Every CTO eventually inherits the same conversation. Someone on the leadership team — often you — looks at a system that has quietly run the business for a decade and says: "It's time to modernize this."The instinct is understandable. The legacy platform is expensive to maintain. It's brittle. Every integration takes longer than it should. Reporting requires three people and a spreadsheet nobody fully trusts. And so the conversation jumps almost immediately to solutions: rewrite it, move it to the cloud, break it into microservices, replace the platform outright.Here's the uncomfortable part: that instinct is often wrong — not because modernization is a bad idea, but because of the order in which it happens. Most modernization programmes start with a technology decision. The right ones start with a legacy modernization risk assessment.Modernization doesn't automatically reduce risk. Done in the wrong order, it multiplies it.When does "legacy" actually become a business risk?Not every old system is a liability. Plenty of ageing platforms still do their job quietly and well. Legacy technology becomes a genuine business problem the moment maintaining the status quo starts limiting what the company can actually do.That shows up in a handful of recognisable patterns:Slow change. Every feature takes longer because engineers are afraid of what else it might break.Integration constraints. Modern APIs, customer platforms, and data tools are difficult or impossible to connect cleanly.Poor visibility. Data is fragmented across systems, and no two teams trust the same number in a meeting.Security and compliance exposure. Unsupported software, outdated access controls, and undocumented components quietly raise your risk profile.Knowledge concentration. A small number of people — sometimes one — understand how a critical part of the system actually works.Growth limitations. The business starts shaping its plans around what the system can support, rather than the other way around.If you recognise two or three of these, modernization is probably the right conversation. But it's still the wrong place to start — that starts with a technical debt assessment, not a technology choice.Modernization is not simply "rebuild or replace"Somewhere along the way, "modernize" became shorthand for "rewrite." It shouldn't be. A company facing legacy risk has considerably more than one lever to pull:Refactor selected componentsImprove specific integrationsMigrate infrastructure without touching the application layerModernize the data layer onlyReplace one application while leaving the rest intactIntroduce APIs around the legacy coreRebuild the system entirelyBuy a replacement platformRun old and new systems in parallel during transitionRetain parts of the existing architecture deliberatelyThe right choice depends on business risk, dependencies, urgency, cost, and operational tolerance — not on which architecture happens to be fashionable this year. Yet in our experience working with Benelux and UK enterprises, the technology decision is usually made before the risk assessment even starts. That's backwards, and it's expensive. Legacy code modernization done this way tends to solve the wrong problem faster.The legacy modernization risk assessment: 7 risk areas to reviewBefore committing to a modernization path, it's worth reviewing the system across seven distinct risk areas. Think of this as due diligence — the same rigour you'd expect before an acquisition, applied to your own technology estate.Risk 1: Business continuityWhich critical processes depend on this system? Which teams touch it every single day? What operations simply cannot stop? What happens if it's unavailable for a few hours — or a few days? Are there seasonal or operational periods (year-end close, peak trading season, audit windows) when migration would be especially dangerous?A technically flawless migration can still be a failure if it disrupts operations the business can't afford to pause.Risk 2: System dependencies and integrationsWhich internal applications connect to this system? Which third parties depend on it? Are the integrations even documented, or are they a patchwork of old APIs, batch jobs, scripts, and manual data transfers built up over years? What breaks downstream if one component changes?Legacy systems almost always have more dependencies than the team initially assumes. Map the dependency chain before you touch the architecture — not after.Risk 3: Data migration risk assessmentWhat data actually needs to move? Is historical data still required, or just assumed to be? Are formats consistent across sources? Are records duplicated, incomplete, or simply wrong in ways nobody has had to confront yet? Who actually owns the data? How will migration accuracy be validated, and will both systems need simultaneous access during the transition?Here's the trap: the application can migrate successfully while the business still loses trust in it, because the data underneath is incomplete or inconsistent. A proper data migration risk assessment catches this before go-live, not after.Risk 4: Undocumented business logic and knowledgeIs the system properly documented — realistically? Which business rules exist only in the code, with no record anywhere else? Are there undocumented exceptions built up from years of edge cases? Who still understands the historical decisions behind how the system behaves? What disappears the day a specific person leaves?The legacy codebase isn't just old technology. It's years of business decisions, encoded in a language most of the organisation can no longer read. Legacy code modernization that skips this step can silently remove functionality nobody realised was load-bearing.Risk 5: Security and complianceReview unsupported technologies, outdated dependencies, access controls, authentication methods, sensitive data handling, audit requirements, regulatory constraints, and third-party dependencies.The nuance that's easy to miss: modernization itself introduces new security risk during the transition, particularly when old and new environments have to coexist for weeks or months. A migration plan that ignores this is a plan with a gap in it.Risk 6: Migration and transition complexityBig-bang cutover, or phased rollout? Can old and new systems genuinely run in parallel? How will data stay synchronised between them? What does rollback actually look like if something goes wrong — not in theory, but in practice? How do users move across? How do you test under real operational conditions rather than a clean staging environment?Most teams plan the destination architecture with real care. Far fewer plan the journey to get there with the same discipline. The transition architecture can be just as important as the target architecture — and it's usually where projects actually fail.Risk 7: Delivery scope, cost, and timelineIs the full rewrite genuinely necessary, or has it become the default answer? Which components create the greatest risk if left untouched? What could be modernized independently, without waiting for a single monolithic programme? What assumptions is the estimate actually built on? What happens if the project runs 30–50% longer than planned — which, statistically, is closer to the norm than the exception?Large modernization programmes become dangerous the moment scope expands faster than business value. This is where progressive modernization earns its keep: prioritise the highest-risk or highest-value components first, rather than attempting to modernize everything simultaneously. A technical debt assessment at this stage tells you exactly where that priority order should start.How to plan a legacy system migrationIf you're wondering specifically how to plan a legacy system migration rather than a full rebuild, the sequence generally looks like this:Run the risk assessment first. Complete the seven-area review above before selecting a target architecture.Map every dependency. Internal systems, third parties, batch jobs, and manual workarounds all need to be on the list.Assess data separately from application logic. A data migration risk assessment is its own workstream, not a subtask of the technical migration.Choose a transition model. Decide between big-bang cutover and phased/parallel-run before you commit to a timeline.Define