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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 wait for the first setbacks they invest in sustainable organizational transformation where humans remain at the centre of decision making from day one what if the key to ai success was simply people human first modernization isn t a trend it s a working philosophy based on a simple truth long term performance is built on strong human foundations not solely on advanced algorithms modernizing means creating systems that are clearer more reliable and more people centered systems capable of evolving at the pace of the people and organizations they support at spiria this belief guides our approach to modernization and ai integration projects we enable organizations to build custom solutions that empower people so ai can truly deliver on its promises because the foundation of ai success lies in an approach that makes artificial intelligence useful sustainable and deeply human what if the best way to succeed with ai was simply to put people back at the heart of modernization read article strategy 5 min read why preparation defines project success how many companies start their modernization project thinking i know exactly what i want why pay for an analysis if you ve heard this in your organization or if you ve said it yourself you re not alone but it s a bit like telling an architect i need a house just start digging tomorrow morning obviously we d never build our dream home without detailed plans soil studies or permits yet that s exactly what we do with our business software systems which are worth thousands of dollars funny logic isn t it so how do you move forward with clarity rather than blindly to ensure good strategic preparation two points make all the difference defining your business objectives and understanding your end users defining your business objectives we already know what we want are you sure often what we think we want is only part of the answer and this certainly can be costly here are some common truths often underestimated in most modernization projects what s hiding under the hood of your systems your applications don t live in isolation they exchange data through applications programming interfaces apis that no longer run smoothly share databases that are sometimes disorganized and rely on small fixes made by your teams that no one currently documents in this context choosing to modify an element of your software without first mapping these interdependencies is like playing jenga with your infrastructure a thorough audit of your systems will reveal these hidden links and help anticipate ripple effects the real hidden costs of migration the price of new technology is never just the license team training data migration integration testing the ramp up period all these indirect costs can often represent a large portion of the total project budget by identifying them from the start we transform unpleasant surprises into controlled budget lines the real impact on your business processes changing systems often means changing how people work your teams have developed countless workarounds to bypass bugs speed up processes and create shortcuts to compensate for current limitations over time these habits become invisible until they stop working taking the time to understand the human impact makes all the difference in ensuring a successful transition when ai reveals everything we preferred to ignore added to all these challenges is artificial intelligence ai which can in some cases complicate the equation wanting to just add ai to a system that s poorly prepared will amplify all your existing issues ai requires clean structured governed data otherwise it will instantly reveal inconsistencies duplicates and obsolete formats without solid foundations ai becomes a problem magnifier rather than a driver of efficiency how do you see more clearly the key a structured approach that leads to project success start by taking time to analyze your entire ecosystem collaborative workshops with all stakeholders not just it then map real data flows audit hidden technical constraints and most importantly define real business objectives with measurable success indicators the result you move from we think that to we know that with a roadmap that anticipates obstacles instead of discovering them along the way at spiria our teams of business analysts developers and designers don t just deliver a plan they join you as technical partners to map your entire ecosystem reveal invisible dependencies and build a roadmap aligned with your business objectives understanding your end users ux ui our teams will adapt is it that simple big trap focusing everything on technology and budget while forgetting those who will actually be using the solution your users their realities are quite different as are their expectations a project that neglects this dimension runs a major risk delivering a system that s robust on paper but unused in practice and obviously a tool that isn t adopted is a wasted investment the 4 pillars of user adoption 1 moving beyond assumptions too many projects start from what leaders think their teams do rather than what they actually do big difference real behaviours real frustrations and real usage contexts only emerge through direct observation and in depth interviews 2 designing the architecture the way your team thinks your users aren t looking to admire your interface they want to accomplish their tasks efficiently organizing features according to their real objectives will transform navigation into a smooth journey indeed what sets good information architecture apart is that it becomes invisible to the user meaning that they find what they are looking for without having any effort 3 prototyping testing before building having real people test interactive prototype s reveals necessary adjustments before they become very expensive to correct whether it s a misplaced button a process that s too long or confusing terminology the prototype will help avoid weeks of redevelopment 4 ai serving the user not the other way around even the most sophisticated artificial intelligence fails its mission if it doesn t match users real habits and constraints an approach centered on their workflow will ensure that ai amplifies efficiency rather than creating new frustrations how do you ensure adoption the key by immersing yourself in field reality from the start not at the end understanding why your users take certain shortcuts identifying their real challenges observing the context in which they work quiet office noisy open area office constant travel this detailed understanding guides each design choice toward real adoption rather than assumptions the result a solution with better adoption rates that protects your investment rather than wasting it at spiria during our in depth analysis phase discovery phase our ux ui design teams dive into your field reality conduct interviews with your various departments observe real journeys and transform pain points into measurable design criterias the foundation of lasting success modernizing on fragile foundations is like building a house directly on the ground without a concrete slab it holds until the first storm companies that invest in serious preparation deliver faster and see their teams naturally adopt new tools in 2025 ai accelerates this logic it amplifies what exists solid foundations become a powerful lever fragile foundations create complications this reality makes preparation even more important yes preparing well requires more time and initial investment but you need to consider the total cost companies that prepare rigorously avoid data flow redesigns successive corrections and months of post launch adjustments the upfront investment pays off quickly when you don t have to rebuild everything six months later while your competitors are correcting their pl...
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