AI interest is high, but real benefits only appear when it’s integrated into daily workflows. A survey of 3,000+ global knowledge workers plus 85+ decision-maker interviews found most people see AI as essential — yet only a minority say it’s deeply embedded in their company tools. The gap is a workflow problem, not a hype problem.

AI has become the new baseline expectation for productivity tools. People want it, leaders are funding it, and vendors are shipping it quickly. But there’s a quieter reality underneath the excitement: in many organisations, AI is still a side experiment rather than something that genuinely changes how work gets done.

That gap is the core theme of recent research published by Notion. The study surveyed 3,000+ global knowledge workers and included 85+ interviews with decision-makers and teams testing AI in daily work. While 93% of respondents said AI is essential to modern productivity software — and 80% said they’d consider switching platforms to get it — only 20% said AI is deeply integrated into their company’s tools.

In other words, demand is soaring. Benefits are more uneven.

Why AI enthusiasm doesn’t automatically translate into results

Most AI initiatives start in the same place: someone tries a tool, gets a quick win, and the organisation concludes it’s “doing AI”.

But the research points to a common failure pattern: AI is often tested in isolated, one-off use cases. That makes it hard to:

• standardise quality (outputs vary by person)

• measure impact (no consistent baseline)

• build trust (teams don’t know what’s reliable)

• scale safely (governance lags behind use)

The biggest constraint is rarely the model itself. It’s the workflow.

What “AI integrated into workflows” actually looks like

Integration means AI touches the moments where teams lose time and context — and does so in a consistent, repeatable way.

Here are three signals you’re moving beyond experimentation:

1) AI supports shared work, not private shortcuts

If AI results live in someone’s chat window, the team doesn’t benefit.

Workflow-level AI is used to summarise decisions, structure project updates, and create reusable outputs that others can review, edit, and build on.

2) AI is attached to the system of record

Teams get real value when AI can act on the same place where work is planned and tracked:

• project updates

• meeting notes and decisions

• docs and knowledge bases

• tasks, owners, and due dates

This reduces “AI sprawl” — where content is generated but not operationalised.

3) There’s a measurable before-and-after

You should be able to point to a metric that improves:

• time from request to first draft

• time from insight to decision

• number of handoffs

• rework and duplicated effort

If you can’t measure it, you can’t scale it.

Practical steps to turn AI interest into real team benefit

If you’re leading adoption, these steps help convert hype into outcomes.

Step 1: Choose one painful, repeatable process

Start with a workflow that’s high frequency and predictable:

• project intake and prioritisation

• weekly status updates and reporting

• customer feedback triage

• meeting output capture (actions, decisions, risks)

Avoid starting with “let’s give everyone AI”. That creates lots of activity and very little evidence.

Step 2: Define the human decision points

AI can help with drafting, summarising, and structuring. It shouldn’t make commitments.

Mark where humans must decide:

• priority calls

• risk acceptance

• final approvals

• policy or legal decisions

Step 3: Add guardrails early

Even in low-risk workflows, set the basics:

• what data is off-limits

• how outputs are reviewed

• what gets logged and retained

• a clear acceptable-use policy

Step 4: Train in role-based patterns

Training is most effective when it’s built around job reality:

• Support: summarising tickets, drafting responses, routing

• Sales: call recap, proposal first drafts, objection handling

• Product: research synthesis, prioritisation summaries

• Ops: process docs, weekly updates, KPI narratives

Step 5: Scale only after you see consistent wins

Once you can demonstrate a measurable improvement on one workflow, expand.

That’s how AI stops being “another tool” and becomes “how we work”.

How Generation Digital can help

If your organisation is moving from AI pilots into production, we can help you:

• identify the best workflow to start with

• design a measurable pilot with governance built in

• train teams in repeatable patterns (not one-off prompts)

• connect AI adoption to collaboration tools your teams already use

Related links:

• Explore Asana’s AI tools: /asana/

• Discover Miro’s innovations: /miro/

• Learn more about Notion: /notion/

Summary

AI demand is real — but benefits depend on integration. The research shows a clear gap between excitement and day-to-day workflow adoption. The way forward is simple (not easy): pick one workflow, measure it, put guardrails in place, and train teams in repeatable patterns. That’s where AI starts to pay back.

Next steps

• Identify one workflow to pilot in the next 2–4 weeks.

• Define 2–3 success metrics (cycle time, rework, satisfaction).

• Build governance and review into the workflow.

• Roll out role-based training so adoption sticks.

6. FAQs

Q1: How many workers were surveyed?
Notion’s research surveyed 3,000+ global knowledge workers.

Q2: What did the interviews focus on?
The 85+ interviews focused on decision-makers and teams actively testing AI in daily work, to understand how AI is (and isn’t) being applied in real workflows.

Q3: Is AI being fully utilised in organisations today?
Not yet. The study found only 20% of knowledge workers say AI is deeply integrated into their company’s tools, despite very high interest.

Q4: What’s the fastest way to get real value from AI at work?
Start with one repeatable workflow, attach AI to the system of record (tasks/docs/updates), set review guardrails, and measure impact before scaling.

Q5: Why do AI pilots often stall?
Because they’re isolated, hard to measure, and not connected to how teams already plan and deliver work — which makes adoption inconsistent and trust fragile.