The digital workplace is not one thing!

Dear Diary

Every now and again, a single phrase does what a dozen trend reports fail to do. It slows the room down.

This is the backdrop for today’s ponderable:

If AI value shows up in spikes rather than averages, are digital workplace leaders looking closely enough at the jagged edges of work?

‘Law is not one thing’, a recent blog published by Matt Pollins1, reflects on the uneven impact of AI across legal work. The value of this piece lies in the recognition that AI does not arrive evenly across a profession. The same holds true for the digital workplace.

All too often, we talk about the digital workplace as though it were a single operating environment. In practice, it is a living system of very different jobs to be done (e.g. intranet search, employee communications, publishing, knowledge management, onboarding, IT help, HR services, collaboration, governance, analytics, community management and workflow automation – the list goes on).

Each of these jobs has a different relationship with AI. Some are already ripe for augmentation. Some are quietly being transformed by retrieval, summarization and automation. Some remain stubbornly human because they depend on trust, judgment, context and organizational politics. The mistake is to average all of this into a single adoption curve.

Anthropic’s refreshed Economic Index2 is useful here because it moves the conversation away from abstract possibility and towards observed patterns of use. Its June 2026 report points to a more granular reality, i.e. AI activity follows human rhythms, produces different types of outputs and shows varying levels of delegation depending on the task and tool surface.

The report is a useful wake-up call because it moves the AI conversation from promise to pattern. For digital workplace leaders, the task is no longer to ask whether AI will transform work, but to understand where value is emerging, how quickly, under what conditions and with what consequences.

The spikes matter more than the average

If we were to draw the digital workplace as a spider chart, the shape would be jagged.

• Search and content discovery would likely show a significant gap between current usage and potential value.
• Employee service navigation would form another spike, given the potential for AI to help people find the right policy, form, process or person without navigating a maze of portals.
• Internal communications would show a different profile again: AI can accelerate drafting, targeting and reuse, while editorial judgment and organizational sensitivity remain firmly human responsibilities.
• Knowledge management may have the most dramatic potential, along with the highest dependency on information quality. AI is very good at finding patterns in well-governed knowledge but struggles when asked to make sense of outdated content, duplicated repositories and unmanaged permissions. In that sense, AI exposes the condition of the digital workplace foundations rather than removing the need for them.

Onboarding, learning and manager enablement sit somewhere else again. Here the prize reaches beyond efficiency into confidence: helping people understand how things get done, who to ask, what good looks like and how to make better decisions faster. This is an experience design challenge more than a technology deployment exercise.

AI will reward the organizations that understand their work

For years, digital workplace strategy has been pulled towards platforms. Which intranet? Which collaboration suite? Which employee app? Which enterprise AI assistant?

Those questions still matter, but they are increasingly insufficient. The organizations that get the most from AI will be the ones that understand the work beneath the platform layer. They will be able to name the tasks, map the journeys, identify the high-friction moments, assess the data dependencies and decide where AI should assist, automate or stay out of the way.

This is where the digital workplace discipline has a particular opportunity. We have always lived at the intersection of technology, communication, knowledge, culture and operations. We know adoption and value are not the same thing. We know that a tool can be heavily used and yet poorly experienced. We know that governance can feel invisible until it fails. We know that employees do not experience the enterprise in org-chart categories.

AI raises the stakes for this work. It asks digital workplace leaders to become more precise: about use cases, guardrails, value measures, content quality, human oversight and the moments where trust is won or lost.

From one big bet to a portfolio of quick wins

The next stage of AI in the digital workplace should not be framed as one heroic transformation programme. It should be managed as a portfolio of quick wins: focused, evidence-rich interventions that reduce friction, build confidence and create visible value while the bigger foundations continue to mature.

The most useful quick wins will be close to the work people already do every day:

• reducing search failure
• summarizing long-form content
• helping employees to navigate services
• supporting communicators with first drafts
• repurposing content across channels.

At the same time, these quick wins should not become disconnected experiments. The best portfolios will connect near-term improvements to more strategic outcomes:

• building organizational memory
• enabling skills-based knowledge discovery
• supporting managers in moments of need
• giving employees a more coherent front door to the enterprise.

The risk is that organizations either overpromise or underuse AI because they keep the conversation at the wrong level. ‘What is our one big AI bet for the digital workplace?’ sounds bold, but it can also become too abstract to manage. A more disciplined question cuts closer to the work itself: ‘Which employee tasks are ready for AI-enabled improvement now, and what has to be true for each quick win to be safe, useful and trusted?’

That shift matters. It moves the discussion from hype to evidence and from ambition to momentum. It helps leaders see where there is genuine value, where the prerequisites are weak and where human judgment remains central. It also creates a better way to prioritize investment: through a managed sequence of visible wins, grounded in the lived work of the organization, rather than through the vendor roadmap alone.

The She-E-O view

From where I sit, leadership requires more than visible enthusiasm for AI at this moment in time. The harder, more valuable, work is to bring nuance, specificity and courage to the conversation:

• Nuance starts with refusing to treat the digital workplace as one thing.
• Specificity gets us beneath platforms and into tasks, journeys and moments of friction.
• Courage means saying plainly that AI will magnify weak foundations before it improves them.

The opportunity for digital workplace leaders is to become sharper sense-makers of where AI belongs in the everyday machinery of work.

If law is not one thing, the digital workplace certainly isn’t either. Accept that and the AI conversation becomes more interesting, more practical and more useful.

The bottom line The next step is to look deliberately for the jagged edges in your own digital workplace. Where is AI value already showing up in sharp spikes? Which tasks are ready for AI-assisted improvement, and which areas are still too dependent on weak foundations, unclear ownership or human judgment to move safely at speed?

If this resonates, now is the moment to stop averaging the digital workplace into one smooth AI story. DWG works with digital workplace teams to examine the spikes, gaps and uneven patterns of readiness across tasks, journeys and friction points, so leaders can distinguish the quick wins worth pursuing from the distracting noise.

DWG can help you map those jagged edges through our diagnostics, peer intelligence and advisory work, then turn the strongest signals into a practical portfolio of AI-enabled improvements.

References

1. Matt Pollins. Law is not one thing (Agents.law, April 9, 2026).
2. Anthropic. Anthropic Economic Index: Understanding AI’s effects on the economy (Anthropic, June 2026).

Categorised in:   → Diary of a She-E-O