Why GenAI alone isn’t enough for digital workplace decisions

Can you rely on GenAI alone for digital workplace decisions?

Generative AI tools can surface large volumes of information quickly. But speed does not always translate into reliability. Outputs can include inaccuracies, oversimplifications or “hallucinations”, which means additional validation is often required before acting on the information provided.

For digital workplace leaders, where decisions can have strategic and financial implications, this creates risk. Time is then spent checking, verifying and contextualising outputs—often offsetting the initial efficiency gains.

A more robust approach combines AI-generated content with insight that has been validated through real-world application and peer experience.

“The discussions go into the detail of what is actually keeping leaders up at night… it is real, practical and immediately relevant.”
– DWG member feedback

This distinction—between information and validated insight—is critical when building confidence in decision-making.

Will GenAI give you insight – or just information?

GenAI excels at synthesising publicly available information. However, much of the value in digital workplace strategy lies beyond what is openly accessible.

Internal practices, programme design decisions, governance approaches and lessons learned are rarely documented in the public domain. As a result, AI-generated outputs can lack the depth and specificity required to inform meaningful decisions.

Digital workplace leaders often need more than a summary of trends – they need insight into what has worked, what has not, and why.

“You get beyond the surface very quickly… people are not holding back or speaking in generalities.”
— DWG member feedback

Access to these kinds of grounded perspectives enables a shift from generalized knowledge to actionable insight.

Why is real-world context critical for digital workplace strategy?

Digital workplaces are shaped by organizational culture, governance structures, technology landscapes and employee needs. What works in one context may not translate directly to another.

GenAI tools, by design, operate without this organizational context. While they can identify patterns, they cannot fully account for the nuances and trade-offs that influence implementation in practice.

This is where learning from peers becomes particularly valuable. Exposure to different organizational approaches helps leaders interpret what might work within their own context.

“You see initiatives being led from different parts of the organisation… that breadth of experience is incredibly valuable.”
— DWG member feedback

Understanding how others have navigated similar challenges provides a more grounded foundation for decision-making.

How do digital workplace leaders validate what works?

Validation is a critical but often overlooked step in the use of GenAI. Without it, there is a risk of acting on incomplete or inaccurate information.

In practice, validation often comes through:

• Comparing inputs from multiple sources.
• Testing assumptions with peers.
• Reviewing case studies and real-world examples.
• Sense-checking recommendations against lived experience.

This process is not always straightforward when working with AI outputs in isolation.

By contrast, environments that enable open, experience-based discussion allow leaders to test ideas quickly and refine their thinking.

“It creates space for candid, meaningful conversations… grounded in their own experience.”
— DWG member feedback

Here, validation becomes built into the process, rather than an additional step.

What role does peer learning play in decision-making?

One of the most consistent challenges for digital workplace leaders is the sense of operating in isolation –particularly when navigating emerging areas such as AI adoption, governance and employee experience.

GenAI tools do not address this challenge. While they can provide information, they do not offer interaction, dialogue or shared learning.

Peer exchange, by contrast, introduces:

• Diverse perspectives across industries and roles.
• Practical examples of what has worked in similar situations.
• Open discussion of challenges as well as successes.

“What stood out most was how much we all learned from one another… the conversations, collaboration, and openness made the event truly valuable.”
— Nicole Wheeler, PNC Financial

This shared learning environment helps leaders avoid common pitfalls and accelerate progress with greater confidence.

How do organizations move from insight to action?

Perhaps the most important distinction between AI-generated outputs and practitioner-led insight is the ability to act.

GenAI can support ideation, drafting and early-stage exploration. However, turning ideas into action requires:

• Prioritisation of initiatives.
• Alignment with organisational goals.
• Clear articulation of value and outcomes.
• Practical next steps for implementation.

Without this, outputs risk remaining conceptual rather than impactful.

Leaders consistently highlight the value of clarity and direction gained through peer exchange and practitioner insight:

“I left with clearer thoughts on many of the initiatives I own, and some solid action items to bring back.”
— DWG member feedback

This progression – from information, to insight, to action – is where the greatest value is created.

What does a practitioner-led approach add that AI cannot?

While AI continues to evolve, there are areas where human expertise and experience remain essential.

These include:

• Interpreting organisational context.
• Navigating complexity and trade-offs.
• Facilitating meaningful discussion and collaboration.
• Translating insight into action.
• Building confidence in decisions.

These capabilities are grounded not in data alone, but in experience, judgement and interaction.

At its core, the difference is not about choosing between AI and other approaches. It is about recognizing what each contributes – and where additional input is needed to move forward effectively.