Marketing

Pilot wave 3 (M9). Impact: medium. See the hub. Marketing’s biggest lever is content scale: the “Protect & Defend” e-shop and a catalog of tens of thousands of SKUs mean product content is a volume problem AI is unusually good at — but it depends on the SoftOne catalog integration being stable (wave 2), hence wave 3.

Current state (assumptions to validate)

Marketing supports very different audiences with a small team: B2B visibility toward shipping companies and yacht management (fairs like Posidonia, sector press, the primeproducts.gr site), the “Protect & Defend” retail brand and e-shop (product content, promotions, social), tender-driven segments where “marketing” means capability documents and references, and the training business (seminar promotion).

Assumptions — to validate in discovery: team ~1–3; product descriptions on the e-shop incomplete or supplier-copied, mostly single-language; campaign material produced ad hoc; no systematic competitor or market monitoring; brand assets scattered. Pain points: content volume vs. team size, bilingual duplication of every piece, product data quality upstream in SoftOne.

Target AI-first operating model

By M12 product content is generated at catalog scale: an agent drafts Greek + English titles, descriptions, and attribute completions from supplier datasheets and SoftOne data, with marketing approving batches rather than writing from scratch. Campaigns, newsletters, seminar announcements, and social posts are drafted by the assistant against an approved brand-voice guide in the knowledge base. A weekly market digest (competitor moves, sector news, upcoming tenders and fairs) arrives automatically. All public-facing output is human-approved before publication — no exceptions.

AI use cases

Use case	Pain addressed	Data needed	Complexity	Impact	Pilot
Bilingual product-content generation for the e-shop (titles, descriptions, attributes)	Content volume, incomplete listings	SoftOne catalog, supplier datasheets, category templates	M	H	Y
Campaign & newsletter drafting against brand-voice guide	Ad hoc production, bilingual duplication	Brand guide in KB, past campaigns	L	M	Y
Market & competitor digest (weekly)	No systematic monitoring	Public web sources, tender platforms	M	M	N
SEO improvement of category/product pages	Weak organic traffic (assumption)	E-shop content, search terms	M	M	N
Capability-document and company-profile drafting for tenders (with sales)	Repeated manual assembly	Reference projects, certifications in KB	L	M	N

Process transformation opportunities

• Content factory: product content becomes a review pipeline (generate → batch-review → publish) instead of a writing task; enriched attributes flow back to improve SoftOne data quality.
• Brand voice as code: one approved bilingual style guide in the KB that every generated draft obeys.
• Always-on market radar replacing occasional manual scanning.

Required data sources

• SoftOne product catalog (via the semantic layer — integrations); supplier datasheets/images; e-shop platform content (platform to identify in discovery).
• Brand assets and past campaigns (SharePoint); certification/reference documents (compliance).

Potential AI agents

• Product Content Agent — drafts bilingual e-shop content per SKU batch; marketing approves before publish.
• Market Digest Agent — compiles the weekly digest from public sources; marketing curates distribution.

Automation opportunities

• n8n: new-SKU-in-SoftOne → content-draft queue; scheduled digest; newsletter assembly and send via the existing tool.

Required integrations

• E-shop CMS/API for draft publishing (read/write with approval step); SoftOne catalog read; M365 for assets.

KPIs

KPI	Baseline	M12 target
E-shop SKUs with complete bilingual content	TBD (M2)	+[X] pp (target set after baseline)
Time per campaign/newsletter produced	TBD	−50%
Organic e-shop traffic	TBD	+20%
Content pieces published per month	TBD	×3

Risks

• Wrong product claims published at scale (safety/PPE products have regulatory claims) → mandatory human batch review; technical-support sign-off for safety-critical categories; standardized disclaimers.
• Generic AI-sounding brand voice → brand guide + few-shot examples from best past copy; periodic tone audit.
• Upstream catalog data quality limits content quality → coordinate with business intelligence data-stewardship work.

Training needs

• Marketing team: prompt craft for content work, batch-review workflow, brand-guide maintenance (M8–M9).

Deliverables

• Bilingual brand-voice guide in the KB; product-content pipeline (pilot M9, production M11); weekly market digest; campaign drafting workflow.

12-month execution milestones

Month	Milestone
M1–M2	Discovery: content inventory, e-shop platform/API check, brand-asset audit
M4	Assistant onboarding (general drafting)
M6–M8	Brand-voice guide written and approved; catalog data readiness with BI
M9	Wave-3 pilot: product-content generation on 2–3 categories + campaign drafting
M10	Pilot evaluation; SEO pass on piloted categories
M11	Content pipeline in production across categories; market digest live
M12	KPI review; content roadmap for year 2