Top 7 Signs You Chose the Wrong Software Vendor
You found a software vendor and started working alongside them. The collaboration went well in the initial weeks, but lately, it’s becoming increasingly frustrating. The cost of choosing the wrong software development company isn’t limited to blown deadlines; it extends to security breaches, rework, downtime, missed opportunities, technical debt, wasted investment, and, sometimes, even reputational damage. Wondering if you’ve partnered with the wrong software vendor? Here are seven critical red flags that can confirm if your project is off track. Checklist – 10 Questions Enterprise Teams Should Ask Before Finalizing a Software Development Partner Critical IT Vendor Red Flags to Watch Out for 1) Poor communication & Lack of Transparency One of the critical factors for successful collaboration is proper communication. If your vendor shares vague updates or, even worse, doesn’t share updates until you ask the team, delays responses, or doesn’t clear queries regarding the project, your project is at significant risk. Also, if you don’t have access to the source code repository or prototypes, the vendor may be hiding a lack of progress. What partners do instead: Software development partners ensure proper communication throughout the project. From the smallest challenges and highest-level architectural shifts to budget risks, they keep you updated about every aspect of the project, allowing you to make necessary strategic decisions. 2) Lack of Proper QA It’s important to deliver the project on time, but it must not come at the cost of thorough testing & Quality Assurance (QA). If the vendor consistently delivers features or applications with bugs or glitches, it indicates that the software development vendor is rushing with the delivery without a solid QA protocol. Such features or applications will require rework, which can result in additional investment. In addition, buggy features or applications can breach security protocols, resulting in legal consequences and penalties. What partners do instead: They take full accountability for the technical outcome, fix the issue, and also perform root-cause analysis on bugs to prevent them from recurring. 3) No Clear Documentation Another red flag to look at in an IT service company is its documentation process. If the IT vendor fails to hand over clean, documented code, you are effectively locked into the vendor even after the project ends. This is because, without access to the code or other technical aspects of the project, your internal team will not be able to fix any future issues or even release an update on its own. What partners do instead: Engineering partners focus on knowledge transfer rather than solely on closing deals. They deliver scalable code and documentation, equipping you to adapt and optimize as your business evolves. 4) Overpromising Results & Underquoting Costs While navigating the deal, the vendor assured to deliver exceptional results within a short period and at a lower budget. The deal sounded exciting, but as the project kicked off, you began to encounter unforeseen complexities, resulting in a budget overrun, features or applications that did not function as anticipated, or even an extended delay in project delivery. What partners do instead: Software development partners avoid setting unrealistic expectations to secure a deal. They offer honest feedback on your roadmap, even if it means telling you that a feature is not feasible within your timeline or budget. This transparency prevents unsustainable projects and supports quality delivery. 5) Lack of Ownership You chose the vendor, assuming you would receive quality outcomes. Instead, your internal team now spends most of its time fixing its bugs and addressing technical gaps. When you raise concerns, the vendor denies ownership and begins treating your project requirements as a checklist. What partners do instead: Engineering partners take accountability for the technical outcome. Rather than following a checklist, they evaluate why a particular technology or platform is ideal for your project. When challenges arise, they put a foot forward to resolve the issue. 6) Lack of Customization Rather than catering to your unique business needs, the software vendor is forcing a “one-size-fits-all” framework. Instead of gaining a competitive edge, you now have increased technical debt and require constant rework to improve your application’s functionality. What partners do instead: Engineering partners believe your success is their success. They offer scalable custom software development solutions, ensuring you have the tools to adapt, optimize, and scale as your business needs evolve. 7) No Senior-Level Involvement Although the initial meetings involved C-level executives or senior developers/architects, it’s only the juniors working on your project. Without the oversight of experienced executives, you cannot effectively escalate issues or resolve concerns about deliverables, leading to mounting frustration and a directionless project without a clear roadmap. What partners do instead: Senior executives ensure they are involved from the beginning to the closing of the project. Experienced developers and architects provide the oversight needed to maintain technical integrity and guide project direction. Choosing a Software Partner is an Engineering Decision Choosing a software partner should not be treated as a tick-the-box exercise but as an engineering decision with long-term business consequences. When choosing a partner, evaluate their culture, ownership mindset, quality of delivery, client reviews, and their stance in terms of after-launch support. The goal isn’t to find a partner offering services at low cost, but one that has your back throughout the process. Checklist – 10 Questions Enterprise Teams Should Ask Before Finalizing a Software Development Partner
Modernization Trap: The Cost of Updating All at Once
As businesses expand, leadership invests in upgrading systems, and the early excitement about starting from scratch sometimes ends in a bigger mess than expected. Systems that are supposed to run smoothly for five years begin throwing multiple issues in the very first year. That’s when teams realize that they have simply swapped old problems with new, shiny ones. The instinct to upgrade is right. The problem is the approach. Modernization is too often treated as a one-time event instead of an ongoing process. Our old systems aren’t just a problem to be solved. They include valuable business logic that drives business growth. So, it is more ideal to adopt Continuous Modernization (CM) as a sustained alternative rather than treating modernization as a one-time obstacle. The Anatomy of a Failed “One-Time Fix” Like many businesses, my team was eager to modernize systems. We wanted to build a future-proof system with a single effort. We believed we considered all critical points before rolling out the new system, including core values, adaptability, technical debt, downtime, and other technical aspects. We transformed a legacy single-tenant system into a cloud-based, multi-tenant platform. We worked in isolation until we matched existing features. All functions performed as expected in development and internal testing. For a brief period, we felt like architects of the future, until the future arrived and broke our illusion. What went wrong? When we onboarded