Balancing Act: AI Investments Strain IT Modernization Efforts

As businesses across Africa race to adopt artificial intelligence (AI), a growing challenge is emerging: how to fund both strategic modernization initiatives and cutting-edge innovation projects.

Recent data from Ensono, a managed services provider, shows that 61% of organizations have had to pause, delay, or scale back IT modernization plans in the past two years. This trend is driven by multiple factors, including tight budgets, cost overruns, and now, increased pressure to invest in AI.

“Organizations are facing a classic dilemma,” explains Brian Klingbeil, chief strategy officer at Ensono. “They need to modernize their core IT systems while simultaneously investing in emerging technologies like AI—and the same dollars often must satisfy both needs.”

The survey revealed that over 70% of modernization projects exceed original budgets, with nearly 30% exceeding by more than 51%. This financial strain is exacerbated as businesses prioritize visible returns from AI investments over longer-term modernization benefits.

The Scope Creep Factor

According to Michele Grant, executive global director at Cognizant, scope creep often contributes to modernization failures. “Organizations discover new complexities in their legacy systems just as they’re trying to move them,” she notes. This discovery problem can be compounded when AI projects promise faster, more visible results.

KJ Kusch, global field CTO at digital adoption platform WalkMe, echoes this sentiment: “We see companies deliberately reallocating modernization funds toward AI initiatives that offer a quicker return on investment.”

The Modernization Paradox

The irony is that AI itself could accelerate modernization efforts by automating tasks like code discovery and cleanup. Yet few organizations have explored this potential—leaving them to navigate complex legacy systems with limited resources.

“Two years ago, the choice was either invest heavily in a risky modernization project or live with an outdated system,” Klingbeil explains. “Now, there’s a third option: using AI to modernize more efficiently.”

Grant emphasizes that IT leaders should phase modernization programs with clear outcomes and designate executive accountability to prevent projects from stalling without defined restart conditions.