How Gen AI Will Disrupt SaaS in Africa: Unbundling ERPs with Agentic AI

June 13, 2025

At the heart of enterprise software is a long-standing contradiction: the more capable a system becomes, the more complicated it tends to get. Historically, SaaS (Software as a Service) was designed to simplify enterprise operations, yet ironically, many businesses are now overwhelmed by bloated platforms, monolithic ERPs, and rigid workflows. In response, a quiet but profound shift is underway. Fueled by generative AI and agentic automation, enterprise software is being unbundled — not by accident, but by necessity.

The central question is not whether AI will disrupt SaaS; rather, it is how quickly this transformation will unfold — and who will benefit most. Based on data from Morgan Stanley forecasts [1], over 50% of CIOs expect to upgrade or replace their ERP systems within the next two years, largely because legacy systems are unable to support the agility, personalization, and intelligence required in the post-COVID business environment. While the incumbents—SAP, Oracle, Microsoft Dynamics—still dominate on-premise deployments, their core architecture remains trapped in 1990s logic: sequential tasks, rigid data models, and explicitly programmed steps.

Yet, as GTM (go-to-market) strategies shift from process-centric to intelligence-driven, these older ERP models no longer suffice. Traditional systems resemble assembly lines—linear, slow, and expert-dependent—while AI-native platforms act more like living organisms: dynamic, self-revising, and responsive to real-time context. The implications of this shift are not merely technical but structural. AI isn’t just streamlining work. It’s redefining what work is.

Based on emerging studies [4][5], Gen AI is poised to drive an unbundling of monolithic ERP systems as shown in Graph 1. Rather than relying on single-vendor platforms that attempt to do everything, African businesses are increasingly inclined towards modular, best-of-breed tools that can snap together like Lego blocks. For instance, a small Kenyan logistics firm might use Zoho for CRM, Xero for finance, BambooHR for talent management, and a local procurement solution like MarketForce—all integrated through AI orchestration layers that manage cross-platform interactions, without human intervention.

At the heart of this shift lies the rise of agentic AI: systems capable of ingesting unstructured data (e.g., PDFs, emails, images), reasoning across multiple workflows, and routing tasks intelligently to the appropriate applications. A system like this doesn’t just update your CRM when a sales call ends—it listens, learns from the transcript, updates the lead status, drafts the follow-up email, books the next meeting, and adjusts your forecasting model based on sentiment trends from past calls. This shift moves SaaS from being a static toolkit to becoming an intelligent partner.

Graph 1

The Rise of the Orchestration Layer: Connecting Intelligence with Context

Despite widespread optimism about AI’s potential, most organizations have yet to reckon with the foundational requirement: context. Generative AI cannot reason without rich, real-time data. It cannot act without connected systems. And most crucially, it cannot improve without memory—closed-loop feedback cycles that allow it to learn from outcomes. Without this, AI becomes a surface-level tool, impressive in demos but chaotic in practice [2].

This is where the orchestration layer enters the equation. Positioned above the fragmented stack of best-in-class tools—finance, HR, CRM, procurement—the orchestration layer serves as the central nervous system of the enterprise. It doesn’t store data like a system of record. Instead, it coordinates workflows, interprets unstructured inputs (emails, call transcripts, documents), and assigns tasks to the right tools using agentic AI models.

Consequently, the implications of this orchestration are two-fold. First, it drastically reduces manual handoffs and data duplication, which have traditionally slowed down SaaS-based operations. Second, it introduces a new paradigm where software no longer waits for user prompts. It anticipates, recommends, and, in some cases, acts autonomously based on learned patterns.

An illustrative case lies in modern CRM systems. Rather than relying on sales reps to update lead statuses, an AI-powered CRM continuously monitors digital signals—proposal revisions, email tone, meeting notes—and updates opportunities in real-time. It mimics what an elite salesperson does instinctively: sense context, interpret nuance, and adjust strategy. Only now, it can do this at scale, 24/7.

The Strategic Advantage for Africa’s AI-Native SaaS Entrepreneurs

While global SaaS incumbents are bogged down by legacy architecture and compliance-heavy AI governance boards, emerging markets—especially across Africa—present a unique opportunity to leapfrog. In fact, several African founders are already building modular, AI-integrated systems tailored for local business realities. Rather than forcing all-in-one platforms on small and medium businesses (SMBs), they are stitching together context-aware tools with shared data backbones and task-routing agents.

Based on recent African tech investment reports [3], vertical SaaS adoption in fintech, agritech, and logistics is outpacing horizontal SaaS growth. These sectors thrive on semi-structured and often chaotic data environments, where traditional ERPs fail to keep up. This makes them ideal testbeds for AI-native orchestration platforms that can turn messy signals into structured actions.

Yet, it remains to be seen whether African policymakers will seize this opportunity by investing in cloud-first infrastructure, AI literacy programs, and open-data initiatives. If they do, African developers may find themselves at the center of the next SaaS revolution—not merely as users, but as architects of new workflows, new revenue models, and new modes of coordination.