hundreds of customers simultaneously, architectural and data-handling flaws became apparent. The core issue was not only design errors but also the decision to use packaged software and large releases rather than continuous delivery, incremental rollouts, and learning from actual usage. The result was multiple reworks to stabilize the system. A Familiar Scenario: The Construction Company I’ve seen this same pattern repeat elsewhere. A construction company (details anonymized) I collaborated with was scaling massively. Their old system, which ran all their work, started slowing down. To fix it, the leaders decided to do a big, one-time modernization project. The decision was to do everything at once. The existing system would be updated, all historical data migrated, and a new, “future-proof” system would be released in one go. The assumption was simple. If the system could be rebuilt and launched successfully, the risk would be behind them. At first, the new system worked well and even improved efficiency in the early weeks. But within six months, problems suddenly began to surface. What had looked like a smooth road developed speed bumps. Users began experiencing lag (again), this time accompanied by data inconsistencies. The team expected isolated issues but not systemic ones. During evaluation, the team found that large historical datasets migrated in a single go led to mapping errors and data corruption. These were not simple migration mistakes. They were the result of trying to concentrate too much change, data, and dependency into a single moment. By the time the problems were identified, the system was deployed across the business, and incremental adjustments were no longer possible. The same pattern showed up in delivery. Once a large modernization investment was approved, stakeholders expected the new system to be live quickly. To meet the due date, the team took shortcuts and ignored core processes, which compounded the structural weaknesses. This was not an execution failure. It was a structural failure from concentrating too much change in a single release, leading to technical debt and slower systems. What a Continuous Modernization Approach Would Have Looked Like The failure wasn’t because of the tools, the people, or the intentions. It was about how change, risk, and learning were managed. By treating modernization as a one-time event, all the uncertainty happened at once, leaving no chance to adjust after launch. A Continuous Modernization approach would have changed that structure. If the risk had been spread out over time, problems with data, performance, or integration would have appeared sooner and on a smaller scale, making them easier to fix. The team could have learned before making final choices and changed those choices if needed. The team could have adjusted, paused, or changed direction based on real results, instead of fixing problems after launch. Choosing Steady Evolution Over Major Overhaul Modernization comes with risks and pressure for quick results. When all the changes happen at once, the risks to data, operations, and company knowledge also pile up. What makes a system succeed or fail is not how big the idea is. It’s whether the organization treats modernization as a single event or an ongoing process. Doing it all at once puts all the risk on launch day, but a continuous approach lets you learn and adjust along the way.
Why Enterprises Are Moving Away From “Vendor-Led” Projects to Partner-Led Engineering Teams
In an environment where market conditions shift quarterly, many enterprises are realizing that opting for a traditional vendor over a strategic partner was a structural mishap. This shift is driven by the reality that “fixed solutions at a fixed cost” no longer support the fluid demands of digital transformation. This doesn’t mean vendor-led models are being abandoned entirely, but we are seeing an evolution toward hybrid models where partner-led engineering teams do the heavy lifting. In this model, the focus shifts from clearing tickets to ensuring multi-year platform evolution. In this blog, let’s take a look at why businesses are choosing long-term software partners over vendor-led solutions. Why Vendor-Led Solutions Are Losing Their Popularity? In addition to creating a disconnection between contractual delivery and actual market value, vendor-led models have a few more drawbacks. They include: 1. Rigidity For true digital transformation, continuous modernization and adaptive engineering are required, but vendor-led solutions often follow a “fixed-scope, fixed-cost” model. The solutions lack flexibility in products, processes, and contracts, leading to failures when market shifts or user needs change. This structural rigidity leads directly to accumulated technical debt, cost overruns, and missed opportunities for enterprise innovation. 2. Knowledge Loss The vendor-led model discourages comprehensive documentation or knowledge transfer once the contract has been signed off. It’s not because the process isn’t documented; it’s because vendors are more focused on completing the project and signing off, resulting in an opaque system that is difficult for the client’s internal teams to maintain. This also increases the dependency on the vendor for troubleshooting and future updates, a costly form of vendor lock-in. 3. Lack of Shared Accountability Though accountability for the deliverables outlined in the contract rests with the vendor, when issues arise, accountability is often unclear. The vendor may finger-point at the client’s team for mishaps, and vice versa. This gets worse if the client has hired multiple vendors. In such an environment, different vendors often manage specific parts of the project, leading to overlapping contracts, inconsistencies, and a lack of shared responsibility. 4. Governance Focused on Compliance Clients often prioritize monitoring contractual compliance, which is important, but this shift also takes focus away from business outcomes. If the product meets the contract and doesn’t meet the market requirements, the vendor is successful, but the business is not. The Rise Of Partner-Led Engineering Teams Partnering with an IT service company offers a wide range of benefits. They include: 1. Engineering Ownership Partner-led projects offer the advantage of engineering ownership. From ideation to deployment and maintenance, software development companies are responsible for the entire development lifecycle. This eliminates the need for technical management at each stage, allowing the client to focus on other key aspects of the project. 2. Risk Mitigation In partner-led projects, one critical benefit is that the team identifies technical blind spots and compliance risks, thereby reducing the consequences of technical oversight. 3. Knowledge Retention A partner company acts as a repository of institutional knowledge. They help clients with seamless documentation and knowledge-transfer processes, allowing them to manage future projects without additional hiring. 4. Business Context Awareness Lack of knowledge of the business context is one of the reasons for a project’s failure. Product thinking teams are trained in business context awareness, ensuring that technology serves the business, not vice versa. 5. Outcome-Driven Delivery The partner-led model focuses on outcome-driven delivery. It measures success by the system’s stability, the accuracy of its output, and its overall speed performance. 