Implementing AI in the company: practical guide 30–60 days

“We want to implement AI” often translates into rushed purchases of tools, isolated pilots, and, after a few months, frustration: it doesn’t integrate into processes, the data isn’t ready, the team is distrustful, or compliance slows progress. AI can provide real advantages, but only when treated as an operational transformation: well-chosen use cases, governed data, controlled risks, and managed adoption. In this article, you will find a practical approach to move from intention to measurable results. You will learn to identify 5–10 typical use cases and select 3 priorities, to execute a phased roadmap (discovery → pilot → scaling) with concrete deliverables, and to define success/ROI metrics. You will also see a minimal governance, security, and compliance plan (GDPR and AI Act at a high level) and a change management approach to achieve real adoption in 30–60 days, without unrealistic promises.

What does “implementing AI” mean in a company

Operational definition: implementing AI means integrating predictive, classification, generation, or assisted automation capabilities within existing (or redesigned) business processes, with clear responsibilities, controlled data, security and compliance, and metrics that demonstrate impact. It is not: “testing a chatbot,” “buying licenses,” “doing a pilot in one department,” or “having a model in a notebook.”

Common mistakes (and why they fail)

Starting with technology, not with the process: a nice demo is created that no one uses in day-to-day operations.
Use cases without an owner: without a “process owner,” there are no decisions or adoption.
Immature data: duplications, inconsistent definitions, confusing permissions; the model inherits the chaos.
Poorly defined success: there is no baseline or metrics; any result is debated.
Ignoring risks (privacy, security, biases): it is halted too late, when there is already dependency.
Not managing change: the team perceives it as control/replacement and passively sabotages it.

2: Principles for successfully implementing AI

Start with business decisions, not with models: what decision/process is improved and how much it is worth.
Choose “value-close” use cases: clear impact, available data, and short adoption cycle.
Define “human in the loop” from day 1: who validates, when, and with what criteria.
Data quality > model sophistication: first consistency, traceability, and permissions.
Design for integration: AI within CRM/ERP/ticketing, not in a separate tab.
Security and compliance “by design”: minimum controls before scaling.
Measure with baseline and experiments: A/B, control groups, or before/after with seasonal adjustment.
Adoption as a product: training, champions, support, and iterative improvements based on feedback.

Use cases by area (2 examples + data prerequisites)

Practical tip for prioritization: select 3 cases that meet (a) clear economic impact, (b) accessible data, (c) simple integration, (d) moderate and controllable risk.

Sales

1. Proposal and email co-pilot (drafts, arguments by sector, meeting summaries). Necessary data: CRM (opportunities, sector, history), commercial templates, product catalog, commercial conditions. 2. Lead scoring and “next best action” (prioritize leads and suggested actions). Necessary data: CRM + marketing automation, conversion history, lead sources, commercial activity.

Customer Service / Support

1. Agent assistant (case summary, suggested response, relevant articles). Necessary data: ticket issuance, knowledge base, policies, resolution history, and tags. 2. Automatic classification and routing (category, urgency, referral). Necessary data: historical tickets with reliable tags, SLA, teams/responsibles.

Finance

1. Invoice automation and reconciliation (extraction, validation, matching). Necessary data: invoices (PDF/EDI), ERP, supplier masters, accounting rules, exception history. 2. Treasury / demand forecasting (forecasting with scenarios). Necessary data: historical collections/payments, sales, seasonality, calendar, business variables.

HR

1. Human Resources assistant for policies and internal inquiries (vacations, permits, onboarding). Necessary data: current policies, applicable agreements, internal portal, FAQs, version traceability. 2. Turnover and climate analytics (drivers, early alerts). Necessary data: HRIS, evaluations, absences, surveys, aggregated and minimized data.

Operations / Supply / Production

1. Predictive maintenance (failure alerts, optimization of downtimes). Necessary data: sensors/SCADA, historical breakdowns, work orders, operating conditions. 2. Planning optimization (shifts, routes, inventory). Necessary data: demand, capacities, times, restrictions, inventory, OTIF.

Marketing

1. Content generation and adaptation (copies, A/B variants, briefs). Necessary data: brand guide, buyer personas, historical performance, allowed claims, creative library. 2. Segmentation and attribution (propensity, cohorts). Necessary data: CDP/CRM, web events, campaigns, conversions, consent, and traceability.

Data, architecture, and tools

1 Typical data sources . Core systems: ERP, CRM, HRIS, ticketing, e-commerce, MES/SCADA. . Documents: contracts, policies, manuals, knowledge base, emails (with control). . Events: web/app analytics, calls (transcription), IoT.

2 Data quality and governance (minimum viable) . Data dictionary (definitions of critical fields). . Data owners by domain. . Quality rules (completeness, uniqueness, timeliness) and a simple dashboard.

3 Integration (to avoid “island pilots”) . Connectors/API to CRM/ERP/ticketing. . Identity management (SSO), role-based permissions, and traceability. . Logging of conversations/actions (for audit and improvement).

4 Build vs Buy (specific decisions) . Buy when: you need speed, standard cases (internal assistant, ticket classification), small team. . Build when: competitive advantage, unique data, deep integration, strict control of costs/risk. . Frequent hybrid: purchased platform + customization + connectors + governance.

Governance, security, and compliance (actionable checklist)

Governance

Executive sponsor and “AI owner” for each use case.
Catalog of use cases with risk level (low/medium/high).
Usage policy: what can be uploaded to external tools and what cannot.
Record of suppliers/models and periodic evaluation.

