As organizations race to tap into the power of generative AI, the path to enterprise-scale adoption remains challenging. On that path, the choice between building GenAI solutions in-house or leveraging a pre-built platform is particularly critical. While in-house enterprise AI development promises more control and customization, the reality is typically fraught with unexpected costs, technical headaches, and scalability challenges.
In this blog, we’ll explore four barriers that can derail in-house GenAI initiatives, ranging from protecting sensitive data and enforcing regulatory and corporate compliance to ensuring enterprise security and scalability. Then, we’ll examine why opting for a prebuilt, enterprise-ready platform outpaces starting from scratch, offering faster deployment, robust security, and scalable innovation that delivers cost-effective AI with measurable impact and ROI.
Access control lists (ACLs) are crucial for ensuring sensitive data remains secure. However, implementing ACLs in retrieval augmented generation (RAG) applications is highly complex. Unlike with standard search, adding ACLs to the vector search exponentially increases computational complexity, leading to performance lags. This technical hurdle often limits in-house solutions from scaling effectively.
Even for enterprises with the capacity to build AI solutions, enforcing ACLs at scale remains a significant challenge, requiring expertise and resources that most internal teams – and many third-party GenAI providers – lack.
In heavily regulated industries like financial services, compliance with regulatory and corporate policies is non-negotiable. This is equally true for human employees and, as they become increasingly relied upon in front and back-of-shop operations, their generative AI-powered counterparts. To minimize legal and operational risks, GenAI systems need AI guardrails to maintain ethical, compliant outputs while aligning with brand voice and regulatory frameworks, for example, ensuring FINRA compliance in the US.
Many in-house proofs of concept (PoCs) struggle to rigorously meet industry compliance standards, creating risks that prevent deployment at scale. Integrating deep industry expertise, the Squirro Enterprise GenAI platform provides the assurance organizations need to clear these hurdles effectively.
In-house GenAI solutions face critical security challenges, including safeguarding sensitive data, meeting information security requirements, and maintaining security during integration with enterprise systems. Each of these tasks requires deep expertise in generative AI security that organizations new to the technology typically lack, increasing the risk of breaches and regulatory issues.
Third-party platforms like ours mitigate these risks with proven, scalable, and secure solutions, allowing organizations to confidently enhance their operations with GenAI while avoiding the pitfalls of in-house development.
Building a single-use GenAI application is relatively straightforward, but scaling to adjacent use cases often means having to start from scratch each time. This creates spiraling development and maintenance costs that strain internal resources.
Scaling in size presents its own challenges. Ingesting millions of live documents across multiple repositories, supporting thousands of users, and managing complex access control lists, can quickly become a significant drain on resources. This not only risks derailing other IT projects but also disrupts everyday operations.
Partnering with scalable AI platform vendors allows organizations to eliminate inefficiencies, accelerating expansion into new use cases without duplicating effort.
Opting for a prebuilt platform offers organizations a strategic advantage, enabling faster GenAI deployment and reduced time to value. By leveraging ready-made solutions, it lets businesses accelerate their AI initiatives to achieve measurable ROI faster. This agility ensures they stay ahead in competitive markets, responding quickly to evolving customer needs.
Prebuilt platforms also eliminate the technical complexities of in-house development. These solutions come pre-configured to address common hurdles like security, compliance, and scalability, freeing their teams to focus on innovation and core business goals.
Perhaps most critically, buying early mitigates risk. Vendors with deep expertise provide proven, enterprise-grade solutions designed to scale seamlessly across departments. This ensures secure, compliant deployments without the delays that often accompany in-house builds.
Squirro offers an enterprise-ready GenAI platform tailored to your needs:
Partnering with Squirro helps organizations overcome the challenges of in-house development, delivering scalable, secure, and transformative AI solutions, as outlined in our white paper on navigating the decision whether to building or buying enterprise GenAI technology.
By investing in our proven, enterprise-ready platform, organizations can unlock the full potential of GenAI, driving innovation and measurable results faster than ever. Ready to transform your operations with Squirro? Be sure to get in touch or book a demo!