6. Flexibility & Scalability IT software development companies offer enterprise engineering services designed to comply with regulations and scale to meet the client’s needs. 7. Continuous Improvement & Modernization A partner-led team thrives on continuous improvement. The team doesn’t end support after release but continues to offer regular, steady improvements to keep the systems updated and running. 8. Shared Responsibility Unlike a vendor, the partner takes accountability for business outcomes, as its success depends on the client’s Key Performance Indicators (KPIs), such as user adoption, technical debt reduction, and ROI. To move beyond the vendor-led model, enterprises require a partner who doesn’t treat a project as merely a static delivery. Future-Oriented Solutions With InApp At InApp, we are not just any IT services company; we are a team that understands the importance of bridging traditional digital transformation services and long-term strategic growth. Our team embeds within your business context so that project completion doesn’t mark the end of our collaboration. We ensure you receive uninterrupted support to keep your core operations running and navigate through high-stakes transitions. By focusing on outcome-driven delivery, we ensure that: How Do We Support Transition? We don’t start coding from day one. Instead, we map your KPI to the technical roadmap. We collaborate with your team or vendor to collect documentation and ensure no Intellectual Property (IP) is lost during the transition. Conclusion So, the next time you are looking for a vendor or partner, evaluate your business and technical roadmap. Ask yourself if you want immediate solutions or a long-term software partner capable of driving sustained growth. FAQs Q. Where does the vendor-led model still work? A. Vendor-led models aren’t obsolete. They remain an efficient choice for low complexity projects with repeated tasks & zero innovation, non-core utility projects, and standalone Proofs-of-Concept (POC). Q. Is the partner-led model merely a service upgrade? No, the partner-led model is more than a service upgrade. It is the structural and operational shift that aligns with the business context and ROI. Q. Why does knowledge loss happen even with competent vendors? A. Knowledge loss even with competent vendors is inevitable due to various factors, including siloed knowledge, employee turnover, technical gaps, poor documentation due to project pressure, and lack of systematic knowledge management processes. Q. How do I move from vendors to partners without disruption? A. The most effective way for the transition from vendors to partners is a parallel approach. Rather than abruptly discontinuing the vendor service, introduce the partner to the project to take over some aspects of the
10 Must-Ask Questions for Enterprise Teams: Your Interactive Checklist for Selecting the Ideal Partner
According to a new report by Grand View Research Inc., the global IT services outsourcing market size is anticipated to reach USD 937.6 billion by 2027, registering a CAGR of 7.7%. For an enterprise team looking to outsource software development, the challenge isn’t just finding a software vendor; it’s navigating a saturated market to avoid significant financial and operational risks. Here’s an interactive, practical evaluation framework, which you can use to assess potential partners using technical criteria and strategic impact. Conclusion: Beyond the Checklist Selecting a software development partner is a critical decision for enterprise leaders, as it shapes future growth.When evaluating partners, look for teams that demonstrate expertise through proven execution, strong governance, and engineering maturity, rather than relying on promises.A reliable partner manages data ownership, problem escalation, and development pipelines with clear processes, allowing you to focus on your core business while they oversee technical operations.
Why Mid-Size Software Companies Deliver Better Results Than Big Vendors
When looking for software solutions providers, our obvious choices are industry giants. The reason is that we equate big names with reputation and security. There is no iota of doubt that they are scalable and secure, but “bigger” doesn’t always guarantee “better”.This blog explores why industry giants may not always be the go-to solution providers and how mid-size software vendors can offer more effective alternatives. The Hidden Costs Of Choosing Industry Giants Company size matters, but it is not the only factor that determines your project’s success. It is essential to look beyond industry reputation and thoroughly evaluate the factors involved in partnering with large software vendors. Some of them include: Why Choose Mid-Size Tech Partners? Mid-size vendors are ideal for agile delivery because they can balance established processes and professionalism with tailored service and cost-effectiveness. Some of the benefits of choosing a mid-size IT outsourcing team include:1) AgilityMid-size teams operate with simple organizational structures and fewer management layers. This enables them to adapt project scope and incorporate client feedback quickly. The teams prioritize delivering value at regular intervals, focusing on continuous development rather than documentation.2) Customized SolutionsMid-size software development companies can deliver tailored solutions rather than standardized services. They focus on providing only essential features using the most optimal technology and platforms.3) FlexibilityVendor flexibility is one of the critical advantages of mid-size software development companies. Mid-size vendors can modify existing components of their services to meet their clients’ evolving requirements. They easily adapt to their clients’ infrastructures, helping minimize disruption. The teams can also efficiently scale up or down quickly to meet the resource allocation requirements. This flexibility ensures improved scalability and seamless operation.4) Faster Time To MarketA client can generate value only if they reach their audience faster than the rest. Mid-sized software vendors adopt Agile methodologies to automate redundant tasks and accelerate delivery.5) Cost-EffectivenessMid-size software development companies follow transparent, straightforward pricing. They are less likely to include hidden licensing fees and mandatory add-ons. This allows businesses to receive maximum value with minimum investment.6) Access To Expertise & Senior LeadershipThe common misconception is that the workforce working with mid-sized companies has limited knowledge and skills. In reality, mid-size companies hire only a workforce that excels in its domain and delivers the best possible solutions. Also, by partnering with mid-size software development companies, clients can collaborate directly with senior developers, designers, decision-makers, and even C-level executives.7) Optimized InfrastructureThe choice of infrastructure affects the project’s quality, cost, and delivery time. Mid-size companies often rely on cloud-based and modern infrastructure that is easy to maintain.With that said, not all mid-size vendors provide quality service. You should consider several factors when determining which software development company is best for your needs. How To Choose The Best Mid-Size Tech Partner? Today, there are many software development companies, and choosing the most suitable for your needs can be daunting. Besides technical expertise, cost, flexibility, and agility, there are a few other factors that you should consider while choosing a software vendor. They include:1) Vendor Background CheckEveryone makes a promise, but only a few keep it. Check your vendors’ websites, industry experience, references, success stories, client reputation, etc. You can also check for their Net Promoter Score (NPS) to measure the vendor’s client loyalty and its performance. This will help you determine if they are technically capable of delivering innovative solutions for your business or are just another fraudulent company.For instance, InApp has an NPS rating of 92, indicating its exceptional customer loyalty. 