Artificial intelligence in business: the guide to getting started

Artificial intelligence for business is no longer a distant technological promise — it is an operational reality measured in concrete results. In most cases, the problem isn't a lack of conviction: it's a lack of execution. In Portugal, around 40% of companies still haven't adopted any AI solution, according to the European SME Survey 2024, even though 41% of European SMEs already have. The gap isn't technological, it's strategic.

At Jelly, we work with companies facing this question every day. What we've learned, project after project, is that the first step is almost always the same: choose the right use case before thinking about tools or budgets. When that order gets reversed, the pilot fails.

In this article, you'll find the use cases with the fastest returns, a phased implementation plan, the most relevant tools for SMEs and the metrics to tell whether your investment is working.

Which artificial intelligence use cases are actually worth the investment

Before thinking about technology, think about processes. The question isn't “how do we use AI”, but “where does AI solve a real problem with available data and measurable impact”. The answer varies from company to company, but some use cases consistently deliver faster returns. This is where intelligent automation in business starts to make practical sense.

Customer service, sales and marketing: where returns arrive fastest

Lead qualification through automated lead scoring is probably the use case with the best ratio of complexity to impact for sales teams. AI analyses behaviour, history and CRM data to prioritise the highest-potential contacts, freeing the sales team to close deals instead of prospecting blindly. In the projects we've worked on, this approach delivered a 40% increase in qualified meetings within the first few months after implementation — a figure in line with the benchmarks in the McKinsey Global AI Survey 2023.

Chatbots and AI agents for customer service solve another classic problem: scale. A well-trained agent responds 24 hours a day and handles repetitive questions with high accuracy, routing more complex cases to the human team. In well-documented implementations, such as those referenced in the Gartner Customer Service Report 2023, this approach cut support costs by around 30% while improving response times. These results depend, of course, on data quality and how well the model is trained.

In marketing, personalisation based on historical data is another area where AI for business shows quick results. Segmenting audiences more precisely and generating content tailored to each profile translates into more relevant campaigns, higher open rates and lower cost per conversion. For teams with limited resources, it's a way to do more without hiring more people.

Operations and finance: intelligent automation that removes errors and frees up time

Invoice processing with AI is one of the best-documented cases in terms of ROI. According to Ardent Partners (AP Technology Study 2023), the cost per invoice can fall from €12–15 to €2–4 after automation — a reduction of over 80% that feeds straight into cash flow. For finance departments processing hundreds of documents a month, the impact is immediate. For anyone looking for specific trends and tools in AI invoice processing, there are practical studies that complement this view.

In HR, automatic candidate screening and job description generation remove hours of repetitive work without compromising the quality of the process. In finance, predictive models for late payment flag risks before they become problems, allowing proactive rather than reactive management.

How to implement artificial intelligence in business, phase by phase

Implementing AI without a structured plan is the shortest route to a pilot that never leaves the drawing board. The good news is that the process can be broken down into phases with clear criteria, without requiring a team of twenty engineers.

Phase 1: diagnosis and choosing the right pilot project

The starting point is to weigh up three variables: expected business impact, technical complexity and the quality of available data. The use case with the highest return and lowest risk is the natural candidate for the first pilot. In the projects we've run at Jelly, a well-scoped pilot — such as automating customer service or lead qualification — reaches payback within 6 to 9 months.

Data governance is the foundation many companies overlook. Before implementing any model, it's essential to assess whether existing data is good enough to train and validate results. Without that, even the most sophisticated technology on the market will produce unreliable outputs. Digital transformation with AI always starts with data, not tools. For practical guidance on how to structure implementation, see our material on implementing artificial intelligence in business.

Phase 2: team, partners and cost structure

For a first project, the internal team starts with three key roles: a product owner who understands the business and sets the objectives, someone from IT responsible for integrations, and a change manager to drive internal adoption. Depending on the complexity of the project, we recommend adding data or machine learning support, either internally or through an external partner. For alternatives to traditional hiring, see the article on Agentic AI and AI Agents for SMEs: when hiring more people is no longer the answer. User resistance is often the biggest obstacle to success — not the technology itself.

