Application inefficiency usually isn’t caused by a major bug. More often, it results from small oversights, technical shortcuts and misaligned processes. When these issues accumulate over time, they can lead to slow performance, rising costs and frustrated users—and they’re often expensive and time-consuming to fix.
Multiple subtle but critical factors, ranging from poor data practices to overlooked performance testing, can drag an application down and limit its scalability, reliability and ROI. Below, members of Forbes Technology Council highlight common root causes of application inefficiency and share their experience to help teams address them effectively.
“A common root cause of application inefficiency is unmanaged identity sprawl. Without visibility into all human and non-human accounts, applications accumulate stale, excessive or conflicting permissions. Leading identity governance technology that leverages digital twins and knowledge graphs can surface these inefficiencies early, optimizing access and reducing operational drag before performance suffers.” – Craig Davies, Gathid
1. Overprovisioning Resources
A frequent but often overlooked cause of application inefficiency is overprovisioning—developers often request far more CPU and memory than workloads actually need. This inflates cloud costs and reduces cluster efficiency. Implementing continuous, data-driven right-sizing ensures resources match real usage, improving performance while maximizing ROI and freeing capacity for other business priorities. – Ben Ofiri, Komodor
2. Acting On Adoption Metrics Too Early
Measuring and acting on feature adoption metrics too early can lead to the inefficient use of time and resources—and technical debt in the long run. Having a game plan for feature adoption and acting at the right time can lead to long-term gains in application efficiency. – Buyan Thyagarajan, Eigen X
3. Underinvesting In Architecture And Design
Underinvesting in the architecture and/or design can cripple app development in many places, causing issues with data flows, integration and more. It not only results in application inefficiency, but can also eventually be the source of massive technical debt. The convenience of doing something more cheaply or quickly in the present at the cost of the future is a temptation IT organizations often struggle with. – Abhijeet Mukkawar, Siemens Digital Industries Software
4. Allowing Silent Data Bloat
A frequent culprit is silent data bloat. As features evolve, unused fields, redundant queries and unoptimized data models creep in. They rarely trigger alarms early on, but over time they slow performance, drive up storage costs and complicate scaling. Regular data audits can catch these before they become a costly drag on the application. – Sandipan Biswas
5. Ignoring Code Scalability During Early Development
A common root cause of application inefficiency is poor code scalability. Teams often prioritize functionality over scalability early on, which can lead to performance issues as the app grows. As traffic increases, inefficient code can cause slowdowns and system crashes. Addressing scalability from the beginning ensures smoother growth and optimal performance under load. – Sandeep Telu, Infosys Consulting
6. Compressing MVP Teams
Misunderstanding how to achieve value is common. Project and Agile leaders think they’re doing the right thing by compressing teams while building an MVP—believing cheaper and faster is always better—but they’ve lost the plot. Creating and conserving optionality in application development consistently beats compression, regardless of industry. – Stephen Fishman, Boomi
7. Not Having A Project Owner
One root cause of inefficiency? It’s fragmented ownership, with no one stakeholder taking responsibility as leader. Performance, security and architecture can sit in separate silos, but if nobody owns the full picture, it’ll show. Bottlenecks hide in handoffs. Cross-functional squads that integrate Dev, Sec and Ops from day one can help, allowing visibility, velocity and resilience to scale together. – Dan Sorensen
8. Using One-Size-Fits-All Caching Strategies
One common issue is the use of one-size-fits-all caching strategies. Teams often implement a single global time-to-live (TTL) or caching layer for convenience, overlooking the fact that not all data ages at the same rate. Some responses stay fresh for hours; others rot in seconds. When stale data drives reprocessing or re-fetching, it both confuses users and crushes performance budgets. – Cristian Randieri, Intellisystem Technologies
9. Letting Invisible Work Queues Build Up
A major cause of inefficiency isn’t destructive code; it’s invisible work queues. Tasks often sit waiting for review, quality assurance or release, quietly slowing delivery. These delays go unnoticed until velocity drops. By mapping the pipeline, visualizing handoffs, limiting work in progress and setting alerts for idle work, teams can uncover hidden blockers and improve flow without extra resources. – Dileep Rai, Hachette Book Group
10. Overlooking Data Latency
Much like a space mission delayed by slow telemetry, apps can stall when data retrieval or processing lags. Teams may optimize code but ignore database queries, network speed or bottlenecks until issues surface under real-world loads. Addressing data latency early—via caching, load balancing and streamlined queries—keeps systems responsive and mission-ready. – Shelli Brunswick, SB Global LLC
11. Accumulating Tech Debt
Technical debt accumulation is the silent killer most teams ignore. Quick fixes, outdated dependencies and a “we’ll refactor later” mentality compound exponentially. AI code analysis tools can now automatically detect debt patterns, estimate refactoring costs and prioritize which areas will cause future bottlenecks—preventing the inevitable slowdown. – Vinod Bijlani, HPE
12. Failing To Optimize Data Access
A frequently overlooked cause of application inefficiency is failing to master “back to basics” data access, which leads to redundant database queries. The classic N+1 problem, where one query spawns N additional queries, is a prime example. This inefficiency is invisible during small-scale development tests but completely cripples an application’s performance under the heavy load of a real production environment. – Anil Pantangi, Capgemini America Inc.
