AI productivity is real, but your org chart is still the bottleneck

Every few months we get a new AI productivity headline…

Anthropic - Estimating AI productivity gains from Claude conversations

This time it’s a big one. Claude-based analysis of real usage logs suggesting material time savings across knowledge work, with an estimated ~1.8% annual productivity lift if adoption scales over a decade.

Before we start ordering “AI saved us 2% GDP” champagne, let’s do two things:

1. Acknowledge the signal is genuinely important.

2. Be honest about what it is and isn’t.

Because both of those truths matter if you’re a CEO, CTO, or CIO trying to turn agentic AI ambition into operating reality.

What the research gets right and why it matters

The Claude report is valuable precisely because it’s not a lab demo. It’s built on millions of real interactions and shows where time is already being saved today.

A few things stood out:

• Big gains cluster in text-heavy, decision-support work: writing, summarising, planning, coding assistance, research synthesis.

• Savings are uneven: some tasks compress dramatically, others barely move.

• AI is already acting like a productivity “exoskeleton” for individuals, especially in roles where thinking and producing language is the job.

That’s a real, grounded indicator of value right now. Not hype. Not vibes. Usage.

If you’re leading an enterprise, this should increase your confidence that AI is not “future value.” It’s present value.

The contrarian part is that task speed ≠ business productivity

Here’s the uncomfortable truth CEOs need to internalise:

AI can accelerate tasks while your organisation stays just as slow.

The report estimates time saved inside the chat. But businesses don’t run inside a chat. They run inside workflows that include:

• Approvals

• Risk checks

• Handoffs

• Data access constraints

• Compliance gates

• Human hesitation

• Legacy incentives

• Meeting gravity

So yes, AI may draft a proposal in 10 minutes. But if the approval chain takes 10 days, your company is still a 10-day company.

This is why the report’s authors are careful: the estimates can’t capture verification time, refinement outside the model, or organisational friction. In other words:

These numbers show the ceiling on the task. Not the realised value of the firm.

“Isn’t this AI marking its own homework?”

A journalist asked me recently whether we should trust a model estimating its own productivity impact. Fair question.

Anthropic tries to validate by testing estimate stability across prompt variants and comparing to real task timings (like JIRA issues). The results look directionally reliable.

But still, self-estimation will systematically skew optimistic.

That doesn’t make the research useless, it makes it a starting point.

At Valliance, we treat model-reported savings as a hypothesis generator as opposed to a KPI. We triangulate with:

• Real baseline timings

• Quality / rework rates

• Business throughput

• Operational telemetry

• Stakeholder satisfaction

Only then do you see net productivity.

Good compass, not a GPS.

The convoy problem - the slowest ship sets the pace

Even if we accept the task-level gains are real, the report highlights a deeper issue - productivity improvements don’t distribute evenly. So organisations get a strange new shape:

• Some steps become 80% faster

• The remaining steps become the bottleneck

• Throughput barely changes

• Leadership wonders why AI “didn’t work”

This isn’t a failure of AI. It’s a failure of systems thinking. Agentic AI doesn’t magically fix a workflow designed for humans moving at human speed.

If anything, it exposes the mismatch faster.

Why this is exactly where Valliance is focused

This is the moment we’re in as an industry:

• Model capability is moving fast.

• Task-level gains are real today.

• Organisational throughput is lagging behind.

So the question shifts from “Can AI help?” to:

“What architecture lets AI act across real enterprise workflows — safely, reliably, and at scale?”

Two things we’re especially bullish on at Valliance:

1) Ontologies as the missing layer for real agentic systems

Most “agents” today are basically: a chat model + tools + hope.

That works in a demo. It breaks in a business.

To make agents dependable beyond a single chat, they need a structured representation of the world they operate in— entities, relationships, permissions, states, and goals.

We’ve seen the Palantir Ontology as one of the most advanced real-world implementations of this idea: A living, governed model of the enterprise that agents can reason over and act within. In plain English:

Agents don’t fail because they can’t think. They fail because they don’t know the rules or the map.

Ontologies give them the map.

2) Evals + synthetic data as the path to production-grade reliability

Agentic ambition dies without reliability. The way we’re pushing that frontier is by scaffolding custom agentic solutions on two pillars:

• Continuous evaluation (evals): so we can measure behaviour, not guess it.

• Synthetic data generation: to expand edge cases, hard scenarios, and long-tail enterprise patterns that your real data doesn’t cover.

This combo is where we’re seeing the clearest step-change in production performance:

Evals tell you where you fail.Synthetic data lets you train for those failures.

That’s how agents graduate from “cool assistant” to “operational teammate.”

What CEOs / CIOs should do now if you want agentic value later

If your ambition is “agentic AI”, systems that plan, act, and execute across workflows, then you need to build the runway today. Here’s the practical playbook we’re seeing work:

1) Start where the value already is

Target high-frequency, text-heavy, decision-dense work first.

That’s where current models shine and where adoption feels “obvious” to teams.

2) Measure net productivity, not chat productivity

Time saved minus time spent verifying, integrating, and fixing.

If you don’t measure the subtraction, you will fool yourself.

3) Redesign workflows around AI speed

If AI collapses one step, remove or compress the downstream friction.

Otherwise you’re installing a jet engine on a horse cart.

4) Build your ontology / operating model

Whether it’s Palantir’s Ontology or another structured layer, agents need a governed world model to operate safely through real processes otherwise workflows will with be brittle and deterministic or suffer compounding error rates.

5) Invest in evals and reliability loops

Agents will not be stable unless you measure them continuously and train them against your real edge cases.

6) Upskill for trust, not just usage

Adoption sticks when teams understand what models are good at, where they fail, and how to work with them.

The future number is a scenario, not a prophecy

The 1.8% annual lift is not a forecast. It’s a “what if adoption becomes universal and models keep improving.” That’s useful for scale imagination, but outcomes depend on you:

• Adoption speed

• Process reinvention

• Data readiness

• Governance maturity

• Change management

• Incentive alignment

Every industrial revolution proved the same thing:

Technology creates potential. Organisations create productivity.

The real takeaway

This research is a meaningful marker. AI is already boosting individual productivity in real work. But the firms that win won’t be the ones that “use AI.”

They’ll be the ones that re-architect work so AI can matter.

Agentic AI ambition is achievable, but only for organisations willing to evolve faster than their org charts.

If you’re a CEO / CIO wrestling with how to turn today’s task gains into tomorrow’s throughput gains, I’d love to hear what’s the biggest bottleneck you see between “AI helps my people” and “AI changes my business”?