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reduce maintenance costs artificial intelligence put your data to work we integrate machine learning models to automate your complex tasks and sharpen decision making digital systems that work for your business our approach brings clarity usability and technical precision to every stage from discovery to design to delivery strategy set a clear direction and reduce project risk with a discovery phase that aligns business goals user needs and technical execution design deliver intuitive effective systems through user centered design that supports adoption and operational efficiency development deliver scalable purpose built software to meet complex requirements evolving needs and seamless operational integration we re a proud canadian company founded and based in canada we are proud to be a bilingual company with experts across the country we work in both french and english and give you direct access to local experts who understand your business environment we build software grounded in your context with smooth accessible collaboration that keeps communication clear and seamless execution contact us our work for over 20 years we ve been supporting industry leaders in bringing their technological ambitions to life proud to be a part of our clients successes here s a glimpse at a few of their stories discover fireprocare manufacturing transformation of fire pump controller maintenance discover cominar real estate digital acceleration of the cominar privilège program discover insurance client insurance finance overhaul of a custom cyber liability underwriting platform discover aseq studentcare insurance finance modernization of a student insurance coverage platform view all projects what they say about us with the business analyst diligently understanding our business requirements and then applying their technical expertise and skill to develop the product we felt spiria was not just a partner but a helpful advisor in developing our new platform michelle chen business process director timber mart spiria showed professionalism transparency and flexibility throughout the entire project constant communication listening and proactive follow ups have resulted in a great deal of mutual trust in addition the reliability and quality of their work have been crucial to our success quentin mosquet director operational excellence tornatech spiria is a creative critical thinking partner that gave us a solution on time on budget and it achieved the outcomes we needed freda molenkamp oudman general manager alberta milk having the spiria team as a software development partner truly helped our ability to design and implement a modern and scalable saas solution without compromising strict timelines and compliance requirements kam lee vice president software development deployment and security aifred health your career at spiria spirians are more than just strategists designers and developers they are agile problem solvers who thrive on challenges and of course technology at spiria you can expect a culture where collaboration curiosity and innovation drive meaningful work discover life at spiria spiria s blog step into our world a hub of stories insights and strategies on all things tech business and of course our culture read article strategy 5 min read the biggest risk in your custom software project is not technology as enterprise technology investments have grown by an average of 8 per year since 2022 mckinsey 2025 one reality remains not every digital transformation project delivers a return on investment roi a boston consulting group study reveals that 70 of digital transformation projects fail to meet their objectives often with serious consequences that same study however underscores just how valuable these investments can be when done right so where do things go wrong rarely at the product artificial intelligence ai or software development level itself the success of a digital transformation is determined well before the first line of code it s mainly based on the strategic planning of the project our thesis a custom software development project is above all a business project so yes strategic planning is critical to reduce risk and ensure your next digital transformation project becomes a growth driver take a moment to debunk 5 myths that could potentially derail your next it project 1 success depends mostly on making the right technology choices technology choices do matter but the greatest risk in a project is rarely technical it s organizational unclear objectives misalignment and low team buy in cause more failures than the choice of language or infrastructure ever could technology is the tool used to address a business challenge not the answer in itself confusing the two means building a solution that works technically but solves nothing concrete the reality a software project is first and foremost a business project enterprise software isn t successful because it works on a technical level it s successful because it simplifies work creates tangible value for your business and is actually adopted by your users 2 we can start coding and adjust later analysis is just a waste of time what s deceptive about application development is that changes can always be made but every change comes at a cost and the later in the process it occurs the higher that cost tends to be jumping into development without a defined foundation is a straight path to scope creep and cost overruns yes projects evolve but every structural change midcourse has significant downstream impacts on budget and timeline the analysis allows you to anticipate critical constraints identify real user needs and validate key assumptions the reality identifying core requirements and critical dependencies during the analysis also called the discovery phase reduces project risk starting development without this ground work is like building a house without blueprints you can always knock down a wall but it costs a lot more than erasing a line on a plan 3 artificial intelligence will make planning obsolete the risk here doesn t lie in your software architecture it comes from your decisions from the very first version to a final product artificial intelligence can generate features at record speed but it cannot define your business strategy the more production capacity increases the more precisely your roadmap needs to be defined the reality ai cannot anticipate the critical dependencies between your various systems nor ensure that the tool will actually address the real pain points of your teams on in your factories on the field or in your office 4 we d rather payfor off the shelf software it s cheaper than custom if an off the shelf solution perfectly meets your needs it s probably the smarter choice and we ll be the first to say so but when the solution touches the core of your operations what sets you apart from your competitors your secret sauce the math changes entirely a well built custom software solution approached as a business project becomes an investment especially if it centralizes your processes replaces several existing tools and saves you time and money in the long run in many industries there are also hybrid approaches to software that combine existing solutions with custom development via apis targeting only what creates unique value for you the reality it s not a question of custom versus off the shelf it s a question of alignment with your business objectives ask yourself