Does every company really need an AI strategy?
Short answer: Yes. Longer answer: It depends on what you mean by "strategy."
According to a 2024 survey by Germany's digital industry association Bitkom, 35 percent of German companies with 20 or more employees now use some form of artificial intelligence - and that number is climbing. Here is the interesting part: Only 12 percent of those companies have a documented AI strategy that goes beyond "we're using ChatGPT now." The rest are flying blind. And that is precisely the problem.
Without a roadmap, here is what happens - and we see this in almost every second initial conversation: Individual departments launch isolated pilot projects. Sales tests a chatbot tool. Accounting experiments with OCR invoice recognition. Manufacturing looks into predictive maintenance. All at the same time, all uncoordinated. IT finds out last. Six months later, the CFO asks what came of all those AI initiatives. The answer is usually disappointing.
An AI strategy is not an 80-page consulting document that gathers dust in a drawer. It is a concrete plan: Where do we stand? Where do we want to go? Which use cases have the biggest lever? And in what order do we tackle them? Companies that skip these questions burn budget - or worse, miss opportunities that competitors are already exploiting.
What decision-makers need to know about machine learning - no CS degree required
You do not need to be a data scientist to make smart AI decisions. But you should understand what your technical people are talking about. Otherwise you end up delegating strategic choices to engineers - and in our experience, that rarely ends well.
Three types of learning at a glance
Machine learning sounds more complicated than it is. At its core, it is software that recognizes patterns in data and derives predictions from them. There are three fundamental approaches, each suited to different tasks:
Supervised learning - the workhorse of ML methods. You feed the algorithm historical data with known outcomes: "These 10,000 loan applications were approved, these 3,000 were rejected." The model learns the patterns and can predict outcomes for new applications. Typical uses: credit scoring, demand forecasting, spam filtering.
Unsupervised learning - no predefined labels here. The model searches for structures in data on its own. Customer groups that behave similarly. Patterns in transaction data that might indicate fraud. Useful wherever you don't yet know exactly what you're looking for.
Reinforcement learning - the algorithm learns through trial and error. Every action is rewarded or penalized. Sounds abstract, but this is the foundation behind autonomous navigation, robot control, and dynamic pricing optimization. For most mid-sized businesses, this is less relevant than the first two approaches right now - but that is changing.
Deep learning and neural networks - what is behind the buzzword?
When someone says "deep learning," they essentially mean: neural networks with many layers. These networks excel at tasks that are difficult for conventional software - image recognition, language processing, text generation. ChatGPT? Built on a deep neural network with billions of parameters.
For practical purposes: deep learning requires lots of data and serious computing power. For a mid-sized company with 50 employees, building your own large language model is neither sensible nor affordable. But leveraging the results of deep learning - via APIs, pretrained models, and cloud services? Any company can do that. The challenge is picking the right method for the right problem. And that is exactly where most in-house attempts fall apart.
Where does your company stand on the AI maturity scale?
Before rushing into use case prioritization, you need an honest assessment. In our projects, we use a four-dimensional model we internally call AKIS - Analysis, Knowledge (competence), Infrastructure, Strategy. Each dimension gets scored, and the result quickly reveals where the real bottlenecks lie.
Analysis: How well do you know your data?
Do you have a complete overview of your data sources? Do you know which data exists at what quality level? Is there a central data warehouse, or are Excel silos multiplying unchecked? Our experience: For roughly 70 percent of the companies we work with, this dimension alone is the biggest blocker. Not missing technology. Not missing budget. Missing data clarity.
Knowledge: Who is actually doing this?
One data scientist is not enough. You need people who can translate between the business side and the technical side. Domain experts who know which business questions actually matter. And ideally someone in management who sees AI not as an IT project but as a strategic priority. This combination is rare. In most mid-sized companies, there is one enthusiastic lone wolf expected to handle everything. That does not work.
Infrastructure: Can your IT handle this?
Cloud or on-premise? What about APIs to your core systems? Is there an ML pipeline, or is everything being cobbled together manually in Jupyter notebooks? Your IT infrastructure does not need to be perfect - but it needs to be extensible. If you are still running Windows Server 2012 with no cloud strategy, AI deployment is going to be painful.
Strategy: Is there a plan - or just good intentions?
Has leadership defined AI as a strategic objective? Is there a dedicated budget? Are responsibilities clear? Or is AI that thing that will happen "at some point"? In our experience, strategic maturity correlates directly with initiative success. Companies that treat AI as a C-level priority deploy projects to production three times more often than those where it remains an IT side project.
The AI roadmap: Five steps from idea to production
Now for the concrete part. Based on more than 40 projects we have guided over the past three years, a proven approach has crystallized. Not a rigid framework - more of a tested rhythm that adapts to industry, company size, and maturity level.
Step 1: Strategic assessment (2–4 weeks)
Everything starts with the AKIS assessment. We talk to leadership, IT management, business units, and - crucially - the people who do the daily work. Not just managers, but also the clerk who has known for ten years which spreadsheet holds the truth.
The outcome is a clear picture: Where the data lives. Where the gaps are. Which processes eat the most time. Which decisions could run better with data support. And - honestly - where AI does not make sense yet.
Step 2: Use case prioritization (2–3 weeks)
The assessment typically surfaces 15 to 25 potential use cases. We rank them on three criteria: business impact, data readiness, and implementation feasibility. The result is a shortlist of three to five use cases to start with.
A practical tip: The first use case should not be the most strategically important one. It should be the one where you get visible results fastest. Why? Because nothing persuades like a working example. The skeptical department head, the CFO, the works council - everyone becomes more receptive once they see AI actually working in their own company. The big strategic plays can come after, with momentum on your side.