2) Security & ComplianceData security is non-negotiable in the digital world. Ensure the vendor complies with industry standards, such as GDPR and CCPA, and follows robust security measures. Also, check if the vendor provides clear terms & conditions regarding data ownership. For instance,For instance, InApp has a dedicated ISO certified cybersecurity team that takes care of end-to-end security aspects, ensuring the client data is safe and secure. 3) Cultural DifferencesMost often, businesses undervalue the importance of cultural fit in IT outsourcing. Cultural fit plays a crucial role in ensuring compatibility between you and the vendor, helping avoid miscommunications during your contract. Also, ensure the vendor shares the same values and respects your business goals.4) Post-Launch Support & An Exit StrategyNot all companies are great at providing post-launch maintenance and support. While discussing a partnership, ensure to check regarding the post-launch support, maintenance, and future updates. Also, ensure the vendor has a well-defined, seamless exit strategy and knowledge transfer process.Finding all these qualities in one software development company can be challenging, but not impossible. A reputable service provider like InApp can review all the factors on your checklist. Why Collaborate With InApp? With over two decades of industry experience, InApp is recognized as a leading enterprise-ready partner. By opting for InApp’s solution, you can receive: Final Thoughts A reputable vendor focuses on delivering niche solutions that meet your specific needs and help you drive digital transformation. Therefore, scrutinize the vendor thoroughly before signing a deal, rather than relying solely on the size of the team. Remember, your one decision can make or break your project.
From AI Experiments to Enterprise-Grade AI: What CTOs Need to Change in 2026
Today, most businesses have adopted AI and have successfully built Proof-of-Concepts (POCs) and pilots, proving AI’s futuristic potential. Sounds great, right? The reality says otherwise. Most organizations fail to convert POCs into scalable AI solutions, and the pilots remain expensive, high-powered experiments that generate zero measurable ROI. In this blog, you will learn why most POCs fail to make it to production and the importance of enterprise AI strategy for organizations in 2026, which is crucial for successful AI adoption in enterprises. Pilot Purgatory Problem: Why It Happens & What It Means For You? According to a report by MIT’s Media Lab, “Despite $30-40 billion in enterprise investment in generative artificial intelligence, AI pilot failure is officially the norm, 95% of corporate AI initiatives show zero return.”AI pilots do fail because of technical issues, but that’s not always the case. Even a technically sound AI pilot hits a wall due to: The cost of non-scaling AI experiments is not limited to financial losses, but also: Beyond The Trial & Error: The Mandate for Enterprise-Grade AI Moving past the failed POC requires a strategic shift in approach. Here are the 5 strategic steps that transform POCs into production-ready AI. AI Governance in Enterprise: Why AI-Readiness Must Begin Concurrently With Model Design? While many believe that responsible AI governance must be established after model deployment, the reality is that it should begin with the design. Why? To identify and resolve any possible inconsistencies and incompleteness in the datasets collected to train the AI model.Also, early-stage AI governance helps in identifying data biases, resource allocation, technical requirements, procedural upgrades, and training to bridge skill gaps, minimizing unexpected costs and disruptions. AI governance establishes ethical frameworks and ensures the model complies with the AI regulations, reducing the risk of legal issues. For the Chief Technology Officer (CTO), these strategic steps and the AI governance are consolidated into a roadmap for enterprise-wide implementation and transformation. Blueprint For Business Transformation: A CTO’s Guide To AI Adoption and Scaling The rise of AI has not only transformed how businesses function but also the role of leadership. While in the past the CTOs were confined to the technological aspects, they must now act as “AI Thought Leaders”. This requires moving beyond building models. Technology & Security To deliver a continuous, measurable ROI of enterprise Machine Learning, the CTO must leverage MLOps. MLOps not only automates the entire lifecycle but also enforces continuous testing at each step throughout deployment. This establishes the necessary AI infrastructure readiness, eliminating the need for rework. With the rise in AI, there is a risk of increased security threats. The CTOs must also focus on building secure systems and approaches for real-time threat detection. These measures can help prevent AI-driven fraud and data sovereignty challenges. Governance & Investment Strategy Beyond technology & security, the CTO must create responsible AI governance frameworks to ensure the models are transparent, trustworthy, scalable, and responsible. The financial focus should shift from funding short-term AI pilots to investing in internal development platforms and modular ERP systems to enable reusable AI capabilities. The CTO must also implement Cloud Financial Operations (FinOps) to optimize cloud usage and resize AI compute and storage resources. Other aspects to be addressed include ethical considerations, cultural shift, AI governance, building a cross-functional team & educating the executive team, determining operational efficiency, and guiding the organization through the challenges of implementing enterprise-grade AI. Navigating through these mandates and implementing the enterprise AI strategy is not as easy as it is said to be, and one would require assistance from an expert & a reliable partner, InApp. Transforming Experiments to Enterprise AI with InApp InApp guides CTOs on defining an organization’s vision, addressing data quality, establishing a governance framework, operationalizing ML models, and regular maintenance & monitoring. By consolidating these mandates, InApp shifts the organizational focus to a scalable enterprise-grade solution that delivers sustained business value. What’s Next? Enterprise-grade AI is the upgrade that helps your business move from reactive to proactive. To achieve this, CTOs must step up as architects of growth and resilience. By aligning skills strategy with enterprise architecture, CTOs can bridge the gap between AI vision and execution, transforming the organization into a future-proof, AI-confident engine.
