Despite proven cost efficiencies from deploying fine-tuned AI models, companies report the primary barrier to adoption is human, not technical. The core challenge is overcoming employee inertia and successfully integrating new tools into existing workflows—a classic change management problem.

Enterprise SaaS companies (the 'henhouse') should be cautious when partnering with foundation model providers (the 'fox'). While offering powerful features, these models have a core incentive to consume proprietary data for training, potentially compromising customer trust, data privacy, and the incumbent's long-term competitive moat.

Off-the-shelf AI models can only go so far. The true bottleneck for enterprise adoption is "digitizing judgment"—capturing the unique, context-specific expertise of employees within that company. A document's meaning can change entirely from one company to another, requiring internal labeling.

The traditional SaaS model of locking customer data within a proprietary ecosystem is dying. Workday's move to integrate with Snowflake exemplifies the shift. The future value for SaaS companies lies in building powerful AI agents that operate on open, centralized data platforms, not in being the system of record.

CIOs report that the unbudgeted 'soft costs' of implementing AI—training, onboarding, and business process change—are the highest they've ever seen. This extreme cost and effort will make companies highly reluctant to switch AI vendors, creating strong defensibility and lock-in for the platforms chosen during this initial wave.

Enterprise buyers purchase tools like Slack because employees love using them, not based on clear ROI. This presents a major adoption hurdle for non-viral, single-player products like enterprise search, which must find creative ways to generate widespread user adoption and love.

Veteran tech executives argue that evolving a business model is much harder than changing technology. A business model creates a deep "rut" that aligns customers, sales incentives, and legal contracts, making strategic shifts (like moving from licensing to SaaS) incredibly painful and complex to execute.

An AI app that is merely a wrapper around a foundation model is at high risk of being absorbed by the model provider. True defensibility comes from integrating AI with proprietary data and workflows to become an indispensable enterprise system of record, like an HR or CRM system.

The proliferation of specialized tech solutions means buyers who fail to engage with a multi-vendor trusted advisor risk selecting suboptimal technology. This single-threaded approach, once a safe bet, is now a significant career risk in a complex ecosystem.