You bought AI like software.
Manage it like labor.

Work That Matters helps companies turn AI from a tool employees occasionally use into reliable organizational capacity they know how to manage.

Most companies are adopting AI like traditional software: buy licenses, train employees, encourage experimentation, and hope productivity improves.

That approach captures only a fraction of AI’s potential.

The larger opportunity emerges when companies begin treating AI as a form of digital labor: work that can be delegated, executed, reviewed, improved, repeated, and eventually orchestrated across the organization.

The challenge is no longer simply:

“How do we get employees to use AI?”

It is:

“How do we reorganize meaningful work so that humans can progressively delegate more of it to AI, while maintaining quality, control, and measurable business value?”

Work That Matters addresses this through a high-touch 12-week company cohort for 12 participants.

Participants learn to examine their own work, identify meaningful tasks, delegate those tasks to AI, review the results, improve the delegation, and turn successful one-off work into reusable organizational capacity.

The primary participant outcome is:

At least 5 hours per week of meaningful work released per participant.

That capacity may come from several tasks and workflows. The objective is not for every participant to build one large agent. It is for each participant to progressively move real work from personal execution into reliable digital execution.

The cohort is therefore not primarily an AI training program. It is the guided adoption and work-redesign layer for the Work That Matters OS.

The OS manages the reusable tasks, workflows, triggers, execution environments, approvals, performance, history, learning, and value created by the organization’s digital labor.

The long-term ambition is for Work That Matters to become the operating system and management layer for digital labor inside organizations.

The core insight is:

You bought AI like software. But meaningful AI ROI increasingly requires managing it like labor.

Traditional software waits for humans to operate it.

Digital labor can increasingly perform work.

That distinction changes the management problem.

A human employee does not need every action written down. People infer intent, understand context, remember what happened last time, notice exceptions, ask questions, learn preferences, navigate ambiguity, and adapt their behavior.

AI exposes how much organizational work depends on those implicit systems.

For AI to perform meaningful work repeatedly, organizations need to become better at defining:

• What work actually needs to happen.
• What result the work should produce.
• What good performance looks like.
• What information and systems are required.
• What AI may do autonomously.
• What requires human judgment or approval.
• When work should begin.
• How exceptions should be handled.
• How performance should improve over time.
• How the organization knows whether the delegated work is creating value.

The scarce organizational capability is therefore not simply prompting or agent building.

It is:

Work design and management for digital labor.

Work That Matters gives organizations a simple language for reasoning about work.

The model deliberately avoids exposing unnecessary technical abstractions to participants.

Task

A task is:

A bounded unit of meaningful work that advances an objective, produces a result that can be checked, and can be delegated as a coherent piece.

Examples include:

• Prepare me for tomorrow’s sales call.
• Draft the weekly operating report.
• Research five qualified prospects.
• Reconcile these two datasets.
• Create and send an invoice.
• Screen this group of applicants.
• Collect new Facebook leads into the company lead tracker.
• Summarize today’s important email.

A task is neutral.

It is not inherently human work or AI work.

The first step is simply learning to see work clearly enough that it can be delegated.

There is also no objectively correct task boundary.

For example, one person may define:

Collect Facebook leads and add them to the company spreadsheet.

Another may separate that into:

Retrieve the Facebook leads.

and:

Add the retrieved leads to the company spreadsheet.

A useful boundary test is:

Would I want to review, reuse, retry, or delegate this piece independently?

If yes, it may deserve to be its own task.

The purpose of Task is to help humans reason about meaningful work, not to create a perfect taxonomy.

Actions and Steps

Below tasks are lower-level execution actions such as:

• Open a browser.
• Navigate to a website.
• Click a button.
• Download a file.
• Type into a field.
• Run a terminal command.
• Parse a document.
• Call a tool.

These generally remain under the hood.

The participant defines the meaningful work.

The system determines how to execute it.

Workflow

A workflow coordinates multiple tasks and the logic connecting them toward a larger outcome.

A workflow may include:

• Sequence.
• Parallel work.
• Conditions.
• Repetition.
• Human review.
• Approvals.
• Handoffs.
• Triggers.

For example:

1. Collect new Facebook leads.
2. Collect website leads.
3. Collect email leads.
4. Validate each lead.
5. Add qualified leads to the CRM.
6. Route uncertain leads to a human.

