AI implementation in your company - how to prepare?

Maybe you're considering an AI implementation in your company but aren't sure how to prepare for your first conversation with a software house. Artificial intelligence is no longer the domain of tech giants. More and more small and medium businesses are discovering the potential of process automation and data analysis powered by AI. Yet enthusiasm is often held back by not knowing where to actually start.

Or maybe you're worried the investment will turn out too costly or a poor fit for your business reality? Those concerns are entirely justified. According to a 2023 McKinsey report, around 40% of AI projects fail to deliver the expected results precisely because of poor preparation and misidentified needs.

This article gathers the most important information to help you understand what to pay attention to before deciding to implement artificial intelligence. It will let you hold informed conversations with potential contractors and avoid the most common pitfalls.

Why business goals matter more than technology

Many companies make a fundamental mistake right at the start: they fall in love with the technology instead of focusing on specific business problems. An AI implementation should answer a real challenge, not act as a trendy add-on to the offer. That's why the first step isn't contacting a software house, but a thorough analysis of internal processes.

Think about which areas of your business generate the highest costs or consume excessive time. It could be customer service, warehouse management, sales data analysis, or recruitment. Each of these areas can be optimised with the right solution. However, without precisely defined expected results, it will be hard to judge the success of the whole undertaking.

How to properly formulate AI implementation goals

Goals should be measurable and specific. Instead of saying "we want to improve customer service," it's better to say: "we want to shorten the average response time to customer enquiries from 4 hours to 30 minutes." Such precision makes it easier both to choose the right solution and to evaluate the project later.

It's also worth preparing documentation, or at least some notes, describing current processes. The documentation doesn't need to be detailed, but it should describe how work currently flows in the area intended for automation. This data will become the basis for the software house's feasibility analysis.

Realistic expectations toward artificial intelligence

Keep in mind that AI is not a magic solution to every problem. Excessive optimism about the technology's capabilities is common. For example, a chatbot won't replace an entire customer service department for complex issues requiring empathy and flexible thinking. However, it can handle around 60-70% of standard enquiries, freeing up employees' time for more demanding tasks.

Data quality and availability as the project's foundation

Artificial intelligence is only as good as the data it was trained on. Before starting any conversations with contractors, it's worth auditing the data you have available. Check whether your company collects the information needed to reach the intended goal, and in what format it's stored.

Data can live in various places: CRM systems, databases, spreadsheets, or even paper documents. The more scattered and unstructured the information, the more work will be needed at the preparation stage. According to Gartner research, companies spend on average 60-80% of AI project time on preparing and cleaning data.

Assessing current technical infrastructure

Another aspect is the existing IT infrastructure. Some AI-based solutions require significant computing resources, while others can run in the cloud without burdening local servers. It's worth knowing what technical capabilities and limitations you have.

If your company uses outdated systems, they may need modernising before an AI implementation. That doesn't mean you have to give up the project, though. Many software houses specialise in integrations with legacy systems and can propose hybrid solutions.

GDPR and data security

Data security issues can't be overlooked, especially when the planned solution will process customers' personal data or sensitive business information. GDPR and other industry regulations must be complied with.

Before talking to a software house, it's worth consulting your legal department or a data protection specialist. This will let you clearly define security requirements and ask potential contractors about their approach to this issue.

The real cost of implementing artificial intelligence

One of the most common questions is: how much does an AI implementation cost? Unfortunately, there's no single universal answer. Projects can range from several thousand euros for simple chatbots to hundreds of thousands for advanced predictive systems or image recognition.

Cost depends on many factors: problem complexity, data availability, integration requirements with existing systems, and the expected level of accuracy. It's worth preparing a budget with a reasonable buffer, since AI projects often require an iterative approach and adjustments along the way.

Different financial cooperation models

Software houses offer various cooperation models. The most common are the fixed-price model, where you get a predefined scope of functionality for a set amount, and the time & material model, where you pay for the team's actual working time.

For AI projects, a time & material model with a defined maximum budget is often recommended. It allows for more flexibility during development, since requirements can evolve as the first versions of the solution are tested. It's also possible to split the project into stages, each with its own budget.

Hidden costs worth remembering

Beyond building the software itself, you need to account for maintenance costs, server costs (if the solution doesn't run in the cloud), tool licences, and employee training. The need for continuous improvement of AI models is also often overlooked - they aren't a "set and forget" solution.

Annual maintenance costs are estimated at 15-25% of the initial investment value. This should be factored into long-term financial planning for the project.

Key criteria for selecting a software house

Not every software house is right for an AI project. It's worth checking a potential partner's experience with similar projects. A portfolio and case studies can say a lot about a team's competence. Pay attention to whether the company has experience in your industry or has delivered projects of a similar scale and complexity.

The working methodology matters just as much. AI projects usually require an agile approach, with quick iterations and hypothesis testing. A software house should be able to present a clear project plan while remaining flexible to changes arising from tests and user feedback.

Questions to ask during the first conversation

During meetings with potential contractors, it's worth asking specific questions. For example: "What similar projects have you delivered?", "What team will be assigned to my project?", "What does the implementation and testing process look like?", or "What support do you offer after the project ends?".

The answers will let you assess not only technical competence but also organisational culture and approach to the client. A good partner should also ask you plenty of questions - about your goals, processes, expectations. If a software house immediately promises the world without deeply understanding your situation, that should raise a red flag.

The importance of communication and transparency

AI projects are inherently unpredictable - the intended model accuracy isn't always achieved right away, and data issues or unexpected technical limitations can arise. That's why transparent communication between both sides is key.

The software house should regularly report on progress, problems, and proposed solutions. On the other hand, you also need to be available for consultation and quick decision-making. Projects where communication is neglected usually end up delayed and over budget.

Change management and employee resistance

An AI implementation usually involves a change in how work is done. Employees may fear that the technology will replace their roles. These concerns aren't entirely unfounded, but it's worth communicating that the goal of automation is to free up time for more valuable tasks, not to cut jobs.

Involving employees early in the project is key. The people who perform the tasks earmarked for automation on a daily basis have the best knowledge of the nuances and exceptions that may not be obvious to outside experts. Their input can significantly affect the implementation's ultimate success.

Training and building internal competence

Once an AI-based system is implemented, employees will need to learn how to use it. The software house should provide appropriate training, but it's also worth considering building internal competence for the longer term.

This doesn't mean every employee has to become a machine learning expert. However, it's worth having people in the organisation who understand the basic principles of how the deployed solution works. This will enable better monitoring of its performance and faster identification of potential problems.

Defining KPIs and a success measurement methodology

Before launching the project, key performance indicators (KPIs) should be defined to assess whether the implementation delivered the expected results. These can be financial (e.g. cost reduction by X%), operational (e.g. shortening a process by Y%), or qualitative (e.g. increased customer satisfaction by Z points in surveys).

Monitoring these indicators should start before the implementation, to have a baseline. Progress should then be measured regularly and results reported to management and the project team. This allows for ongoing adjustment of the solution and maximising return on investment.

Implementing AI in a business is a complex process that requires careful preparation. Before deciding to talk to a software house, it's worth precisely defining your business goals, taking stock of your available data, preparing a realistic budget, and choosing the right technology partner. Preparing the whole organisation for the coming changes is equally important.

Remember that artificial intelligence isn't a goal in itself, but a tool for solving specific business problems. The better you prepare for the project, the greater the chance of its success and a real return on investment.

Ready for the next step? Take advantage of a free consultation with our experts, who will help you assess the potential of AI in your business and plan the optimal implementation. Get in touch with us today and find out how technology can help your company gain a competitive edge.