Nnaemeka Egbuhuzor: Beyond The Hype: How AI, SaaS, ERP are Reshaping Finance In Emerging Markets
By Salami Adeyinka
Nnaemeka Egbuhuzor is a driving force in Africa’s Artificial Intelligence and Enterprise Resource Planning (ERP) systems, leveraging his expertise as a technology expert product leader to empower organizations and his community.
Why this shift matters now
Nigeria’s banks are racing to modernize, weaving AI into SaaS, ERP, and CRM stacks to cut operating costs, speed decisions, and elevate customer experience. Few understand the stakes better than product leader and Columbia Business School MBA candidate Egbuhuzor, who has helped financial institutions rethink workflows around data, automation, and cloud. His vantage point is pragmatic: AI can convert fragmented, manual processes into real‑time, data‑led operations, if institutions execute well and measure what matters. That means moving beyond pilots toward production systems that improve risk, compliance, and customer journeys in ways executives can track.
The friction slowing adoption
Execution is where ambitions stall. Legacy, siloed systems make clean data hard to marshal, blunting AI’s impact and forcing teams to reconcile formats across core banking, payments, and lending platforms. “Most Nigerian businesses operate with legacy systems that don’t communicate well with AI‑driven platforms,” Egbuhuzor notes—a gap that breeds inefficiency, failed proofs‑of‑concept, and dashboards leadership can’t trust. Talent is another constraint: machine learning, cloud architecture, and data‑science skills remain scarce, pushing firms toward costly consulting support. Security concerns and regulatory ambiguity compound the hesitation; boards want clear guidance on encryption, model risk, and data residency before committing critical workloads to the cloud.
What it will take to scale
Egbuhuzor argues for a three‑part playbook. First, upgrade the data foundation, APIs, event streaming, and master‑data management, so systems integrate cleanly and models have reliable inputs. Assign ownership for data quality and model monitoring with service‑level targets that business units, not just IT, can understand. Second, invest in people: build applied AI and analytics skills in risk, compliance, operations, and marketing, and redesign processes so employees actually use the tools. That includes change‑management basics, training, incentives, and frontline champions, so adoption sticks. Third, pursue regulatory clarity that builds trust without throttling innovation. Shared standards on auditability, explainability, and security will unlock cloud adoption while protecting consumers.
The bottom line
AI‑powered SaaS, ERP, and CRM can make the sector faster, safer, and more customer‑centric. Institutions that align data, talent, and policy will move from pilots to measurable gains: shorter onboarding, quicker compliance cycles, earlier anomaly detection, tighter collections, and higher lifetime value. The technology is ready; the differentiator is disciplined execution, governed by transparent KPIs and cross‑functional ownership. Start narrow but meaningful (fraud analytics, collections, or frontline assistance), prove value quickly, then scale. Banks that master these fundamentals will convert AI from buzzword to competitive moat; those that wait will find catch‑up costs compounding.