The important distinction is:

Task = atomic unit of meaningful work.

Workflow = coordination of tasks and control logic.

Actions/tool calls = execution primitives underneath the task.

Workflows are intentionally not the starting abstraction for participants.

They become useful after participants already have reliable tasks worth orchestrating.

The initial commercial product is a high-touch 12-week company engagement.

Price: $60,000 per company

Participants: 12

A company selects 12 people who own meaningful work inside the organization. These will often be middle and upper managers, operators, functional leaders, and selected individual contributors with enough context and authority to redesign how work gets done.

Over 12 weeks, participants learn how to:

• Recognize meaningful units of work.
• Identify tasks worth delegating.
• Give AI real work rather than artificial exercises.
• Review AI execution.
• Improve poorly specified tasks.
• Define what good performance means.
• Save successful work as reusable tasks.
• Progressively automate reliable tasks.
• Combine tasks into workflows.
• Measure released capacity and business value.

The success target is:

At least 5 hours per week of cumulative capacity released for each participant.

Across 12 participants, that represents a target of at least:

60 hours per week of organizational capacity created.

The five hours do not need to come from one agent, one workflow, or one dramatic automation.

A participant might release:

• 45 minutes from daily email triage.
• 90 minutes from weekly reporting.
• 60 minutes from meeting preparation.
• 90 minutes from prospect research.
• 60 minutes from follow-up communication.

The objective is not to create impressive demos.

It is to move meaningful work from:

I personally execute this

toward:

I can reliably delegate this.

Start With Constrained Work

The first question is not:

“Where can we use AI?”

It is:

“What important work is consuming capacity, delayed, backlogged, expensive, or not getting done?”

Strong opportunities often include work that:

• Repeats frequently.
• Consumes meaningful human time.
• Blocks revenue or throughput.
• Requires scarce expertise.
• Scales with headcount.
• Produces significant rework or errors.
• Is consistently late or backlogged.
• Matters but is neglected because nobody has enough capacity.

The objective is to start with actual work rather than interesting AI capabilities.

Learn to See Tasks

Participants then decompose their work into meaningful delegatable units.

They do not need to model every click, tool call, input, output, or exception before beginning.

They need to be able to answer two fundamental questions:

What do I want done?

and:

How will I know whether it was done well?

This produces the beginning of a Task.

Delegate Real Work

Participants give the AI a real task they would otherwise have performed themselves.

The initial experience should feel more like delegation than configuration:

Describe the work → let AI attempt it → inspect the result.

The system determines the lower-level actions and tools required.

Review and Improve

The participant then evaluates the work.

They identify:

• What was correct.
• What was missing.
• What was unnecessary.
• What judgment was wrong.
• What information the AI should have considered.
• What should happen differently next time.

This review loop is fundamental.

The participant is learning to manage delegated work rather than merely prompt a chatbot.

Save Successful Work

Once useful one-off work has been completed, Work That Matters can help convert the interaction into a reusable Task.

For example:

Save this as a reusable task?

WTM can infer the likely task definition from the successful execution and allow the participant to edit it.

This reduces the need for users to create elaborate specifications before they know whether the work can be delegated successfully.

The progression becomes:

Do the work → improve the work → save the work → reuse the work.

Build Reliability Before Automation

At the beginning of the program, tasks are human-triggered.

A human decides:

Run this task now.

This is intentional.

Participants first need to develop the ability to define, delegate, review, and improve work.

Automating a poorly designed task merely creates bad work without requiring someone to initiate it.

Once a task has become reliable, additional trigger types become useful.

Add Scheduled Triggers

A stable reusable task can later run on a schedule.

For example:

Every weekday at 8:00 AM, summarize the important email from the previous day.

The important conceptual distinction is:

The task defines what work happens.

The trigger defines when the work starts.

Add Event Triggers

Later, tasks and workflows may also begin when something happens.

For example:

When a qualified lead submits the website form, prepare the account research.

Event-driven execution allows digital labor to become increasingly embedded in normal organizational operations.

Condition-based triggers may become useful later as the product matures.

Combine Tasks Into Workflows

Only after participants have several useful tasks does orchestration become valuable.

At that point the product can introduce the idea:

These tasks belong together. Let’s coordinate them as a workflow.

Workflows should feel like an earned abstraction rather than something participants must understand before receiving value.