rollback criteria in advance. Know what "this isn't working" looks like before day one of the migration, not after.Sequence by risk and value, not by convenience. Migrate the highest-risk or highest-value components first.Set a measurable business outcome. A migration without a defined success metric is hard to call finished — or failed.Rebuild, migrate, replace, refactor — how do you actually choose?There's no universal answer, but there is a useful starting framework:Software modernization approaches (by Sigli)None of these are rigid rules. The point is that "rebuild" is one option among several — not the default.Why "rewrite everything" is often the highest-risk optionFull rewrites are seductive for good reason. They promise clean architecture, the removal of technical debt in one motion, modern technology across the board, and simpler development from day one.They also tend to come with long delivery cycles, requirements that shift halfway through, quietly lost business logic nobody remembers to re-specify, real migration complexity, delayed ROI, and the burden of maintaining two systems in parallel for far longer than anyone budgeted for.A complete rewrite can absolutely be the right call. But it should be a conclusion you arrive at after the risk assessment — not the assumption you walk in with.What a proper risk review should actually produceDone well, a legacy modernization risk assessment gives leadership clarity on six things:Current-state risks — what is genuinely creating business or technical exposure today, versus what's simply uncomfortableDependencies — the systems, data, integrations, workflows, and people affected by changeModernization priorities — what needs attention first, and what can safely waitRecommended approach — rebuild, replace, migrate, integrate, refactor, or a phased combination of theseTransition risks — downtime, data migration, coexistence, security exposure, and user adoptionDelivery roadmap — what changes first, and what follows once the foundation is stableA pre-modernization checklistBefore approving a modernization programme, confirm: Critical business processes are mapped System dependencies are understood Integrations are documented Data quality and migration requirements are known Hidden business logic has been investigated Security and compliance risks are understood Downtime tolerance is defined Old/new system coexistence is planned User impact is understood Modernization scope is prioritised Rollback and testing approach is defined Expected business outcome is measurableModernization Programme Approval (by Sigli)A few unchecked boxes don't mean "don't modernize." They mean those uncertainties need to be resolved before you commit to a delivery approach — not discovered halfway through one.Modernize in the right orderLegacy modernization shouldn't begin with a preference for the cloud, for microservices, for a new framework, or for a full rewrite. It should begin with three questions: What is creating risk today? What could create risk during the transition? And what should change first — versus what can safely remain exactly as it is?The goal was never to modernize as much as possible. The goal is to reduce the right risks, protect business continuity, and build a technology foundation that actually supports where the business needs to go next.‍Not sure where your biggest modernization risks actually sit? Sigli offers a free Pre-Build Risk Review to help CTOs and technology leaders map dependencies, data risks, and transition complexity before committing to a delivery approach.Book your free risk review →

AI projects often begin with questions about models, vendors, architecture, and use cases:Which model should we use?Should we build or buy?Do we need RAG?Should we use an external API or host something ourselves?Those questions matter but they may come too early. Before deciding how to build an AI solution, there is a more fundamental question to answer: Can your data safely and reliably support it?Many problems that appear later as “AI issues” are actually data issues that existed long before the AI project started. Poor data quality, fragmented systems, unclear ownership, weak permissions, privacy restrictions, historical bias, and outdated documentation can all become significantly more expensive once a model, integrations, and production workflows are built around them.That is why AI readiness should start with a data risk review. The objective is not to make your data perfect. It is to identify the weaknesses that could make the planned AI solution unreliable, unsafe, non-compliant, unexpectedly expensive, or difficult to operate before development begins.AI Data Readiness Starts With Data QualityThe first risk is also the most basic: data quality. An AI system can only work with the information available to it. If that information is inaccurate, incomplete, duplicated, inconsistent, or outdated, the AI may reproduce those problems at scale.Typical warning signs include:duplicate customer or product records;missing values in important fields;different formats across systems;outdated documents;conflicting values for the same customer or transaction;manually maintained spreadsheets with unclear controls;inconsistent labels or classifications.For traditional software, bad data may cause an incorrect dashboard or an incomplete report.With AI, the problem can be harder to detect because the output may still sound convincing.A customer-support assistant, for example, may generate a perfectly written answer based on an obsolete policy document. A forecasting model may work technically while relying on inconsistent historical records. An AI sales assistant may recommend the wrong next action because CRM fields are incomplete.The pre-build question is therefore not simply: Do we have data? It is: Is the data sufficiently accurate, complete, current, and consistent for this particular use case?AI Data Readiness Depends on Access and IntegrationOrganizations frequently discover that the required information exists, but is distributed across many systems.A single AI use case may depend on information from:CRM;ERP;data warehouses;SharePoint;document repositories;legacy applications;spreadsheets;external APIs;third-party platforms.That creates a hidden scope risk. What initially looks like an AI-development project can quickly become a data-integration project involving APIs, migrations, transformations, identity management, data pipelines, and legacy-system constraints. This is particularly common with enterprise AI assistants.A company may say: “We want an AI assistant that understands all of our customer information.”The next question should be: Where does that information actually live, and can it be connected reliably?If customer contracts are in one system, pricing in another, support history in a third, and account status in manually maintained spreadsheets, the integration effort may become more significant than the model implementation itself.Before building, map the data sources, dependencies, owners, update frequency, and access requirements. That exercise can materially change the architecture, timeline, and business case.Is Your Data Representative Enough for AI?Another common mistake is assuming that having a lot of historical data automatically makes a company AI-ready.Volume and representativeness are not the same thing.A dataset can contain millions of records while poorly representing:particular customer groups;countries or regions;languages;products;new users;unusual transactions;edge cases;changing market conditions.This matters because an AI system learns from the reality represented in the data, not necessarily the reality in which it will eventually operate. Imagine an AI support system trained mostly on English-language conversations. Its overall performance may look strong during evaluation, while its performance for Dutch- or French-speaking customers is significantly worse.The same issue applies to predictive systems. A model can achieve high average accuracy but perform poorly for a commercially important customer segment or unusual operational scenario.Before development, ask: Does our data represent the customers, situations, languages, and environments the AI will actually encounter?And do not look only at average model performance. Important