Why Operationalize Generative AI?

Successfully operationalizing Generative AI is the only way to get a return on the massive investments being made. While 62% of companies use AI agents regularly, analysis shows 95% get zero ROI.

Simple pilots fail because they do not build the trust required for enterprise-wide use.

True operationalization moves beyond personal productivity tools. It tackles the main problem of the Verification Tax. This is the cost of employees double-checking confidently wrong AI outputs. 

By building a trusted, dependable, and secure operational engine, enterprises can finally bridge the GenAI Divide. You can operationalize Generative AI to get significant value instead of getting stuck in pilot purgatory.

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4 Reasons to Operationalize Generative AI

1. To Prevent Massive Investment But Zero Return

The enterprise landscape is defined by a stark contradiction. Conflicting market data shows both 74% ROI and a 95% zero return failure rate. Both are true. The important part is to understand this GenAI Divide. 

There is an 83% success rate for personal, general-purpose tools (like copilots). But there is a 95% failure rate for the high-value, task-specific, or embedded GenAI systems that promise true business change.

2. To Avoid the Verification Tax

The primary cause of this 95% failure rate is the Verification Tax. Practitioners identify this as the main operational problem. 

AI pilots stall because the models are confidently wrong. They produce hallucinations with the same authority as correct facts. 

This forces human employees to double-check every single output. This manual process completely removes any potential ROI and destroys user trust.

3. The Need for Enterprise-Grade Trust

Simple, consumer-grade tools are sufficient for personal productivity. But they lack the security, accuracy, and governance for main enterprise tasks.

To be truly valuable, AI must be embedded in processes, which requires it to be trusted. 

The single greatest differentiator for high-maturity AI companies (the successful 5%) when they operationalize Generative AI is trust.

In 57% of these high-maturity companies, business units trust and are ready to use new AI solutions. This is compared to just 14% in low-maturity companies.

4. From Point Solutions to a Platform-Led Method

The 95% of companies who fail are those stuck with fragmented, isolated point solutions in pilot purgatory. 

The 5% who succeed are those who embed AI into processes. This requires a planning shift to a platform-led method. 70% of enterprises are now prioritizing this. This method is easier to grow as it uses existing, familiar systems (like your CRM or ERP). 

When you operationalize Generative AI, it also gives the model secure access to proprietary, enterprise-wide data. This allows for accurate and holistic insights rather than isolated, untrustworthy guesses.

The 5-Phase Lifecycle: How to Operationalize Generative AI From PoC to Production

To cross the GenAI Divide, leaders must guide their companies through a structured, 5-phase operational lifecycle. This plan synthesizes multiple expert frameworks. It gives a clear path from initial idea to continuous, in-production value.

1. Planning Assessment and Scoping

Before any code is written, a strict planning filter must be applied. This phase, when you operationalize Generative AI helps established the business case. It weeds out the trivial PoCs that lead to the 95% failure rate. Key steps include:

• Define the Business Case: Identify a clear path and business value for the AI project. This is a primary challenge. Without it, companies are left with fragmented efforts.
• Filter with the Cost Equation: Apply a proven model: 1) Break jobs into tasks. 2) Assess each task using the generative AI cost equation. This equation must include not just API fees. It must also include the less-obvious, potentially more significant costs of building mechanisms to detect and fix errors (the Verification Tax).
• Triage Internal Demand: Use a Use / Compose / Build model. Use cases are solved with existing tools (like Microsoft Copilot). Compose cases use low-code tools (like Copilot Studio). This reserves expensive Build resources for only the highest-value projects that have passed the cost equation.
• Direct Build Efforts: For Build projects, direct attention to the 75% value quadrant. Analysis shows about 75% of GenAI's value is concentrated in four functions: Customer Operations, Marketing & Sales, Software Engineering, and R&D.

2. Planning, Data, & Model Selection

Once a use case is approved, this phase operationalizes Generative AI, designing the technical and data structure. This is where the basic decisions are made to support a trustworthy system that can grow when you operationalize Generative AI. Key steps include:

• Assess Data Landscape: Check the quality, availability, and location of the data needed for the use case. This includes identifying proprietary enterprise content that will ground the model.
• Make the Build vs. Buy LLMOps Decision: Leaders face an important choice for the operational platform itself. Building in-house often presents an Illusion of Control. It results in a fragile, high-maintenance stack. Buying a purpose-built GenAl control plane acts as an Accelerator, greatly shortening the path to ROI.
• Standardize on RAG: Adopt Retrieval-Augmented Generation (RAG) as the default enterprise structure. RAG is the key pattern for securely connecting LLMs to proprietary data. This greatly lowers hallucinations, builds trust, and solves the stale data problem of foundation models.
• Select Data Pipelines: Choose the vector database and data ingestion pipelines (ETL). These will be used to change enterprise documents into a queryable, vectorized format for the RAG system.

