The current mass-adoption phase for AI tools means buying decisions that would normally take 5-7 years are being compressed into 1-2 years. Startups that don't secure customers now risk being shut out, as enterprises will lock in with their chosen vendors for the subsequent half-decade.

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.

Traditional SaaS switching costs were based on painful data migrations, which LLMs may now automate. The new moat for AI companies is creating deep, customized integrations into a customer's unique operational workflows. This is achieved through long, hands-on pilot periods that make the AI solution indispensable and hard to replace.

While AI expands software's capabilities, vendors may not capture the value. Companies could use AI to build solutions in-house more cheaply. Furthermore, traditional "per-seat" pricing models are undermined when AI reduces the number of employees required, potentially shrinking revenue even as the software delivers more value.

The excitement around AI often overshadows its practical business implications. Implementing LLMs involves significant compute costs that scale with usage. Product leaders must analyze the ROI of different models to ensure financial viability before committing to a solution.

The biggest resistance to adopting AI coding tools in large companies isn't security or technical limitations, but the challenge of teaching teams new workflows. Success requires not just providing the tool, but actively training people to change their daily habits to leverage it effectively.

Enterprise software budgets are growing, but the money is being reallocated. CIOs are forced to cut functional, "good-to-have" apps to pay for price increases from core vendors and to fund new AI tools. This means even happy customers of non-mission-critical software may churn as budgets are redirected to top priorities.

Historically, labor costs dwarfed software spending. As AI automates tasks, software budgets will balloon, turning into a primary corporate expense. This forces CFOs to scrutinize software ROI with the same rigor they once applied only to their workforce.

An enterprise CIO confirms that once a company invests time training a generative AI solution, the cost to switch vendors becomes prohibitive. This means early-stage AI startups can build a powerful moat simply by being the first vendor to get implemented and trained.