In 2025, talking about “implementing AI” in a company sounds about as generic as “installing electricity”. Both can be used for a huge range of tasks, but every organisation uses them differently depending on their needs and processes.
Artificial intelligence can be applied to tasks such as analysing data, serving customers, forecasting demand or automating processes, among many others. The mistake is to approach this technology as if it were a magical, one-size-fits-all solution that will “fix” every problem in the company. In practice, the organisations that extract the most value from AI are those that integrate it strategically into their business processes.
In this article, we set out what we see as the 5 key steps companies should take to successfully implement artificial intelligence.
5 steps to get started with AI in your company
Implementing AI in a company is a transformation that involves people, processes and technology.
These five steps provide a practical and strategic guide to getting started with AI in the organisation. From identifying the right profiles and selecting use cases with real impact, to launching pilots, building internal capabilities and, finally, scaling solutions that work.
Step 1: Start with people
Contrary to what many might think, the first step is not choosing a model or a platform. It is identifying the right people inside the organisation to drive the change.
You need a core group of sponsors who understand the business in depth (processes, bottlenecks, metrics, etc.) and who also have a basic level of AI literacy. They should know what AI can and cannot do, where its limits are and how its outputs should be validated. This combination is what allows you to connect real needs with technological capabilities and avoid AI ending up as another “showcase project” with little real impact.
In parallel, it is advisable to define clear and responsible ground rules from the outset, such as usage policies, human review, data privacy, security and risk management.
This human dimension also requires structured upskilling and reskilling initiatives. According to the World Economic Forum (link here), the evolution of roles and skills over the coming years will increasingly depend on stronger digital competencies, data skills and collaboration with intelligent systems.
Step 2: Identify use cases
Once the sponsoring team is in place, the next step is to identify specific use cases and prioritise them.
Specialised AI partners can be very helpful at this stage by providing expert guidance. You can also use structured decision-making methodologies, paying particular attention to variables such as:
- Expected economic value: cost savings or additional revenue.
- Ease of implementation: data availability and quality, technical complexity, organiational change required.
- Associated risk: privacy, bias, security or dependency on third parties.
Studies such as this from McKinsey & Company show that the greatest AI opportunities are often found in cross-functional areas such as customer service, sales and marketing, back office and operations.
These are the domains where the largest productivity gains and time savings typically appear in tasks such as drafting documents, searching for information, analysing data or providing support. This is why it makes sense to start with processes that rely on reliable, repeatable data and leave high regulatory risk or highly uncertain return-on-investment cases for later.
Research from MIT Sloan Management Review and BCG indicates that when AI is introduced, traditional KPIs should be reviewed and extended, because new metrics often provide a clearer picture of impact. For example, data quality or the proportion of AI outputs that are effectively reviewed by humans can be just as important as classic financial indicators.
Step 3: Move fast and launch initiatives
Once use cases have been prioritised, it is important to move quickly.
It is better to launch a tightly scoped 60-90 day pilot than to spend six months writing documentation. The purpose of agile pilots is not perfection but learning. They help validate hypotheses, quantify benefits, understand data or integration limitations and fine-tune the design of the human–AI workflow.
Before launching a pilot, you should define the baseline, the success KPIs and clear “go/no-go” criteria. In other words, what results would justify scaling the solution and what outcomes would indicate that it should be stopped or redesigned.
Companies that manage to scale AI successfully combine speed with solid governance. They professionalise the transition from pilot to real production early on. This involves adapting processes, ensuring operational support, monitoring results and controlling costs.
Studies from Deloitte and MIT Sloan Management Review point out that the main bottlenecks are not only technical: data governance, risk management, executive sponsorship and post-launch support are equally important. That is why pilots should be designed from day one with an eye on how the solution will operate once it is in production.
Step 4: Build technological and human capabilities
Once the first lessons have been learned, the focus shifts to building capabilities. This needs to happen on several levels.
On the technology side, everything starts with access to the right tools and with data: its quality, traceability, security, accessibility and a privacy-by-design approach are all crucial.
A very useful pattern in many projects is Retrieval-Augmented Generation (RAG) applied to corporate knowledge. It helps keep context up to date and reduces model “hallucinations”.
On the human side, the ideal path is to build a hybrid in-house team that combines increasingly specialised AI profiles such as data engineers, prompt and conversation designers and process specialists.
In Europe, it is important to align these efforts with AI Act requirements, which include classifying systems by risk level, ensuring proper data management and documenting high-risk solutions in detail.
Continuous reskilling is another critical piece. According to the World Economic Forum’s Future of Jobs Report, task reconfiguration driven by AI requires sustained training programmes to maintain employability and productivity. Companies that approach this proactively not only reduce the friction of the transition, they also increase their ability to capture value in the medium and long term.
Step 5: Scale up
When a use case has proven its value and is properly governed, it is time to scale it. This can be done horizontally (the same solution in more units or countries) or vertically (the same family of tasks in adjacent processes).
To scale in a robust way, it is essential to standardise what has been learned. That means having reusable components, deployment templates and a catalogue of best practices that sets out the required data, monitoring metrics, usage limits and the roles involved.
At the same time, you need to keep a close eye on the external environment. That includes available investment, the evolution of talent and regulation. Reports such as Stanford University’s AI Index show an ever-faster pace of innovation, new models and funding, alongside tighter regulation and wider enterprise adoption.
This is why it is advisable to review the company’s AI roadmap at least once a year. It should be adjusted to this constantly changing landscape, so that new improvements are informed both by internal learning and by external developments.
At this point, AI stops being a novelty inside the organisation and becomes another tool embedded in processes and ways of working.
Getting started with AI in your company means activating a process of change
Ultimately, getting started with AI is much more than a technology project. It means setting in motion a transformation that combines strategic vision, operational discipline and cultural readiness. Organisations that approach it methodically manage to turn AI into a real lever for productivity and competitive advantage.
At Holistic Data Solutions, we support organisations at every stage of this journey. From opportunity assessment and use case selection through to pilot execution, production deployment and team training.
If you would like to explore how to get started with AI in your company in a safe, measurable and high-impact way, contact us.
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