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Is Your Workforce Ready to Use AI With Confidence, Clarity and Impact?

Organizations are investing in AI to help employees work faster, reduce manual effort and improve the quality of everyday outputs. But access alone does not create value. Real gains depend on whether AI is connected to the workflows people use every day, supported by trustworthy information and reinforced with the training, communication and guardrails employees need to use it confidently.

When AI is introduced without a clear plan for rollout and adoption, employees may not understand where it fits, which tools are approved or how to validate what it produces. That can lead to inconsistent use, limited impact and new risks as people turn to unsanctioned tools. This is especially concerning in light of the fact that, for organizations with high levels of shadow AI, the average cost of a breach increases by $670,000.¹

When organizations take a strategic approach, AI feels like a practical advantage built into how work already happens.

Use this checklist to evaluate where you stand today and identify the areas where CDW can help you move from AI access to AI adoption.

Five Areas to Strengthen Workforce AI Adoption

Strategy and Business Alignment: Start With Outcomes, Not Just Access

Empowering people with AI starts with clarity about why you are adopting it and where it can make the biggest difference. Before you scale access, align on the workflows, roles and business outcomes that matter most so AI supports higher-value work instead of adding another layer of change.

Have you identified the business goals AI should support, such as workforce productivity, faster decision-making, improved quality or better employee experience?
Have you pinpointed the roles, teams and workflows where AI can deliver the most practical value in day-to-day work?
Have you prioritized a short list of use cases with clear owners, realistic expectations and measurable outcomes?
Have you defined where AI should assist people, where human review is required and where automation is not the goal?
Do employees and managers understand why AI is being introduced and how it’s meant to support work rather than add confusion or resistance?

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People, Skills and Change Enablement: Help AI Become a Habit, Not a Novelty

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Training matters, but adoption requires more than training alone. Employees need communication, reinforcement, feedback loops and peer support so AI becomes part of how work gets done instead of a feature they try once and abandon.

Do you have an enablement plan that includes communication, onboarding, training and ongoing reinforcement for different user groups?
Have you identified champions or superusers who can model effective AI use and help adoption spread peer-to-peer?
Are employees being taught how to prompt effectively, review outputs critically and apply their own expertise to improve results?
Do managers understand how to reinforce responsible AI use and set expectations for when employees should verify or escalate AI-generated outputs?
Are you measuring adoption in ways that go beyond licenses assigned, such as active usage, confidence, time saved or workflow improvement?
Do you have a way to gather employee feedback and refine your rollout based on how people actually use AI in daily work?

Technology and Tool Integration: Put AI Where Work Already Happens

Employees are more likely to use AI consistently when it is embedded in the tools they already rely on. The goal is to reduce friction and context switching so AI supports work within the flow of work, not outside it.

Is AI available through the major productivity, collaboration and service platforms your employees already use?
Have you identified where employees still leave their normal workflow to search for information, complete repetitive tasks or create first drafts manually?
Are you evaluating where AI can fit naturally into everyday tasks instead of forcing employees to adopt disconnected point tools?
Have you considered how AI could support use cases such as summarization, drafting, search, service interactions or knowledge retrieval across functions?
Do your current tools, integrations and support models make it easy for employees to use sanctioned AI in practical, repeatable ways?

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Data Readiness and Information Access: Make Better Answers Easier to Find and Trust

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AI outcomes depend on the quality and accessibility of the information employees need. If knowledge is hard to find, poorly organized or inconsistent, AI may produce polished outputs that still create confusion.

Have you identified the knowledge sources employees rely on most and assessed whether they are accessible, current and organized well enough to support AI use?
Can employees use AI to find and summarize trusted enterprise information more easily?
Have you addressed gaps in content quality, ownership or structure that may make AI responses incomplete, outdated or inconsistent?
Do employees know how to validate AI-generated answers against trusted sources when accuracy matters most?
Are you reducing friction between users and enterprise knowledge bases so AI can improve access to information instead of adding another layer of uncertainty?

Security: Apply Guardrails That Reduce Risk Without Stalling Adoption

AI readiness includes practical guardrails. The goal is not to perfect every policy before anyone starts. It’s to protect the most sensitive data and highest-risk use cases first while still enabling employees to gain value from lower-risk applications and sanctioned tools.

Have you identified which data, workflows and user groups carry the highest security, privacy or compliance risk?
Do employees know which AI tools are approved, what information should not be entered and when outputs must be verified before use?
Have you established clear policies for identity, access, privacy and responsible AI use that support secure adoption across the workforce?
Are you monitoring for unsanctioned AI usage and addressing the workflow gaps that may be driving employees to unofficial tools?
Do you have a plan to refine governance and guardrails over time as AI expands into more workflows and business functions?

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Source: ¹ IBM, “Cost of Data Breach Report 2025,” July 2025

Why CDW

CDW helps organizations move from “we made AI available” to “our people are using AI productively, confidently and responsibly at scale.”

Business-led AI planning: We help identify the roles, workflows and use cases where AI can reduce friction, improve quality and support measurable productivity gains.
Enablement that supports adoption: CDW can help with AI enablement workshops, communication planning, champions programs and training strategies that turn access into action.
Practical deployment across major platforms: We help organizations support licensing, deployment and optimization across the major productivity and collaboration platforms while aligning tools to real employee workflows.
Information access and AI experiences: We help assess knowledge access, data readiness, and chatbot or knowledge retrieval opportunities so employees can find answers faster and work with more confidence.
Support from strategy through execution: CDW can bring together expertise across digital workspace, data, infrastructure, security and services, including deployment accelerators, proof-of-concept support and pilot services where needed.