Security

SSO + role-based access control (RBAC).
Encryption in transit and at rest; key management.
Logging and auditing (who consulted what, when, and result).
Protection against rapid injection and exfiltration (filters, tool isolation, validations).
Robustness/red team testing before production.

GDPR (minimums)

Clear legal basis; purpose limitation; data minimization.
DPIA when applicable (high risk, sensitive data, systematic evaluation).
Contracts with processors (DPA), sub-processors, international transfers.
Defined rights of data subjects and retention/deletion.
Pseudonymization/anonymization when feasible.

AI Act (high level, practical approach)

Classify the case: does it affect employment, credit, education, essential services, or other sensitive areas?
If it is of higher risk: documentation, traceability, data quality, human oversight, and strict risk management.
Transparency: inform when there is interaction with automated systems (depending on the context).

Change management and adoption

Minimum plan (30–60 days)

1 Sponsor message (day 1–3): “AI to increase capacity and quality, not to improvise cuts”; measurable objectives. 2 Impact map by roles: what tasks change and what is expected from each role. 3 Training by profiles (2–3 formats): . 60–90 min for executives (risks, metrics, decisions). . 2–3 h for users (practical cases + “do/don't”). . 1 h for IT/Security/Legal (controls, operation, incidents). 4 Champions (1 per area): weekly feedback, help standardize best practices. 5 Support and improvement: doubt channel, office hours, biweekly improvement backlog. 6 Incentives: objectives linked to responsible use (quality, time savings, reduction of incidents), not just “number of alerts.”

How to measure impact (ROI)

1 Leading metrics (anticipate success) . % active weekly users / adoption by team. . Average time per task (before/after). . Perceived quality (internal NPS, internal CSAT). . Human correction rate / rework. . Security/compliance incident rate.

2 Lagging metrics (business outcome) . Increased conversion, reduced abandonment, improved OTIF. . Reduced cost per ticket / administrative cost. . Decreased errors (invoices, reconciliation, claims). . Reduced cycle (lead → proposal → closure).

Simple calculation example (practical)

Case: agent assistant in support. . Inputs/month: 2,000 . Average saving: 2 minutes/ticket (measured in pilot) . Hours saved: 2,000 × 2 / 60 = 66.7 h/month . Loaded hour cost: €30 . Monthly value: 66.7 × 30 = €2,001 . Monthly cost (licenses + operation): €900 .Net monthly ROI: 2,001 – 900 = €1,101 Additionally, add quality metrics (e.g., +5 CSAT points) to avoid optimizing only for cost.

Final checklist: “First 30 days” (12–15 actions)

Appoint executive sponsor and program manager.
Identify 5–10 use cases and document them (value, data, risk, effort).
Select 3 priorities with an impact/effort/risk matrix.
Define baseline and KPIs by case (time, cost, quality, risk).
Quick data inventory: where they are, quality, permissions, owner.
Approve internal AI usage policy (includes prohibited data).
Review GDPR: legal basis, minimization, retention, DPIA if applicable.
Evaluate suppliers/tools (security, DPA, audit, logs).
Design minimum integration with systems (SSO, CRM/ERP/ticketing).
Define “human in the loop” and validation criteria.
Prepare communication plan (what changes, why, when).
Select champions and pilot users (10–30 people).
Execute practical training and usage guides (do/don't).
Execute practical training and usage guides (do/don't).
Launch controlled pilot with weekly metric review.

Successfully implementing AI does not depend on “having the best model,” but on doing the basics well: choosing use cases with owners and value, preparing data and minimum controls, integrating into real work, and managing adoption with discipline. In 30–60 days, it is feasible to move from intention to useful and measurable pilots, and lay the groundwork for scaling without surprises in security, compliance, or costs. If you share your sector and your current systems (ERP/CRM/ticketing) and 2–3 business objectives, prepare a prioritization of use cases and a 60-day roadmap with metrics and risks.

Frequently asked questions

1) Where do I start if I don’t have “perfect” data? With a use case with data already available and controllable (e.g., tickets and knowledge base). At the same time, define owners and quality rules for critical data. 2) What use cases usually provide value faster? Agent assistant in support, classification/routing, commercial/marketing drafts with brand guide, data extraction from invoices, automation, and administrative tasks. 3) How long does it take to see ROI? In operational cases (support, back office), it can take between 4 and 8 weeks if there is a baseline, minimum integration, and committed pilot users. 4) What are the most common risks with generative AI? Information leakage, incorrect responses with apparent security, rapid injection, and uncontrolled cost dependency. Mitigate with permissions, logging, validations, and limits. 5) What do I need to comply with GDPR? Minimization, legal basis, access control, DPA with suppliers, retention/deletion, DPIA when there is high risk, and usage traceability. 6) Does the AI Act affect me already? It depends on the case. If it falls into sensitive areas (employment, credit, essential services), it requires a reinforced approach: risk assessment, traceability, and human oversight. 7) Minimum team to start? Sponsor, process owner (business), IT/security, data manager, and a legal representative/DPO. For the pilot: 1 PM/PO + 1–2 technical profiles + champion users.

How to get started?

If you don’t know, the best thing is to let a company with experience in these cases guide you. Integrating AI can be a good start, contact info@netretina.ai and we will help you step by step.