How long does it take to implement an AI project in an SME?

Simple automation projects take 4-8 weeks. LLM integrations into existing SaaS can take 8-16 weeks. Custom systems with complex agents need 3-6 months.

What's the minimum budget to start with AI in H2?

For basic automation, budget €5-15k. LLM integrations start at €15-30k. Custom projects with bespoke development start from €30k.

How do I prioritise between different AI use cases?

Use the RICE framework: assess Reach (how many people it affects), Impact (how much it improves), Confidence (certainty of results) and Effort (required effort). Prioritise cases with high RICE scores.

Is it better to hire internally or work with consultancies?

For your first project, we recommend external consultancy to validate the approach. Then you can build internal capacity or maintain a hybrid partnership depending on complexity.

How do I measure ROI of AI projects?

Define metrics before implementation: time saved, errors reduced, processes automated. Measure current baseline and compare after 3-6 months of effective use.

AI Planning for H2: Practical Checklist for Portuguese SMEs

Current state assessment: where does your company stand?

Before planning new AI projects, conduct an honest diagnosis of your current situation. Many SMEs skip this step and end up with uncoordinated or redundant implementations.

Start by mapping the manual processes that consume most of your team's time. Identify where there's repetitive work, data processing, or tasks requiring pattern-based decisions. These are natural candidates for automation.

Also assess your company's technological maturity. Do you have documented APIs? Is your data organised? Does your team have experience with integrations? This analysis determines whether you can start with no-code solutions like n8n or need custom development.

Prioritisation framework for use cases

Use the RICE model to evaluate each AI opportunity identified. Calculate Reach (how many people or processes it affects), Impact (expected improvement from 1-3), Confidence (result certainty from 0-100%) and Effort (development weeks).

Prioritise cases with clear ROI and relatively simple implementation. For example: automating support email triage has high reach, medium impact, high confidence and low effort. A sales forecasting system might have high impact but low confidence if you lack sufficient historical data.

Repetitive workflow automation (high RICE score)
Chatbot integration for basic support (medium effort, immediate impact)
Automatic document analysis (high impact if processing many files)
Content personalisation (high reach, but needs quality data)

Realistic budget for the second half

Set a budget that includes not just development, but also tools, training and maintenance. For basic automation with tools like Make or n8n, budget €5-15k including setup and initial workflows.

If you plan to integrate LLMs into your existing SaaS, investment rises to €15-30k. This includes development with SDKs like Vercel AI or LangChain, security testing and prompt optimisation.

For custom projects with smart agents or complex systems, reserve €30-60k. These projects require specific architecture, extensive testing and careful integration with existing systems.

Implementation timeline by quarter

Structure your plan in realistic phases. In Q3, focus on basic automation projects that can go live in 6-8 weeks. This generates quick results and builds team confidence.

Reserve Q4 for more complex projects needing 12-16 weeks. LLM integrations or custom agent development fit this window, allowing adequate testing before year-end.

Avoid overloading your team with multiple parallel projects. It's better to do 2-3 implementations well than 5 half-finished projects.

Choosing suppliers and tools

For basic automation, evaluate no-code platforms like n8n (open-source) or Make (more user-friendly). Both integrate well with Portuguese APIs and have active communities for support.

If you need custom development, look for teams experienced with LangChain, OpenAI SDK or Anthropic Claude. Check cases similar to your sector and ask for references from other clients.

Also consider post-implementation support. AI tools need regular adjustments as models evolve and your data changes.

Metrics and KPIs to track results

Define specific metrics before starting any project. For automation, measure hours saved per week, error reduction and processing speed. For chatbots, track automatic resolution rate and customer satisfaction.

Use tools like Google Analytics to measure conversion impact, or internal systems for productivity tracking. Establish clear baselines in the first 30 days to compare improvements.

Review metrics monthly and adjust strategy as needed. AI isn't 'implement and forget' — it needs continuous optimisation to maintain results.