Measure Released Capacity

The program continuously measures what work has actually moved.

The first practical metric is time released:

How many hours of meaningful human execution no longer need to be performed manually each week?

Additional business measures may include:

• Revenue supported.
• Throughput increased.
• Cycle time reduced.
• Backlog reduced.
• Errors prevented.
• Outside spend reduced.
• Incremental hiring avoided.
• Customer responsiveness improved.
• New work made possible.

The goal is not theoretical productivity.

It is demonstrable organizational capacity.

The cohort is the guided adoption layer.

The OS is the persistent operating layer.

The Work That Matters OS manages the digital work participants create and allows that capability to continue expanding after the 12-week engagement.

The OS ultimately manages:

• Tasks.
• Workflows.
• Triggers.
• Execution history.
• Human review.
• Approvals.
• Authority boundaries.
• Performance expectations.
• Exceptions.
• Corrections.
• Outputs.
• Outcomes.
• Operational learning.
• Capacity released.
• Business value created.

The OS should not force humans to navigate all of this structure manually.

Its purpose is to make delegation easier, not to turn work into configuration.

The guiding principle is:

Humans define meaningful work. The system manages the execution complexity underneath it.

A core product capability is the Work Computer.

Each participant receives a persistent cloud computer that acts as the runtime for their delegated work.

The participant’s physical computer is primarily the console.

The Work Computer is where digital labor operates.

It includes capabilities such as:

• A persistent desktop.
• Browser access.
• Persistent authenticated sessions.
• Filesystem.
• Terminal.
• Code execution.
• Access to web applications.
• Human remote access.
• AI computer-use access.

The participant manually authenticates into the systems they already use.

Authentication and MFA remain human responsibilities.

WTM does not need to become a giant library of custom OAuth integrations before it can perform meaningful work.

Once authenticated, the Work Computer can operate many of the same systems the participant already uses:

• Gmail.
• Microsoft 365.
• CRM platforms.
• Internal admin tools.
• Web applications.
• Spreadsheets.
• Communication tools.
• File systems.
• Browser-based business software.

The human and AI should operate the same persistent environment.

A participant should be able to:

• Watch the AI work.
• Inspect the environment.
• Take over when necessary.
• Resume control.
• Leave authenticated sessions available for future delegated work.

This allows Work That Matters to benefit from increasingly powerful general-purpose computer-use agents without needing to build the best general-purpose computer agent itself.

The Work Computer is infrastructure.

It is not the moat.

Work That Matters should maintain a clear division of labor.

The Human Is Responsible For

• Identifying meaningful work.
• Defining the task.
• Deciding why the work matters.
• Defining what good looks like.
• Choosing what should be delegated.
• Reviewing results.
• Correcting judgment.
• Establishing authority.
• Deciding what still requires human involvement.

The System Is Responsible For

• Determining lower-level actions.
• Selecting tools and capabilities.
• Operating the computer.
• Managing execution details.
• Remembering relevant execution history.
• Detecting recurring patterns.
• Applying prior corrections where appropriate.
• Surfacing exceptions.
• Recording outcomes.
• Helping improve future performance.

This division prevents WTM from becoming another low-code workflow builder where users must manually construct every internal execution step.

The employees most capable of designing effective digital labor may also feel most threatened by it.

The people closest to the work understand:

• Hidden exceptions.
• Informal rules.
• Workarounds.
• Failure modes.
• Institutional history.
• Which decisions require judgment.
• Which seemingly simple processes are actually complicated.

This creates an important tension:

The person most qualified to teach AI how the work operates may believe they are training their replacement.

The answer cannot simply be better marketing.

Each engagement should establish a clear internal transformation charter.

The company should communicate that:

• AI adoption is necessary.
• The company cannot promise that every role will remain unchanged forever.
• The objective is to expand organizational capability rather than simply eliminate headcount.
• Employees will participate in redesigning the work they understand.
• Human expertise and institutional knowledge are assets.
• Digital autonomy must be earned.
• New capacity should allow the organization to accomplish valuable work it could not previously perform.
• People who successfully create and manage digital capacity should become increasingly valuable to the organization.

Where possible, employees should become owners and managers of new organizational capability rather than simply sources from whom process knowledge is extracted.

Work That Matters has two complementary revenue layers.

12-Week Company Cohort

$60,000 per company

12 participants

The company purchases the engagement rather than individual seats.