On costs, SaaS-based AI solutions for business typically start between €20 and €100 per month (indicative figures, subject to variation by plan and region). Custom implementation projects with an external partner range from €10,000 to €50,000 for a well-scoped pilot. Working with a specialist agency on the first project, rather than building internal capability from scratch, reduces risk and shortens the time to first result.

Phase 3: AI tools for mid-sized companies

Tool selection should follow the use case, not the other way round. There are affordable, well-documented solutions for the most common SME scenarios. What we share below is what we see working on the ground, along with what would be our first choice for each scenario. A practical list of tools and resources for SMEs is available in our guide to AI tools for business, which can help with the initial selection.

Automation, productivity and content creation

ChatGPT / Claude: content creation, customer support and document analysis. Pro plans from approximately €20 per month (USD prices subject to conversion and variation).

Zapier: automates workflows across more than 7,000 applications with no code — CRM, email and spreadsheets included. Pro plan from €20 per month. It's the first tool we reach for when the goal is connecting systems without custom development.

Notion AI: project organisation, meeting summaries and internal documentation. Included in Pro plans from around €10 per user per month (USD figure subject to variation).

Sales, CRM and customer service

Salesforce Einstein / Agentforce offers predictive sales models and AI agents built directly into the CRM, with an add-on from €25 per user per month. For companies that need custom chatbots without a dedicated machine learning team, Google's Vertex AI is an affordable pay-as-you-go option for smaller-scale pilots. The two solutions have different adoption curves, and the choice depends on the CRM already in use and the technical maturity of the team.

If you're looking for a practical guide to implementing agents in customer service, see our article How to implement AI agents in customer service.

Phase 4: how to measure AI ROI and know whether it's working

Without metrics defined before you start, there's no way of knowing whether the project delivered value. Measurement should begin at the diagnosis stage, not after implementation.

Metrics and benchmarks for SMEs

The most reliable KPIs in the early phases are cost per qualified lead, average customer response time and conversion rate. These indicators capture the direct impact on the most common use cases and allow a rigorous before-and-after comparison. According to the IDC Future of Work 2023 report, the average benchmark for time reduction in automated processes is 70%. The same report indicates that 91% of SMEs that adopt AI with a clear business objective report a direct increase in revenue.

Realistic timelines and mistakes to avoid

Payback in 6 to 9 months is the typical scenario for SMEs that start with a well-defined pilot. The most common mistake we see at Jelly is measuring AI as an IT project rather than a business project. If there's no business KPI attached from the outset, the project has no clear success criterion and rarely scales beyond the pilot.

In Portugal, the two biggest real barriers to adoption aren't technological: they're a shortage of internal skills and a lack of data governance. Solving those two things before moving to implementation is what separates the pilots that reach production from those that stay in experimentation. A recent study highlights that delays in AI adoption can undermine a company's competitiveness, reinforcing the need for structured action in the short term — see Deloitte's study on lagging AI adoption for more context.

Where to start, in practice

Adopting artificial intelligence in a mid-sized company doesn't require a team of engineers or a multinational's budget. It requires clarity on the use case, data of at least minimum quality and a structured, phased plan. Those elements are worth more than any single tool, and that's exactly the order in which the process should unfold.

If your company wants to take its first concrete steps with AI, Jelly can help design a practical pilot, from diagnosis through to implementation. Not as a software vendor, but as a partner that understands both the business and the technical side, and that has already guided companies across various sectors through this transition with measurable results. Also worth checking are the open PRR applications, Artificial Intelligence in SMEs, if you're looking for funding and support in Portugal.

AI doesn't replace strategy. It amplifies it. And the companies that start now, with clear objectives and the right partners, build a competitive advantage that becomes increasingly hard to close.