is already planning to incorporate artificial intelligence into its processes. Are you one of them? Then this article is for you.
We'll take a step-by-step look at the key areas you shouldn't overlook when implementing AI - so that it delivers real results. Using AI alone is not enough. "Just like any other change, AI must have a clear strategy, goals and support across the business.
If you're still hesitant about implementing AI, check out our article
Risks of unguided AI adoption
Don't rush headlong into implementing AI, it could end up doing more harm than good.
Fragmented use of tools The use of different tools leads to chaos and an inability to measure effectiveness.
Loss of confidence in results AI may not always be right, if we don't test the results enough and learn to get them right, the team may lose confidence in them.
Security and legal risks Non-existent internal rules for the use of AI lead to sensitive data leakage or regulatory breaches (e.g. GDPR, AI Act)
Unnecessarily high costs Companies often pay for tools they don't need or duplicate. Many paths to greater efficiency also lead through automation, not necessarily AI.
Unrealistic expectations AI is seen as a magic wand, but it doesn't work without the right deployment. This leads to teams losing motivation to use AI.
Step 1: Map the baseline
Before you start the actual implementation, think about what your current situation is.
Find out if you have the technical infrastructurethat will allow AI tools to integrate seamlessly.
Look at the processes: Where are there repetitive routine tasks that AI could simplify? What metrics are you already tracking today (e.g. lead time or time-to-market) that AI could help improve?
Evaluate your data: If your goal is to better evaluate your data, start by assessing its current state. Without relevant and quality data, AI doesn't work well. Just like a human who is given inadequate data
Context of use: in which situations will the data be used (e.g. chatbot for customers, capacity planning, demand forecasting)?
Relevance: is the data relevant to the goal it aims to achieve? If there is "nothing to see", don't try to "boil the stew". Rather, change the goal or fill in the data first.
Quality and completeness: are key columns/events missing? Are there holes or inconsistencies in the data?
Structure and accessibility: is the data easily accessible and structured (ideally in CRM/ERP/DWH/logging)?
Legal framework (GDPR): if you work with personal or sensitive data (especially user data analysis), have a legal basis for this use. If you are only dealing with internal process automation, you can often get by without processing personal data.
Focus on the team
Anyone want to be an AI ambassador?
Do colleagues have enough time to learn and try new things?
Someone may already be informally testing ChatGPT or other tools today - see what experience they have with it.
Does your company already have any rules or guidelines for using AI?
Step 2: Clarify goals and expectations
Why did you decide to start using AI and what do you expect from it?
Your goal must be based on a real need. So its determination may be preceded by audit of all your processes, where you should ideally find out exactly where your heel is pushing you and where AI can help.
Is your goal to accelerate development? To automate routine tasks or reduce the burden on the team? It's also good to set an ultimate goal, for example, is it the need to save money, be more competitive or introduce innovation into the company.
Determine the indicators of success
How will you know if your AI implementation is paying off and how will you measure it? Be clear from the start what you want to measure - and track it continuously, not just "at the end of the project".
Knowing the data before implementation is crucial for measurement, otherwise you will find it hard to evaluate it. Before you start tracking metrics, think about what you really want to measure and how it relates to your business goals.
Set clear metrics to track, these are based on your goals. The most common are:
Time-to-marketa: Are we delivering faster than before?
Lead timea: How long does it take to get an idea into production?
Velocitya: Can we do more work in the same amount of time?
ROIa: Does AI implementation have a positive return on investment?
Team satisfactiona: Does the wiring increase or does it overload?
Cost per deploymenta: Are deployment costs coming down?
Beware, it's not enough to just have metrics - it's important to understand them. Before you start measuring, think about what data you'll need to calculate a given metric and how you'll get it.
Practical tips for measuring:
DORA Metrics: The DORA (DevOps Research and Assessment) metrics - Deployment Frequency, Lead Time for Changes, Change Failure Rate and Mean Time to Recovery - are very useful for optimizing software development and DevOps processes. These metrics help you identify bottlenecks in the process and improve efficiency.
Automate data collection: use tools to automate data collection and processing to minimise manual work and reduce the risk of errors.
Visualize data: use charts and dashboards to visualize metrics and more easily spot trends and anomalies.
Review metrics regularly: make sure your metrics are still relevant and in line with your current goals.
You may find that you don't need to use AI for some processes, but can get by with regular automation. So start with it.
Tools such as Make a or Zapier can help you.
Step 3: Create a realistic strategy and roadmap
Find a pilot project
Choose the first specific use-case you want to solve by implementing AI. Ideally, this should be a quick win, meaning a project that will have a big impact but won't take too long.
Such a project, in addition to testing your strategy, will help motivate your team to take on other similar projects.
An example of this would be the deployment of a chatbot for the most common customer queries. Such a project can be finished in a week.
Plan the implementation in time
Create a roadmap where you break down the introduction of AI into your processes. Be realistic. Implementation shouldn't burden your team, but help them be more efficient. Schedule projects according to your team's priorities and time availability. Don't forget to assign an owner to each project who will be responsible for it.
Choose the appropriate tools
Select the appropriate tools you need for your strategy. Think about which ones fit best in your stack and consult with your team. You may need to test more tools initially. However, remember to review their use regularly so that you don't pay unnecessarily for duplicates.
For starters, you can probably get by with tools such as:
For general work or working with documents: ChatGPT, Gemini
Establish internal rules for working with AI - what's allowed, what's forbidden and what are the approved solutions. Determine what types of data are risky to process with AI tools (e.g. customer data)
Make sure you are using AI in compliance with all regulations (AI Act) and assess the potential risks.
Create a simple internal framework that summarizes how you work with AI. This will simplify onboarding new team members.
Educate the team
Explain to the team why it's important to use AI safely and introduce them to the potential risks they should learn to recognise for themselves. (e.g. AI hallucinations, data leaks, errors in generated code)
Don't forget to train all teams that will be working with AI, not just IT.
Repeat the training periodically and update the information.
Step 5: Involve the team
Internal communication and working with management
Share goals and expected benefits across the company. Then focus specifically on team leaders, managers or techleads who can push the information further into the team.
Incorporate AI implementation into your company's strategic plans and regularly communicate the results - what's working, where the obstacles are, what's next.
Don't forget to ask for feedback regularly. Make sure the AI is delivering what you expected to the team and not just causing more wrinkles.
Involvement of ambassadors
Identify individuals who have the desire to be AI "drivers" - across roles. (development, marketing, HR)
AI ambassadors help motivate the team and inspire more confidence than if you were only trying to implement from the top.
It is important to give ambassadors enough time and support - regular meetups, community involvement, time to experiment.
Ambassadors can use their newfound knowledge to inspire others, share information about new developments or train teams.
Conclusion
Implementing AI can help your business be more efficient, save costs or increase your competitiveness. However, it should be approached thoughtfully and strategically so that its adoption really brings the desired results.
Otherwise, we risk unnecessary investment, demotivation of the team or loss of important data.
Still, there is no need to be afraid of implementation, it is not about delaying with lengthy processes that lead nowhere, but to invest dedicated time in the beginning in a strategy that will pay you back many times over.
If you still don't know how to implement AI or would just like some guidance in its early stages, get in touch. We'd be happy to help.
