AI in the Modern Workplace: Excitement Is High, but Execution Is Everything

CDW’s latest proprietary research confirms what many IT leaders are already seeing firsthand: artificial intelligence-enhanced collaboration has officially entered the mainstream.

Nearly 80% of respondents identified AI-enhanced collaboration as the top emerging workplace technology trend. The enthusiasm is real. But excitement alone doesn’t translate into impact. The next phase of AI in the workplace will be defined by governance, integration and disciplined execution.

Following are key themes from the research and what they mean for organizations moving from experimentation to operational AI.

When we talk about AI-enhanced collaboration, we’re not talking about stand-alone AI platforms or novelty chatbots. We’re talking about integrating AI directly into the tools employees already use every day.

Think of such practical and commonplace tools as AI-generated meeting summaries, automated scheduling, workflow automation, task extraction, and follow-ups and integrated copilots within productivity tools.

The shift is subtle but powerful: AI is moving from being a chatbot that answers questions to a teammate embedded in your applications.

That practical, everyday usefulness is likely why AI-enhanced collaboration ranked No. 1 among emerging trends — outpacing more futuristic concepts like agentic AI or immersive technologies. These are tangible productivity multipliers, not experimental features.

In the survey, respondents were asked whether their organization was exploring AI tools and, if so, what they hope these tools will help with. The research shows that automation and workflow efficiency dominated open-ended responses (approximately 51% of responses).

Leaders aren’t chasing AI for its novelty. They want it to eliminate repetitive tasks, reduce context switching across tools, minimize manual note-taking and follow-ups, and streamline reporting and analysis.

These tasks all tie directly to productivity, which aligns with the results of the research. More than two-thirds (68%) of respondents want AI to boost employee productivity.

But there’s a second mandate that’s equal in importance: 68% want AI to improve security and compliance.

That tells us something critical. Organizations aren’t just looking for speed; they’re looking for speed with safety. AI must accelerate work without increasing risk.

Here’s where things get more nuanced.

While nearly 80% are excited about AI-enhanced collaboration, many organizations are discovering that AI success isn’t automatic. AI is only as good as the data it can access. If that data is stale, redundant, ambiguous, poorly classified or unsecured, then the outputs will reflect those weaknesses.

That’s why AI governance and readiness are so important.

The research shows:

• 58% of respondents have introduced ethical-use policies
• 57% of respondents have cleaned up stale or outdated data

That’s encouraging progress. But it also means roughly 40% of organizations have not yet taken those foundational steps.

AI implementation without governance is risky. One of the biggest hazards we’re seeing is data leakage and oversharing, in which AI surfaces confidential information that was loosely stored and never properly classified.

Historically, data often lived indefinitely inside organizations. Now that AI can surface information instantly, poor data hygiene becomes immediately visible and potentially dangerous. As experts often say, AI is 10% technology and 90% governance.

Even with growing maturity, integration remains a significant obstacle. Approximately 10% of respondents explicitly cited fragmented data and poor system integration as a barrier.

AI cannot analyze what it cannot see. If customer relationship management systems, financial platforms, HR systems and collaboration tools remain siloed, employees will continue spending time searching across applications. AI cannot generate comprehensive insights unless those environments are connected.

When integration is done well, the experience shifts dramatically. Instead of checking four different applications before a meeting, a person can use AI to generate a contextual summary pulling data from each system. But that outcome requires deliberate integration. It doesn’t happen automatically.

The data suggests organizations are moving beyond experimentation. The introduction of ethical policies, data cleanup efforts and readiness assessments signals that AI maturity is increasing.

However, excitement still outpaces enablement. Many organizations adopted AI tools quickly, only to realize they needed to step back and formalize:

• Data classification and labeling
• Retention and expiration policies
• Trusted data source identification
• Access controls
• Cross-functional governance models

AI should not be treated as a departmental experiment. It should be handled like any major business initiative, with executive sponsorship and cross-organizational alignment.

When asked about the one limitation in their current working environment respondents hoped AI could help solve, personalization and user support emerged as key themes. Employees expect AI to provide contextual help, smarter digital assistants, AI-driven training and an improved user experience.

Additionally, 45% of survey respondents expect AI to support more equitable hybrid work experiences.

That’s significant. AI isn’t just a productivity tool; it’s becoming a mechanism for visibility and inclusion across distributed teams. Meeting summaries, task extraction and contextual recaps help ensure that remote employees aren’t disadvantaged by time zone gaps or missed sessions.

The biggest gap today isn’t in AI capability — the models are advancing rapidly. The gap is in execution. To close it, IT leaders should:

1. Focus on fewer use cases, especially those that will deliver high impact.
   Meeting summaries, workflow automation and integrated copilots offer quick wins.
2. Invest in data hygiene as much as AI licenses.
   Clean, classified, governed data is the foundation of trustworthy AI.
3. Break down silos deliberately.
   Integration is not optional — it’s the enabler of meaningful AI outcomes.
4. Treat AI as an organizational initiative.
   Governance, policy, training and change management matter as much as the technology itself.

The research shows strong enthusiasm. But AI value will not come from excitement alone. It will come from disciplined integration, responsible governance and thoughtful enablement.

AI has already become part of the modern workplace. The organizations that thrive will be the ones that prepare their data, their policies and their people — not just their platforms.