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World desk5 min

39 Years in Tech, One Hard Lesson: Lungi Sangqu on Why Buying Software Won’t Fix Broken Processes

Lungi Sangqu argues that software cannot repair a broken process on its own. Her interview highlights workflow redesign, access, governance, and measuring real service improvements.
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Buying software can digitize a broken process without fixing it. In a TechCabal interview syndicated by RJB World on October 2, 2026, technology leader Lungi Sangqu argues that organizations should first define the problem, decide who will operate the system, and establish how it will improve service. The interview’s examples—from UNISA’s online assignment marking to the risks of technology managed only by IT—show why adoption depends on redesigning work, preparing people, and measuring outcomes, not merely launching a platform.

Why a software purchase cannot repair a broken process

A system can make an existing workflow faster or move it online while leaving its underlying problems intact. If responsibilities are unclear, approvals are slow, or the service itself is poorly designed, software may simply reproduce those problems in digital form.

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Sangqu’s starting point is therefore not a product shortlist but a set of operational questions: Why does the organization need the system? Who will operate it? What should improve for the people using or receiving the service? In the interview, she cautions against counting acquired platforms as a measure of success. The relevant test is whether technology helps an organization serve customers or citizens better.

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“A successful launch is only a milestone,” Sangqu says in the interview. “The real test is whether the organisation can sustain the change and whether the people it serves experience a tangible improvement.” The quote and the views in this article are attributed to Sangqu as presented in the syndicated interview; they have not been independently corroborated.

Digital transformation changes work, not just tools

The interview’s UNISA example illustrates the difference between digitization and transformation. It reports that the university had moved assignment marking fully online by August 2013. Sangqu’s account describes this as more than putting an existing task on a screen: workflows had to be redesigned and staff trained to use the new way of working.

That shift also exposed an access problem. The interview says some rural students faced friction because they lacked compatible devices or reliable broadband. A digital process can be better organized and still be difficult to use for people who cannot reliably reach it. That makes access part of the service design, rather than a separate technical detail to consider after launch.

Before committing to a system, leaders should identify which steps in the current process need to change, who will need training, and whether affected users can realistically access the service. Those answers help distinguish a tool that supports a redesigned process from one that merely moves the old process online.

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Technology decisions need organization-wide ownership

Software choices can change budgets, staffing, service models, and day-to-day work. The interview argues that these consequences make technology an executive responsibility shared with IT—not an IT-only procurement decision. Technical teams may understand architecture and security, but they cannot alone decide what the organization’s service should become or authorize every change in staffing and operating practice.

Sangqu links slow approvals and information silos with the emergence of “shadow IT”: teams adopting tools outside formal oversight when approved routes do not meet their needs. She calls it “a governance and trust problem.” In the interview’s framing, unmanaged tools can create security and information-management risks, while an overly restrictive process can leave teams unable to solve legitimate problems. The balance is to make approved routes responsive and clear, while retaining visibility over data and systems.

How to assess a proposed system before buying

Use the interview’s emphasis on purpose, readiness, and outcomes to evaluate a proposal on its actual fit—not on novelty or vendor claims.

  • Problem and outcome: State the operational problem in concrete terms and identify the service or productivity improvement the system is expected to deliver.
  • Process fit: Map the current workflow and specify what will change. If the underlying process is confusing or duplicative, decide how it will be redesigned before automating it.
  • Ownership and readiness: Name the people responsible for operating the system, the leaders accountable for the change, and the staff training required.
  • Reliability, security, and data control: Assess whether the system can be trusted for the work and whether the organization can govern the information it handles.
  • Affordability and suitability: Compare options against the organization’s needs and constraints. The interview recommends judging local and international providers on merits such as reliability, security, affordability, and fit—not origin alone.
  • Access: Consider the devices, connectivity, and other practical requirements for every group expected to use the service.
  • Evidence of value: Define how the organization will tell whether users receive a tangible improvement, rather than treating purchase or launch as the result.

These checks do not guarantee a successful implementation. They make explicit the operational assumptions a purchase depends on, so leaders can address gaps before they become adoption problems.

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AI needs a use case, sound data, and human oversight

Sangqu applies the same problem-first principle to artificial intelligence. Her advice in the interview is to begin with a clear operational use case, check the integrity of the data, and keep human oversight. Those are recommendations attributed to her—not a tested implementation framework—and they point to basic questions an organization should answer before introducing AI into a workflow.

Leaders should also consider how changed tasks will affect workers. Automation does not remove the need to plan for the people whose responsibilities shift. Sangqu warns: “We cannot ask young professionals to innovate while requiring them to spend their days repairing avoidable gaps in legacy systems.” The interview’s broader point is that new technology cannot deliver its intended value if people are left working around preventable problems in the systems and processes around it.

Measure what improves after launch

Evaluation should continue after deployment. The interview contrasts counting platforms with asking whether technology has helped an organization serve people better. That makes the intended outcome—such as a more usable service or a more effective workflow—the basis for assessing whether a system is working.

In the interview, Sangqu describes a career spanning public institutions, banking, logistics, rail, and higher education. It says she encountered computer science in 1987, began as a software programmer in the early 1990s, and had an operational scope at DHL covering 49 African countries. These career details, like the UNISA milestone, are reported by the syndicated interview rather than independently verified.

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The principle behind her lesson is practical: identify the organizational problem, prepare the process and people for change, and choose technology that can serve a defined purpose. A purchase is an input to that work, not proof that the work is finished.

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