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Guest contributor Adi Aloni draws on her experience leading CS at Folloze to explore how AI is reshaping the classic platform vs. point solution debate.
During my time as SVP of Customer Success at Folloze, I experienced this tension from both sides. As a customer success leader, I lived in the constant pull between wanting everything in one place and needing best-of-breed capabilities. I felt this daily—should we invest in integrating our scattered customer data systems (commercial data, product telemetry, usage data, customer outcomes data) or add another specialized tool that promised better insights? Each system represented a moment when we'd swung toward 'best tool for the job,' but the cumulative effect meant constantly stitching together customer views. This meant hours of manual data reconciliation, delayed insights when customers needed immediate attention, and the constant risk of making decisions on incomplete information—all while knowing that a single source of truth remained frustratingly out of reach.
Meanwhile, our enterprise customers were going through their own cycles. Every few years, they'd reorganize from corporate-heavy structures to field-heavy ones and back again. Periods of budget pressures tended to drive consolidation—bringing together tools, teams, and processes to drive efficiency and cut costs.
These same forces are still at work today, but AI has fundamentally changed their speed and stakes.
The fundamental drivers haven't changed—companies are still chasing efficiency and sustainable costs. But AI has dramatically raised the stakes around integration complexity and operational risk. Choosing between platforms and point solutions means managing the cascading effects of those choices.
The appeal of platform consolidation has intensified with AI. When I think about the hours my team spent stitching together customer views across multiple systems, unified data and consistent workflows become even more critical when AI needs clean, integrated data to function effectively. However, specialized AI tools continue to innovate faster in narrow domains, creating pressure to adopt best-of-breed solutions despite integration challenges.
What's genuinely new is that companies aren't just choosing between two approaches anymore—AI has created new hybrid positions. Companies increasingly use tools like Zapier and Airtable to create custom AI solutions, attempting to bridge the gap between platform limitations and point solution complexity. But this DIY approach illustrates a fundamental challenge: while it solves immediate functional gaps, point solutions can result in cost-prohibitive and resource-intensive implications downstream—including unexpected maintenance costs, data privacy and security issues, and integration incompatibility.
McKinsey research on AI platform building reveals why this matters more than ever: "Companies can enable innovation while managing for risk if they are deliberate in building a platform—a centralized set of validated services...that are easy to find and use (and reuse). Integrating these capabilities into a single platform ensures that products satisfy compliance requirements much more efficiently, which, in our experience, helps to virtually eliminate 30 to 50 percent of the nonessential work typically required."
Another recent McKinsey survey offers revealing insights into how organizations are actually structuring their AI implementations. "Some essential elements for deploying AI tend to be fully or partially centralized. For risk and compliance, as well as data governance, organizations often use a fully centralized model such as a center of excellence."
Think about what this means for the platform vs. point solution decision. If your data is fragmented across multiple systems, you're essentially asking your centralized risk and compliance teams to manage exposures across a sprawling ecosystem of tools. Each additional point solution creates new governance touchpoints, new data lineage challenges, and new compliance requirements.
Another interesting aspect is the change management one: "The value of AI comes from rewiring how companies run, and the latest survey shows that, out of 25 attributes tested for organizations of all sizes, the redesign of workflows has the biggest effect on an organization's ability to see EBIT impact from its use of gen AI." This finding validates something that is becoming widely accepted—AI's real value isn't in automating what we already do, but in fundamentally changing how we work.
However, we need to acknowledge that organizational readiness varies. Some teams might be ready to reimagine their entire customer journey, while others need to prove value within familiar processes first.
The data suggests that companies achieving the highest returns are those that build with evolution in mind—starting with practical improvements and scaling toward fundamental redesign, supported by centralized governance structures that can manage the complexity and risk that comes with that growth.
If I were leading customer success today, I'd start with an honest assessment: are we looking for immediate wins within current processes, or are we building for long-term transformation potential? Because that choice determines your platform selection and implementation approach, not whether you choose platforms at all.
If we want both immediate value and future optionality, I'd prioritize platforms that can deliver quick wins without requiring wholesale process changes upfront. The integration complexity I experienced at Folloze taught me that having fewer, more capable tools often beats having the "best" tool for every specific function, especially when you need those tools to work together as your sophistication grows.
If we need purely incremental improvement with minimal change, point solutions can deliver immediate value. But I'd be honest about the architectural limitations this creates and plan for the eventual integration challenges.
Most importantly, I'd build with change in mind. The AI landscape is evolving too quickly to make permanent architectural decisions. Whatever approach I chose would need flexibility to adapt as both capabilities and organizational readiness evolve.
The choice between platforms and point solutions shouldn't be based on feature comparisons. These four questions matter more:
1. Do we want immediate wins within current processes, or are we building for future transformation potential? If your team needs to prove value quickly without changing established workflows, platforms can start there and evolve. If you only need specific improvements with no plans for broader change, point solutions might be sufficient—but understand the architectural constraints you're accepting.
2. Can our governance structure handle distributed risk management? With risk and compliance increasingly centralized, every additional point solution creates new governance complexity. Ask yourself: does our compliance team have the bandwidth to manage AI risks across multiple vendors and data flows, or do we need the unified governance that platforms provide?
3. What's our tolerance for integration debt? Each point solution is a bet that the short-term value outweighs long-term maintenance costs and security risks. If you're already struggling to maintain clean data across systems, adding more tools won't solve that problem—it will amplify it.
4. How quickly is our competitive landscape changing? If you're in a stable market, you might afford the luxury of best-of-breed specialization. But if you're racing against AI-enabled competitors, the speed and consistency of platform approaches often matter more than having the absolute best tool for each function.
Your answers to these questions should drive your architecture decisions—not vendor demos or feature comparisons. Pick the right approach for your specific context—and build the capability to evolve as both your organization and the AI landscape mature.
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