Can AI be deployed in Critical Processes? Addressing the Key Challenges

by Ivo Bernardo

You’ve seen it before: a company starts deploying AI but struggles to move beyond the most high-level use cases. Yet, current AI technology has the potential to power near-fully automated business workflows—so what’s missing?

The challenge lies in how you manage the error. In critical business processes, AI errors can be extremely costly and deploying these systems becomes far more complex. AI will always fail at some point—and businesses need to reconcile this reality with the need for reliability in critical processes.

For example, we’ve heard this story countless times from our customers:

  • Banks are happy to use AI for processing documentation—but automatic loan approvals? Out of the question.
  • Pharmaceutical companies embrace AI copilots for employees—but automating FDA or EMA requests? Too risky.
  • Telcos rely on AI for high-level customer support—but letting AI adjust pricing or plans autonomously? Not a chance.

Based on this dillemma, we’ve identified five crucial factors that determine the success of AI deployments in critical processes, all of which are the main vectors in our product, Gen-OS.

To capture the value to be generated by AI, organizations must navigate key challenges and strategically integrate GenAI into their workflows. Below, we break down the essential considerations for maximizing GenAI’s effectiveness.

1. Structured Build Process

One of the biggest challenges in deploying GenAI is ensuring organizations follow a structured development process. While drag-and-drop tools can be useful for non-critical tasks, they often fall short when security, integration, and domain-specific expertise are required. This misalignment can lead to poor implementations or a productization of something that can't be productized. Here are our recommendations:

  • Adopt a rigorous development framework that includes domain-specific fine-tuning and customization.
  • Ensure model alignment with business needs instead of relying solely on generic, pre-built solutions.
  • Recognize the limitations of off-the-shelf solutions, which typically struggle to grasp the nuances of your business.
  • Evaluate vertical AI tools carefully—they may address specific challenges, but how many different AI solutions do you want to manage?
  • For critical processes, rely on expert-built solutions—GenAI applications that will be deployed in critical processes should be developed by data scientists with deep technical knowledge to ensure reliability, performance and constant iteration that doesn't depend on someone else's roadmap. In your personal life, you would not want your next password management app to be built by vibe coding, right?

2. Human-in-the-Loop Mechanism

AI models are not perfect and never will be. Period.

They can hallucinate, make biased decisions, or generate inappropriate content. Having human oversight is crucial to ensure outputs are reliable and aligned with business objectives.

Human-in-the-Loop Mechanism in Gen-OS

From our experience, if you want to deploy AI in critical processes you need to guarantee the following:

  • Implement continuous human review processes
  • Create feedback loops for refining AI-generated content.

53% of companies' boards are worried about managing AI systems' outputs. You probably should too.

3. Integration with Enterprise Systems

GenAI models do not operate in isolation; they need to integrate with existing enterprise software, APIs, and workflows. Proper integration ensures that AI-powered solutions can access relevant data, interact with other systems, and provide meaningful insights in real time.

Without this level of connectivity, AI risks becoming another siloed tool that lacks the ability to improve decision-making, automate processes, or drive efficiency across an organization.

A critical GenAI solution needs APIs and middleware to connect GenAI with core enterprise applications.

If you don't enable it? Probably, you will end up on the bad side of this statistic: 55% of workers use unapproved AI tools. Your employees will use AI no matter what. Your goal is to provide them with the safest environment possible.

4. Performance Monitoring and Continuous Optimization

Deploying a GenAI model is just the beginning. To ensure long-term success, organizations must proactively monitor performance, establish continuous feedback loops, and regularly adjust RAG systems to adapt to new business roles and documents.

Integrating fresh content, refining prompts, and addressing biases will always be essential to maintaining accuracy and relevance, particularly in critical processes.

Without ongoing oversight, AI models risk drifting from their intended purpose, producing outdated or misleading outputs.

Gen-OS Knowledge Center lets you update content for each AI Agent

5. Scaling from Proof-of-Concept to Production

The combination of the former parameters enable you to jump from POC to Production much better.

There is a significant gap in the value generated between proof-of-concept (POC) projects and fully deployed, production-level enterprise systems. While POCs demonstrate the potential of a technology and can generate initial excitement, they often fall short when scaled to real-world, enterprise-level applications. This gap arises due to the factors we've discussed before.

Approximately 90% of GenAI POCs never reached production. Can you imagine the wasted value on these solutions?

Final Thoughts

To successfully navigate the complexities of deploying GenAI in critical processes, you need to follow a structured approach, integrate human oversight, ensure enterprise system integration, continuously monitor performance, and scale effectively from proof-of-concept to production.

At DareData, we understand these challenges and are committed to helping businesses unlock the full potential of AI in their workflows.

If you're looking for the right partner to take your AI integration to the next level and ensure reliable, scalable AI deployments, get in touch with us at ivo@daredata.ai.

Learn more about our AI ops platform, Gen-OS, and discover how we can help you build smarter, more efficient systems that drive long-term success for your business.