3. Building and Experimentation (The Inner Loop)

When you operationalize Generative AI, this is the iterative development phase where the main system is built. This is an inner loop of prompt engineering and model customization. Key steps include:

• Data Curation and Vectorization: Prepare and enrich the proprietary data identified in Phase 2. This includes chunking documents and running them through an embedding model to store in the vector database.
• Iterative Prompt Engineering: Systematically develop and test the system prompts that will guide the LLM's behavior. This includes curating a library of tested base prompts for different tasks.
• Understand LLMOps vs. MLOps: Recognize this is a new discipline. Traditional MLOps uses clearly defined metrics (like an F1 score). LLMOps operates in an unclear world. It requires new benchmarks (like ROUGE or BLEU scores) to measure the quality of generated text, not just its binary correctness.
• Build Orchestration (RAG Process): Use tools (like LangChain or Semantic Kernel) to build the system logic. This follows the standard RAG pattern: 1) User prompt goes to retrieval system. 2) System queries vector DB. 3) Top-ranked, relevant documents are retrieved. 4) This context is stuffed into the prompt. 5) The LLM generates a response constrained to that context.

4. Operationalization and Deployment (The Outer Loop)

Once the inner loop produces a dependable model, it is time to operationalize it. This outer loop when you operationalize Generative AI involves building the strong, secure, and growable system to release the system to users. Key steps include:

• Strict Model Validation: Validate the model against a golden set of question-answer pairs. This includes A/B testing different prompts or models. This helps see which performs best against your defined business metrics.
• Secure System Deployment: Deploy the GenAI system into a production environment. It should be containerized and managed within your existing CI/CD and DevOps processes.
• Connect with Main Systems: Execute the platform-led plan by embedding the new AI system directly into main enterprise systems (like SAP, Salesforce, or Microsoft Dynamics). This makes it part of the user's natural process.
• Establish LLMOps Baselines: Before launch, establish the baseline performance metrics for latency, cost, and quality. This baseline is needed for detecting drift in the next phase.

5. Optimization and Continuous Improvement

Generative AI is not a deploy-and-forget system. When you operationalize Generative AI, this phase is the ongoing operational engine required to maintain value and trust after deployment. This is the engine that fights the Verification Tax. Key steps include:

• Continuous Performance Monitoring: Use a dedicated LLMOps platform. This will continuously monitor for drift, hallucinations, cost, and silent failures (where the API is up but the answers are wrong).
• Use User Feedback: Actively seek user feedback. This is the most direct way to measure the Verification Tax. Track metrics like retry patterns (a user re-phrasing a question) and user satisfaction scores.
• Create a Human-in-the-Loop (HITL) Process: Build a process for human experts to review, correct, and log model failures. This feedback is used to create the accuracy flywheel that improves the model over time.
• Expand and Improve: Use the performance data and user feedback to continuously improve the system. Expand successful models to new business units and retire underperforming ones.

5 Major In-Production Challenges When Operationalizing Generative AI

Once deployed, operationalizing GenAI presents unique, ongoing challenges. In production challenges, when you operationalize Generative AI, include traditional software monitoring tools that are blind to these. Managing these realities is the main function of a mature LLMOps practice.

1. Detecting Silent Failures and LLM Drift

LLMs are non-deterministic. They demand real-time monitoring to detect silent failures. The primary threat is drift. There are two important types:

1. Prompt Drift (Input Drift):
Happens when user inputs change over time (e.g., new slang, new product questions). These no longer match the training baseline.

2. Output Drift (Concept Drift):
Happens when the vendor (e.g., OpenAI) updates the foundation model. This causes its responses to change unexpectedly, even for the same prompts.

2. Managing the 3 Vectors of RAG Drift

Retrieval-Augmented Generation (RAG) solves the hallucination problem for companies looking to operationalize Generative AI. But it also creates a more complex, three-part failure point. The system's output depends on the user prompt, the LLM, and the retrieved data. 

