Okay, what’s up?
Artificial intelligence is currently everywhere. In keynotes. In pitch decks. In LinkedIn posts with rocket emojis 🚀.
And sometimes even in places where nobody has seriously ordered them. The feeling is:
If you don’t have AI today, you will be irrelevant tomorrow.
However, the reality in many companies is different. There are overstretched IT teams, unclear responsibilities, security concerns, a whole collection of different AI tools, a progressive boss, a mood somewhere between euphoria and rejection, enthusiastic consultants – and the quiet question in the background: “Is this really helping us right now – or are we just building the next technological construction site?” … Welcome to the AI madness.
Between AI chaos and pilotitis
In the last two or three years, AI has shot from a niche topic to the top of every board agenda – but that doesn’t mean that companies really know how to use AI effectively. A look at the reality of AI deployment in 2025 shows a very mixed picture: a lot of movement, a lot of hype – and yet surprisingly little clear strategy. Current data shows that the majority of companies are using AI at least somewhere today: Around 78% of companies worldwide use AI in at least one business function – such as marketing, customer service or data analysis.(The Global Statistics). However, this does not mean that AI is strategically anchored. Many of these applications are selective tools – not embedded in well thought-out business processes.
AI tools in everyday life, but without governance
Generative AI assistants such as ChatGPT, Copilot, DeepL & Co. have changed digital work: Almost half of companies already use such tools for writing and translation tasks, research or simple automation.(PwC). But beware: in many cases, this is done
The data from company surveys show two sides of the coin: on the one hand, AI has arrived in everyday life: Recruitment, onboarding, training, communication – people are at least experimenting everywhere.(manpowergroup.de). On the other hand, many companies lack
Skills and culture problem: AI cannot replace someone who does not know how to manage it
People are a central hub in the field of AI experimentation. Studies show that although a large proportion of the workforce uses AI tools, they do not feel sufficiently prepared for them: in a global survey, only around 36% said they were well enough prepared to use AI effectively on the job.(BCG Global). This leads to two typical patterns in companies: Wild growth in tool use (employees use AI “just like that” – but without common rules, without data and security specifications) andstrategic blockades (at management level, there is either no plan for how AI should contribute to value creation, or the tools remain stuck in proof-of-concepts because no one is “leading them into the organization”).
Industry and size differences: Large companies vs. medium-sized companies
There is a clear effect of company size: large corporations integrate AI in several areas at the same time – from customer service to supply chain to product development.(The Global Statistics). Although small and medium-sized companies are following the trend, they are often only at the entry level: selective use cases, less deep integration, more experimental use without governance.
AI practice is not a sprint, but a marathon
AI adoption in 2025 is clearly on the rise – almost every company is using some form of AI tool today. But:
- Many deployments are selective, not strategic.
- Governance, data strategy and security are lagging behind.
- Skills gaps and cultural hurdles slow down real added value.
Companies need to get out of the “tool chaos” and into a strategy-centric AI practice if they want to realize real, sustainable efficiency and innovation gains. Otherwise, AI will remain a buzzword – and not a real build AI driver for digital transformation.
3 inconvenient truths about AI in software development
Before we talk about what companies need, it’s worth taking a quick look at what they often underestimate:
- AI does not write “good” code – only plausible code.
Generated code often looks correct, but is difficult to maintain, poorly documented and not always secure. - AI increases complexity if it is used in an uncontrolled manner.
Different tools, models, prompts and frameworks quickly lead to a new proliferation – this time with the AI label. - Responsibility always remains with the company.
The organization, not the model, is responsible for security, compliance, maintainability and operation.
In short: AI can speed things up. But AI can also accelerate problems.
Build AI – another buzzword?
No, simply reality: many people who have already looked more closely at the use of AI in software development have come to the salutary conclusion that AI is best not to write software, but to provide targeted support for software development. This is precisely where a mature, pragmatic approach comes in: Build AI. The idea is simple – but effective: AI does not become a developer, but an assistant.
For the Business Orchestration & Automation Platform Simplifier, this means in concrete terms:
- The productive code is always generated by the platform.
- AI provides support – within a controlled, safe framework.
The result:
✔ No uncontrolled code fragments
✔ No dependency on “magic” prompts
✔ No breaks in governance, security or data flow
In other words: AI should be used where it is strong – not where it creates risks. But where does Build AI really create added value, and immediately? Instead of vision slides, Simplifier offers very concrete application scenarios:
- AI-supported translation of workflows
Digital business processes can be automatically translated into 37 languages – consistently, quickly and without manual maintenance. Ideal for international organizations and scaling processes. - AI Workflow Creation
An AI-supported wizard helps to create user tasks, integrations, variables and process logic. All of this is based on the business requirements – not on detailed technical knowledge. - AI code assistant for Business Objects
Citizen developers can formulate complex filter logic, business rules or data queries without having to be a programmer. The platform ensures that this results in clean, maintainable code. - AI App Creation
UI structures, integrations and user stories are automatically created from requirements. This massively accelerates app development – without any loss of control. - AI-assisted integration
AI provides support with connectors, data mapping and transformations – directly in the process modeler, which is particularly helpful in complex system landscapes. - AI-based testing
Oh, how nice: no more writing test scripts. Instead, record real user behavior, automatically generate test scripts and continuously ensure quality. A huge lever for stability and time-to-market.
What companies really need (and what they don’t)
No question: they need control instead of a black box, speed without security risks, AI as an amplifier – not as a replacement for architecture. And they certainly need platforms that create order, not new silos.
You do not need to:
- AI for the sake of AI
- generated code without ownership
- new tool explosions
- Buzzwords without economic effect
The crucial question is not: “Are we using AI?”, but rather: “Are we using AI in such a way that it really makes us faster, safer and more capable of acting?” Build AI answers this question pragmatically: with clear responsibility, stable platform logic and measurable added value. Not everyone needs an AI revolution. But every company needs a clear plan for how AI can work in a meaningful way.
How companies approach AI sensibly without getting lost in the hype
The most important first step with AI is not the tool selection, but an uncomfortable question: What problem is annoying us so much today that we finally want to solve it structurally? Successful companies don’t start with “What can AI do?”, but with “Where are we losing time, money or quality – every single day?”. AI unfolds its value where processes are clearly understood, responsibilities are defined and data is reasonably clean. Only then will AI become an accelerator instead of another experiment. It is also crucial not to see AI as a substitute for thinking, but as an amplifier: for good processes, for specialist knowledge from the teams, for better decisions. Those who translate AI into small, measurable steps – instead of proclaiming it a major revolution – build trust, create acceptance and learn faster. AI needs direction, not euphoria. Structure, not playfulness. And platforms that grow with us – instead of creating the next silo system.
Sources:
– 78% of companies use AI in one function (e.g. marketing, service, etc.) – global adoption 2025(The Global Statistics)
– 45% of companies work with generative AI tools such as ChatGPT or Copilot.(PwC)
– 93% of employees use unauthorized AI tools at work.(CRN)
– Only a third of companies have an integrated AI strategy.(henleyresearch.com)
– Only 36% feel prepared for AI use.(BCG Global)