differences can disappear inside a single headline accuracy number.Historical Data Can Automate Historical BiasHistorical business data is often treated as objective truth. Data contains the results of previous business processes, decisions, incentives, policies, and human judgments.If those processes contained bias or inconsistency, an AI system may learn and scale them.Consider labels such as:“high-quality lead”;“good candidate”;“high-risk customer”;“priority case”;“likely to churn.”They may look like factual categories in a database, but they often reflect human decisions or previous business rules.The question is therefore not only whether the dataset is technically clean.It is: Which historical patterns do we actually want the AI to reproduce?There is also the risk of proxy variables. Removing an obvious sensitive attribute does not automatically remove bias if other variables indirectly correlate with it. Location, education, employment history, income indicators, or purchasing behavior may unintentionally recreate distinctions that the organization did not intend the model to make. Bias assessment should therefore happen during data preparation, not only after a model has been trained.AI Data Readiness Also Means Privacy and ComplianceAI projects often involve personal or sensitive information. Customer conversations, employee records, support tickets, behavioral data, documents, financial information, and transaction histories can all contain personal data. For European organizations, this creates immediate GDPR considerations.Before connecting that data to an AI system, organizations need to establish:why the data is being used;whether there is a lawful basis for that use;whether the new AI purpose is compatible with the original purpose;whether sensitive or special-category data is involved;whether all of the information is actually necessary;who will receive or process the data;how long it will be retained;where it will be processed;whether it can later be corrected or deleted.This is where a technically simple AI idea can become a compliance problem. For example, a business may already have thousands of customer conversations available in its support platform. That does not automatically mean those conversations can be reused for every possible AI-training or analytics purpose.The useful pre-build question is: Are we technically able to use this data, and are we actually permitted to use it in this way?Those are different questions. The EU AI Act adds another layer for certain AI use cases, particularly where systems fall into higher-risk categories. Depending on the application, requirements around governance, documentation, transparency, and data quality may directly influence how the system should be designed. Compliance therefore needs to be considered before architecture decisions become difficult to reverse.Security and Access Controls Are Part of AI Data ReadinessSecurity is another area where AI projects can expose risks that already existed in the underlying data environment. Consider an internal AI assistant connected to company documents. The initial requirement may sound straightforward: “Let employees search our internal knowledge using natural language.” But it creates a more important question: Should every employee be able to search every document?If HR, Finance, Legal, Sales, and management documents are placed into one retrieval system without preserving existing authorization rules, the AI can effectively flatten security boundaries that were previously enforced by separate applications. A safer architecture looks conceptually like: User → identity → authorization → permitted data → retrieval → model → answerrather than: User → AI → entire company knowledge baseThe same principle applies to AI agents that can take actions. If an AI system can update CRM records, send messages, query internal databases, approve workflows, or trigger APIs, access controls become even more important.Before development, determine:which datasets the AI can access;what each user should be able to retrieve;whether existing permissions will be inherited;whether the AI can only read information or also change it;which external vendors receive data;what gets logged;how activity can be audited.The core principle should be least privilege: Give the AI only the access it actually needs.Can You Trace Where the Information Came From?Data provenance and lineage become increasingly important as AI architectures become more complex. An AI system may depend on information that has passed through several steps: source application → data pipeline → transformation → warehouse → embedding → retrieval system → modelIf the final answer is wrong, can you trace the problem backwards?Can you determine:which source was used;which version of the information was retrieved;what transformations were applied;when the source was updated;who owns it?Without that traceability, debugging AI outputs becomes much harder. It can also create compliance and governance problems, particularly when third-party datasets or external information sources are involved. The ability to answer “Where did this information come from?” should therefore be designed into the system rather than added later.Data Risk Does Not End When the AI Goes LivePassing the initial data review does not mean the risk disappears. Business data changes. Customers behave differently. Products evolve. Policies are replaced. New regions are added. Data pipelines change. Documents become outdated.That can lead to data drift or a growing gap between the information used during development and the environment in which the system currently operates. A model that performed well at launch can become less reliable even if the model itself has not changed.For production AI, organizations therefore need processes for:monitoring data quality;detecting drift;identifying stale information;reviewing access;correcting bad source data;handling user feedback;updating documentation;removing obsolete sources.Deployment is not the end of data governance. It is the beginning of another phase.Make an AI Data Readiness Go/No-Go Decision Before DevelopmentA pre-build data review does not need to become a six-month governance programme. Its purpose is much simpler: identify the data issues that could materially change the feasibility, risk, cost, architecture, or expected outcome of the AI project before significant development money is committed.A practical review can be structured around six questions:1. Can we trust the data?Is it sufficiently accurate, complete, current, and consistent?2. Can we access it?Can the necessary sources actually be connected without unexpected integration or infrastructure work?3. Does it represent reality?Does the data cover the users, scenarios, languages, and edge cases the AI will encounter?4. Can we legally and safely use it?Are privacy, security, contractual, regulatory, and access requirements understood?5. Do we understand and govern it?Are ownership, definitions, provenance, metadata, and authoritative sources clear?6. Can we maintain it?Can the organization monitor quality, update information, manage access, and correct errors after deployment? The outcome should not always be “go.”Sometimes the right next step will be: AI development → proceedBut sometimes the sequence should instead be: data cleanup → integration → governance → AIAnd sometimes the review may reveal that the use case itself needs to change. That is not a failure of the AI initiative. It is exactly what risk assessment is supposed to uncover.Do Not Make the Data Perfect. Make the Risk Visible.AI creates pressure to move quickly. A prototype can often be built in days or weeks, which makes it tempting to start with the model and deal with data problems later. But a working prototype does not prove that a system can operate reliably at scale.The larger risk is committing to a model, architecture, vendor, budget, and delivery plan before understanding whether the underlying data can actually support them. Before asking “Which AI should we build?”, ask: “What would have to be true about our data for this AI to work safely and reliably?”That question may expose work that needs to happen first. And discovering it before the build is almost always cheaper than discovering it after.

