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Agentic Workflow Implementation

Turn recurring tasks
into connected workflows.

Agentic Workflow Implementation by Brand & Story turns recurring research, coordination and content tasks into connected, AI-assisted processes — with clear outputs, human review and an owner in your team.

For teams in sport, brands, media and beyond. Start with one useful workflow.

Explore the work, not the jargon

One process.
A connected way to work.

Select an example, then explore each step. See what AI prepares, where a person decides and what the team receives.

Illustrative workflow

From scattered updates to a decision-ready brief.

Where manual handoffs slow things down

Someone checks several sources, copies updates into a document, reconciles conflicting information and chases missing context. The next reporting cycle starts from scratch.

Inputs ready

Approved sources

Project updates, selected documents and a defined reporting question.

The control that matters

Access is limited to the sources agreed for this workflow. Missing or outdated inputs are flagged.

Draft prepared

AI-assisted synthesis

A structured draft with source links, changes, open questions and suggested priorities.

The control that matters

The workflow separates evidence from interpretation. Unsupported statements and conflicting inputs are marked for review.

Waiting for approval

Your decision

A named owner checks the evidence, corrects the draft and decides what belongs in the final brief.

The control that matters

The draft stays in the review queue until approved. Uncertainty returns to the owner instead of becoming an automatic decision.

Approved output

Approved brief

The reviewed version goes to the agreed workspace, with its sources and an activity record.

The control that matters

Distribution is restricted to the agreed destination. A failed delivery is visible and can be retried without sending duplicate versions.

Illustrative workflow

From a shared inbox to an owned next step.

Where manual handoffs slow things down

Requests arrive in different formats. People read, copy, classify and forward them. Missing details surface late, and ownership depends on who noticed the message.

Request received

Incoming request

An enquiry from an agreed inbox or form, with the information the requester supplied.

The control that matters

Only the agreed channel and necessary fields enter the workflow. Attachments and message text are treated as input, not instructions to the system.

Routing proposed

Suggested routing

A summary, proposed category, missing information and a suggested responsible person.

The control that matters

Rules handle clear cases. AI assists where the request needs interpretation. Ambiguous or sensitive cases go to a person.

Waiting for approval

Owner confirmation

A responsible colleague confirms the route and checks any draft response.

The control that matters

No external reply is sent by this illustrative workflow without approval. The reviewer can correct the classification or take over.

Handoff recorded

Assigned next step

An approved task or response in the existing team workflow, with a traceable owner.

The control that matters

The original request remains linked. Failed handoffs enter an exception queue instead of disappearing between tools.

Illustrative workflow

From approved source material to review-ready content.

Where manual handoffs slow things down

The team repeatedly finds the source, rewrites the same context for different formats and checks which version is approved. Publication status lives in separate conversations.

Brief connected

Source material

An approved briefing, factual sources and the organisation’s writing guidelines.

The control that matters

The workflow works within the approved source set and respects usage permissions. It does not invent evidence or client results.

Drafts prepared

Structured drafts

Draft versions for the agreed formats, with source references and outstanding questions.

The control that matters

The workflow applies the brief and flags missing substantiation. It prepares material for an editor rather than deciding what the brand should claim.

Waiting for approval

Editorial approval

An editor checks facts, voice, rights and the final message.

The control that matters

Publication remains a human decision. Rejected drafts return for revision; approval applies to a specific version.

Assets ready

Approved assets

Approved versions in the agreed content workspace, with clear status and ownership.

The control that matters

Publishing integrations, if required, are scoped separately. The default handover keeps the team in control of what goes public.

Concept examples, not client case studies. This illustration does not process data or perform actions.

The service

Agentic Workflow Implementation, from first process to team handover.

AI workflow automation connects recurring steps across the tools your team already uses. AI agent implementation is useful where a step requires interpretation or a choice of action; predictable steps can remain ordinary automation.

