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Audit → first workflow → pilot

What an AI audit should deliver

A useful AI audit ends with a roadmap, risks, and a recommended pilot, not a slide deck of tools.

From audit to pilot

How to hand off a free AI audit into a funded 6–10 week pilot with owner, KPI, and scope.

Choosing the first AI workflow

How to pick the first process to automate with AI so the pilot earns trust instead of creating noise.

Scoring use cases in an AI audit

Score use cases on value, effort, data readiness, and risk. The highest score with an owner becomes the pilot. The audit is a ranking tool, not a brainstor…

When to hire vs pilot with partners

Hire when the capability is core and recurring. Pilot with a partner when you need speed, a template, and a clear go/no-go without a permanent headcount be…

Pilots & measurement

Owners, KPIs, go/no-go

AI pilot KPI examples

Concrete KPIs that prove an AI pilot worked in 6–10 weeks for reporting, triage, drafting, and analysis jobs.

Owner not committee for AI pilots

AI pilots need one process owner who can change the workflow. Committees can advise. They cannot run a six-week go/no-go.

Baseline before you automate

Measure the current time, volume, or error rate on one real cycle before you automate. Without a baseline, every pilot looks like a success story.

Go/no-go after six weeks

At six to ten weeks, keep the pilot only if the primary KPI moved and the owner wants the workflow. Otherwise redesign. Do not expand a fuzzy demo.

Measuring hours returned from AI

Hours returned is the honest time the team gets back after review, not the time the model spends generating text. Baseline one cycle, then compare.

Cheap vs risky AI experiments

Cheap experiments use public or synthetic data and internal drafts. Risky experiments touch customers, money, or personal data without a review gate. Separ…

What leadership should ask in an AI demo

Leadership should ask who owns the workflow, what KPI moves in six weeks, and where data goes. If the demo cannot answer, it is theater.

Safe AI & privacy

GDPR, vendors, review gates

GDPR-safe AI workflows

A practical privacy pattern for Slovak and EU SMEs using generative AI in daily work without treating compliance as a slide.

Vendor questions for AI tools

Ask where data is processed, whether prompts are used for training, how long logs are kept, and how you export or delete them. If a vendor cannot answer th…

Human review gate for AI outputs

A human review gate is a named person and checklist that must approve AI output before it leaves the team. Without it, speed rises and trust falls.

Redacting personal data before prompts

Redact names, IDs, and secrets before prompts so the model never sees what your policy forbids. If you cannot redact, do not run the workflow on live data.

Building an approved AI tools list

An approved tools list names what staff may use for work data, for public text only, and what is banned. Update it when vendors change terms.

When not to use generative AI

Skip generative AI when the cost of a wrong answer is immediate legal, safety, or irreversible customer harm and you have no reviewer. Use rules or humans…

Internal vs customer-facing AI outputs

Internal drafts can tolerate more error with review. Customer-facing text needs stricter gates, tone rules, and often a smaller scope.

AI for HR admin without leaking data

HR admin AI stays on redacted or synthetic examples unless the tool is contracted for personal data. Policies and contracts beat clever prompts.

Everyday workflows

Support, inbox, CRM, finance, helpdesk

Reporting automation with AI

How to replace weekly manual reporting with a reviewed AI workflow that returns hours without losing trust.

AI for customer support drafts

Use AI to draft support replies from a template and knowledge snippets, then require an agent review before send. Measure time-to-reviewed-reply and reopen…

AI for inbox triage

Route inbound mail or tickets to the right queue with AI suggestions and a human confirm step. Watch misroute rate as closely as speed.

AI from meeting notes to actions

Turn meeting notes into owners and due dates with a fixed prompt workflow, then confirm actions with attendees. The KPI is actions captured without a secon…

CRM hygiene prompts that stick

How to use AI prompts for CRM field cleanup without creating a second shadow database or fake data quality.

Helpdesk side-car AI patterns

How to attach AI next to a helpdesk without replacing the ticket system of record or skipping agent review.

Vendor invoice triage with AI

Using AI to sort and draft invoice exceptions while keeping amounts and approvals in finance systems of record.

AI and finance packs for SMEs

Finance packs can use AI for draft commentary after numbers are frozen from systems of record. Never let the model invent totals. Review before leadership…

Board pack narrative layer

How to add an AI-assisted narrative layer to board packs without inventing numbers or burying the decision.

Data & decisions

Analysis without a DS team

Data analysis without a data science team

SMEs can get decision-ready insights with focused pipelines and clear KPIs before hiring a full data science team.

Decision-ready outputs, not more dashboards

Decision-ready means an owner can act after one review. More dashboards without a decision cadence are inventory, not insight.

Source of truth hierarchy

How to rank systems and documents so AI answers cite the right source when tools disagree.

System of record vs AI scratchpad

Why AI chats are a scratchpad, not a system of record, and how to keep official decisions in the right tools.

Spreadsheet formula assist limits

Where AI help on spreadsheet formulas helps, where it breaks, and how to keep humans accountable for numbers.

Documenting AI assumptions

Write the assumptions behind a workflow: data sources, definitions, and what the model is not allowed to decide. Reviewers need that list more than another…

Fundraising & VC

Narrative, metrics, data room

VC narrative for deeptech

The story and metrics investors expect from Slovak and EU deeptech founders during fundraising.

Fundraising data room basics

A fundraising data room matches the pitch: metrics, contracts, IP, and use of funds. If the deck and the room disagree, diligence stops.

Pitch metrics investors distrust

Investors distrust vanity metrics, unsourced TAM, and precision on science timelines you cannot defend. Prefer fewer numbers you can audit.

Grant vs equity storytelling

Grant stories emphasize public benefit and milestones. Equity stories emphasize buyers, margins, and ownership. Label which audience you are speaking to.

Naming the buyer in deeptech pitches

Name the budget holder and the job they pay for today. Deeptech without a buyer is a lab update, not a venture narrative.

Runway, milestones, and product claims

Tie runway to milestones you can show, and keep product claims inside what the milestone proves. AI adjectives do not extend runway.

Training & operations

Adoption, prompts, rollback

Practical knowledge for AI, data, and consulting