Brysa: Tech Debt Is a Business Risk, Not an Engineering One, and Most UK Companies Are Underestimating the Cost

Consultancy reports companies are losing up to 42% of engineering capacity to legacy debt, with high-debt organisations seeing 25-50% slower feature delivery

LONDON, UNITED KINGDOM, May 20, 2026 /EINPresswire.com/ -- Brysa, a UK-based AI and data consultancy, is warning that technical debt is widely misunderstood as a purely engineering issue, when in practice it shapes scalability, revenue, and the speed at which a business can grow.

The consultancy cites industry data showing that 23-42% of engineering time goes into managing technical debt, and that organisations carrying high levels of debt see 25-50% slower feature delivery due to compounding complexity and maintenance overhead. Brysa argues that the impact of these losses is felt across the business - in delayed roadmaps, rising infrastructure costs, customer churn from unreliable performance, and an inability to support AI and automation initiatives that depend on clean, unified data.

"Tech debt isn't buried in code. It starts with business decisions," said Satish Thiagarajan, founder of Brysa. "Teams take a shortcut to hit a deadline or seize a market opportunity, and it's often the right call in the moment. The problem is that 'we'll fix it later' rarely happens. Temporary solutions become permanent architecture, and the cost shows up years later as slower releases, fragile systems, and growth ceilings nobody saw coming."

Brysa argues that the framing of tech debt as an engineering backlog issue leads to systematic underinvestment in remediation. New investment in AI and digital capability tends to compound existing debt rather than relieve it, because the underlying systems were never built to support what's being layered on top.

The consultancy identifies ten warning signs that a company is accumulating dangerous levels of tech debt: releases regularly introducing regressions; engineers reluctant to modify systems for fear of unintended consequences; lengthening QA cycles and frequent hotfixes; duplicate systems and inconsistent data across teams; roadmaps repeatedly delayed by technical complications; simple features taking disproportionately long to build; increasing reliance on workarounds instead of permanent fixes; production issues that are hard to diagnose; long onboarding cycles for new engineers; and cross-team dependencies creating bottlenecks.

The long-term cost, Brysa says, is structural rather than cosmetic. Refactoring becomes exponentially more expensive as complexity builds. Security vulnerabilities increase as outdated systems lose maintenance support. Integration challenges multiply, making it harder to connect new tools and platforms. Data inconsistency blocks AI initiatives that rely on clean, unified inputs. And growth ceilings appear unexpectedly when systems can no longer support scale.

Brysa recommends seven disciplines for managing tech debt without slowing delivery. The first is a tech debt audit involving engineering and customer-facing teams, not just code review, to make debt visible and measurable. The second is categorising debt as critical, moderate, or acceptable, so remediation effort reflects business impact rather than engineering preference. The third is allocating a fixed 15-25% of every sprint to refactoring, treating it as part of regular workflow rather than a deferred project.

The remaining four focus on prevention: improving code quality standards and peer review processes to catch shortcuts early; automating testing and CI/CD pipelines so changes can be shipped without introducing instability; investing in modular, API-first architecture to limit the blast radius of future shortcuts; and improving documentation and knowledge sharing to reduce reliance on tribal knowledge.

"High-performing teams don't try to eliminate tech debt entirely," Thiagarajan added. "They manage It deliberately, the same way they manage product roadmaps or revenue targets. The companies that get this right keep shipping fast while strengthening the foundation underneath. The ones that don't end up funding AI and digital initiatives on systems that can't support them, and wonder why the returns never materialise."

The consultancy is urging technology leaders to commission a tech debt audit before committing to 2026 AI and digital investments, on the basis that capital directed at advanced capability without first addressing foundational debt is unlikely to deliver expected returns.

Brysa PR Team
Brysa Limited
+44 7867 865102
email us here
Visit us on social media:
LinkedIn

Legal Disclaimer:

EIN Presswire provides this news content "as is" without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.