AI is working, but only for the individual | Tech Radar

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Many implementations of AI continue to fall short when it comes to improving team collaboration

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Despite widespread enthusiasm for AI tools among employees and organizations, many implementations continue to fall short when it comes to improving team collaboration.

Companies have typically only focused on the outcomes they want to achieve, without giving equal attention to how those outcomes will be delivered in practice.

Part of the challenge is that AI is still widely viewed as a tool for boosting individual productivity. While this does create value, it overlooks a far greater opportunity to transform how teams work together.

AI fatigue is real and it’s time for leaders to close the organizational gap

The 70% rule: Why your AI strategy is a people strategy

As a result, a gap is emerging between AI investment and meaningful business impact. In one study, only 15% of AI decision makers reported a revenue lift for their organization over twelve months, suggesting that AI’s potential is still not translating into results.

To realize the full potential of AI, organizations need to change their mindset. Instead of thinking of individual outputs, leaders must now think about how to turn AI adoption, experimentation and success into a team game.

It’s the rollout, not the tech that’s holding back ROI

In recent months, a wave of high-profile job-cuts attributed to AI have signaled a fundamental shift in how organizations are structured.

Yet in the rush to adopt the technology, many have prioritized access to the tech over a robust strategy, with employees vocalizing their concerns over a lack of clear guidance on how they should be used or how they are trained.

As a result, employees are left to navigate AI on their own, while leaders continue to cite gaps in technical skills and workforce readiness as the primary barriers to implementation.

Without clear direction usage becomes inconsistent and siloed, limiting opportunities to scale impact across teams. More concerning, however, is this lack of oversight increases the risk of inaccurate outputs or the mishandling of sensitive data which can expose organizations to operational and reputational risk.

The next challenge lies in how effectively organizations enable teams to work together, something that still feels out of reach for many businesses. While nine out of 10 decision makers view collaboration tools as critical to AI success, 75% say tools remain focused on individual productivity.

Closing AI learning gaps between leaders and employees

Why so many businesses are still on the wrong side of the AI divide

Organizations need to move beyond isolated use cases such as meeting summaries or copy generation and instead integrate AI into shared workflows, bringing it into the environments teams already use together, rather than relying on standalone tools.

This shift reduces the friction of switching between platforms and helps standardize how AI is applied across teams. It leads to more consistent outputs and stronger alignment around shared ways of working.

Progress also accelerates when teams experiment together rather than in siloes, sharing insights and adapting in real time. Organizations need an open dialogue to see what really is and isn’t working. Transparency is critical to turn these learnings into a meaningful advantage, particularly in a period defined by ongoing change and uncertainty.

Strong leadership is another important factor when it comes to driving impact with AI. Measurable business transformation depends on leaders embedding AI into day-to-day operations and clearly articulating where it fits within the broader strategy.

When a senior team leads by example and demonstrates how they use AI themselves, it signals that adoption is a core part of how the organization is evolving and how to get the most out of the technology.

We have also found that as AI gradually becomes more embedded in workflows, knowledge that once sat with individuals becomes accessible across the team. This opens up new opportunities for shared learning, supported by communities of practice where teams exchange insights and tackle challenges together.

Many organizations are reinforcing this through initiatives such as internal AI days or hackathons, where teams showcase experiments and working prototypes. These efforts not only highlight practical use cases but help to normalize AI as a collaborative tool, with cultural shifts that compound over time to drive meaningful, organization-wide impact.

They also allow people to get hands on and help teams simultaneously upskill the knowledge, engaging in a collaborative way whilst solving problems with AI.

Success will not come from isolated experimentation

AI has already proven its potential to reshape how work gets done, but real impact will depend on how organizations choose to implement it. The gap between investment and results is not a reflection of the technology itself, but of how it is introduced, embedded and scaled across teams.

Closing this gap requires a shift in mindset. Success will not come from isolated experimentation or individual productivity gains alone, but from enabling teams to work differently together. This means putting the right structures in place, embedding AI into shared workflows and fostering a culture where learning is collective and continuous.

Organizations that take this approach will be better positioned to move beyond incremental improvements and unlock meaningful, organization-wide transformation.

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Many implementations of AI continue to fall short when it comes to improving team collaboration
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