This creates three drift vectors. The LLM can drift (as described above). The user's prompts can drift (e.g., users start asking about new products not in the data). Most deceptively, the data can drift.

If the retrieval logic is changed, or if the underlying documents or embeddings in the vector database are updated, the context stuffed into the prompt will be different.

This will cause the LLM's final output to drift. This happens even if the LLM and the user's prompt have not changed.

3. Prompt Injection

Working to operationalize Generative AI creates a new, serious vulnerability: Prompt Injection.

There are two primary threat vectors for an enterprise:

1. Direct Attack:
A malicious user tricks an LLM into sharing its original prompt or bypassing safety protocols (a jailbreak).

2. Indirect Attack:
This more deceptive threat includes an attacker embedding a malicious prompt inside a document (like a PDF resume or a support ticket). When the enterprise's trusted RAG system retrieves this document, the malicious prompt is indirectly injected into the LLM's context. This can potentially damage the entire system.

4. Managing Operational Costs with Growth

The computational cost of generative AI can be significant and unpredictable. Cost estimates can vary 500-1,000% from initial projections.

A main LLMOps function is financial governance. This requires using tools to monitor LLM API usage and cost-per-request in real-time, user by user. 

Without this, costs can grow out of control. Best practices when looking to operationalize Generative AI include using pay-as-you-go cloud models to avoid over-provisioning. They also include establishing clear data governance policies to manage the large amount of data being processed and stored.

5. Avoiding Shadow AI and Legacy IT Blockers

A primary challenge for leaders is Shadow AI. Without central governance, individual business units will create their own undocumented and rogue systems. This creates massive, unmanaged security and compliance risks.

At the same time, leaders must battle legacy IT. Over 50% of companies report that legacy IT systems are a primary blocker to expanding AI. All this happens while handling a complex web of emerging rules.

What are the Best Practices for Governance and Security?

1. Establish a Centralized Governance Model

Enterprises must anchor their plan in a formal risk model. An emerging global standard is the NIST AI Risk Management Framework.

A specific Generative Artificial Intelligence Profile was also released to help companies identify unique risks. An actionable first step is to set up an AI Ethics Board within 30 days.

2. Invest in a Purpose-Built LLMOps Platform

High-performing teams do not use a single, monolithic tool. They build modular and composable LLMOps stacks.

When looking to operationalize Generative AI this allows teams to swap components as better technology emerges. The Build vs. Buy decision is important. 

3. Create a Multi-Layered Security Defense

To defend against new attacks like Prompt Injection, a multi-layered defense is required. First, deploy LLM Firewalls or Guardrails.

This is a new class of security tool that includes a specific filter to detect and block jailbreak attempts. Second, also look to sanitize and validate all inputs and outputs when you operationalize Generative AI.

4. Protect Data and IP with Contractual Guarantees

Protecting proprietary data is a primary concern for 95% of enterprises. This requires strong internal controls like Role-Based Access Control (RBAC).

It also means encrypting all data at rest and in transit, and masking sensitive data in non-production environments. Importantly, it also requires external vendor management. 

5. Use Privacy-Preserving Techniques (PII Redaction)

A main governance challenge for RAG is handling documents that contain Personally Identifiable Information (PII).

The simplest and most common method is Prompt Redaction. This includes using a privacy-preserving tool to scan and redact the prompt. This masks out PII data before sending it to the API provider. 

6. Build an Accuracy Flywheel with User Feedback

The final best practice is to create a closed-loop system that continuously improves. The Verification Tax that kills 95% of projects is the direct cost of a system that lacks an accuracy flywheel and trust.

By using a strong LLMOps monitoring (Phase 5) and actively capturing user feedback (both via explicit thumbs up/down buttons and implicit retry pattern metrics), you create a Human-in-the-Loop (HITL) process.

How to Operationalize Generative AI Using Enterprise AI Companies

If you’re wondering how to operationalize Generative AI without the technical experts in-house, engaging Generative AI experts during your transition from Proof of Concept to production can help simplify the entire process. At Entrans, our GenAI specialists bring extensive knowledge of data modeling, RAG structure, LLMOps, DAX functions, and Power Query. Meaning, your data validation and migration process can be done a lot quicker and with far fewer issues.Learn how our team of certified Generative AI experts can help you go live quicker. Curious to know more? Book a free 20-minute consultation call!