Slow To Adopt, Hard To Displace

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Recent industry analysis confirms that **established enterprise vendors** like Microsoft, Salesforce, and SAP are maintaining their dominance in AI integration, despite widespread reports of slow adoption and internal resistance to change. This resilience is driven by their deep data control and embedded infrastructure, making them difficult to displace.

According to Thorsten Meyer and industry analysts, the core reason for this paradox is that the same factors causing enterprises to be slow in adopting AI—such as high switching costs, data gravity, and regulatory constraints—also create a durable moat against disruption. Major platforms like Microsoft Copilot and SAP’s Joule are now embedded as operational control planes, making them the default environments for enterprise AI deployment.

While many disruptors have attempted to challenge these incumbents, most have failed to unseat their entrenched position. Instead, incumbents have absorbed AI capabilities into their existing systems, reinforcing their control and making switching increasingly costly. The result is a landscape where incumbents are both slow to change and remarkably resilient, with their market share largely intact.

Implications for Disruptors and Enterprises

This dynamic challenges the common narrative that AI disruption will swiftly topple established firms. Instead, it reveals that the **inertia of incumbents** can serve as a powerful barrier to displacement, especially when their core assets—trust, data, and integrated workflows—are reinforced by AI. For enterprises, this means strategic patience and careful evaluation of AI investments are crucial, as their existing vendors are likely to deepen their dominance.

Industry Trends and Historical Patterns

Historically, industries have shown that core infrastructure and data control create formidable barriers to disruption. Recent AI investments have followed this pattern, with incumbents embedding AI into their platforms rather than creating entirely new categories. As Meyer notes, the AI landscape in 2026 is characterized by convergence, with vendors shipping similar architectures based on trusted enterprise data and governance, rather than outright innovation or category disruption.

This trend underscores a shift from the expected "disruptor wins" narrative to one where incumbents leverage their structural advantages, making the landscape more stable and resistant to rapid change.

"The slowness of enterprises to adopt AI is the same factor that makes them hard to displace—it's a durable moat, not a weakness."

— Thorsten Meyer

Unresolved Questions About Future Disruption

It remains unclear whether emerging technologies or new regulatory environments could eventually erode the incumbents' moats. Additionally, the pace of AI innovation and shifts in enterprise priorities might alter the current landscape, but specific timelines or impacts are still uncertain.

Next Steps for Disruptors and Enterprises

Disruptors should reconsider their assumptions about rapid displacement and focus on understanding the deep integration and data advantages of incumbents. Meanwhile, enterprises are likely to continue deepening AI integration within their trusted platforms, reinforcing their market position. Monitoring regulatory changes and technological breakthroughs will be key to assessing future shifts.

Key Questions

Why are incumbents so slow to adopt AI?

Incumbents face high switching costs, data gravity, regulatory constraints, and complex organizational inertia, all of which slow AI adoption but also create a durable moat.

Can disruptors still challenge these incumbents?

While possible in theory, most disruptors underestimate the strength of incumbents' embedded systems and data control, making outright displacement difficult in the near term.

What does this mean for enterprise AI investments?

Enterprises are likely to continue investing within existing vendor ecosystems, reinforcing their dominance rather than seeking quick, disruptive change.

Will regulatory changes impact this dynamic?

Potential regulatory shifts could influence data governance and vendor competition, but the current structural advantages of incumbents are deeply rooted and may persist despite policy changes.

Source: ThorstenMeyerAI.com