Enterprise AI and Data: A Practical Guide to Building at Scale
Bring Strategy, Data, Governance, Infrastructure and Operations Together To Turn AI Ambition Into Measurable Outcomes
AI Adoption Is Accelerating. Creating Value Takes More.
AI is no longer a future initiative. Organizations are investing, experimenting and putting AI into the hands of employees now. But the path to driving real value becomes more complex as AI moves from individual tools and isolated pilots into core workflows. At that point, performance depends not only on the AI itself but on the quality and accessibility of the data behind it, the controls around it, the infrastructure supporting it and the organization’s ability to integrate AI into business processes.
98%
of technology decision-makers are piloting, implementing or upgrading AI technologies.1
The Pilot-to-Production Gap Is Still Real
Pilots can prove an idea works. Production requires the systems and processes supporting it to work as well. Scaling AI introduces integration, security, governance, infrastructure and ongoing operational demands that controlled experiments can often avoid. The challenge is no longer simply proving that AI can work but ensuring it can operate reliably at enterprise scale.
25%
of organizations surveyed had moved 40% or more of their AI experiments into production.2
AI Governance Must Keep Pace With AI Risks
AI depends on the information it can access, the decisions it can influence and the actions it is allowed to take. As organizations adopt more advanced use cases, including agentic AI, governance requirements expand in scope. Policies for data use, security, oversight and accountability need to reflect evolving regulatory requirements and growing concerns around data residency and sovereignty.
73%
of organizations cite data privacy and security among their top AI risks.2
AI Economics Change as Usage Grows
AI costs can come through software subscriptions, API consumption or the infrastructure an organization owns and operates. As usage and reasoning complexity increase, token consumption, compute demand and operating costs can become harder to predict. Organizations need visibility into how model choice, infrastructure and usage patterns affect the economics of each workload.
What It Takes To Build Enterprise AI
Enterprise AI depends on more than any single model, application or infrastructure investment. It requires a connected set of capabilities across strategy, data, governance, infrastructure, operations and workforce readiness. The exact approach varies by organization and use case, but these areas need to work together to support AI effectively at scale.
70%
of respondents say they are personally ready for AI, but only 27% of leaders say their organizations are ready for the changes ahead.3
Business Alignment and Highest-Value Use Cases
Prioritize the business problems and opportunities where AI can have the greatest impact, then define the outcomes and measures that will determine success. Clear priorities help focus investment on use cases that are both valuable and viable, rather than on experimentation for its own sake.
An AI-Ready Data Foundation
AI-ready data depends on access, quality, ownership, lineage, metadata and governance. Organizations need a clear strategy for making trusted, relevant data available to the models, applications and people that need it.
Governance, Security and Sovereignty
Establish policies for data use, model and agent oversight, identity and access, risk classification, human approval and monitoring. Data residency and sovereignty requirements may also shape architecture, deployment and vendor decisions.
AI Workload Characteristics Drive Infrastructure Decisions
Training, inference, retrieval-augmented generation, agents and physical AI can place very different demands on compute, storage, networking, power and cooling. Infrastructure needs to support current workloads while preserving flexibility as demand, scale and workload characteristics change.
AI Development and Operations
Production AI needs repeatable processes for deployment, integration, monitoring and improvement. Depending on the environment, that can include machine learning operations (MLOps) or large language model operations (LLMOps) practices, orchestration, observability, model management and cost controls.
AI Adoption Requires Changes to Roles and Workflows
Effective adoption requires more than access to AI tools. Organizations need to prepare employees for changes to workflows, responsibilities and decision-making processes. Training is important, but so are process redesign, role clarity and clear expectations for where AI can support employees and where human judgment remains essential.
Identify Constraints Before You Scale
Before expanding pilots or making new investments, assess the business, data, technology and workforce environment AI will rely on. These questions can help identify the constraints and dependencies that should shape your priorities and roadmap.
Where Can AI Create the Most Value in the Near Future?
Identify the workflows, customer experiences or operational problems where AI can deliver meaningful business impact. Balance potential value against complexity, risk and organizational readiness to determine which use cases should move first.
What Condition Is Your Data In?
