The world is witnessing one of the most fundamental paradigm shifts in the history of information technology, and that is now an undeniable fact. This train is already moving. For any company standing in its way it will bring nothing but disaster, and we can clearly see that it has not yet reached full speed.
Artificial intelligence has stopped being an experimental research topic or an analytical tool limited to certain algorithms. It has become a central backbone that fundamentally transforms how organizations make decisions, how their business models work and how engineering is practiced. Looking at it as nothing more than advanced machine learning, and failing to place it at the very center of your processes, leads to an outcome where falling behind is obvious even under today's conditions. In today's digital ecosystem, competitive advantage is measured not only by owning big data, but by the ability to turn that data into real-time, deterministic and actionable enterprise intelligence.
The generative AI applications that entered our lives in recent years first drew attention in areas such as text generation, summarization, image creation and coding assistance. Now we are beginning to see AI stop being a standalone application and become a natural part of companies' daily operations.
We are starting to witness a curious and motivated vibe coder, without much infrastructure behind them, single-handedly producing in a matter of days, and at sufficient quality, the kind of groundbreaking product that once took hundreds of engineers years of R&D. Evaluated as a cost-benefit analysis, the difference is truly dramatic.
At Wiseapp Business Solutions we focus on the digital transformation of businesses. We provide end-to-end support, from the implementation of IFS ERP solutions to integrations, and from process analysis to user training. We define AI not as a mere trend or a superficial assistant attached to user interfaces, but as a strategic value creator positioned at the center of enterprise architectures. We follow every current development and design processes that create added value for our customers.
From manufacturing to procurement, from sales to logistics, from planning to maintenance and service, different operations rest on the same truth. Companies generate enormous amounts of data, but real value emerges only when that data reaches the right person, at the right time, as the right action. In complex systems where source data sits in various databases, volumes reach very high levels and millions of records are produced every day, anomaly detection can be incredibly difficult and costly. With effective use of AI, these anomalies can now be detected at full speed, independently of individuals, and presented dynamically to decision makers. At this point, informing companies about the practical use of AI and getting consultancy on the subject is essential, so that the first button of the shirt is not fastened wrong and disastrous results are avoided.
Only a few years ago, when a purchasing manager asked "Which orders carry the highest delay risk this week?" or a production manager asked "Show next week's capacity bottlenecks and suggest alternatives", we could only imagine the day AI would answer. Today AI does more than give those answers and produce reports. Within the required permissions and controls, it can prepare a notification to the supplier, assign tasks to the relevant users or start the next process.
This transformation has taken us from the "Assistant" era, in which AI could only give reasonable answers to the questions it was asked, to the "Agent" era, in which it can follow existing systems according to context, distribute tasks, act within defined boundaries and maintain internal standardization by staying consistent with earlier decisions.
On the other hand, AI models can be wrong in an extremely convincing way. Applying a suggestion that sounds right but has not been tested to an enterprise database can have consequences that are hard to reverse. That is why our principle is clear: AI proposes, humans verify, the system tests. No change that goes to a production environment moves forward by skipping these three stages.
Nor is AI a magic layer that turns bad or scattered data into good data by itself. Master data quality, correct authorization, traceability of transaction history and processes that are standardized as far as possible are still critically important. In fact, we believe these topics will matter even more as AI becomes widespread. When automation speeds up, a well-designed process multiplies value quickly, while a poorly designed one multiplies risk at the same speed.
We also do not believe every process should be automated. Instead of an uncontrolled structure in which AI decides in place of people, building a system that helps people make better and faster decisions is the more sustainable approach. Especially for transactions with high financial, legal or operational impact, we believe keeping human control inside the process will remain important for a long time.
Another key topic in enterprise AI strategy is where the models will run. Companies face two basic approaches today: using commercial AI models offered as a service over the internet, or running open-source models on their own servers, in their own data centers or in private cloud environments. Both approaches have significant advantages, and both have disadvantages that must be weighed against the organization's security policies, use case, transaction volume and technical competence.
The main advantage of online AI services is that companies gain fast access to extremely powerful models without large upfront investments. Most of the operational burden, such as infrastructure setup, GPU management, model updates and scaling, is carried by the service provider. Access to new model capabilities also usually happens without any infrastructure change. This approach offers real agility for rapid prototyping, for applications with low or variable usage, and for companies that want to benefit from constantly improving models.
On the other side is running open-source models on the organization's own infrastructure. Its biggest advantage is far greater control over data. Sensitive company data can be processed without leaving the organization, model access can be integrated with the company's existing identity and authorization infrastructure, and the model can be tailored to a specific industry or use case when needed. For high-volume, continuously running applications, the cost per transaction can also become more predictable once the right infrastructure is in place. In addition, reducing dependence on a single provider can give companies more technological flexibility in the long run.
Running models in-house, however, brings a broader technical responsibility than it first appears. High-performance GPU infrastructure has to be bought or rented, model services have to be scaled, and security updates, performance optimization, backup, monitoring and the model life cycle all have to be managed. The fact that an open-source model can be downloaded for free does not mean it is free to operate. Hardware, energy, operations and expert staff can make up a significant share of the total cost of ownership. Companies that want to host their own model should therefore assess not only the security advantage, but also whether they can run this infrastructure sustainably. In terms of output quality, we can also clearly see that open-source models are closing the gap with the industry's leading models.
At WiseApp we do not believe a single method is right for every company. On the contrary, we expect hybrid AI architectures to become widespread in the coming period. In-house models can be used where sensitive data is processed or where there are high-volume standard tasks, while cloud-based models can serve more advanced reasoning or low-volume special operations. Some tasks can be solved far more safely and economically with classic software rules, without any AI model at all.
Our core approach is to build a model-independent AI architecture instead of making companies dependent on a specific model or provider. That way a commercial model used today can be replaced by another service in the future, certain tasks can be routed to an in-house open-source model, and different models can be used for different tasks within the same system.
In an environment where AI is evolving this fast, we believe the right investment is not only choosing today's most powerful model, but building a flexible, secure and sustainable infrastructure that can adapt to tomorrow's models as well.
Author: Oğuzhan Korkmaz, Founder — WiseApp Business Solutions. This article was first published in Turkish on The Prowess.
Meet our team for an IFS ERP roadmap tailored to your business.
Meet the Team