A useful agent has a job, access to the right information and a boundary. We define those first, then build and test the workflow around them. Where a simple rule is enough, we use a simple rule.

01 · Define

Choose work worth connecting.

Map the current process, inputs, handoffs and decision points. Agree the expected result, a named owner and how improvement will be measured.

You receive: a workflow blueprint, integration scope and acceptance criteria.

02 · Build & test

Make the process work.

Connect the agreed tools. Configure AI-assisted steps, review points and exception handling. Test normal, incomplete and unexpected inputs before a controlled release.

You receive: a working pilot and a documented test and release decision.

03 · Transfer

Give the team ownership.

Train the people who run and review the workflow. Document how to handle failures, change permissions and check quality and cost as tools evolve.

You receive: an operating guide, team handover and agreed support responsibilities.

A practical starting point

Start where the work repeats.
And the outcome is clear.

A strong first workflow

  • The task recurs and uses identifiable sources.
  • Someone can describe a good result and review it.
  • The team can grant the necessary tool and data access.
  • There is an owner for the process after launch.

Resolve these first

  • A process with unclear responsibilities or changing goals.
  • Data the team cannot reliably access or use.
  • A task where errors cannot be caught before harm occurs.
  • A business case based only on replacing people with AI.

We compare completion time, rework, exceptions and running cost against the current process. A pilot earns the next step through its results.

One advisory practice · Connected capabilities

Your customer relationships.
Your team’s ability to deliver.

The Fan Relationship System connects audiences, data, platforms and value creation. Agentic workflows support the people operating that system — and can improve internal work even when a fan or customer relationship is not the task.

Direct access · Defined scope

Work with the person
responsible for the outcome.

Ralph Scherzer brings operational experience in digital platforms, communications and commercial systems. That perspective connects a workflow to the people, responsibilities and business result around it.

He is currently completing the AI Integration Expert programme at Leaders of AI.

Each engagement starts with a defined scope. Integrations requiring specialist software engineering or regulated decision-making are assessed separately, with responsibilities agreed before work begins.

Explore Ralph’s background ↗
Before we start

Practical questions.
Clear answers.

What is Agentic Workflow Implementation?

Agentic Workflow Implementation connects an AI model, business tools and defined decision rules into a working process. The implementation specifies the inputs, permitted actions, human review points and expected output. An agent is useful where a task needs interpretation or a choice of steps; predictable steps can use ordinary automation.

What do we receive from an engagement?

The agreed scope can include a workflow blueprint, a connected pilot, a test record, a controlled release and team handover. Before building, we define the workflow boundary, integrations, acceptance criteria and responsibilities. Additional workflows, custom engineering and ongoing support are priced and agreed separately.

Do we have to replace our existing tools?

Usually the first step is to assess what can work with your current environment. Tool choice follows the process, available integrations, access controls and operating requirements. A specific platform is selected during scoping; compatibility with every existing system is not assumed.

How do we stay in control of data and actions?

We agree which data the workflow may access, which actions it may perform and which decisions need approval. The scope includes checks for inappropriate instructions in input content, failed integrations and unexpected outputs. Sensitive data, retention, hosting and vendor terms are reviewed with your responsible IT or privacy contacts before deployment.

How long does implementation take, and what does it cost?

That depends on the number of integrations, the quality of the inputs and the review requirements. We start with a focused conversation and then define a scoped proposal with milestones, fees and acceptance criteria. Software subscriptions and any continuing support are made explicit before work starts.

Who operates the workflow after handover?

A named owner in your organisation receives the operating documentation and training. The handover covers exceptions, permissions, quality checks and changes to tools or models. Monitoring and maintenance responsibilities are agreed explicitly; ongoing support is a separate scope rather than an assumed subscription.

Bring one recurring task

Where does your team
keep moving work by hand?

Tell us what triggers the task, which tools are involved and what a good result looks like. We use the first conversation to assess whether a focused implementation makes sense.

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