The Expensive Mistake Happens Before Development StartsEvery year, enterprise organizations and scale-ups pour millions of dollars into custom software applications that ultimately stall, exceed budgets, or fail to deliver a clear return on investment. When these projects collapse, the blame is typically assigned to technical execution: bad code, missed milestones, or poor engineering performance.However, looking beneath the surface reveals a different reality. The most expensive mistakes are almost always made before a single line of code is written.The root cause of technical failure is rarely execution; it is strategy. Without a rigorous, objective project risk assessment, leadership teams frequently greenlight massive initiatives based on internal momentum rather than empirical data. They leap directly into choosing how to build a solution before properly validating if they should build it at all.When an organization bypasses early-stage validation, they default to highly risky assumptions. Launching into development without an objective framework sets off a costly domino effect: misallocated capital, distracted engineering teams, and missed market opportunities. To avoid these traps, decision-makers must treat technology acquisition not just as an engineering task, but as a core financial and strategic risk management exercise.Why “Build” is Not Always the First AnswerThere is a powerful, persistent trap in modern business operations: the default assumption that a unique organizational challenge requires a custom-coded software application. It is easy to see why this mindset prevails. Custom software feels bespoke, highly tailored, and prestigious. It signals innovation to stakeholders and promises total control over functionality.However, jumping straight to a custom build introduces massive, long-term hidden costs that rarely appear on an initial proposal. When you choose to design, engineer, and deploy software from scratch, you aren't just paying for the upfront development hours. You are committing your organization to a permanent lifecycle of software ownership. This includes continuous technical maintenance, security patches, compliance updates, and a constant drain on internal engineering resources.Before committing an internal or external engineering team to an extensive roadmap, leadership must ask a fundamental question: Does this application deliver a genuine, defensible competitive advantage, or are we simply reinventing a wheel that already exists? As we often emphasize when assessing early technical roadmaps, learning why saying no is sometimes the best service can save an organization hundreds of thousands of dollars in unneeded development.When to Build Custom SoftwareBuilding proprietary software is the correct strategic path when the technology itself is your primary competitive advantage. If your business model relies on a highly proprietary operational workflow, a unique algorithm, or a core customer-facing product offering that simply does not exist anywhere else in the market, then the decision to build or buy software leans heavily toward a custom build.You should build when:It forms your core Intellectual Property (IP): The software directly generates your top-line revenue or serves as the primary value proposition that differentiates you from your closest competitors.Extreme, non-negotiable customization is required: Your workflows are so highly specialized or subject to strict regulatory frameworks that forcing them into a pre-packaged platform would actively break your operations.You require complete data and UX autonomy: You need total, unrestricted control over the user experience, architecture, and data pipelines for strategic positioning or deep monetization.When these conditions are met, the long-term total cost of ownership is justified by the immense market value and defensibility the software creates.When to Buy an Existing ToolYou do not need to design and manufacture a custom engine when a perfectly optimized, road-tested one is already sitting on the lot. At Sigli, we advocate for the smartest, most capital-efficient path forward, and frequently, our independent evaluation leads us to advise clients not to build custom software.Buying commercial off-the-shelf (COTS) software or subscribing to an established Software-as-a-Service (SaaS) platform is the ideal choice for standardized business functions. Operational utilities like human resource management, standard CRM pipelines, general ledger accounting, and baseline communication tools rarely need to be built internally.You should buy when:Speed to market is paramount: You need the solution operational within days or weeks to capture an immediate market opportunity, rather than waiting quarters for a development cycle.The business problem is a market commodity: Dozens of other organizations face the exact same operational bottleneck, and an entire industry of specialized software vendors already competes to solve it.Product maintenance is outside your core competencies: You prefer to shift the ongoing burden of cloud infrastructure, compliance, data security, and feature updates to a trusted enterprise vendor whose entire business model depends on perfecting that single tool.When to AutomateIn many instances, the optimal solution isn't acquiring a new, complex piece of software, but rather optimizing and linking the capabilities you already possess. Modern automation allows businesses to eliminate inefficiencies without shifting their entire technology infrastructure.For instance, leveraging custom integrations or specialized AI agents for real-world operations offers a practical guide to scaling capabilities without undertaking a massive software redesign. Positioning automation as your primary path is highly practical because it bypasses the friction, retraining costs, and structural risks associated with deploying massive software architectures.You should automate when:The operational bottleneck is manual human labor: Employees are burning high-value hours copy-pasting data between spreadsheets, manually generating PDFs, or routing routine emails.The underlying process is highly predictable: The business workflow follows strict, repeatable, logic-based rules that do not require deep subjective human nuance or emotional intelligence.You require rapid, high-ROI wins: You need to unlock massive operational efficiency and cost reductions in a matter of weeks without disrupting daily staff workflows or altering customer-facing interfaces.When to Integrate FirstBefore signing a contract for a new SaaS application or reviewing an engineering proposal for code development, take a critical look at your existing technology stack. A significant portion of corporate friction doesn't stem from a lack of software, but rather from "software silos", isolated platforms that hold valuable data but refuse to communicate with one another.Instead of engineering a brand-new application to act as a single source of truth, the most elegant, cost-effective strategic pillar is often system integration. Utilizing APIs, webhooks, or dedicated middleware to