One part of the research landscape that isn’t going away anytime soon is of course, AI. The term has been thrown around so much it can feel like a catch-all promise of innovation, but a growing misalignment is emerging that demands our attention.
Multiple industry surveys show persistent perception gaps between leaders and employees in AI adoption and workplace impact. According to Qualtrics, “83% of leaders say AI tools have made their teams more efficient, but just 65% of contributors share that view.” Gartner’s 2025 workplace survey also echoes this misalignment, finding that rushed, leader‑driven AI deployments without sufficient workforce engagement lead to uneven adoption experiences. In short, AI’s promise has leadership buy-in, but its reality is landing very differently on the ground. So what can be done? Leaders, this is where you come in.
In short: AI’s promise has leadership buy-in, but its reality is landing very differently on the ground.
As leaders push forward, individual contributors are raising red flags, not because they are resistant to change, but because implementation isn’t matching up. Left unaddressed, this growing gap will only deepen organizational friction. So what can be done? Leaders, this is where you come in.
1. Align on the Goal Before You Accelerate
This may not sound groundbreaking, but moving fast without a shared understanding of success is a recipe for failure. Alignment isn’t just about agreeing that AI matters. It’s about agreeing on why it matters and where it should be applied.
Your research strategy should be clearly understood and supported by the people closest to the work. That alignment is critical when selecting AI tools and workflows. Often, the issue isn’t that organizations are using too much AI, it’s that they’re not using it in ways that best support their work. Talk to your teams early and often, and choose workflows that solve real problems.
2. Invest in Enablement, Not Just Technology
Buying a research platform with powerful AI capabilities isn’t enough. Successful adoption depends on whether teams can realistically learn, trust, and integrate those tools into their daily workflows. Here are some good questions to consider:
- How intuitive is the platform for everyday users?
- What training or onboarding support is available?
- Who has access to the platform and who knows what tools to use to best understand the data ?
Without thoughtful enablement, even the best AI tools will fall short of their potential.
Why This Matters to Leaders
For leaders, AI misalignment isn’t a communication issue, it’s becoming a performance risk. If teams don’t see AI making their work better, leaders won’t get the results they’re expecting. Speed alone doesn’t create advantage. In fact, speed without alignment often creates friction, slows adoption, and undermines confidence in the very tools meant to drive progress.
AI transformation doesn’t fail because the technology isn’t good enough. It fails when leaders assume alignment instead of intentionally building it. The organizations that succeed won’t be the ones that move the fastest. It will be the ones that ensure everyone is moving forward together.
