Work Transformation

New work. New responsibilities. People and agents together.

Start with the work your enterprise needs done. Define what changes for the people and agents involved, then shape a simulation around your systems, data and rules.

Reimagine a workflow Reimagine a role

Work transformation in practice

What work do you want to change?

Build on the work your people already do. These examples show how software delivery, testing and business teams could add AI capabilities, what they would produce and what needs checking.

Task Intelligence follows each workflow across the teams it passes through and classifies every task as automate, augment or human-only. Those are proposals. The simulation tests them before your owners decide. Explore the Task Intelligence framework →

Software engineering and QA

Build a website-testing agent

The work today: Engineers repeat browser journeys, reproduce failures and assemble test reports.
What changes: Your team builds an agent that exercises agreed journeys, runs executable checks and records failures. Engineers verify findings and decide what to fix.
Bespoke to your enterprise: Your test application, user journeys, access boundaries and release criteria.
What the team produces: A testing agent, repeatable checks and a failure report.
What needs checking: Known defects detected, reproducible failures and false positives reviewed. Test completion alone is not a pass.
What to measure after rollout: Defects that reach release and time per regression cycle, against your current release.

Read the testing-agent guide

IT services and software delivery

Build a software factory

The work today: Delivery teams repeat requirements, implementation, testing and review for each change.
What changes: Your team builds a repeatable process in which agents prepare changes, independent checks test them and engineers review the result before release.
Bespoke to your enterprise: Your client requirements, repository, dependencies, coding standards and release controls.
What the team produces: A build process, reviewable software changes, test results and a record of failures and repairs.
What needs checking: Required behavior, regression failures and dependencies checked against the built application. The release owner decides what ships.
What to measure after rollout: Changes rejected at review and lead time from requirement to release.

Read the software-factory guide

Finance operations

Generate invoices with AI

The work today: People collect order details, check amounts and prepare invoices across systems.
What changes: Your team builds or configures a workflow to prepare invoice drafts and surface missing details. People resolve exceptions and approve issue.
Bespoke to your enterprise: Your order data, billing rules, accounting interfaces and approval limits.
What the team produces: An invoice workflow, checked drafts and an exception queue.
What needs checking: Correct customer, amounts and tax treatment; missing or duplicate orders; approval before issue.
What to measure after rollout: Invoices reworked or reissued and time from order to issued invoice.

Read the invoice guide

Bring the work your team is asking about.

Prepare for Forward Deployed Engineering →

Connect stakeholder discovery to a bounded build, integration decisions and demonstration.

Build an AI software factory →

Define a repeatable software product, build stages, independent verification and release ownership.

Fine-tune a model for a specific task →

Scope training data, a baseline, held-out evaluation and deployment constraints.

Test a website for security weaknesses →

Agree an authorized target, test boundaries, validated findings and remediation checks.

Build or adapt an AI model →

Start with the task and data. Distinguish model development, adaptation and training from scratch.

Build an agentic workflow →

Connect tools, decisions and human handoffs around an agreed enterprise work product.

Enrich leads and prepare outreach →

Define sources, CRM fields, message review and the sales decisions people keep.

Bring one baseline. Your operating owner sets the target.

Illustrative examples for scoping, not customer results or a fixed catalogue.

Transform the workflow, including who does what.

Generating an invoice is part of a larger flow from order to payment. Testing a website is part of deciding whether a release can proceed. Codify the relevant work: inputs, systems, rules, decisions, exceptions and handoffs. Then agree what AI changes, what people own and which improvement the enterprise wants to measure.

AI changes that division of work. An existing role may take on new responsibilities without changing its title. A new agent may join a familiar process. Start with the work that needs to change and the tools you already use, then decide how much of the workflow needs to be reimagined.

Make the project real in your enterprise context.

We scope custom business environments around your enterprise requirements: the systems, records, rules and people involved in the work. Reuse suitable test systems and represent the parts needed to practise realistic situations together.