13. Treating Components In Isolation
Ignoring end-to-end data flow and treating components in isolation is a common root cause. Without holistic profiling, teams miss how upstream bottlenecks, inefficient data serialization or chatty APIs cascade into latency and resource waste. Early systemwide observability and cross-team collaboration are crucial to catch inefficiencies before they snowball. – Katerina Axelsson, Tastry
14. Developing ‘For Now’ Instead Of Designing For Growth
Teams must strike a balance between developing apps “for now” and designing them “for the future”—and failing to do so typically results in applications that do not scale well with growth. It’s relatively easy to throw more compute power at a scalability problem, but that cost should not increase in proportion to growth. Initial releases must address how products can scale efficiently. – Jason Penkethman, Simpro Group
15. Skipping Defined Performance Budgeting And Early Instrumentation
Teams often overlook the absence of defined performance budgets and early instrumentation. Without clear targets or automated benchmarks integrated into the CI pipeline, performance regressions accumulate unnoticed—until they cause significant slowdowns. Embedding lightweight performance tests and real-time metrics from the outset prevents these issues from spiraling out of control. – Charles Morey, MobilEyes Inc.
16. Prioritizing Speed Over Scalable Design
In the early stages of development, teams often prioritize rapid delivery over thoughtful, scalable design. However, shortcuts taken early can have long-term consequences, such as performance bottlenecks, maintenance headaches and hidden technical debt. Without a solid platform and ongoing monitoring, these issues compound over time, making them far more costly and complex to fix later. – Tiago Azevedo, OutSystems
17. Failing To Batch Data Calls
Here’s a mistake: Instead of batching or consolidating calls, the app makes many small requests for related data. This works fine in development, but in production, latency and network overhead pile up fast. Combine multiple requests into a single call whenever possible. Instead of 10 calls to get details for 10 products, send a single request with a list of product IDs and return all the details at once. – Len Covello, Engage People Inc.
18. Allowing Unchecked Identity Sprawl
A common root cause of application inefficiency is unmanaged identity sprawl. Without visibility into all human and non-human accounts, applications accumulate stale, excessive or conflicting permissions. Leading identity governance technology that leverages digital twins and knowledge graphs can surface these inefficiencies early, optimizing access and reducing operational drag before performance suffers. – Craig Davies, Gathid
19. Skipping Load And Stress Testing
A common root cause of application inefficiency is skipping load and stress testing in CI/CD pipelines. Issues like inefficient SQL queries, missing indexes, poor caching and redundant API calls may pass QA and UAT, only to surface under real-world load. Without load and stress testing under realistic conditions, inefficiencies in scaling, response time or fault tolerance may remain undetected. – Harikrishnan Muthukrishnan, Florida Blue
20. Building A Solution In Search Of A Problem
A common root cause of application inefficiency that teams often overlook is building a solution in search of a problem. This inefficiency arises because team leaders exclude the developers they manage from conversations with the stakeholders who will use the software, thereby missing out on their feedback as it’s being built. – Lane Campbell, GovSoft