At full participation, this is economically equivalent to $5,000 per participant.

The cohort creates the initial organizational capability, identifies valuable work, develops internal champions, and moves meaningful work into the WTM platform.

Work That Matters OS

$60,000 per company per year

The OS is a platform fee rather than a traditional per-seat license.

Model and token usage are passed through to the customer.

The customer therefore pays:

• $60,000/year for the Work That Matters OS.
• Variable model usage based on how much work the organization delegates.

First-Year Customer Economics

A standard customer purchasing the cohort and continuing on the OS produces:

From year two forward:

$60,000 ARR per retained customer plus pass-through model usage.

Illustratively:

These figures exclude new cohort revenue.

Economic Logic

The economics work only if Work That Matters creates substantially more value than it costs.

The initial capacity target itself creates a useful benchmark.

Twelve participants each releasing five hours per week creates approximately:

60 hours of organizational capacity per week.

That is roughly 3,000 hours per year if sustained.

The true value will depend on what those hours represent.

Released executive capacity, increased sales throughput, avoided operational bottlenecks, improved customer response, reduced external spend, and newly possible work may be worth substantially more than simple salary-hour calculations suggest.

The intended customer value should comfortably exceed the $120,000 first-year cost.

Why Not Per Seat

The product's long-term value does not primarily depend on how many humans log into the application.

It depends on how much valuable work the organization runs through it.

A company with relatively few employees may operate substantial digital capacity.

The initial pricing model is therefore intentionally simple:

$60,000 annual platform fee + pass-through model usage.

The initial go-to-market is founder-led and problem-led.

The buyer does not primarily need another AI platform.

The buyer needs evidence that AI can create meaningful business capacity inside the actual organization.

The initial sales motion is:

1. Identify companies with meaningful capacity constraints or disappointing AI ROI.
2. Diagnose where important work is consuming scarce capacity.
3. Enroll 12 participants who own meaningful work.
4. Run the 12-week cohort.
5. Create at least five hours per week of released capacity per participant.
6. Build internal champions who know how to identify and delegate additional work.
7. Keep the resulting tasks and workflows running inside Work That Matters OS.
8. Expand digital capacity across additional employees and functions.

Each early customer serves two purposes:

Revenue and product discovery.

Repeated engagements reveal:

• Which work is easiest to delegate.
• Which task definitions create reliable execution.
• Which review patterns matter.
• Which governance structures work.
• Which trigger patterns become useful.
• Which workflows recur.
• Which exceptions require humans.
• Which metrics credibly demonstrate value.
• Which organizational conditions predict adoption or failure.

The progression is:

Engagements create patterns → patterns become methodology → methodology becomes product.

The business has a natural expansion loop:

1. Sell the company cohort.
2. Identify constrained work.
3. Teach participants to define and delegate tasks.
4. Create measurable released capacity.
5. Turn successful work into reusable tasks.
6. Build internal champions.
7. Keep the tasks running inside WTM.
8. Add triggers as tasks become reliable.
9. Combine tasks into workflows.
10. Expand into additional work.
11. Accumulate execution history, corrections, exceptions, and outcomes.
12. Use that operational intelligence to improve performance and identify additional opportunities.

The services layer creates behavior change and initial adoption.

The software layer creates continuity, organizational learning, expansion, and recurring revenue.

The cohort should make the OS increasingly difficult to remove for the right reason:

Real work now depends on it.

Work That Matters should not attempt to win by building the world’s best general-purpose AI agent.

OpenAI, Anthropic, Microsoft, Google, Amazon, and others will continue investing enormous resources in increasingly capable general-purpose models and computer-use systems.

WTM should benefit from that competition.

The strategic position is one layer above it:

Work That Matters is the management layer that turns general-purpose AI agents into reliable organizational capacity and measurable ROI.

The moat has three major components.

Work Management

WTM becomes the place where the organization defines and manages:

• Reusable tasks.
• Workflows.
• Triggers.
• Success criteria.
• Authority.
• Human review.
• Approvals.
• Execution.
• Performance.
• Value.

The models underneath may change.

The organizational work remains.

Operational Intelligence

The deeper knowledge accumulated by WTM is not simply a collection of company documents.

The more valuable knowledge comes from doing the work.

