Production AI success: From gen AI promise to business impact

Generative AI (gen AI) has the potential to significantly improve customer engagement, reduce costs, and boost productivity. However, many enterprises struggle to move beyond initial experiments to widespread and scalable implementations. This article examines 6 common obstacles to enterprise AI adoption—as highlighted in this Harvard Business Review report—and how Red Hat can help you overcome them.

1. Defining a strategic path and clear business value for AI

Many organizations initiate gen AI projects without a defined strategy or roadmap. This can lead to fragmented efforts that make it difficult to demonstrate a return on investment (ROI). A well-articulated strategy is crucial for successful adoption. You may also find your organization struggling to align AI solutions with specific, impactful business use cases. Demonstrating clear business value can often be a challenge. 

How Red Hat helps:

• Strategic alignment workshops: Red Hat Consulting will collaborate with your cross-functional teams to clarify business goals and identify high-value gen AI use cases. These tailored workshops help your organization streamline data generation, refine data, and customize language models. This helps create a prioritized roadmap for experimentation, deployment, and scaling, directly tying AI initiatives to your strategic business objectives.
• Business-first use case identification: We will work with your teams to surface pain points and find opportunities where AI can deliver real value so projects address actual business problems. Red Hat's approach focuses on building and running AI solutions that align with how your business operates, from initial AI experiments to launching and scaling in production.
• Pilot-to-production co-creation: Red Hat works alongside your internal teams to rapidly build, test, and demonstrate value through proofs-of-concept, further laying the groundwork for enterprise-wide scaling. This collaborative approach will help your organization see the real-world impact of AI solutions, building confidence and momentum for broader adoption.

2. Addressing the AI talent gap

A global shortage of skilled AI talent is a growing problem. This isn't just a bottleneck—it can prevent your organization from fully harnessing AI's transformative potential. Addressing this gap requires strategies for developing both future and current workforces.

How Red Hat helps:

• Upskilling and reskilling programs: We offer our Red Hat Learning Subscription, customized workshops, and role-based learning paths for AI-related technologies. These resources give your teams access to an extensive curriculum of self-paced training, hands-on labs, and expert-led videos, helping them develop foundational skills and address emerging challenges.
• Skills transfer through co-creation: This approach moves beyond traditional training sessions by fostering a "learn-by-doing" environment. In an AI Incubator residency, Red Hat AI experts will work side-by-side with your team to help you build a roadmap for your AI journey. This direct collaboration provides real-world experience, allowing your team members to observe, participate in, and eventually lead complex tasks related to AI application development, deployment, and management.
• Open source tooling for broader talent pools: Red Hat's AI stack, built on widely adopted open source, community-developed tools such as vLLM, Kubeflow and llm-d, reduces reliance on proprietary platforms and rare specialists. These upstream community projects are where developers collaborate on the core tools and components that enable scalable, portable, and simplified AI workflows on Kubernetes.
• Vendor-neutral talent strategy: Red Hat promotes open standards and tools that can help your organization reduce its dependence on hard-to-hire, vendor-specific skills. This approach can also help you attract talent interested in open source technologies and foster a more diverse and accessible talent pool, making it easier for you to acquire and retain skilled AI professionals.

3. Establishing a robust and scalable AI infrastructure

Many existing IT and data infrastructures are unprepared for gen AI integration. AI requires scalable, flexible, and cost-effective data storage and processing capabilities, and the computational demands of AI training and inferencing can quickly overwhelm traditional setups. Infrastructure limitations are a major scaling hurdle.

How Red Hat helps:

• Hybrid cloud platform for AI: Red Hat AI provides containerized, GPU-accelerated environments for model training, fine-tuning, and inference. With Red Hat AI Inference Server, optimized model inference is delivered across hybrid cloud environments, offering scalability and efficiency whether deployed on-premises, in the public cloud, or at the edge—even in disconnected environments. All building on the guiding principle of supporting any model, any accelerator, any cloud.
• Data and model lifecycle management: Our AI stack and integrations, particularly through Red Hat OpenShift AI, help automate data preparation, model training, tuning, and deployment. OpenShift AI also supports comprehensive lifecycle management and data science pipelines for AI models at scale, enabling reproducibility and compliance across hybrid environments.
• Infrastructure automation: Red Hat Ansible Automation Platform can be used to automate provisioning and management of AI infrastructure, helping reduce the complexity and manual nature of day-to-day IT management. This helps your team be more efficient and gives them time to focus on more strategic work.
• Infrastructure readiness assessments: Red Hat Consulting can help identify bottlenecks in your existing infrastructure and work with your teams to develop roadmaps for modernization and scaling. These assessments can help you understand your current state and lay out a clear roadmap to build a more robust, scalable foundation for your AI workloads.

