Breaking Out of the POC Trap: Turning Generative AI Trials into Real Results
Generative AI, led by technologies like ChatGPT, has captured global attention, revolutionizing industries and how businesses operate. Many organizations are experimenting with generative AI in areas such as customer service, sales management, compliance, business operations, supply chain etc. However, despite its potential, most initiatives remain stuck in the Proof of Concept (POC) stage, unable to transition into production and yield tangible business benefits.
Why? Let’s explore the challenges holding businesses back and the essential features needed for a robust generative AI stack.
Key Challenges in Operationalizing Generative AI
1. Security Gaps
Generative AI like ChatGPT/Large Language Model excels in generating human-like text but when use alone would lack the necessary security features required by businesses. Especially in heavily regulated industries like financial services, features such as user access management and security certifications are non-negotiable. Without these, businesses risk exposing sensitive systems to potential misuse or breaches.
2. Accuracy Concerns
The problem of hallucination—where large language models (LLMs) generate incorrect or fabricated information—is a real and persistent issue. Businesses require factual and grounded outputs, especially in business operations and responses to customers. Erroneous output can lead to regulatory fines, reputational damage, or operational inefficiencies.
3. Data Privacy Compliance
Generative AI often processes sensitive data during business operations. When data is passed to LLMs for tasks like understanding, chunking, or generating responses, it may inadvertently leave the organization’s secure environment, particularly when using publicly hosted models like OpenAI’s ChatGPT. This poses a significant risk of violating stringent privacy regulations like GDPR and PDPA, especially if personal data is involved.
4. Appropriate and Aligned Output
In customer-facing processes like sales and support, AI-generated responses must adhere to corporate language and branding standards. Beyond filtering vulgar or inappropriate content, businesses need AI outputs to reflect their values and tone, ensuring a cohesive customer and employee experience.
Essential Features to Deploy Generative AI to Production
To overcome these challenges, businesses need an integrated generative AI solution with the following critical features:
1. Advanced Security
Security must go beyond basic RBAC and certifications. Generative AI platforms often need to connect to multiple data sources, each with varying user access rights across active directories. For instance, consolidating access control for systems like ERP, CRM, and HR platforms into a single AI interface ensures secure and seamless integration without compromising data integrity. To achieve this, an advanced user access rights management system is essential, capable of merging and reconciling diverse access rights from different active directories across systems.
2. Taxonomy Knowledge Graph
An organization’s taxonomy—its structured understanding of concepts, terms, and relationships—must be well-defined. A taxonomy knowledge graph ensures:
For example, a retail company’s knowledge graph could map product categories, customer preferences, and supplier details, ensuring AI outputs align with the business’s specific context.
3. Data Privacy Screen
A robust data privacy screen ensures sensitive data is masked before sending requests to the LLM. Post-processing, the data can be unmasked before being returned to the user. This mechanism enables organizations to leverage generative AI without breaching privacy regulations.
?4. AI Guardrails
AI guardrails are essential to manage the tone, language, and appropriateness of outputs. Beyond preventing vulgarities or offensive content, guardrails enforce corporate language and branding standards, ensuring a consistent customer and employee experience.
For instance, AI outputs for marketing materials should align with the company’s creative and promotional guidelines.
A Technology Scan: Moving Beyond POC with End-to-End Solutions
Many software providers offer individual components of these capabilities, but few deliver a comprehensive generative AI stack. Squirro is one such platform, offering an integrated solution with key features, including:
Squirro’s end-to-end stack helps businesses move beyond POCs, enabling real operational use of generative AI to achieve tangible outcomes.
Ready to Explore Generative AI for Your Business?
Don’t let generative AI remain a POC experiment. Contact us at [email protected] to learn how our solutions can help your business overcome these challenges and unlock the full potential of AI in real-world operations.