out hype allows the executive team to focus on the 20% of initiatives that will drive 80% of the impact.The more hype-driven a market becomes, the more valuable restraint becomes.Challenging a brief early is the fastest route to a useful outcome. What truly slows an organization down is committing to the wrong solution and discovering the mismatch six months into the roadmap.Good service is not blind agreement. It is the discipline to challenge the wrong path before it becomes an expensive one.Is your current roadmap built on solid outcomes or just fast agreement?If you are evaluating an AI, automation, or transformation initiative and want a grounded, outside view before committing to a specific path, let’s talk.

Today, 70% of the workforce is not actively looking for a new job. Due to this, posting an ad and hoping for the best doesn’t work anymore.Does it mean that hiring is fully broken? This episode of the Innovantage podcast offers a solution. Its host and Sigli’s CBDO, Max Golikov, sat down with Juris Zalāns, co-founder of Talenme, to speak about the ongoing changes in the recruitment and HR space.In this conversation, Juris explained how digital tools are reshaping this sphere and why human trust is still the most powerful algorithm of all.Juris’s entrepreneurial journey began in the corporate world. At the beginning of his path, he held senior roles in procurement, logistics, and resource planning. He had a stable career, but he wanted to create solutions beyond the limits of traditional corporate responsibilities. He started contributing to initiatives outside his formal role and was looking for ways to deliver additional value within the organization.This initiative eventually led to his first startup. He introduced a contract-based discount system for employees. It happened more than a decade ago. But his solution resembled models that are only now gaining traction in the Baltic region.How Talenme helps businesses find employeesNow, Juris and his team are building Talenme. It is a referral-driven hiring engine designed to transform how companies find talent. Instead of relying only on traditional recruitment agencies or headhunters, the platform enables businesses to tap into broader human networks to identify suitable candidates.The concept is quite simple. Companies publish open roles along with a referral bonus. As a result, anyone can recommend qualified candidates. In this model, individuals become active contributors to the hiring process. They help organizations discover talent through trusted connections instead of cold outreach. If someone knows a strong candidate for a role, they can recommend them and receive a reward once the hire is successful.This approach aims to democratize headhunting and turn recruitment into a marketplace.Additionally, the model allows recruitment agencies and professionals to reuse their existing candidate pipelines more efficiently. Candidates who may not fit one role can still be matched with other opportunities within the ecosystem. This makes the hiring process more dynamic and cost-effective.What makes Talenme stand out?A key differentiator of Talenme is the crowd effect. The offered hiring approach is built around the reality that most talent is not actively searching for jobs. Today, only a small portion of the market actively applies for open roles. As a result, companies often compete for the same limited pool of candidates on traditional platforms.Talenme addresses this gap by focusing on access to passive talent through human networks. The model leverages personal connections as the primary channel to reach qualified specialists who may otherwise remain inaccessible. With its incentives for referrals, Talenme improves reach and significantly reduces sourcing limitations.Behind the scenes: Lean team, constant iterationTalenme is a compact, highly focused team. It consists of the co-founders and a CTO. Product development, sales, and marketing teams are freelancers who work part-time. This lean setup allows flexibility. But it demands significant multitasking and continuous prioritization.Operating with limited resources means balancing multiple responsibilities (client communication, bug fixing, financial management, and strategic planning) simultaneously. Due to this, startup execution often feels like constant grinding, with late nights and ongoing problem-solving becoming part of the daily routine.Another key challenge is aligning the product with real market needs. Initial ideas about features and client expectations often evolve when the product meets real users. It is crucial to listen to customers instead of relying on assumptions.Rethinking work-life balance in startupsJuris believes that the idea of traditional work-life balance in the startup world is largely a myth. Instead of balance, founders operate in a constant state of shifting priorities, where the most urgent tasks demand immediate attention. To build a startup, you need to keep a sustained focus. Apart from this, you should be ready to accept trade-offs rather than expect a perfectly structured routine.Startup life inevitably influences personal relationships, social time, and overall lifestyle. High responsibility and ongoing decision-making often blur the boundaries between professional and personal spheres.Key achievements in the first yearDuring its first year, Talenme reached several milestones that demonstrate early market validation and steady progress. One notable achievement was generating over $20,000 in revenue within the same year the product was pre-launched. It is a very significant step for an early-stage