bridge the gap between your existing ERP, CRM, legacy databases, and communication channels can unlock the exact operational capabilities you need at a fraction of the budget.You should integrate when:Operational data is highly fragmented: Your team is forced to engage in double or triple data entry across multiple legacy systems to complete a single transaction or client update.You possess powerful but isolated software utilities: Your current platforms function perfectly well on an individual basis, but would unlock tenfold value if data flowed seamlessly between them in real-time.When to Clarify or Rethink FirstIf an initial project plan feels ambiguous, if requirements fluctuate week-to-week, or if key internal stakeholders cannot clearly articulate what an absolute success looks like, it is time to hit the brakes. Proceeding straight into procurement or engineering under these conditions means your entire technical framework is built on unverified project assumptions rather than concrete business realities.Taking a step back to clarify or completely rethink your approach is not a failure; it is a vital, risk-mitigating strategic pause. This is particularly true for hype-driven tech initiatives; understanding when AI is the wrong answer highlights exactly what businesses should focus on fixing internally before investing in complex new solutions.You should clarify or rethink when:Scope creep manifests before a kickoff: The list of requested features keeps expanding rapidly before a single prototype has been validated.Direct end-user validation is entirely missing: You have not conducted objective user testing or gathered empirical proof that your internal staff or target customers will actually adopt the software.The projected ROI is fundamentally vague: You cannot clearly tie the financial investment of development back to a measurable reduction in operational costs or a direct acceleration of business revenue.The Practical Pre-Build Risk ChecklistTo ensure your leadership team does not fall victim to a misaligned, overly expensive technology strategy, run your next proposed initiative through this practical technology risk assessment checklist before signing off on the budget.How Sigli HelpsNavigating these technical pathways objectively can be incredibly challenging from inside an organization, especially when internal biases, personal preferences, or development momentum push for a specific custom outcome. That is exactly where Sigli steps in. We act as your independent technology partner, providing an unbiased software project audit to thoroughly stress-test your ideas before you commit substantial capital.We do not operate with a default bias toward pushing custom builds. Whether your ideal roadmap involves tailored software development, advanced business process automation, strategic AI implementation, or modernizing legacy tools through seamless system integration, our singular priority is safeguarding your technology investments. We help you navigate the complex question of how to choose a software development partner by utilizing an upfront Discovery Service to ensure you are solving the right problem with the right framework.Check Your Build Risk Before You CommitUnsure whether your next organizational move should be to build, buy, automate, or completely rethink your software plan? Avoid the catastrophic pitfalls of unvalidated development pipelines and turn early-stage uncertainty into a clear, highly actionable, risk-mitigated technical roadmap.Before you sign off on the proposal or approve the build, ensure your architecture, strategy, and budget are completely bulletproof.👉 Check Your Build Risk Before You Commit

The math of the startup world is brutal: 99% of new ventures fail within 36 months. Most of the time, it’s not because the tech is bad or the team is lazy. Quite often, it happens because they are trying to solve a problem that nobody actually cares about.In this episode of the Innovantage podcast hosted by Sigli’s CBDO, Max Golikov, Raimonds Kulbergs, founder of BADideas.fund, shares his journey from building companies to backing them. Apart from this, he also explains why early ideas are often wrong in their first form, and why the next wave of venture capital will look very different in the age of AI.From corporate life to founder mindsetBefore becoming a full-time investor, Raimonds was a three-time founder with one early exit behind him. But his path did not start in startups. It started in consulting.He worked at Ernst & Young and Deloitte, advising clients across the Baltics and Ukraine. The work gave him exposure to buying and selling businesses, and to how companies operate at scale. But he understood that he was not making the decisions himself. In fact, he was helping others make them.Over time, that became uncomfortable. He wanted to be in the driver’s seat: to build, to decide, and to take responsibility for the outcome. Eventually, he reached a point where staying in consulting felt safer than leaving it. And that was exactly why he left.Kulbergs’ first startup focused on a very specific problem: helping universities improve how they collect donations from alumni.Like many first-time founders, Raimonds quickly discovered how different building is from advising. Mistakes came fast. But thanks to this experience, he could understand how startups actually fail and what it takes to survive the early stages.At one point, Raimonds joined the 500 Startups accelerator (now 500 Global) in Silicon Valley. The program gave him frameworks, tools, and exposure to how top founders think. But applying those lessons was not immediate. Real startups rarely follow frameworks cleanly.Kulbergs also worked on a few smaller projects. One was a collaboration with a Baltic bank to build a Klarna-style product for the region. Another involved a marketplace model developed together with a co-founder of Pipedrive in Estonia.These projects were shorter in scope, but they added more layers to his experience as a builder.A crossroads and a longer viewAt a certain point, Raimonds began thinking more about his next chapter not in months, but in decades. He started evaluating his career in ten-year blocks, asking a simple question: “If money were not a constraint, what would actually be worth doing?”The answer was not more startups of his own. He decided to help founders avoid mistakes and guide them through uncertainty.Startup investing became the natural path forward. It allowed him to stay close to founders, but across many more companies than he could ever build himself.There was just one problem: credibility.In venture capital, track record matters. But building a track record requires capital, access, and a structure that usually does not exist at the beginning. While working in Silicon Valley and later in Boston, Raimonds noticed a different model emerging. Founder-led communities were pooling resources and investing together.That idea did not seem common in Europe at the time.Building a founder-led fundBack in Europe, he saw an opportunity. Instead of waiting for a traditional VC path to open, he decided to