A useful analogy may lie in telecom. Just as Africa bypassed landlines to become a mobile-first continent, it may similarly bypass the bloated legacy SaaS architectures and become a proving ground for AI-native, modular enterprise systems. Given that ERP sub-markets like supply chain, planning, and order management remain over 70% on-premise even globally [6], African module disrupters and orchestration platforms have ample runway to offer vertical-specific solutions, from agriculture to education.

Consider the potential impact in a sector like logistics: An agentic AI layer could monitor fuel receipts, GPS data, WhatsApp updates, and inventory logs to predict delivery delays, reroute shipments in real-time, and flag fraudulent activity. This would be nearly impossible using conventional software logic, but well within reach for an AI-native system trained on contextual signals and organizational intent.

Despite widespread optimism, significant hurdles remain. African firms still face data fragmentation, regulatory ambiguity, and insufficient digital infrastructure. AI cannot generate value from what it cannot see or learn from. Many businesses operate informally, and enterprise-grade data isn’t always structured, labelled, or digitized. Furthermore, without enforceable data governance and ethical AI policies, agentic systems may inadvertently entrench biases, reinforce inequality, or leak sensitive information. The question, then, is not whether Gen AI can unbundle SaaS and ERPs, but whether African businesses can design the right institutional scaffolding to support this leap.

Still, the opportunity cost of inaction is immense as shown in Graph 2. According to McKinsey [7], AI-native SaaS could generate $4.4 trillion in global economic value by 2030, with Africa potentially capturing $120 billion if structural constraints are addressed [8]. This will not come from selling tools but from building systems that learn and compound over time. And unlike their industrial predecessors, these AI agents do not sleep, go on leave, or ask for end-of-year bonuses.

Graph 2

Final Thoughts

Despite the hype surrounding generative AI, the real disruption isn’t in flashy chatbots or AI avatars—it’s in workflow design. What’s changing is not the interface but the infrastructure. The SaaS of the future will not be a suite of apps but an ecosystem of agents, choreographed by context, driven by data, and evolving through feedback.

Unbundling the ERP isn’t just a matter of preference. It’s becoming a survival strategy for organizations seeking agility and intelligence in a volatile economy. The winners won’t be those with the most features. They’ll be those who can turn unstructured chaos into structured, compounding intelligence.

And in that regard, African founders—nimble, hungry, and unencumbered—might be better positioned than we think.

References

[1] Morgan Stanley. 2025. “Upcoming ERP Upgrades Are Set to Drive a Back-Office Supercycle – Morgan Stanley.” Linkedin.com. 2025. https://www.linkedin.com/in/christopherquintero/overlay/1731299378095/single-media-viewer/?profileId=ACoAABMy-QwBE5NrCKssVDSz8KAoSs-D2RAFUOQ.

[2] Sinha, Chandni. 2025. “Why AI Fails without Real-Time Data—and How to Fix It.” Ibm.com. 2025. https://www.ibm.com/products/blog/ai-real-time-data.

[3] Vested World. 2025. “Where Is the SaaS Revolution in Africa? – Investor X – Webflow Ecommerce Website Template.” Vestedworld.com. 2025. https://www.vestedworld.com/perspectives/where-is-the-saas-revolution-in-africa.

[4] Srivastava, Jatin, Jilco Schuurmans, Koushik Ray, Loïc Mesnage, Nick Butler, Sharonee Dutta, Ted Kubit, and Till Janner. 2025. “GenAI Can Revolutionize ERP Transformations.” BCG Global. April 25, 2025. https://www.bcg.com/publications/2025/gen-ai-can-revolutionize-erp-transformations.

[5] Nishar, Deep, and Nitin Nohria. 2025. “How Gen AI Could Disrupt SaaS—and Change the Companies That Use It.” Harvard Business Review. May 21, 2025. https://hbr.org/2025/05/how-gen-ai-could-disrupt-saas-and-change-the-companies-that-use-it.

[6] Radu, Adrian. 2025. “The Great ERP Unbundling: Seizing Startup Upside in the Replacement Super Cycle.” Lightspeed Venture Partners. May 29, 2025. https://lsvp.com/stories/the-great-erp-unbundling/.

[7] Chui, Michael, Eric Hazan, Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, and Rodney Zemmel. 2023. “Economic Potential of Generative AI | McKinsey.” Www.mckinsey.com. McKinsey. June 14, 2023. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier.

[8] McKinsey. 2025. “Africa’s Gen AI Potential.” McKinsey & Company. May 29, 2025. https://www.mckinsey.com/featured-insights/sustainable-inclusive-growth/charts/africas-gen-ai-potential.

Want to stay ahead of the curve and learn more about this groundbreaking innovation? Subscribe to our newsletter and follow us on X and LinkedIn to join the conversation and be part of the future of real estate in Africa.