Simulations try those situations, including missing information and decisions that need people. Evaluations check the actions, results and handoffs. Your domain experts review whether the environment reflects the agreed work; separate checks assess whether it was performed correctly.

Explore business environments for the new workforce

Prepare people for the work their role will do next.

A finance specialist may configure an invoice workflow. A developer may build a testing agent. An AI engineer may build or fine-tune a model for an agreed task. The starting point is the work the enterprise needs, then the capabilities, responsibilities and evidence required of the person doing it. An existing role can change, or a person can prepare for a new one.

Project Readiness connects those responsibilities to enterprise-specific simulations. Build on current capability, approved tools and your existing enablement. Define what people must produce and check, then choose the environment and assessment that help them demonstrate it.

Explore existing-role transitions and new-role preparation

Illustrative new-role preparation: FDE

From a stakeholder conversation to a demonstrated solution.

A Forward Deployed Engineer connects a customer's business problem to a working solution. The FDE discovers the workflow and its constraints, agrees a bounded problem, and builds in a sandbox with the scenario's data and tools. An AI agent can help draft code and tests; the FDE verifies the work and demonstrates it to the stakeholder.

When the problem and scope are clear

The FDE agrees the problem, available data, constraints and decision owners with the stakeholder. The agent helps with the build inside that scope. The FDE checks its outputs and explains how the solution addresses the agreed problem.

When the proposed solution assumes unavailable data

The agent produces convincing code that depends on data the customer cannot provide. The FDE recognizes the unsupported assumption, returns to the stakeholder and revises the problem or build scope before proceeding.

What the simulation should reveal

Can the FDE uncover the real problem, question an agent's assumptions and handle the missing data? Does the agent work within the agreed boundaries? Can the stakeholder understand the demonstration, its limits and the decisions they need to make? Review these as separate questions about the same workflow.

In discovery practice, an AI-simulated stakeholder helps assess the FDE's questioning and judgment. That differs from evaluating an agent contributing to the build. This illustrative workflow describes preparation; customer approval and production acceptance are separately agreed.

Check the people, the agent and the work between them.

Review what was built and performed, including the decisions, errors and corrections. A person's capability, an agent's performance and acceptance of the shared workflow answer different questions.

Evidence	What it needs to show
Human capability	What a person can demonstrate: applying context, checking outputs, making authorized decisions and handling exceptions. Skill Validation Assessments provide evidence for the human responsibilities and scenarios assessed.
Agent behavior	How the selected agent behaves against agreed criteria: using the permitted tools and evidence, staying within its authority, and escalating when it cannot proceed. Human skill assessments do not establish this.
Shared workflow	Whether the combined sequence works in the agreed scenarios: information reaches the right owner, decisions respect authority, and exceptions have a usable path. The organization's operating owners decide acceptance for real use.

Agree agent and workflow tests separately. Preparation evidence does not replace integration, security review or production acceptance.

Scope the environment and evidence for shared work

Onboard to the work, then revisit it as it changes.

When a person or an agent joins the workforce, begin with its contribution to the workflow. Define responsibilities, access, required evidence, handoffs and escalation. Decide which scenarios need rehearsal and what evidence the owners need before assigning the work.

A new model, tool or agent capability can change those requirements. So can a change in policy or in the people who own the decisions. Review the affected work, repeat the relevant scenarios and update preparation where needed. Agree the review responsibility and cadence rather than assuming readiness lasts indefinitely.

Task Intelligence is the framework underneath.

Start with the role and workflow. Task Intelligence examines the detailed work within them: where agents can contribute, where people remain responsible, and where both are needed. That understanding informs the redesign, simulations and preparation.

Explore the Task Intelligence framework

Start with one piece of work you want to improve.

Bring the work your team wants to change: preparing invoices, enriching leads, building a testing agent or developing a model. Agree the enterprise context, the first project, who will do what and the evidence needed for the next decision.

Reimagine a role Reimagine a workflow Choose practice for the target role Prepare Forward Deployed Engineers

Discuss the work you want to change