Enterprise AI: A Guide to Moving from Pilots to Production
Build the right strategy, data foundation and infrastructure to scale AI with CDW.
AI Urgency Is Real. Scaling It Is Harder Than It Looks.
Most organizations feel pressure to “do something with AI,” but enthusiasm alone doesn’t translate into measurable outcomes. Challenges typically start appearing after the first proof-of-concept projects, when teams run into the realities of data readiness, governance, security and infrastructure scale. And this is about the time when organization leaders are looking for proof of ROI.
The good news is that these challenges are solvable when AI initiatives are treated like an operating model shift, not a one-time experiment.
The pilot-to-production gap is the warning sign
AI pilots are often easy to start and difficult to finish. The hardest work begins when you need repeatable processes, trusted data and clear ownership across IT, security and individual lines of business.
88%
of AI pilots fail to reach production.1
Data is the foundation
If data is inconsistent, ungoverned or hard to access, AI becomes “artificial ignorance” fast. Enterprise AI depends on clean pipelines, strong metadata practices and a governance model that supports responsible scaling.
63%
of organizations either do not have or are unsure if they have the right data management practices for AI.2
Trust, security and compliance can make or break adoption
As AI expands beyond chatbots and into agents and workflow automation, risk increases. Securing the AI stack means protecting data, models, tokens and the systems AI connects to while aligning to frameworks and regulations that matter to your environment.
NEARLY
70%
of organizations cite the rapid pace of AI development as their leading security concern related to GenAI adoption.3
Key Considerations for Building Production-Ready AI
Moving from AI exploration to measurable outcomes goes faster when you start with clarity on what you are trying to solve and what will limit you if you scale. Use the questions below to help refine priorities, uncover dependencies and avoid the common “we’ll fix it later” traps.
What business outcomes are you solving for first?
Before you invest in GPUs, cloud capacity or a new platform, get specific on the outcome. Are you reducing cycle time, improving forecasting accuracy, modernizing the service desk or enabling end users with embedded AI? Clear outcomes prevent “AI for AI’s sake” and help you select the right approach, whether generative, predictive or agentic.
Is your data aligned to the use case and risk profile?
AI-ready data requirements are different from traditional BI. You may need stronger lineage, tighter access controls, better metadata and new governance for prompt, model and agent outputs.
Do you have the right tools to operationalize AI, not just test it?
Pilots can run on spreadsheets and small sandboxes. Production needs an operating model: MLOps for machine learning or LLMOps for large language model practices, monitoring, cost controls and repeatable deployment patterns across environments.
How will you secure the AI pipeline end to end?
Enterprise AI expands the attack surface. Consider how you will prevent poisoning, protect sensitive data, and manage identity and access when AI connects to enterprise systems and workflows.
What infrastructure will support your workloads at scale??
Plan for training vs. inference, latency, throughput, data gravity, power and cooling realities and where workloads should run across on-premises, colocation and cloud.
Common pitfalls to avoid
Starting with tooling instead of strategy
Buying platforms before aligning stakeholders and outcomes creates fragmentation and weak adoption.
Treating governance as “phase two”
If governance is bolted on later, it rarely catches up to use case sprawl and risk exposure.
Overestimating internal capacity for day-two operations
Ensure adequate skilled resources are in place for monitoring, patching, model management and cost governance for long-term success.
A Practical Path Forward, Organized Around How AI Programs Mature
CDW helps you navigate the AI spectrum from strategy and governance through scalable environments and outcome-driven solutions. We’ll meet you where you are on the journey, then help you build toward production-ready capabilities in a structured way.
Define your AI strategy
Strategically plan for AI adoption and expansion by aligning initiatives to executive priorities, selecting high-value use cases and setting measurable success criteria. CDW can help translate ambition into a roadmap that balances speed with responsible scaling.