Every execution can generate information about:

• What happened.
• What succeeded.
• What failed.
• What the human corrected.
• Which exception occurred.
• Which decision was approved.
• Which output performed well.
• Which instruction improved performance.
• What happened afterward.

Over hundreds or thousands of executions, the system begins accumulating organizational experience.

This is different from generic enterprise search or RAG.

The important memory is:

What the organization has learned through repeatedly doing the work.

Measurable Value

WTM should make digital labor economically visible.

Leadership should be able to see:

• What work is delegated.
• How frequently it runs.
• How reliably it performs.
• Where humans still intervene.
• How much capacity has been released.
• What business outcomes are associated with the work.
• Where the next delegation opportunities exist.

The long-term defensibility comes from combining:

work definition + execution history + organizational learning + measurable value.

Work That Matters begins with individual people learning to delegate meaningful portions of their existing work.

But the long-term implication is larger.

Today, most organizations scale by adding human execution capacity.

More customers require more support staff.

More leads require more sales capacity.

More transactions require more operations staff.

More projects require more coordinators.

As digital labor improves, that relationship can weaken.

An employee may increasingly spend less time personally executing routine complicated work and more time:

• Owning outcomes.
• Designing work.
• Exercising judgment.
• Solving novel problems.
• Managing digital capacity.
• Handling exceptions.
• Creating new capabilities.
• Deciding what deserves human attention.

The organization begins to move from:

Humans using AI tools

toward:

Humans managing portfolios of human and digital work.

A single person may eventually command productive capacity that previously required an entire team.

The strategic sequence is:

Today

Help 12 people inside a company identify and delegate enough real work to release at least five hours per week each.

Next

Become the system where that delegated work is saved, operated, governed, measured, and expanded.

Then

Allow tasks to become increasingly autonomous through schedules, events, workflows, and carefully governed authority.

Then

Accumulate operational intelligence from thousands of executions, corrections, exceptions, and outcomes.

Eventually

Become the operating system through which companies organize and manage substantial portions of their digital labor.

Not an AI Training Company

Participants will learn a great deal about AI, but education is not the product outcome.

Released organizational capacity is.

Not a Generic AI Consultancy

High-touch services are necessary initially, but the purpose is to discover and implement a repeatable system that ultimately lives in software.

Not an Agent Builder

The product should not begin with:

“Create an agent.”

The starting point is:

“What work are you trying to get done?”

General-purpose agent construction will increasingly commoditize.

Not Zapier

Participants should not have to manually construct every step, integration, condition, and API call required to perform their work.

Humans define meaningful work.

The system handles execution complexity.

Not a Connector Company

WTM should use APIs and native integrations when they materially improve reliability, but the product should not depend on building hundreds of bespoke connectors before it becomes useful.

The Work Computer provides a broad execution surface across the applications people already use.

Not a Computer-Use Infrastructure Company

The Work Computer is necessary infrastructure.

The long-term value is not the remote desktop itself.

The value is the management system governing the work running through it.

Not a Headcount-Reduction Consultancy

AI will inevitably change staffing requirements in some areas.

WTM should not deny that.

But the product should optimize for:

More capability per human

rather than:

The same capability with fewer humans.

Not an AI Demo Factory

A clever autonomous demo has little strategic value by itself.

WTM should be judged by meaningful work delegated, reliable execution, released capacity, and business results.

Services Trap

The company could become a profitable AI transformation consultancy without creating a scalable software business.

Mitigation: Treat every engagement as product discovery. Identify recurring task patterns, governance structures, execution needs, measurement systems, and reusable product primitives.

Premature Product Complexity

The long-term digital-labor vision can easily produce too many concepts, forms, configuration screens, and abstractions.

Mitigation: Expose structure only when it helps the participant perform meaningful work. Start with tasks. Introduce additional abstractions when users have earned the need for them.

Automating Unreliable Work

Scheduling or event-triggering poorly defined tasks can create unattended failure.

Mitigation: Begin with human-triggered execution. Introduce automated triggers only after tasks demonstrate acceptable reliability.

General-Purpose Agent Commoditization

Underlying models and computer-use agents will continue improving rapidly.

Mitigation: Remain model- and infrastructure-agnostic. Own the organizational layer: work definition, management, governance, history, evaluation, learning, and value.

Difficult ROI Measurement

The value of released capacity can be difficult to convert into precise financial ROI.