4. Addressing AI risks and regulatory compliance

Enterprises face significant data privacy and cybersecurity challenges when deploying AI. These include safeguarding AI systems against threats like algorithmic bias, hallucination, and data poisoning, all of which can compromise the integrity and reliability of a system.

At the same time, evolving regulatory requirements add another layer of complexity. Stringent data privacy rules and data residency mandates can directly impact deployment strategies and increase compliance overhead. This dual challenge of technical security and regulatory adherence requires a proactive approach to AI governance.

How Red Hat helps:

• Responsible AI and governance frameworks: Red Hat can help you build a framework that helps address ethical considerations, data privacy concerns, and security threats from the outset. This includes integrating features like algorithmic bias detection, AI guardrails and human-centric design, so you can develop and deploy AI more responsibly.
• Defense in depth: Red Hat's security approach emphasizes secure-by-design principles to enhance the protection of AI systems and data. This layered strategy, often augmented by tools like Red Hat Insights, adds capabilities to better identify, prioritize, and mitigate risks, helping enhance your overall security posture.
• Flexible deployment for data sovereignty: Red Hat's hybrid cloud strategy enables you to deploy AI on-premises, in sovereign clouds, or across multiple regions. This enables careful control over data residency and helps meet evolving regulatory requirements, such as GDPR or local data protection laws.
• Automated policy enforcement: Ansible Automation Platform helps enforce policies at scale. This offers consistency and reduces human error in regulated environments, so AI-driven automation functions within defined internal and external policies.
• Open Source Assurance program: Red Hat offers intellectual property assurances for Red Hat-provided open source software and AI models. This program defends customers against infringement claims within stated limitations, providing greater comfort and enhanced protection when deploying Red Hat solutions.

5. Cultivating executive sponsorship

A primary barrier for your organization may be that leadership does not perceive gen AI as a critical strategic priority. Strong executive support is a key success factor for driving AI adoption at speed and scale. Without high-level backing, AI initiatives often struggle to secure adequate resources, overcome organizational resistance, and integrate deeply into core business functions.

How Red Hat helps:

• Executive education and briefings: Red Hat experts offer tailored workshops to demystify gen AI, showcase real-world use cases, and highlight competitive risks of inaction. These briefings, often conducted at Red Hat's Executive Briefing Centers or through dedicated virtual sessions, provide a focused environment for IT leaders to engage directly with Red Hat strategists and technical leaders.
• Business value framing: We help frame AI use cases in terms of clear business outcomes like revenue growth, cost savings, and risk reduction, emphasizing direct ROI for your organization. By translating technical AI capabilities into concrete financial or efficiency gains, Red Hat can help your leaders clearly understand the direct value AI initiatives can bring to your enterprise.
• Quick-win pilots: By working with your teams to co-develop rapid proofs-of-concept, Red Hat helps prove AI's feasibility and value. This builds confidence among stakeholders and creates internal success stories that you can use to champion broader adoption within your organization.

6. Overcoming employee reluctance

Finally, your employees may exhibit reluctance to adopt new AI technologies. Fostering transparency, inclusion, and enablement can help overcome this friction, encouraging your teams to adopt and experiment with AI, with a goal of developing internal AI champions.

• Inclusive adoption strategy: Red Hat will help you involve employees early, co-develop solutions that address pain points, and position AI as an augmentation tool. This approach empowers employees, helping them see AI as a way to free up time for more creative work, which can lead to more enthusiastic adoption of tools and approaches.
• Hands-on training and enablement: Our consulting and learning programs can help upskill both technical and non-technical employees, empowering them to use and manage AI tools effectively. The Red Hat AI learning hub, for instance, provides a range of resources from foundational AI concepts to advanced model training and deployment.
• Iterative change management: Red Hat consultants can work with your organization to introduce AI in small, manageable steps, allowing teams to test, iterate, and provide feedback. This approach fosters adaptability and helps reduce resistance to new technologies.
• Cultural alignment: Finally, Red Hat's open source principles promote collaboration, community building, and continuous feedback loops to improve AI usage and employee experience. This open culture encourages shared responsibility and the free exchange of ideas, which are essential for successful AI integration.

Building a foundation for AI success

Remaining competitive today means embracing gen AI, as standing still risks falling behind. At the same time, it's important to approach AI with a thoughtful, disciplined strategy, making AI a foundational part of your business plan, choosing the right vendors, and building on scalable open infrastructure.

Red Hat can help you establish this solid foundation for your AI strategy by co-creating solutions with your teams on our hybrid cloud platform built for AI. With a clear roadmap and a solid foundation, your organization will be poised to turn AI's transformative potential into a competitive advantage.

Download the Harvard Business Review report: Navigating the Generative AI Landscape.