build something closer to what he had seen in the US.He decided to bring experienced founders together, let them invest collectively, and learn from each deal as a group.There was also a personal motivation behind it. He wanted to be surrounded by people who were more experienced in certain aspects of company building. In that environment, the group as a whole could see better deals and make better decisions.That thinking eventually led to the creation of BADideas.fund.It began as a collective of founders who decided to back other founders. The idea was simple: people who have built companies understand other builders better than most institutional investors.Why founders actually joinBuilding a startup can be isolating. Founders are constantly under pressure, and most do not have time to connect with others in the same situation. Yet that connection is often exactly what is missing.The goal became to create a space where founders could share what they are building, learn from each other, and spot emerging ideas early.Over time, the fund became less about investing alone and more about exchanging experience.What started with around a dozen founders has grown into a community of roughly 250 members across 20 countries.Many of them have built or scaled well-known companies, including HubSpot, Shopify, Dropbox, Uber, Bolt, Vinted, NordVPN, Pipedrive, Printify, and Printful.Why startup investing is mainly about portfolio thinkingAccording to Raimonds, startup investing only works when it is treated as a portfolio game instead of a set of isolated bets.In early-stage investing, concentration is the most common mistake. Many angel investors back a few startups heavily, then lose momentum. Capital runs out, patience fades, and deal flow slows down. But returns do not come from a handful of bets. They come from spreading risk.Statistically, moving from a portfolio of 10 startups to 20 can significantly improve outcomes. More exposure to high-upside outcomes increases the chance that one or two companies drive returns for the entire portfolio.However, diversification only works if the input is strong.If the quality of startups being evaluated is low, even a large portfolio will underperform. That is why deal flow matters just as much as capital allocation.There are two main paths to access it. One is to build a personal brand that attracts founders directly. The other is to join a network or community where high-quality deals are already being sourced and filtered collectively.Meanwhile, Raimonds sees startups as one of the most compelling asset classes if approached correctly.Unlike public markets, most of the value creation happens before companies ever reach IPO. By the time companies are listed, much of the growth has already been captured by venture investors.How to win startup fundingIn early-stage investing, the product is not the main factor. Neither is traction, nor market size, nor the current version of the idea.The team is. In the beginning, most startups are incomplete. The idea will change. The market will shift. The product will evolve. What matters is whether the founders can navigate that uncertainty and still build something meaningful.Raimonds described strong founders as people who are not fully comfortable with the status quo. There is usually a certain restlessness, which is an internal push that things can be done better or faster.He also looks for patterns in how founders process challenges. Personal history, including difficult experiences, can become a source of resilience if it has been reflected on and turned into a strength rather than avoided.Integrity is another important point, as well as the ability to articulate ideas, which is a form of influence.In BADideas.fund’s evaluation process, the team accounts for roughly half of the decision.Building for the future According to Raimonds, strong founders do not build for the present. They try to extend their thinking 12 to 18 months into the future. This often leads to ideas that do not make sense right now and may not generate revenue immediately.But if the timing is right, being early becomes an advantage. The first movers are often the ones who define the category before others even notice it exists.Building a startup today is significantly easier than it was 10 or 20 years ago. Infrastructure barriers have dropped. Tools are widely available, while distribution channels are more accessible.What remains difficult is understanding the customer.Instead of relying on heavy infrastructure or large teams, founders now win by being close to users. That can mean simple but consistent actions: talking to customers directly, observing how they work, or spending time inside their environment to understand real pain points.A year wasted on the wrong customers: Lessons learnedOne of the biggest early mistakes that Raimonds made in his work was conducting customer discovery too late (and doing it poorly).At the time, he believed his idea was obviously needed. He invested his own savings, built financial projections, and convinced himself the path to profitability was clear. On paper, everything worked.In reality, he had never properly validated the assumption.He spent nearly a year targeting universities in continental Europe, assuming they needed a better way to raise alumni donations. It seemed logical, but it was wrong. Leadership simply did not see it as a priority.If he had spoken directly to decision-makers earlier, he would have learned that much sooner. Once he realized the mistake, he pivoted. If continental Europe was not the right market, he moved to the UK, where universities had a stronger culture of alumni giving.That shift worked. He landed institutions like Oxford and Cambridge colleges, and London Business School. But the market was too small.The US had both the scale and the culture for large-scale university donations. The problem was access.Without a strong network, entering that market was difficult. That is what eventually led him to an accelerator like 500 Startups and a move to Silicon Valley. From there, he was able to land customers such as Georgetown, West Point, and UC Berkeley.The hidden risk after raising moneyFundraising brought a different kind of mistake. Like many founders, he experienced the post-raise high. He believed that everything had finally clicked. Without clear milestones, it is easy for a startup to drift.Founders need to work backwards from the next major milestone: what has to be achieved before the next round, what signals will attract strong investors, and what needs to be true to hire great talent.You don’t need top accelerators anymoreRaimonds does not believe that joining a top accelerator is essential for startup success today.Accelerators are still useful, but they are not as unique as they once were. The venture ecosystem has changed, and so have the tools available to founders.Despite its strengths, the traditional VC model has clear limits. It is constrained by time and human bandwidth.This is exactly where innovation is now possible, especially with the rise of AI and better tools for sourcing, analyzing, and