Designing for Diversity: How Multilingual and Inclusive UX Expands Product Reach
Your website loads in less than 3 seconds and includes strategically placed feature buttons, but you are losing a significant portion of your audience. You are not alone. For example, the older Hispanic people in the United States are less likely to have received influenza vaccinations compared to their English-speaking counterparts. The reason is that the federal Centers for Disease Control and Prevention (CDC) website’s information is often delayed, contains translation errors, and lacks culturally appropriate content. In today’s competitive digital world, merely a “nice-to-have” look won’t translate as intended. What you truly need is an inclusive design and multilingual interface. What Is Inclusive Design? Today, accessibility in UX design plays a critical role. One way to achieve this is to adopt inclusive design. Inclusive design is an approach that offers accessible digital experiences that consider a wide range of human diversity and are accessible to everyone, regardless of their needs, physical abilities, or backgrounds. Such designs comply with the Web Content Accessibility Guidelines (WCAG) and the Americans with Disabilities Act (ADA). What Is Multilingual UX Design? Multilingual UX Design is an approach to designing websites that can communicate with users in multiple languages. This involves implementing Internationalization (I18N), which provides the flexibility required for successful UX localization. How Inclusive & Localized Design Unlocks New Markets? Inclusive and UX localization have become key strategies for connecting with diverse user personas. These global UX strategies focus on tailored solutions that align with individual needs, enabling everyone to access products and services without additional adaptation. For businesses, this is the key to local market adoption in underserved sectors. UX Design For Elderly & Differently Abled Demographics For years, many websites have been designed without considering the special needs of elderly and specially abled people. By adopting this “design for everyone” approach, businesses can now reach underserved audiences. For example, an aesthetic-only design may not be accessible to people with visual impairments. This issue can be overcome with inclusive design. That is, adding screen reader support, ARIA landmarks, and descriptive alt text for all images, which can help visually impaired people navigate through the site easily. UX Design For Linguistic & Cultural Barriers Localization and cross-cultural user experience design enable businesses to reach people from diverse cultural backgrounds and linguistic groups. Localization is more than translating the content into regional languages; it’s about meeting the regional UX preferences. For example, Western languages read from Left To Right (LTR), but few languages, such as Arabic, Persian, & Hebrew, follow a Right-To-Left (RTL) format. Since reading direction affects user interfaces and screen navigation, businesses must consider demographics before finalizing the placement of menus. Similarly, when localizing for Chinese, Japanese, and Korean (CJK) languages, it is essential to consider font size, increased line spacing, and whitespace to improve readability and reduce cognitive load. Culturally adaptive designs must meet the needs of cultural norms (imagery, colors, symbols, and time & date formats) and technology. For example, in China, white color is considered unlucky and is associated with funerals, whereas in the USA, white is associated with weddings & purity. Ignoring such deeply rooted factors can offend customers, resulting in a negative impact on business. The Rise Of Inclusive Product Development Factors such as fueling innovation, changing demographics, increased market reach, customer loyalty, and brand recognition are contributing to the growing development of inclusive products. Some of the popular inclusive products include: Google Voice Assistants Google Voice Assistants are evolving continuously. These platforms are designed to support and adapt to the region-specific languages and various global accents. While Google’s Automatic Speech Recognition (ASR) systems support over 125 languages, the “Voice Match” feature for Google Assistant allows the system to learn a user’s speaking style phrases to improve its accuracy. For users with low vision or blindness, Google Assistant is fully compatible with screen readers. The platforms use TalkBack, the Android screen reader, and VoiceOver on iOS devices. The TalkBack lets users use the phone through voice and gestures. It also reads aloud the text, buttons, icons, and other elements navigated to on the screen. The low-vision accessibility feature in Google Assistant allows users to adjust font size, high-contrast color, color correction, etc., to improve readability. Apple Voice Assistant Apple’s Voice Assistant, Siri, is designed to adapt and respond to users in their specific accents. Siri can also sound natural and remember previous conversations to provide personalized responses. Siri can seamlessly work with Apple’s built-in screen reader, VoiceOver. The VoiceOver is an accessibility feature for users who are blind or have low vision. It can read the text and interface elements for users and help them navigate through their device. VoiceOver includes customization options such as selecting a custom voice, adjusting the speaking rate, and personalizing the rotor. In addition to the magnifier and display adjust settings, Apple offers Braille Access in its devices. The Braille Access is integrated with the VoiceOver. It allows users to take notes in Braille format, perform calculations, and use Live Captions features. Beyond tech giants, service providers like InApp excel at providing scalable and tailored UX solutions by incorporating a global UX strategy. InApp’s UI/UX Development Solutions InApp’s software localization services focus on adaptation and development of UI/UX for global users. The team leverages advanced technologies to create a scalable design while prioritizing inclusive, multilingual product design requirements. The team designs page layouts to accommodate reading direction (i.e., Left To Right (LTR) & Right-To-Left (RTL)) and other language requirements based on the specific region. The InApp design team conducts deep research into cultural norms to ensure the colors, symbols, date, and currency formats feel native to the regions and sectors. InApp adheres to global standards such as WCAG to make sure the interface is accessible to people with disabilities. Additionally, the team emphasizes responsive and adaptive layouts to suit global screen sizes, resolutions, and orientations. Furthermore, the interface is tailored to deliver a smooth digital experience even on devices that are old or run on slower internet speeds. Why Should You Embrace