startup, especially given how challenging it is to secure initial traction and paying customers.The team also succeeded in attracting strong market interest. Major retail chains and well-known IT companies began testing the platform through pilot programs.Early feedback has been particularly positive from organizations that previously relied on internal referral programs and are now exploring how to scale them externally.In addition to this, maintaining team cohesion has been an important internal achievement. Despite the high workload and limited time, the team has preserved a collaborative and supportive culture.Smart communication in small teamsIn a small startup team, effective communication is less about sharing everything and more about sharing what truly matters. It makes no sense to overwhelm team members with constant updates. The focus should be on providing the information they need to perform their roles efficiently. Overcommunication can create noise and distract from execution.For example, high-level updates about fundraising or long-term strategy may be shared as context. At the same time, detailed discussions should center on areas where the team can actively contribute (for example, product development or feature decisions).However, transparency remains essential from the very beginning. Setting clear expectations helps align the team and attract people who genuinely believe in the vision. In early-stage startups, motivation is often driven by the opportunity to build something meaningful. Meanwhile, financial incentives are less important.Talent acquisition trends in 2026One of the defining trends in talent acquisition is the growing dominance of passive candidates. Unemployment rates are historically low across Europe. The majority of qualified professionals are already employed. Organizations need to rethink their recruitment strategies. They can’t rely only on inbound applications. Businesses must focus on engagement and employer branding to attract and retain talent. Employee-centric approaches (increasing workplace satisfaction, maintaining strong internal culture, etc.) are becoming key competitive advantages.Another trend is the growing reliance on external hiring channels. Many companies are outsourcing HR processes to recruitment agencies or headhunters. At the same time, gig work and flexible talent models continue to expand. This enables businesses to scale faster without fully increasing headcount.Financial pressure also impacts hiring decisions. Rising salaries and slower wage growth stabilization mean startups and smaller companies must be more resourceful when they are competing with large corporations for skilled professionals.Role of AI in hiring: Support tool, not a decision makerThe popularity of AI in talent acquisition is growing. But its role remains complex and often misunderstood. Automated screening tools and AI-powered applicant tracking systems promise faster candidate evaluation. However, real-world cases show that overreliance on automation can lead to flawed outcomes. Quite often, qualified candidates are automatically rejected due to rigid filtering criteria.At the same time, both candidates and recruiters are now using AI in parallel. Applicants generate AI-enhanced resumes, while HR teams rely on AI-assisted scoring and shortlisting. This creates a loop where automation influences both sides of the hiring process and raises concerns about transparency and decision accuracy.Juris explained that AI works best as a support feature rather than a replacement for human judgment. It can help structure data, speed up candidate scoring, and improve efficiency. However, blindly following AI recommendations can reduce hiring quality, especially in nuanced roles where context and interpersonal fit matter.Regulatory uncertainty, including emerging AI legislation, is also making corporations more cautious about full-scale automation in HR processes. As a result, many organizations are adopting a hybrid approach. They try to combine AI-driven insights with human validation.Referral-driven hiring models highlight the continued importance of human interaction. Personal context and reputation signals often provide stronger candidate validation than algorithmic filtering alone.Future of AI in talent matchmakingWhile organizations increasingly rely on AI for screening and data analysis, trust in automated decisions still has clear limits. Recruiters and hiring managers tend to treat AI outputs as guidance (not absolute truth), especially in high-stakes hiring decisions.According to Juris, in the future, AI is likely to evolve into intelligent matchmaking. Instead of only filtering resumes, it may help identify strong candidate-role connections by analyzing skills and network relevance. For example, platforms could allow users to connect their professional networks and receive notifications when suitable roles match the profiles of people they know. This would transform recruitment into a more proactive process.However, even with advanced AI capabilities, the human element will remain essential. A recommendation from a trusted contact carries social proof and additional insight into candidate fit that automated systems often miss.Referrals vs. cold applications: Who gets reviewed first?When it comes to reviewing CVs, referrals often get priority over cold applications. This approach is not about favoritism, but efficiency. Referrals signal trust and reduce uncertainty. This allows hiring managers to focus their energy on the most promising candidates.However, this system can pose challenges for those who are