From Pilot to Production: Operationalizing AI for Enterprise Success

Artificial intelligence is no longer a theoretical concept; it’s a practical tool for driving business innovation and efficiency. Yet, despite its promise, many enterprises are struggling to move their AI projects from the pilot phase to full-scale production. According to Forrester, only 15% of businesses have successfully scaled their AI projects beyond the initial pilot. This challenge highlights a crucial distinction: the difference between deploying AI and operationalizing it.

Deploying AI simply involves setting up and running a model in a specific environment.

Operationalizing AI, on the other hand, is a more comprehensive approach that integrates AI into existing workflows, ensures scalability and maintains compliance and sustainability over time. It’s about moving from isolated projects to a cohesive, enterprise-wide AI strategy. This is a strategic imperative for businesses that want to ensure their AI investments deliver measurable returns.

The playbook for operationalizing agentic AI

To help your business bridge the gap from proof of concept to production, we’ve created a new guide: “How to Operationalize AI Agentic Workflows for Your Enterprise.” This guide provides a clear, five-step playbook to help you successfully integrate AI into your existing workflows and unlock its full potential.

Step 1: Define business-aligned use cases

The first step is to identify specific areas where AI can simplify processes and align those uses with your business goals. This requires a needs assessment with key stakeholders to understand their pain points. By prioritizing high-impact areas, such as enhancing threat detection for a CISO or automating supply chain management for a CTO, you can set clear objectives and define measurable key performance indicators (KPIs) for each use case.

Step 2: Implement a secure AI foundation

Before you can deploy any AI, you need a secure and scalable infrastructure to support it. This involves evaluating your current IT systems, choosing a trusted platform that offers robust observability and security, and ensuring your data is clean and ready for AI workloads. A strong foundation is critical for protecting sensitive data and maintaining compliance, especially when leveraging diverse open-source components.

Step 3: Develop and deploy agentic workflows

This is where you bring your AI models to life. The goal is to create AI agents and workflows that drive efficiency and innovation. Our guide recommends developing and training AI models using advanced algorithms and using tools like Langflow to build your workflows. Importantly, this step also includes providing training for employees on how to interact with these new systems to ensure a smooth transition.

Step 4: Monitor, measure and optimize performance

Deployment is not the final step; continuous improvement is key to long-term success. You must define and track key performance metrics to ensure your AI workflows are performing as expected. Regular audits are also essential to ensure compliance and ethical standards are being met. By continuously monitoring and measuring the impact of AI on business outcomes, such as supply chain efficiency or customer satisfaction, you can refine and optimize your systems over time.

Step 5: Scale and standardize across departments

The ultimate goal is to expand the use of AI throughout your organization and standardize processes. This requires fostering collaboration between different business units and ensuring your infrastructure can support growing needs. By standardizing AI usage and security protocols across all departments, you can ensure consistent and predictable ROI across the entire organization.

Why choose a trusted platform?

Navigating the challenges of operationalizing AI—from dealing with black-box systems to managing complex infrastructure and adhering to ever-shifting regulations—is a significant undertaking. Choosing a trusted platform is key to overcoming these hurdles. SUSE provides an AI platform that offers a secure, scalable, and open environment for your AI innovation, ensuring you have complete control and ownership.

By following the steps outlined in this guide and leveraging a trusted platform, you can ensure your AI initiatives are both controlled and trusted, ultimately meeting your ROI goals and future-proofing your investments.

Ready to get started? Download the free guide and take the first step toward a successful AI strategy.