My Journey as a Data Science Lead: Vision, People, and Real Impact
Starting something new is always an adventure, especially when it involves navigating the exciting yet often complex world of Data Science and AI. My own journey in establishing a Data Science/AI Center of Excellence (CoE) at InApp has been a whirlwind of learning, building, and transforming. Many are embarking on this journey, and I wanted to share some insights from my experience to help others on their path. Building a CoE from the ground up is not just about technology. It is about understanding people, processes, and possibilities. When I was hired to establish InApp’s first Data Science CoE, I knew this would be more than just hiring data scientists and buying software. It would be about creating a foundation for our organization’s AI-driven future. Understanding Before Building When I first stepped into this role, the canvas was essentially blank. I didn’t spend my first few months writing code or building models. Instead, I became a student of the company. This meant diving deep into our business, getting to know our clients, and assessing where we stood in terms of data and AI readiness. I understood our company culture, our risk appetite, and what success looked like for different stakeholders. Creating the Vision With this understanding, I started by taking a snapshot of the present, understanding our strengths, and identifying areas where we could grow. This foundational understanding was key to envisioning a “future state” for the CoE, i.e., what did we want to achieve? What impact did we want to make? And what does success mean? This clear vision helped us pinpoint the gaps in skills, technology, and processes that we needed to bridge to get from our current reality to our aspirational future. The roadmap I built was not a technical document filled with buzzwords but a clear picture of how Artificial Intelligence could transform our daily operations and customer experience. This roadmap became our north star, breaking down the journey into manageable phases with clear milestones and success metrics. Building the Right Team At the heart of any successful endeavor are the people. Building a strong team was paramount, but here’s where I took an unconventional approach. Instead of immediately hiring expensive senior data scientists/AI engineers, I started with bright, eager interns fresh out of graduate school. The interns underwent a comprehensive immersion program that combined hands-on training and real-world challenges. The interns learned our systems inside-out, understood our business challenges thoroughly, and brought fresh perspectives without preconceived notions. After months of training and proving themselves on real projects, I hired the best performing interns as full-time employees. Today, I have a dedicated team of amazing professionals who are not just technically competent but also deeply aligned with our company’s goals. From Proof to Production Once the team was in place, it was time to roll up our sleeves and get to work. We plunged into Proof-of-Concept (POC) projects, demonstrating the tangible value that AI could bring. The real test came with our first Proof-of-Concepts (POCs). We started small, kept the scope tight, and aimed for quick wins. One of our earliest POC projects that seamlessly converted to production involved an agentic workflow for market intelligence. Salary benchmarking used to take days with manual research across job sites, APIs, and internal reports to compare roles, levels, and locations. We took that pain to production with a conversational system that reads plain language questions, pulls data from job boards, salary APIs, and internal knowledge bases, then normalizes, de-duplicates, and builds filterable comparisons. What took two days now takes two minutes with one click CSV or Excel export; a significant reduction in analysis time. The system has achieved over 95% data accuracy, improved decision turnaround by 10x, and reduced manual research effort by nearly 80%. More than a feature, it marked the shift from experiments to enterprise impact. Leaders could explore “what if” scenarios in real time, and our CoE proved it could deliver robust, maintainable AI that changes how the business makes decisions. We did not build “cool demos.” Every POC had a path to production with data pipelines defined, security reviewed, owners named, and a lightweight runbook ready. That made handoffs smooth and outcomes predictable. The end goal was always to move beyond theoretical possibilities and convert these POCs into fully-fledged AI projects that delivered real business impact. This phase was crucial for building credibility and showcasing the power of our CoE. Each successful POC and related AI project that we delivered built confidence across the organization and demonstrated tangible value. The key was choosing projects with a clear business impact and manageable complexity. Success bred success, and soon, different departments approached us with their challenges. We gradually moved from simple automation to more sophisticated AI solutions, ensuring we could deliver and maintain what we built. Scaling and Governance Now, as we scale, my focus has shifted to setting up robust processes, systems, and infrastructure that will enable the entire organization to become “AI native.” It is about embedding AI thinking and capabilities into the very fabric of our company, making it a natural part of how we operate. This is not about technology alone but more about fostering a culture of innovation and data-driven decision-making. Throughout this journey, one aspect that has remained a constant focus is the responsible implementation of AI. We are actively working on incorporating AI Governance, Ethics, and Privacy guidelines. These aren’t just buzzwords; they are essential principles that will ensure our AI initiatives create a positive impact and build trust with our customers. We plan to roll out these guidelines across the organization soon, ensuring that as we embrace AI, we do so with integrity and a strong sense of responsibility. The journey continues, but the foundation remains strong…
Rethinking Data Governance for the AI Era: What CXOs Need to Know in 2025