just starting their careers. Early-career candidates should concentrate on learning and demonstrating potential. It’s also vital to recognize that building a reputation and network takes time.When it comes to recruitment in startups, referrals are highly effective for hard-to-fill mid- and senior-level roles, particularly in IT, fintech, and sales. But giants also rely on recommendations as well.Data from 2024 shows that companies like Accenture hire roughly one in three employees through internal referrals.However, for junior or high-volume positions like internships, referrals play a different role. They can accelerate hiring when speed matters. But they are less critical since candidates are more accessible through traditional channels.Juris emphasized that resumes are rarely the deciding factor.In his experience, hiring decisions are guided more by attitude and self-motivation than by formal degrees or prior titles. The ideal candidate is eager to learn and capable of taking initiative without constant supervision. For startups, assembling a team of motivated learners can have a huge impact on future growth. Rise of project-based careersAs studies show, younger generations move through more jobs over their careers. According to a recent survey, Gen Z workers are expected to change jobs 20-30 times, compared to 10-12 times for millennials. It is still not fully a gig economy since many professional roles require onboarding and company-specific knowledge. However, this trend points toward a project economy. It means that employees work on discrete assignments within organizations before moving on to the next project. Platforms like Fiverr demonstrate the growing popularity of short-term, skill-based work. In this environment, hiring solutions that accelerate the path from job posting to onboarding are crucial. The most effective recruitment approaches will be those that quickly connect companies with the right talent.Evaluation of career experienceHiring decisions often come down to the trade-off between breadth and depth of experience. For example, imagine that you consider two engineers. One of them has a long-term journey at a single company. This specialist has gained deep expertise in a specific product or enterprise. The other has multiple short-term project experiences across several companies. Thanks to this, this engineer has managed to accumulate diverse skills and exposure to different industries and workflows.The optimal choice depends on the organization’s current needs. If the goal is rapidly scaling knowledge across new domains, a candidate with multiple project experiences may be a good choice. This specialist can bring fresh insights and accelerate learning within the team. On the other hand, if the priority is leveraging existing expertise for execution and specialization, a candidate with long-term experience may deliver more consistent results.Why salary isn’t the top priority for Gen ZFor Gen Z, career choices are increasingly driven by lifestyle alignment and work-life balance rather than purely financial incentives. Unlike previous generations, many young professionals in Europe face less financial pressure. Very often, they are able to rely on family support while pursuing early career opportunities. As a result, their priorities shift toward roles that offer meaningful work and allow them to maintain a healthy work-life balance.This trend challenges companies to reconsider traditional compensation-focused recruitment strategies. High salaries alone are no longer sufficient to attract or retain talent. Employers must also foster flexible work environments that prioritize employee well-being and personal growth.Future of HR tech: AI and human interactionSpeaking about the future of HR technology, Juris highlighted the growing role of the combination of AI-driven analytics and human-centered interactions. AI is expected to become an essential tool in HR processes, from talent sourcing to workflow optimization. Its power in data analysis and pattern recognition can help HR teams make faster, more informed decisions.However, AI can’t replace the human elements critical to recruitment and workforce management. Skills like reading interpersonal cues, detecting honesty or motivation, and building relationships remain inherently human. Missteps with fully automated interactions (such as AI-generated calls or outreach) can seriously frustrate candidates.Regional regulations also shape the adoption of HR tech. Centralized systems like blockchain-based CVs could simplify hiring across borders. However, varying legal frameworks in different countries require human judgment to ensure compliance.Why technology in HR is long overdueHR has historically lagged behind other business functions in adopting technology. Marketing, logistics, and production have been fully digitalized. But HR still relies heavily on manual processes for recruitment and talent development.In such conditions, despite being the most human function, HR is in the most need of tech support. Digital solutions can cover data analytics, candidate matchmaking, operational automation, and many other tasks. This can allow HR professionals to focus on human-centered work.The right balance between digitalization and human participation remains key to increased efficiency across every function. And HR is not an exception here.If you want to learn more about the role of modern technologies in business and our everyday life, stay tuned! The next episodes of the Innovantage podcast will offer new insights and perspectives on these aspects from industry experts and tech leaders.