Nnaemeka Egbuhuzor: Why AI-Powered SaaS ERP and CRM Implementations are Struggling in Nigeria

Promise vs. reality

AI‑first SaaS, ERP, and CRM platforms promise efficiency and sharper decisions across finance, oil & gas, healthcare, and manufacturing.

On paper, the case is overwhelming: automated workflows, richer analytics, elastic scale. In practice, many Nigerian deployments stall at “partial”, costly pilots, thin ROI, and lingering scepticism.

Product manager and AI researcher Nnaemeka Egbuhuzor calls AI “essential” for competitiveness, yet says adoption lags because of deep structural issues, not hype. The paradox: leaders believe in the destination but are stuck in the weeds of getting there.

Five blockers you can’t ignore

Data is the first failure point. Disjointed sources, inconsistent formats, and poor lineage undermine models and starve dashboards of trustworthy signals.

Culture follows close behind: teams cling to manual checkpoints and “how we’ve always done it,” leaving new tools idle or misused. Security and regulation create additional drag; ambiguity around cloud security, data sovereignty, and AI governance keeps workloads on‑prem and experiments small. Talent is scarce where it matters most, applied ML, data engineering, and enterprise integration, so projects rely on external vendors and lose momentum post‑launch.

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Finally, cost myopia skews decisions: executives overweight upfront licensing and integration fees while undervaluing lifecycle gains from automation, compliance, and scale.

A pragmatic fix

Start with the data layer. Standardize schemas, instrument event streaming, and enforce data‑quality SLAs so models aren’t learning from noise. Build skills where value accrues, risk, operations, finance, through targeted upskilling, vendor apprenticeships, and incentives that reward adoption, not just tool procurement. Clarify the rules: sector regulators can set baseline controls for encryption, logging, and model auditability to de‑risk cloud usage. Recast business cases around outcomes that executives track, fraud losses avoided, days‑to‑close reduced, cost‑to‑serve lowered, so benefits outweigh sticker shock. And sequence work in “crawl‑walk‑run” phases: small production wins, then broader rollouts once data, security, and change‑management patterns harden.

The takeaway

Hesitation is expensive. Firms that professionalize data, upskill teams, and embrace cloud‑friendly rules will compress pilot timelines and convert AI into hard ROI. The rest will watch costs creep and customer expectations rise.

The play is not to chase every shiny feature but to operationalize a few high‑leverage use cases and measure relentlessly. Treat AI adoption as an enterprise change program, not a tool install and the payoff compounds: better decisions, safer operations, and teams freed from drudgery to focus on growth.

Source: Legit.ng