The rapid advancement of Artificial Intelligence (AI) technology has significantly transformed the way industries operate. According to McKinsey & Company’s “State of AI” 2025 report, 78% of companies are now using AI in at least one business function, a 55% increase from 2023. Though AI models are known for contributing to improving overall efficiency and providing ROI, models are likely to underperform, not because of coding errors or incorrect algorithms, but because of poor data governance. What is AI Data Governance & Why Does It Matter? AI data governance is a framework that enables the management and control of data used in AI systems and applications within an organization. Responsible AI data governance establishes the standards, processes, and policies that oversee the collection, utilization, processing, and storage of data. It also ensures AI compliance by enabling data quality management and preventing the breach of confidential information. Like any other technology, AI can have both good and bad impacts. If AI models are not governed properly, they can lead to unintended consequences such as unreliable results, data breaches, financial setbacks, harm to an organization’s reputation, and attract regulatory scrutiny. However, with proper AI governance, businesses can convert these risks into opportunities. AI governance can enhance the reliability of AI results, reduce compliance risks, evaluate risks, ensure transparency, and build trust among stakeholders. Roadblocks in Implementing Responsible AI Data Governance Implementing AI-ready data governance is easier said than done. Some of the hardships faced during the implementation include: Technical Challenges 1. Opacity (Black Box Problem): AI models such as Large Language Models (LLMs) and deep learning operate as opaque systems. This opacity can complicate the process of tracing data points that led to a specific decision. 2. Fragmentation of Data Silos Data silos (information silos) are nothing but pockets of information stored in different information systems or subsystems that don’t connect with each other. Due to the existence of data silos, teams may lack full access to integrated datasets and may find it challenging to implement uniform data governance policies, which can compromise AI-readiness. 3. Diverse and Unstructured Data Types: Unstructured data, including text, video, and audio, lacks predefined formats. Since AI and Generative AI (GenAI) require governing vast quantities of unstructured complex data and synthetic or third-party datasets, it is difficult to ensure the quality and relevance of datasets. Organizational Challenges 1. Skills Gap: The skill gap in understanding AI concepts and tools is widening faster than imagined. According to DataCamp’s State of Data and AI Literacy report, 62% of leaders recognize an AI literacy skill gap in their organizations. Yet only 25% were able to implement AI training programs in their organizations. Lack of knowledge about AI can not only prevent teams from understanding bias-detection methods and fairness metrics, but also from using the technical tools required to enforce AI and implement responsible AI governance. 2. Designate Responsibility: Using AI models would require enterprises to hire roles such as a Chief Data Officer or a Data Protection Officer and assign them the responsibility of overseeing AI data. However, in the absence of a unified enterprise data strategy, it becomes challenging to assign data accountability. Spread of Shadow AI 1. Data Leakage Risk: Shadow AI can bypass the organization’s security stack, including firewalls, proxies, and Data Loss Prevention (DLP) tools. If employees upload sensitive files or client data into unauthorized AI tools, the systems will save logs and may leak the data. Since unauthorized AI tools are not governed by an organization’s security policies, it is impossible to track or control the flow of sensitive data. 2. Regulatory Compliance Failure: Unauthorized AI tools can bypass mandated compliance, such as GDPR and HIPAA. This can lead to financial fines (up to 4% of global revenue under GDPR) for a single unmonitored employee and a mandatory public breach disclosure. 3. Lack of Traceability: One of the critical aspects of compliance reporting is the ability to track data. Since outputs generated by shadow AI often lack an audit trail, it is nearly impossible to verify what data was used and how it was processed. This makes the shadow AI untrustworthy in a regulated context. How Poor Data Governance Leads to Biased or Unreliable AI outputs? When an organization fails to manage data effectively, it undermines the fundamentals of the business—reliability, trustworthiness, and ethical integrity —beyond the technical glitches. Let’s take a look at how poor data governance can negatively impact organizations. 1. Biased Decisions If the data used to train AI agents is mismanaged, it can yield flawed or biased outcomes. Poor governance not only fails to ensure data is fair, diverse, and representative but also leads to poor decision-making by individuals or organizations. 2. Unreliable and Unstable AI Output A core failure of data governance is the lack of rigorous quality checks, which leads to inaccurate predictions and poor data quality management. AI models may learn ambiguous patterns due to inconsistent data. When models encounter real-time data, they may produce incorrect outputs, affecting decision-making and business performance. 3. Irrelevant Datasets Responsible AI data governance should frequently define data relevance and timeliness. If this critical aspect is ignored, AI systems can become obsolete when deployed. For instance, a predictive model trained on retail sales data collected before a major economic shift is essentially irrelevant to the present time. 4. Accountability A human can own their mistake, but can an AI model own its mistake? Poor data governance fails to assign clear data accountability, making it difficult to trace errors to the corrupted dataset. If no one owns the data, who will be responsible for the biased or unreliable AI outputs? How Can Enterprises Move from Passive Data Stewardship to Active, AI-Ready Governance? For years, passive data governance has played an important role in ensuring data compliance. But compliance is necessarily not equal to AI-readiness. AI-readiness requires data traceability, drift monitoring, and bias detection, in addition to regulatory compliance. This isn’t optional but a necessity. Top 3 Challenges with Passive Governance in the AI
