An AI Workflow Design,
in full.
Not consultancy to redo every time, but a reusable playbook. This is the Marketing department example, open and free: what to really automate, with which tools, in which phases and with which controls — starting with tone-of-voice oversight. The exact shape of what we graft into your business.
What to really automate
The pattern of what works: high-frequency, data-driven optimisation loops, not strategy or brand voice. AI tunes the levers and prepares the drafts — creative direction stays a human decision.
- Adaptive campaign and budget optimisation al posto di: Manual bid and budget adjustment
- Dynamic lead scoring al posto di: Static MQL rules
- Copy drafts and ad variants al posto di: Hand-written first draft
- Content personalisation engines al posto di: Static segment targeting
- Predictive churn prevention al posto di: Reactive win-back campaigns
- Campaign performance summaries al posto di: Manual dashboard review
The best-documented use cases report a marked return on personalisation and content generation, but the available figures are self-reported aggregates from vendors and analysts, not independently verified: read them as direction, not a promise of results.
Two philosophies, not a shopping list
The 2026 market splits into two models. The right choice depends on how stable your spend is and the success criteria you can define, not on the most-cited tool.
Copilot with approval
The AI suggests, a person confirms the launch. A channel-bound optimiser paired with a copy-generation tool: it costs less, keeps every choice attributable to a human decision and avoids the percentage-of-spend structure that only pays off on large budgets.
Autonomous on spend
The AI decides and executes across the whole budget, with minimal oversight. It only makes sense with high, stable spend and clean KPIs: without clear success criteria it becomes the number-one cause of projects abandoned within the first 90 days.
Our default choice for an SME is the with-approval model: cheaper, less risky and with the best evidence to hand. Picking the exact vendor — and the due diligence on its data-processing terms — is part of the graft.
The phases of the graft
Every item in the playbook carries a phase, so halfway through you filter by "what comes next" instead of re-reading the pilots you've already shipped.
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1
Pilot
First 30 daysA single high-frequency use case (usually budget optimisation or copy drafts), a named owner, the success criteria written BEFORE you start — the number-one cause of abandonment in these projects is exactly an objective left vague.
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2
Scale
First 90 daysThe workflow that worked is extended to the other use cases and integrated with the tools you already use. The guardrails on brand tone of voice are consolidated: here oversight is not optional.
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3
Ongoing
Steady stateContinuous monitoring of adoption and results, periodic review of vendors and risks. The playbook stays alive: it gets updated, not archived.
The compliance overlay, and who governs it
Compliance overlay
Personalisation and lead scoring touch personal data of prospects and customers: they raise the same GDPR questions as the rest of the playbook — an impact assessment (DPIA) when needed, data minimisation on the enrichment tools. Campaign optimisation at SME scale isn't in itself a high-risk category under the EU AI Act, but the vendor's processing terms — where the data ends up and where it is processed — require the same due diligence as any software choice.
Tone-of-voice oversight
It's the risk specific to marketing: not a legal risk, but a trust one. Content-generating agents tend to drift out of the brand voice — it's one of the top reasons these projects get abandoned. The workflow treats it as a named control, not a hope: a step of human review or voice checking before publication, always.
The AI owner
No big-enterprise Center of Excellence: in an SME one or two people named as AI owner are enough. Five responsibilities stay with them — setting priorities, who decides what, enabling the team, reusable standards and monitoring adoption and results — without assuming a whole dedicated department.
The same pattern, other departments.
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Sales
Lead scoring, automated follow-ups, CRM hygiene.
See the full example -
Operations
Process automation, reporting and anomaly detection.
See the full example -
Finance & Administration
Accounts payable, month-end close and cash forecasting, with a human on the money.
See the full example -
HR & People
Screening, onboarding and an internal knowledge base, with high-risk hiring kept under oversight.
See the full example -
Customer support
Ticket deflection, triage and agent-assist, with answers anchored to sources — no hallucinations.
See the full example
This is the example. We graft yours.
The other departments follow the same pattern. Start from the free assessment to find out where it makes sense to begin, or let's talk directly.
Example for guidance only: it does not constitute legal advice or a compliance assessment.