Mitigation: Begin with observable time released and connect it to stronger business measures wherever possible. Make assumptions explicit rather than manufacturing false precision.

Employee Resistance

Employees may reasonably fear that delegating their work to AI makes them less necessary.

Mitigation: Position participants as designers and owners of new capacity, maintain transparent governance, prioritize expansion-oriented use cases, and create clear organizational expectations before the engagement begins.

Security and Enterprise Access

Digital labor operating inside authenticated business environments introduces legitimate security, identity, audit, and compliance concerns.

Mitigation: Keep authentication human-controlled, maintain clear authority boundaries, create auditable execution history, support human takeover, and design the Work Computer layer to accommodate enterprise deployment and security requirements.

Misaligned Customers

Customers primarily seeking immediate headcount elimination may create poor incentives, employee resistance, and weak long-term platform adoption.

Mitigation: Prioritize companies where additional capacity has obvious value.

Several important questions remain to be resolved through early customers and product use.

Ideal Customer Profile

Which industries, company sizes, organizational structures, and operational profiles produce the fastest path to meaningful released capacity?

Buyer

Is the strongest initial buyer the CEO, COO, transformation leader, CIO, business-unit leader, or another operator?

Participant Selection

Which 12 employees should a company choose to maximize both measurable results and future internal adoption?

5-Hour Measurement

What evidence should qualify as credible proof that a participant has released five hours per week?

Commercial Guarantee

Should the 5-hour outcome become part of a contractual guarantee, and if so, under what participation and measurement requirements?

Task Reliability

What evidence should cause WTM to recommend that a task is stable enough for scheduled or event-triggered execution?

Expansion Motion

Once 12 participants have meaningful delegated work running through the system, what is the best mechanism for spreading task delegation across the organization?

Governance

How much authority, approval logic, auditability, and administrative oversight do enterprise customers require before digital work can operate unattended?

Pricing Evolution

As customers operate substantially more digital labor, should pricing eventually reflect usage, organizational complexity, support level, security requirements, or deployed capacity?

Operational Intelligence

At what scale does accumulated execution history become meaningfully better than starting fresh with a general-purpose agent?

1. Prove the participant outcome. Demonstrate that 12 people can each release at least five hours per week through delegated work.

2. Make Task delegation excellent. The core experience should make it easy to give AI real work, review what happened, improve it, and reuse successful work.

3. Avoid premature workflow complexity. Participants should receive substantial value before they need orchestration concepts.

4. Build the Work Computer into a reliable execution substrate. Humans and AI should be able to operate the same persistent authenticated environment safely and consistently.

5. Build durable execution. Real delegated work must eventually survive application restarts, run unattended, preserve history, and support reliable long-running execution.

6. Measure released capacity. The product needs a credible mechanism for determining how much meaningful human work has actually moved.

7. Learn when automation is earned. Establish the conditions under which WTM should recommend scheduled or event-triggered execution.

8. Convert successful one-off work into reusable tasks. This should become a core product loop.

9. Turn cohort participants into internal champions. The company should leave the program with people who know how to continue identifying and delegating work.

10. Convert adoption into recurring OS usage. By the end of the cohort, meaningful company work should already be running through WTM.

11. Capture operational learning. Preserve runs, corrections, exceptions, approvals, and outcomes so the system becomes more valuable through use.

12. Protect the strategic layer. Do not spend company resources competing with frontier model providers on capabilities they are structurally better positioned to build.

Work That Matters helps companies turn AI from a tool employees use into reliable organizational capacity they know how to manage.

A company sends 12 participants through a 12-week program.

Each participant learns to break meaningful work into tasks, delegate those tasks to AI, review the work, improve it, and turn successful execution into reusable digital capacity.

The target is at least five hours per week of released capacity per participant.

Initially, humans decide when tasks run.

As tasks become reliable, they can run on schedules, respond to events, and combine into larger workflows.

The Work That Matters OS becomes the system through which the company defines, runs, governs, measures, and improves that digital work.

Underneath, increasingly capable general-purpose AI agents operate persistent Work Computers and the applications employees already use.

Above them, WTM manages the work itself:

What needs to happen. What good looks like. What AI owns. When it runs. What happened. What the organization learned. And what value was created.

The long-term goal is not simply to help companies automate more tasks.

It is to build the management system for organizations in which humans increasingly direct, improve, and expand portfolios of digital labor.