Building The Bridge Between Legacy & Modern Systems: Lessons From 2 Decades of Innovation
Integrating next-generation technology with legacy systems is more important than ever in today’s fast-evolving digital landscape. However, most organizations struggle to modernize seamlessly. The reason? Poor modernization strategy! With over 25 years of industry experience, InApp understands that transitioning from legacy systems to modern technology is easier said than done. Through this blog, InApp will share its journey in legacy modernization. But first, let’s quickly understand what a legacy system is. What Are Legacy Systems? Legacy systems are software applications that are built on older frameworks, databases, and programming languages. These systems lack modern features and may not operate as intended while working with the emerging technologies. Challenges Associated With Integrating Legacy Into Modern Technologies Dependencies Legacy systems have been around for years. They depend on many other processes, systems, and databases. This makes it hard to upgrade or replace the systems all at once. Compatibility Issues As mentioned earlier, legacy systems use older technologies or architectures. These systems are not easily compatible with modern technologies like AI models, cloud-native stacks, microservices, and APIs. This creates a complex and significant gap that has to be bridged to enable smooth communication between the two systems. Data Migration & Integration Data migration was another big challenge during modernization. Legacy systems often store large amounts of data in old formats and structures. This can make it hard and time-consuming for the team to move data to a new system. Keeping data consistent during the transition process was critical. Even a small change or data error could disrupt the entire operation. The organization has to ensure the data remains accurate and complete, which can be a daunting, time-consuming task. Risk of Business Disruption During the transition process, there is a high potential for system downtime or functionality loss. This disruption can affect daily operations and may result in revenue losses. Compliance Data protection regulations are changing with the evolution of the digital landscape. Though legacy systems comply with some regulations, they cannot comply with GDPR or CCPA regulations. This requires going through additional steps to ensure proper compliance. What Factors Does InApp Consider While Integrating Modern Technologies Into Legacy Systems? Various factors are considered while transitioning from legacy systems to modern technology. They include: What to Move and What to Retain The first step is to determine whether the entire legacy system needs to be migrated or only a selected part. This is done because migration can cause disruption, and it is critical to determine if the business can tolerate the downtime. Is the Transition Necessary Not every legacy system needs to be moved to cloud-native development systems. InApp inspects if the current system complies with the regulation, whether it can be upgraded rather than replaced, and whether modernization will lead to enterprise transformation. Determining Business Driver It is essential to identify the business driver before proceeding with the migration. Who is asking, why they are asking, and what kind of modernization they are asking for helps in determining the solution. Are the executives demanding more features or the customers? For instance, if the executives are asking for more features, then chances are they are looking for KPIs, workflows, dashboards, etc. If customers are asking for more, then it is necessary to conduct an exhaustive analysis. In addition, the amount of data available plays an important role. For example, does a business need to verify if they have a 10-year or 20-year data backlog? Will the data be stored for a long time? If data will not be stored for long-term use, it is required to rethink whether modernization is needed. Cost Cost plays a critical role in legacy modernization. It requires an initial investment and regular maintenance costs. They need to inspect if they are ready to bear the additional cost required for the transition process. For example, if the business has already invested $5 million in a legacy system, are they willing to bear additional cost for infrastructure changes, such as cloud migration and other aspects like data migration, maintenance, operations, etc., involved in modernization? Also, is it performance over scale? Or is it visuals over performance? These aspects also determine the cost to a good extent. Key Technologies Used By InApp for Legacy Modernization Microservice architecture and containerization are two key technologies used to meet the demands of modernization. Microservices Microservices architecture breaks the monolithic application into smaller segments. Each microservice operates independently, and that is why failure in a single section will not impact other services. This decentralized approach helps in improving scalability, debugging, and maintenance capabilities. Containerization Containers are lightweight and executable software packages that offer a practical method to deploy services. They are considered highly efficient, easily portable, and more modifiable. The containers provide an isolated environment for each service and ensure uniform operation despite differences that might occur between development and staging. Why Don’t All Legacy Systems Need To Be Scrapped? Although there is hype around the adoption of modern systems, it is not necessarily a mandate to scrap the legacy system. If the legacy system is reliable, cost-effective, and easy to use, the business can still have this system in place. If required, the system can be upgraded, and operations can continue normally. For example, if a team decides to continue to use .NET, they can certainly do so by simply upgrading .NET to the latest version. However, if the old system poses security or compliance issues, transitioning to new technologies such as React, Node.js, etc. is ideal. Layered AI Integration Into Legacy Platforms To Augment Functions AI integration into existing systems can be more challenging than anticipated. To layer AI into current systems, businesses can deploy a model that receives a copy of the data but is not used to generate output. This allows businesses to test the performance of the model with zero risk. Gradually, the AI model can be used to analyze a small set of read-only data and monitor logs to provide its insights. Another approach is to introduce an API