supporting startups. BADideas.fund is now exploring this direction.Instead of relying solely on traditional partner-led workflows, the idea is to build systems that help process deal flow, support decision-making, and extend what a small team can do.Why most VCs may become irrelevantVenture capital firms without a real community layer will struggle to stay relevant.Capital alone is no longer enough. If you compare two funds, one that only deploys money and another that combines capital with a strong founder community, the second will consistently win.Startups do not just need funding. They need access to experience and real-world context from people who have already built companies.Founders want stories, mistakes, and honest guidance from people who have actually been through it. As more of the world becomes automated, relationships become more valuable.The hard part now is matching the right founder with the right experience at the right time. In practice, this is a context problem: understanding what a startup is struggling with, and knowing who has actually solved something similar before.If done well, this model could outperform traditional venture funds.Corporate experience vs startup mindsetCorporate culture, according to Raimonds, often slows companies down, but it also creates the conditions that startups later exploit. Large organizations tend to move cautiously, which opens space for faster teams to disrupt them.At the same time, his own corporate background gave him a useful foundation, such as decision-making frameworks and an understanding of how larger systems operate.Meanwhile, today, too many founders still start with small, incremental ideas. Even in strong technical ecosystems like Ukraine or Central and Eastern Europe, Raimonds often sees startups built around safe concepts rather than bold ones. A calendar app can become a company, but it is not where transformative outcomes begin. Founders who spend time in ecosystems like Silicon Valley tend to think differently. Being around people working on aggressive ideas naturally raises the baseline of what feels possible.For Raimonds, returning to Silicon Valley regularly is about reading the mood: what founders are building, what problems they are chasing, and how the ecosystem feels emotionally.For example, at a recent SaaStr conference, the message from experienced operators was that the old growth expectations in SaaS are no longer enough. Investors are increasingly focused on extreme outliers. Quite often, these are companies with the potential for massive, category-defining growth. Now, attention and capital are concentrating on a small number of breakout AI-driven companies. Everything else competes for less funding and less focus.The future of VC is community plus AIIt is easy to underestimate how fast AI is progressing. Even tools like ChatGPT feel imperfect in the moment, but compared to a year ago, the progress is already dramatic.In tech right now, founders often hold two conflicting emotions.On one hand, everything feels possible. Building has never been easier.On the other hand, there is a constant sense of being behind, like the world is moving faster than you can keep up.Raimonds believes the next 18 to 24 months will be one of the most intense periods in tech history. In speculative scenarios like “AI in 2027,” researchers imagine systems equivalent to hundreds of thousands of PhD-level minds operating continuously, pushing the limits of what companies can do. Even if it takes five or ten years, it is still a very short horizon in historical terms.However, Raimonds does not think startups need to brand themselves around AI. In fact, he believes most should not.AI is not a category anymore. It is a layer that should already be embedded in how companies build, design, and operate.Developers use coding copilots to move faster. Designers use tools like Lovable to prototype in minutes. The advantage is not in having AI, but in how naturally it is used across the workflow.Role of experimentsSmall teams can move quickly because they have no legacy systems, no heavy feature sets, and no long-standing customer expectations. Everything is flexible.But once a team reaches 20 or 30 people, complexity builds as there is more pressure and less time to experiment. At that point, even when urgency increases, experimentation often decreases.The solution is a small but consistent effort. Even a short daily block of time spent testing new tools or workflows can compound over time. Without it, teams risk slowly falling behind while thinking they are still operating efficiently.Top 3 things to know before founding a startupMost early mistakes come from skipping the basics. In his conversation with Max, Raimonds shared the following practical tips.Do real customer discovery. Ignoring this step usually leads to building something nobody truly wants.Don’t chase revenue too early. Early sales can be misleading. They often push founders to optimize for short-term income instead of long-term product-market fit.Dream big enough and think about the future. Strong founders combine two things: big ambition and deep immersion in the problem space.It may look too time-consuming at first, but it prevents much bigger mistakes later. Nevertheless, second-time founders tend to do this better. They slow down earlier, validate more carefully, and avoid premature scaling.Want to find out more insights from business and tech experts? That’s exactly what you can expect from the Innovantage podcast. Stay tuned for the next episode!‍

When eCommerce growth slows down, the storefront often gets the blame first.If conversion rates dip or user engagement drops, teams frequently assume they need a cosmetic redesign, a new CMS, a better product detail page, or a complete replatforming project. Sometimes, they do.But more often than not, the real bottleneck is hidden safely out of sight:Backend logic has become too fragile and complex to change safely.Legacy integrations frequently drop data or fail under minor load spikes.Technical debt slows down code releases from days to weeks.Performance issues require constant firefighting rather than systematic fixes.The engineering team spends more time maintaining what exists than building what is next.The Reality Check: The storefront is where customers see your brand. The backend is where your business feels the actual cost of growth. Before changing what your customers see, you need to check what your engineering team is fighting behind the scenes.Why Backend Problems Quickly Become Business ProblemsBackend modernization is rarely just a technical preference or a cleaning exercise for developers. It is a direct driver of business agility.When your backend architecture no longer fits your operations, it creates an invisible tax on your entire organization. A slow, fragile backend does not just frustrate engineers; it paralyzes your business roadmap.When every new feature requires complex workarounds, when minor bug fixes take weeks to deploy, and when external integrations become impossible to maintain, the company loses market velocity exactly when it