- AI strategy workshops and roadmap development
- Governance models aligned to your environment and risk profile
- Use case prioritization based on value, feasibility and readiness
Build AI toolsets
Design and deploy the data ecosystem and tooling needed to store, transform and operationalize data for AI. Add advanced AI tools and AIOps to improve model management, visibility and operational efficiency.
- Data platforms, integration and modernization to reduce silos
- MLOps or LLMOps foundations for deployment, monitoring and iteration
- Automation and AIOps to reduce manual work and improve reliability
Solve business problems with AI
The fastest route to ROI is solving specific business problems with clear outcomes. CDW helps identify the right approach and deploy AI solutions that improve forecasting, customer experience, service desk performance and more.
- Outcome-driven AI use cases tied to operational KPIs
- Integration with enterprise systems so AI can take action, not just answer questions
- Accelerators and repeatable patterns to reduce time to value
Architect the right AI infrastructure
AI performance depends on the environment underneath it. CDW provides infrastructure options for your most demanding workloads, from GPU-ready compute and AI-ready data centers to scalable hybrid and cloud architectures that match your requirements.
- Infrastructure design for training, inference and agentic workflows
- Workload placement across on-premises, cloud and hybrid environments
- Architecture guidance that accounts for performance, cost control and governance
Empower people with AI
AI value scales when it reaches the people doing the work. CDW helps enable end-user productivity with embedded AI tools and practical adoption support so AI becomes part of day-to-day workflows, not a separate experiment.
- Enablement for tools like Copilot, Gemini and enterprise chat experiences
- Change management and role-based training that drive adoption
- Guardrails that help users work faster while protecting sensitive data
Why CDW for Enterprise AI
CDW helps organizations move from experimentation to measurable outcomes by connecting strategy, data, security, people and infrastructure into a practical path forward. Whether you’re defining your first priority use cases or scaling AI across the enterprise, we bring the expertise and breadth to help you execute with confidence.
ONLY
2%
of organizations are “highly ready” to scale enterprise AI.4
Services to get you from idea to execution
Scaling AI requires more than implementation support. CDW can help you define a roadmap, establish governance and operational practices, then stand up repeatable processes for deployment, monitoring and iteration. The goal is to reduce friction between IT, security and the business so AI initiatives have clear ownership and a sustainable operating model once they go live.
The right technology for your AI build
CDW helps you select and integrate the platforms and tools that fit your use cases, environment and risk profile, from data and analytics foundations to AI platforms and automation. We focus on interoperability and long-term manageability so you can avoid tool sprawl and build a stack that supports both near-term pilots and production-scale deployments.
Solutions designed for real-world outcomes
AI value is proven in the workflow. CDW helps you connect AI to the business problems that matter most, then integrate solutions into the systems and processes where work actually happens. By prioritizing outcomes like productivity, service improvement and smarter decision-making, we help you measure impact, scale what works and refine your approach as needs evolve.
Four Ways CDW AI Services Drive Value
There is no single “silver bullet” for AI success. It is a set of coordinated decisions across strategy, data, security, people and infrastructure. CDW helps you move faster with less risk by making each step practical and connected.
Accelerate time to value — Prioritize use cases, build an execution roadmap and reduce the pilot-to-production gap with proven strategies and expert guidance.
Build trusted foundations for responsible scaling — Strengthen governance, quality and security so AI can expand without creating uncontrolled risk.
Increase productivity across the organization — Enable end users with embedded AI tools and adoption support so value is not limited to technical teams.
Create scalable performance and cost control — Design infrastructure and workload placement that support your AI requirements while managing operational complexity.
Sources:
1 CIO, “88% of AI pilots fail to reach production — but that’s not all on IT,” March 2025
2 Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” February 2025
3 Thales, “2025 Thales Data Threat Report,” May 2025
4 F5, “2025 State of AI Application Strategy Report: AI Readiness,” 2025
Turning AI Ambition into Measurable Outcomes
Take the next step in your AI journey. Connect with CDW to schedule an AI strategy workshop or platform assessment and identify your next best move toward production-ready AI.