Rethinking the Risk and Impact of Enterprise Software Closed Data Models to AI Strategy

Why Salesforce’s Slack Data Policy Signals a Strategic Shift

Salesforce’s decision to restrict how Slack data can be used by external AI tools marks a pivotal moment in enterprise technology. By limiting how customers can leverage their own data, particularly for AI training or third-party search functions, Salesforce is moving toward a closed-data model that locks value within its own ecosystem.

This policy shift spotlights an emerging strategic fault line in enterprise software: open-data models, where customers retain portability and control, versus closed-data models, where access and usage are constrained by the vendor. While Salesforce frames its policy around privacy, trust, and responsible AI use, the implications reach far beyond data security. This is a competitive positioning move – and one that will shape how enterprises select and manage software providers in the AI era.

Defining the Models: Control vs. Containment

The choice between open and closed data models is no longer a technical preference, but rather a strategic fault line. Open models offer enterprises the freedom to innovate, integrate, and build AI capabilities on their own terms, while closed models promise turnkey convenience at the cost of autonomy. As vendors like Salesforce and Google increasingly restrict how enterprise data can be used, the risk isn’t just about functionality, but about leverage. CIOs and digital leaders must now confront a defining question: are we building toward control, or are we being contained? This tension sets the stage for a broader debate—one that will shape how we evaluate platforms, partnerships, and the future of enterprise AI.

Historical Parallels: This Has Happened Before

This open-closed debate is not new as it echoes prior strategic splits along four themes:

1. 1. Perpetual Licensing vs. Subscriptions: The move to SaaS shifted control away from the buyer. Today, if a subscription ends, access—and sometimes even your data—disappears.
   2. On-Premises vs. Cloud: Enterprises gave up physical control for scalability and cost efficiency, introducing new concerns around vendor lock-in and data sovereignty.
   3. Open-Source vs. Proprietary Software: Vendors who once embraced openness for growth later restricted access to protect their IP—especially when cloud hyperscalers began monetizing their code without contributing back.
   4. Best-of-Breed vs. Integrated Suites: From ERP to productivity platforms, software buyers have long had to choose between flexibility with fragmentation or consistency with constraint.

As seen in the table below, each decade brought a new balance between buyer control and vendor lock-in, culminating in today’s debate over closed versus open data models in the age of AI. The timeline below highlights how these dynamics unfolded.

Table 1: Prior strategic splits by theme and decade.

Salesforce’s move with Slack is the latest evolution of these dynamics, now applied to AI data access.

What’s Driving the Shift to Closed Data?

Three primary forces are driving vendors to restrict data access—and each one carries strategic implications for enterprise buyers.

First, AI has become a competitive moat. As foundational models converge in capability, the true differentiator is proprietary data. Platforms like Slack contain vast troves of enterprise conversation data—an invaluable training corpus for AI. By locking down access, Salesforce ensures that only its own AI products, such as Einstein Copilot and the broader Agentforce ecosystem, can capitalize on that value. This isn’t just about product development. Rather, it’s about controlling the future of enterprise intelligence.

Second, there’s growing pressure on infrastructure and cost. Open data models, especially those that support high-volume API access for AI indexing or training, place significant strain on backend systems. Without a clear monetization path, this becomes unsustainable. Salesforce’s move mirrors what Reddit did when it began charging for API access: signaling that the era of free, open data pipelines, particularly for AI use, is over unless it aligns with the vendor’s business model.

Third, vendors are positioning these restrictions as a trust and compliance play. By limiting data export and third-party usage, they reduce exposure to breaches, regulatory violations (e.g., GDPR, HIPAA), and reputational risk. For highly regulated industries, this narrative resonates. But it also masks a deeper shift: the consolidation of control under the guise of privacy.

These forces are converging to reshape the enterprise data landscape—and they set the stage for a deeper examination of the open vs. closed debate that follows.

Implications for Enterprise Buyers

The result of these changes is a rebalancing of power. Enterprises must now assess vendors not just on product capabilities, but on data usage rights, including the ability to train models, extract insights, and integrate with outside systems.

This means evaluating:

• • Data Portability: Can we extract all of our data, in usable formats, without excessive cost or friction?
  • AI Flexibility: Can we apply our own models to the data—or only the vendor’s built-in AI?
  • Exit Strategy: If we leave the platform, what data and derived intelligence do we retain?
  • Contractual Rights: Are there explicit clauses in our agreements covering AI use, model training, and third-party access?

These questions are no longer academic—they’re essential to long-term platform risk management and innovation enablement.

Risk Assessment for Services

As Slack’s updated terms of service restrict long-term access to message data by third-party AI platforms, the implications for our enterprise data strategy are both immediate and far-reaching. This policy shift not only limits our ability to integrate Slack data into broader knowledge systems but also signals a broader industry trend toward closed data ecosystems, even where platform owners retain exclusive control over user-generated content.

The most pressing strategic and operational exposures resulting from this change merit immediate consideration. These risks (as noted in Table 2) ranging from data silos and vendor lock-in to innovation constraints and compliance complexity, carrying high potential impact across our collaboration, AI, and governance initiatives. Each item is paired with targeted mitigation strategies to inform near-term decisions and long-term platform strategy.

Table 2: Risk Assessment of Services

Given the pace of change in enterprise AI and the growing importance of data interoperability, these risks warrant immediate executive attention and coordinated action across IT, legal, and strategic planning functions.

Strategic Recommendations for Executives

1. 1. Make Data Rights a C-Level Issue
      CIOs, CTOs, and legal teams must treat data usage as a first-order concern in software procurement. This is no longer the sole domain of IT architects or contract lawyers. It’s strategic and in the domain of the boardroom.
   2. Demand AI Usage Transparency
      Insist that vendors disclose their own rights to use your data for AI training—and negotiate reciprocal rights. Push for clarity on what AI capabilities you can bring to the platform.
   3. Plan for Portability
      Build an exit strategy from day one. Ensure all mission-critical data can be exported, in formats that can be reused in future systems or AI models.
   4. Align on Ecosystem Strategy
      Determine whether your organization prefers:
      • A closed ecosystem with built-in AI and limited external integration, or
      • An open architecture that supports custom models, best-of-breed tools, and freedom to experiment.

There is no one-size-fits-all answer either, but only alignment with the enterprise’s strategic goals.

A New Era of Software Selection Criteria

The Slack policy update is a clear marker of where enterprise software is heading. As AI becomes embedded in every tool, data usage rights will define how much value organizations can extract from their platforms.

Closed-data vendors will promise performance, security, and control—but with trade-offs in flexibility and interoperability. Open-data platforms will appeal to innovation-driven enterprises that want to experiment, integrate, and own their insights.

Executives must now navigate a landscape where software capabilities and data freedom are two sides of the same coin. The stakes are high—not just for operational efficiency, but for AI leadership, compliance assurance, and digital independence.

In software, we may still trust. However, when it comes to data, we must negotiate.

By Andre Sole, Partner, and David Acklin, Senior Director