whether the solution addresses a challenge that sets you apart whether it integrates into your environment and whether it saves costs time or enables something your competitors can t do that will give you your answer 5 delivery marks the end of the project software is a living asset and its deployment is not a finish line it s the moment when users take ownership of the solution when new learning begins and new ideas emerge this is especially true knowing we rarely aim for the perfect solution right out of the gate you ship a first version addressing core needs like a house once built it needs ongoing maintenance and improvements to preserve its usefulness and value underestimating adoption support and evolution costs in the initial business case can be a major mistake the reality plan for an annual software maintenance and enhancement budget based on the level of risk you re willing to carry once the solution is in place it needs to be maintained to preserve its value and relevance these 5 myths share a common thread they shift attention toward where risk is lowest and away from where it s highest you invest in technology pick the right tools kick off development all to discover along the way that objectives were vague that users were never consulted or that the solution is solving the wrong problem worth repeating 70 of digital transformation projects fail to meet their objectives bcg 2020 not because the technology fell short because strategic planning wasn t taken seriously a software project is above all a business project and even your sharpest colleagues and managers could fall for these misconceptions why not share this article with them so your next project lands in the 30 that succeed read article artificial intelligence 5 min read human first modernization the silent accelerator of ai artificial intelligence ai promises a lot but it s true potential often remains untapped why well not because technology lacks maturity but because we forget that real world use by people remains the real driving force behind any digital transformation and yes no one understands humans better than humans this is where the human first approach comes in a simple philosophy that places people at the heart of technological changes and thus designing tools and systems that genuinely work for the individuals who use them the goal isn t to strengthen the technology itself it s to strengthen our understanding of the work your teams do before adding a layer of artificial intelligence on top of it you can have the most advanced systems in the world but if your teams aren t ready nothing will move forward at spiria we see it every day successful organizations don t just modernize their infrastructure they modernize how their teams interact with their tools their data and their workflows they lay the groundwork for ai to become useful understandable and sustainable this article explores how human centred software modernization naturally prepares organizations to welcome ai that integrates itself smoothly supports people and enhances their work for long term ai success modernizing for ai starts with modernizing for humans modernizing first then wondering how to drive ai adoption internally how many organizations have made this mistake the desire to integrate ai is natural it represents progress innovation and efficiency but in reality it often struggles to fit into environments that were never designed to support how teams actually interact with them the result these systems that seem modern on paper remain underused the teams using them are frustrated and bypass these new tools to return to old habits it s a disappointing roi that leads leadership to question ai itself in our previous article why legacy systems break under ai pressure we explored the technical obstacles of fragmented data rigid architectures and technical debt but these technical obstacles are only part of the problem the other part the one we overlook far too often lies in human and organizational silos that s why a people centered approach is essential modernization must be guided not only by technical requirements but by the people who depend on these systems every day it s about clarifying simplifying and streamlining the experience so tools become coherent easy to use and aligned with day to day work ai only creates value when it enters an environment teams already understand modernizing for ai therefore means modernizing for humans first the three foundations of human readiness clarity trust collaboration before diving any further let s remember one key thing human first is the approach while human ready is the outcome in other words an organization reaches this state when modernization is intentionally designed for people and put into practice these pillars are the foundation for this 1 clarity clarity translates to making systems readable workflows understandable and tools intuitive it is about operational transparency not technical transparency what data does ai use why does it recommend one action over another what are its limits teams need to understand what a system does how it does it and why it does it clarity reduces uncertainty and opens the door to natural confident use of ai it helps users know when to trust the algorithm and when their own judgment should take the lead 2 trust trust is the invisible core of any technological adoption it grows gradually but it starts with evidence building trust in ai requires reliable systems consistent results and tangible improvements in day to day work people need to see that ai simplifies their work rather than complicates it and that it respects operational realities instead of ignoring them ongoing training is crucial not only at the beginning of a project but over time giving teams the space to explore ask questions and build technological intuition when trust settles in ai becomes genuinely useful 3 collaboration collaboration is what brings everything else to life ai projects rarely fail because of algorithms they failed because people weren t involved early enough preparing teams to collaborate with ai means understanding their real pain points their critical decisions and their operational constraints this knowledge is what allows ai to find its appropriate role in the workflow ai can optimize a process but only humans can understand nuance context and intent this complementarity is where its true value lies from ai ready to human ready two concepts often confused many organizations aim to become ai ready with upgraded infrastructure centralized data and new intelligent tools but none of these matter if people aren t ready to use them being human ready is different it is the outcome of a human first approach it means having simple systems clarified processes and tools that match real world usage it means creating an environment where teams understand the technology trust it and can apply it with discernment and this people preparation must come first too many organizations treat modernization as an isolated it initiative they invest millions in new infrastructures without ever questioning user experience but what is the point of a high performance system if no one wants to use it organizations that succeed with ai don t just deploy new tools they prepare their teams to integrate them into daily practices even before the first deployment they adjust processes early they clarify roles and responsibilities before ai arrives they build trust during the design phase and do not...