Nimble small and medium-sized businesses will increasingly use AI to build custom internal tools, especially for CRM. They will opt to create the 20% of features they actually need, rather than pay for complex, expensive enterprise software where they ignore 80% of the functionality.
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The rise of AI services companies like Invisible and Palantir, which build custom on-prem solutions, marks a reversal of the standardized cloud SaaS trend. Enterprises now prioritize proprietary, custom AI applications to gain a competitive edge.
The one-size-fits-all SaaS model is becoming obsolete in the enterprise. The future lies in creating "hyper-personalized systems of agility" that are custom-configured for each client. This involves unifying a company's fragmented data and building bespoke intelligence and workflows on top of their legacy systems.
Previously, building sophisticated digital experiences required large, expensive development teams. AI and agentic tools level the playing field, allowing smaller businesses to compete on capabilities that were once out of reach. This creates a new 'guy in the garage' threat for established players.
"Vibe coding" platforms, which allow users to create apps from natural language, pose a direct threat to the B2B SaaS market. For simple workflows, it is becoming faster for a team to build its own personalized app than to navigate the sales, procurement, and integration process for an existing SaaS product.
AI is becoming the new UI, allowing users to generate bespoke interfaces for specific workflows on the fly. This fundamentally threatens the core value proposition of many SaaS companies, which is essentially selling a complex UX built on a database. The entire ecosystem will need to adapt.
The surprising success of Dia's custom "Skills" feature revealed a huge user demand for personalized tools. This suggests a key value of AI is enabling non-technical users to build "handmade software" for their specific, just-in-time needs, moving beyond one-size-fits-all applications.
The primary value of AI app builders isn't just for MVPs, but for creating disposable, single-purpose internal tools. For example, automatically generating personalized client summary decks from intake forms, replacing the need for a full-time employee.
In the future, it will be easier for businesses to build their own custom software (e.g., Salesforce) through prompting than to buy and configure an off-the-shelf solution. This shift towards "liquid software" will fundamentally challenge the one-size-fits-all SaaS model, especially for companies that currently rely on implementation partners.
The biggest misconception is that SMBs aren't ready for AI. In reality, their lack of corporate bureaucracy allows them to be more agile and move faster than large enterprises. The key for vendors is to provide accessible, scalable solutions with a low entry point, enabling them to take small, quick steps.
Large companies integrate AI through three primary methods: buying third-party vendor solutions (e.g., Harvey for legal), building custom internal tools to improve efficiency, or embedding AI directly into their customer-facing products. Understanding these pathways is critical for any B2B AI startup's go-to-market strategy.
