Over the last two years, AI adoption at work has exploded.

According to McKinsey, over 70% of employees now use some form of generative AI in their day-to-day work, often without formal rollout or training. Productivity experiments routinely show individual gains of 20–40% in writing, analysis, and research-heavy tasks. On paper, this should translate into faster, more effective teams.

But in practice, it hasn’t.

Despite widespread AI usage, most teams report that work still feels fragmented, repetitive, and slow. Meetings haven’t disappeared. Context still gets lost. Decisions still need to be re-explained. And leaders still struggle to point to clear, team-level ROI from AI investments.

So what’s going on?

If AI genuinely made individuals faster, why didn’t team productivity improve at the same rate?

AI Succeeded at Individual Productivity

There’s no debate about this part.

Multiple studies—including research from Harvard Business School—show that individuals using AI complete tasks faster and with higher perceived quality. Knowledge workers write better drafts, generate ideas more quickly, and spend less time staring at blank pages.

For individual contributors, AI works because:

• tasks are well-scoped
• success is immediate
• feedback loops are short
• context lives with one person

In other words, AI fits neatly into personal workflows.

But teams don’t operate like individuals at scale.

Teams Don’t Fail at Execution. They Fail at Shared Understanding.

Team productivity is not just the sum of individual output.

It depends on:

• shared context
• consistent understanding
• decision continuity
• alignment over time

And this is where AI falls short today.

AI improved how fast people produce work, but it did very little to improve how teams align around that work.

1. AI Usage Is Still Largely Private

Most AI interactions happen in isolation.

Employees open a private chat, ask a question, generate an answer, and move on. The insight may influence their thinking, but it rarely becomes part of a shared system.

This mirrors a broader trend in workplace tools. Microsoft’s Work Trend Index found that over 60% of knowledge workers say information is scattered across too many tools, making it difficult for teams to stay aligned.

AI didn’t fix this fragmentation. It quietly reinforced it.

The result is “local optimization”: individuals get smarter, while teams remain misaligned.

2. AI Accelerates Tasks, Not Collaboration

AI excels at execution: drafting, summarizing, analyzing, and rewriting.

But collaboration is not an execution problem. It’s an alignment problem.

Research from MIT Sloan shows that teams lose significant productivity not because tasks take too long, but because:

• assumptions are misaligned
• decisions are poorly documented
• rationale is forgotten
• context doesn’t carry forward

AI answers questions, but it doesn’t automatically:

• capture why a decision was made
• reconcile conflicting viewpoints
• ensure shared understanding across stakeholders

As a result, teams move faster individually—but still slow down collectively.

3. AI Doesn’t Create Organizational Memory

One of the most expensive inefficiencies in modern teams is repeated context rebuilding.

Decisions are discussed in meetings, summarized in chats, partially documented in notes, and then slowly forgotten. When someone new joins—or when a decision is revisited weeks later—the same explanations resurface.

According to Atlassian, teams spend nearly 40% of their time searching for information or re-explaining work. AI reduced search friction, but it didn’t solve memory loss.

Without a shared memory system, teams don’t compound learning. They reset it.

4. Fragmentation Undermines AI’s Gains

Modern work already spans Slack, docs, tickets, meetings, whiteboards, and dashboards. AI simply joined this fragmented ecosystem.

Context gets dropped every time work moves between tools. Intent gets lost. Decisions lose traceability.

AI-generated output often travels from:
Chat → Doc → Slack → Meeting → Task → Another Chat

Each transition strips meaning.

So while AI increased output velocity, it didn’t increase continuity. And continuity—not speed—is what drives team efficiency over time.

5. Teams Still Depend on Key Individuals

Despite advanced tools, most teams still rely on a few people who “just know”:

• why things are the way they are
• how decisions connect
• what tradeoffs were considered

AI didn’t eliminate this dependency. In many cases, it amplified it by increasing the volume of work without strengthening shared understanding.

When knowledge lives in people instead of systems, teams don’t scale. They bottleneck.

The Missing Link: From Intelligence to Alignment

AI has clearly improved individual intelligence at work.

What it hasn’t done—yet—is improve collective intelligence.

Teams don’t need more answers.
They need:

• persistent context
• retained decisions
• shared understanding
• memory that compounds

Until AI helps teams think together, not just work faster alone, team productivity will continue to lag behind individual gains.

What Needs to Change

The next phase of AI at work won’t be defined by better prompts or faster models.

It will be defined by systems that:

• capture context automatically
• preserve decisions over time
• make alignment durable
• allow intelligence to compound across teams

That gap—between individual acceleration and team-level impact—is exactly what we’re focused on solving at WorkLLM.

Not by making people faster.

But by helping teams finally move forward—with shared understanding, not repeated explanations.