Step 3: Proof of concept / pilot project (6–12 weeks)
The pilot is the proving ground. Theory meets reality: real data, real model, real users. The scope is deliberately narrow - one process, one department, one clearly defined success metric.
We have learned that the pilot needs to prove two things simultaneously: It must demonstrate technically that the solution works. And it must demonstrate organizationally that employees can and want to work with it. The best ML model is worthless if the end user ignores or works around it. That is why we involve the eventual users from day one - not just at go-live.
Step 4: Scaling and integration (ongoing)
The step most people underestimate. A working pilot is not a production solution. The transition requires work on architecture, monitoring, data quality assurance, IT integration, and training. In a typical project, effort doubles between pilot and production - and that is normal.
Concretely: The model gets embedded into the existing IT landscape. It gets monitoring that detects model drift. There are feedback loops where users can flag misclassifications. And there is a clear owner - not the team that built the prototype, but an operations team that maintains the solution long-term.
Step 5: Continuous evolution
AI is not a project with a start and end date. Models degrade as the data landscape shifts. New use cases emerge. Regulation evolves. Companies that succeed with AI have understood that it is an ongoing process - similar to quality management or IT security.
We recommend quarterly reviews: Which models are performing as planned? Where are the deviations? Which new technologies have become relevant? And which use cases from the original long list are now ripe for implementation?
Which AI trends should you watch in 2025?
The AI landscape moves fast. Too fast for most companies. Here are the developments we consider particularly relevant for 2025 and beyond - not because they generate the most hype, but because they are becoming practically usable for mid-sized businesses.
Generative AI is growing up
The first wave was experimentation: generating text, creating images, producing code snippets. The second wave is business: automated report generation from enterprise data. Contract drafts based on internal templates. Customer communications that match the company's tone. According to a Capgemini study, 82 percent of large European enterprises plan to deploy generative AI in at least one business process by the end of 2025. For mid-sized companies, the estimate sits at 40 to 50 percent - and rising fast.
Multimodal models
GPT-4o, Gemini 1.5, Claude 3 - the latest models process not just text but also images, audio, and video simultaneously. For businesses, this opens up entirely new possibilities: quality inspection via camera images and text descriptions. Technical documentation generated from photos and voice input. Customer support that analyzes screenshots. This is not science fiction - the APIs are available and costs are dropping rapidly.
Edge AI - intelligence without the cloud
Not everything belongs in the cloud. For applications with real-time requirements or data privacy concerns, Edge AI is gaining traction: AI models running directly on end devices. On the factory floor. In vehicles. At the point of sale. The hardware is getting smaller, cheaper, and more powerful. NVIDIA, Qualcomm, and Apple are investing heavily in AI chips for edge devices. For companies in regulated industries - healthcare, finance, automotive - this is a decisive advantage, because sensitive data never has to leave the company network.
AI regulation is getting real
The EU AI Act is just the beginning. Industry-specific regulations will follow - in financial services (DORA), healthcare, and automotive. If you are building an AI strategy now, bake compliance in from the start. Not as a brake, but as a quality marker. Regulatory-clean AI solutions build trust - with customers, regulators, and your own employees.
What mistakes do companies make most often when introducing AI?
After three years of intensive project work in the DACH region, we have developed a pretty good sense for what derails AI initiatives. Spoiler: It is almost never the technology.
"Let's just do something with AI"
The single most common mistake, by far. Technology-driven initiatives without a clear business case. The question is not "What can we do with AI?" but "Which business problem are we solving - and is AI the right tool for it?" Sometimes the answer is: No, a simple RPA automation is perfectly sufficient. That sounds unsexy but saves 80 percent of the budget.
Ignoring data quality
The classic garbage-in-garbage-out problem. We have seen projects where companies spent six months building an ML model - only to discover that the training data was inconsistent. Wrong customer IDs. Duplicate entries. Missing fields. Data cleanup would have taken two months. The failed model cost six months and 200,000 euros.
Forgetting change management
Deploying technology is the easy part. Bringing people along is hard. Implementing AI tools without talking to affected teams first breeds mistrust and sabotage. That sounds dramatic, but we have seen it: employees who deliberately entered wrong data to prove that "the AI doesn't work anyway." The fix? Involve people early. Communicate transparently. Be honest about what changes - even when it is uncomfortable.
Scaling too fast
A successful pilot is tempting. The urge to roll out immediately to all locations is strong. But a pilot runs under controlled conditions with motivated users. Rolling out to a skeptical workforce with heterogeneous IT infrastructure is a completely different challenge. Better approach: Scale in stages. One more location first. Then the next. Learn and adjust at every step.
The most successful AI projects are not the most technically ambitious ones - they are the ones where someone thought things through before writing the first line of code.
What is the next step for your company?
AI strategy sounds like a grand undertaking. It does not have to be. The most important step is the first one: Create clarity. Where do we stand? Where are the biggest levers? What can we realistically achieve with existing data and resources - in the next six months, not the next five years?
Technology is evolving rapidly. Generative AI, multimodal models, edge computing - the possibilities are expanding. But the fundamentals remain: Clear goals. Clean data. Realistic expectations. And a plan that thinks beyond the first pilot.
At rwQUANTICAL, we work with mid-sized companies and enterprises that are serious about AI. From the initial assessment to use case evaluation to production-ready AI solutions - always with the goal of creating measurable business value. Not hype. Results.
If you want to find out where your company stands on the AI maturity scale and which next steps are worth taking - let's have a conversation.