needs to scale.The Ripple Effect of Backend Drag:Feature Delivery Speed: New market opportunities are missed because the platform cannot support rapid deployments.Customer Experience: Latency at checkout, inaccurate inventory syncs, and broken user flows directly damage brand trust.Platform Reliability: High-traffic events (like seasonal sales) turn into high-stress infrastructure panics.Operational Cost: Support workloads skyrocket as customer service agents manually resolve issues caused by system exceptions.6 Signs Your eCommerce Backend Is Slowing GrowthHow do you know if your platform is suffering from backend drag rather than frontend friction? Look for these six distinct symptoms:Sign 1: New features take longer than they shouldThe business wants to launch a new loyalty tier or a localized checkout option, but the engineering estimate comes back at six months. Each change requires a massive effort because the code lacks modularity.Sign 2: Small changes create unexpected bugsYou deploy a minor update to the shipping calculator, and suddenly the checkout page stops processing payments. This whack-a-mole pattern points to high technical debt, hidden dependencies, or a fragile architecture.Sign 3: Developers rely on workaroundsThe system works, but only because the team knows exactly which parts of the codebase not to touch. New features are bolted onto the side using custom scripts or fragile middleware rather than being integrated properly.Sign 4: Performance problems appear during growthThe site runs fine with average traffic, but more customers, expanding product catalogs, or concurrent API requests expose severe architectural limitations. Response times slow to a crawl when data volumes scale.Sign 5: Integrations are difficult to maintainConnecting a new ERP, CRM, or inventory management system feels like open-heart surgery. The backend is not designed to support clean, reliable, and secure data exchanges.Sign 6: Support and development teams keep solving the same issuesThe same data synchronization errors or checkout bugs recur month after month. Recurring bugs mean your team is treating the symptoms rather than fixing the root structural cause.Why a Redesign Will Not Fix Backend RiskIt is tempting to throw a beautiful new interface over a struggling system. A slick frontend redesign can temporarily boost user metrics, improve initial page loads, and make the platform feel modern to stakeholders.However, a better interface can only hide backend problems for a while. It cannot remove them.A frontend redesign will not solve slow release cycles, fragile business logic, weak integration architecture, or unpredictable data flows. If your underlying platform is structurally compromised, a new frontend is simply an expensive coat of paint on an unreliable engine. You will still face the same delivery delays, the same high maintenance overhead, and the same scaling limits.Case Example: Modernizing the System Behind a No-Code eCommerce PlatformAt Sigli, we prioritize identifying whether backend risk is the actual blocker before recommending any major development work.We partnered with a No-Code eCommerce Website Builder facing this precise challenge. From the outside, the product functioned as intended, but the underlying platform needed to evolve to support rapid growth. The challenge was not cosmetic; it was about platform stability, capability, and engineering velocity.The Practical Approach:Instead of suggesting a risky, ground-up rewrite, the focus shifted to targeted, risk-led modernization:Backend Refactoring: Cleaning up core modules to eliminate technical debt and clear out legacy dependencies.Functionality Expansion: Restructuring the architecture so new features could be integrated cleanly without breaking existing components.Workflow Optimization: Overhauling internal development processes to reduce friction around bug fixes and deployment cycles.The Result: The client achieved faster, significantly more reliable feature delivery and reduced the burden on customer support. The system became easier to maintain and scale because the core backend risk was mitigated systematically.Broadening the Scope: In a similar engagement involving the Unification of a Complex Suite of Software Products, the risk was not feature development but fragmented architecture. Separate codebases and duplicated logic were driving up maintenance costs. By consolidating the underlying architecture, the business cleared away massive operational overhead and stabilized its roadmap.What to Check Before Starting Backend ModernizationBackend modernization should never begin with a blanket directive to "rewrite everything." Blanket rewrites introduce massive delivery risks and rarely solve the immediate business problem. Instead, modernization should start with a practical risk review.Before committing resources, ask your technical and product leadership these core questions:Which specific features are currently the hardest and most expensive to deliver?Where do bugs repeat most frequently in the system?Which parts of the codebase do developers openly avoid touching out of fear?Which external integrations or APIs create the most friction and data drops?Where exactly does platform performance begin to break down under load?What specific technical debt is actively blocking the immediate product roadmap?What components can be refactored or optimized instead of completely rebuilt?Modernize, Rebuild, or Leave It Alone?To avoid expensive technology mistakes, you need a pragmatic strategy for different parts of your platform. You can use this simple framework to evaluate your next steps:How Sigli Helps: Challenge the Brief, Deliver the SystemWe do not believe in taking a project brief blindly and writing code. We help technology and business leaders challenge their assumptions to ensure they are solving the right problem before investing capital.When partnering with Sigli for backend modernization, we help you:Isolate the Real Risk: Diagnose whether your primary blocker is frontend friction, backend architecture, or internal delivery workflows.Scope Pragmatically: Decide precisely what needs to be modernized, what should be completely rebuilt, and what should be left alone to preserve budget and timeline.Deliver Predictable Engineering: Execute targeted backend improvements that directly enhance delivery speed, platform maintainability, and scalability.Empower Internal Teams: Provide clean, well-documented codebases and modern deployment workflows so your team can maintain velocity long after launch.We focus on minimizing technology mistakes so you can deliver working systems that predictably support business growth.Is Backend Risk Slowing Your Roadmap?Before you invest in an expensive new frontend, a complex CMS, or an unpredictable platform rebuild, get a practical second opinion.[Schedule a Backend & Platform Risk Review with Sigli Today]

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

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

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

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