Evaluate data quality, accessibility, ownership, lineage, privacy and security. Determine whether the information required is usable, well governed and available to the systems that need it.
Are the Right Controls in Place for Each AI Use Case?
Assess how systems are approved, inventoried, monitored and reviewed and whether those controls reflect the sensitivity of the data and the potential impact of each use case. For agentic AI, define what actions systems can take independently and where human approval is required.
Can Your Environment Support the Workloads You Want To Run?
Evaluate existing infrastructure, cloud services, data platforms and AI tools against the anticipated demands of priority workloads. Identify gaps in capacity, integration, security or scalability before expanding the environment.
Do You Have Visibility Into AI Costs?
Assess visibility into token consumption, compute utilization, software costs and ongoing operations. Establish clear ownership for monitoring costs, optimizing resources and maintaining performance over time.
Is the Workforce Ready for AI-Enabled Workflows?
Evaluate the skills, adoption barriers and workflows most likely to change. Identify where employees will need training, new responsibilities or additional oversight as AI becomes embedded in day-to-day operations.
Assumptions That Can Derail AI Planning
- Governance best practices can wait until a pilot proves value. Governance decisions often need to be made earlier, especially when sensitive data, regulated environments or autonomous actions are involved.
- AI strategy starts with choosing the right LLM. In practice, use cases, data, governance and operating requirements should shape technology choices.
- Buying GPU capacity makes an organization AI-ready. Infrastructure is only one part of the foundation needed to support production AI.
Build an Actionable Roadmap From Foundation to Enterprise Scale
Once priorities and constraints are clear, turn them into a phased roadmap for moving AI from planning to production and scale. That roadmap should address the decisions and investments required across strategy, data and AI toolsets, infrastructure, implementation and workforce enablement over time.
Define the Strategy and Prioritize Use Cases
Sequence use cases by value, feasibility, risk and readiness, then set governance principles and success criteria.
Prepare the Data and AI Toolsets
Prepare the data required for priority use cases, then modernize and connect platforms to make trusted data accessible. Establish the toolsets needed to build, integrate and manage AI solutions.
Architect Infrastructure Around the Workload
Decide which workloads belong in cloud, on-premises or hybrid environments and where AI-ready infrastructure or AI factory approaches may fit. Balance performance, security, sovereignty and economics as requirements evolve.
Put AI Into the Workflows and Operations That Matter Most
Connect AI to enterprise data, applications and processes. Match generative, predictive, agentic or physical AI to the need, then measure performance and expand what works.
Prepare Your Workforce and Optimize Over Time
Support employees with role-based enablement, workflow redesign and governance for AI tools such as Microsoft Copilot and Google Gemini. Monitor adoption, the impact on team performance and costs over time.
Why CDW for AI and Data
Few organizations need help with only one aspect of AI. CDW brings together expertise across AI, data, cloud, cybersecurity, infrastructure, networking, applications and workforce technology to connect business priorities with the technology and change management capabilities required to support them.
Start With the Business Outcome
CDW helps organizations define the outcomes that matter, then determine the data, governance, infrastructure and operational capabilities required to support them.
Connect Decision-Making Across the AI Ecosystem
Data, infrastructure, cloud, security, applications and workforce technology influence one another. CDW helps coordinate decisions across the environment so dependencies can be addressed before they become barriers to scale.
Build the Right Environment, Not a Preset Stack
CDW helps evaluate technologies based on use case, existing environment, risk profile and economics. Our broad partner ecosystem gives organizations the flexibility to choose the right approach for each workload rather than forcing every workload into the same stack.
Support Throughout the AI Lifecycle
CDW can support organizations from strategy workshops and assessments through architecture, implementation, governance, optimization and managed services as workloads and AI capabilities evolve.
Sources:
1 Foundry, “AI Priorities Study 2026,” 2026
2 Deloitte, “State of AI in the Enterprise: The Untapped Edge,” January 2026
3 McKinsey, “From Adoption to Impact: Three Horizons of AI Transformation,” July 2026
Turn Your AI Priorities Into Action
The right next step depends on your priorities, readiness and existing environment. CDW can help assess where you are, identify what needs to happen next and build a practical plan to move priority AI initiatives forward.