Why companies often fail with their own AI projects - and how SaaS platforms help with AI document processing
- Technology
- Marius Gotschol
According to a study, 80% of AI projects fail before they bring real added value. Companies invest time and money in their own AI developmentsbut in the end all that often remains is an expensive prototype with no productive benefit. The reasons are well known: Data quality, scalability, high costs and a lack of expertise. What begins as an ambitious innovation project often ends up in a technology labyrinth without a clear business case.
The pressure to find efficient solutions is particularly high in the field of AI document processing, where companies work with large volumes of unstructured data on a daily basis. But does it make sense to develop your own AI architecture? Or is there a faster, more reliable way?
The answer lies in SaaS platforms that specialize in document processing with artificial intelligence. They offer ready-made, proven solutions that are immediately ready for use - without years of development work. In this article, we show why it is often not worth setting up your own AI and how companies can become productive immediately with a SaaS platform.
We also spoke to our Senior Account Executive Martin Jarosch to find out first-hand what challenges companies face when developing their own AI - and why SaaS platforms are often the better choice.
High costs, complex technology, little success - the dilemma of in-house AI development
High development costs
Setting up your own AI solution means more than just launching an ambitious project - it is a long-term investment in time, money and human resources. Companies not only have to collect and process large amounts of data, but also train models, further develop algorithms and carry out continuous tests. However, it often remains with an initial version that never makes the leap into productive use because either the budget is exceeded or the benefits cannot be proven clearly enough.
Technical complexity
An AI solution is not a static system - it must be constantly optimized, further developed and adapted to new circumstances. Especially in the field of document processing, it is not enough for an AI to deliver good results once. It must be able to handle new document types, improve itself and meet regulatory requirements. If there is a lack of know-how or infrastructure, the system quickly becomes a problem instead of a solution.
Lack of experts
AI development is expert work - and these experts are rare and expensive. Data scientists, machine learning engineers and AI specialists are in high demand, and not every company can afford its own team. It is becoming particularly difficult for small and medium-sized companies to compete with large tech corporations for the best talent. Without the right experts, the project often gets bogged down in complicated details or doesn't even get past the concept phase.
But what does this mean in practice? Many companies start with promising prototypes, but when it comes to productive use, they come up against unexpected hurdles. Martin Jarosch regularly experiences this in discussions with companies.
Martin, can you give us a specific example from your experience where a company failed to implement its own AI solution? What were the biggest stumbling blocks?
You first have to differentiate between the two: What does "fail" actually mean? Many companies, especially large corporations, are trying to develop AI solutions with their own teams of data scientists and IT-savvy employees. They draw on open source technologies and existing LLMs, build initial prototypes and test what is possible. This often results in something presentable, as AI is accessible to many people today and it is not the biggest challenge to get a neural network or a machine learning model to do something cool.
The point at which companies fail is usually not the prototype itself - they get it right. The problem begins when it comes to transforming it into a stable, productive solution. Suddenly they are faced with completely different challenges: How do I get it through internal IT security? How do I ensure that the solution is scalable and works reliably not just with a test volume, but with thousands of documents every day? How do I integrate it into existing business processes so that it really adds value for the specialist departments?
Companies often underestimate what it means to operate an AI application in the long term. After all, it is not enough to build a solution once - it has to be stable, maintained, developed and adapted to new requirements. Models such as LLMs are changing rapidly. What works well today can suddenly deliver different results in a month's time and disrupt the entire production process. Who then takes care of it? Who keeps the system up to date? Who ensures that it remains compliant, especially with regard to new regulations such as the EU AI Act?
Many companies realize at some point that they have reached a dead end with their prototype. They are faced with the decision of either making huge investments to build a truly production-ready solution or scrapping the project - and in many cases the prototype then disappears into a drawer. This is why more and more companies are turning to specialized SaaS platforms such as Buildsimple. The aim here is not just to quickly build a prototype, but to create a real business application - with experts who only deal with these challenges on a daily basis. This ensures that AI doesn't just remain an experiment, but really brings productive benefits.
What is AI document processing?
AI Document Processing is the use of artificial intelligence to analyze, classify and extract information from documents. Various technologies such as Natural Language Processing (NLP), Optical Character Recognition (OCR) and machine learning are combined to automatically capture and process content. As a result, manual processes can be drastically reduced and errors minimized.
Benefit
By using AI, documents can be processed in real time, allowing companies to significantly speed up their workflows. Instead of employees having to check and transfer information manually, the AI automatically recognizes relevant content and converts it into structured formats. This not only reduces processing times, but also minimizes typical sources of error such as typos or missing information. Automated document processing also ensures greater transparency and enables better traceability of processes.
Typical use cases
Companies use AI document processing primarily in areas where a large number of documents have to be processed on a daily basis:
- Invoice processing: Automatic extraction of invoice data such as amount, payment deadline and supplier details, direct forwarding to ERP systems for faster processing.
- Contract analysis: Identification and classification of clauses, deadlines and contract details to facilitate legal audits and compliance checks.
- Claims processing: Recording and analysis of insurance documents, damage reports and expert opinions for faster processing of claims.
What is a SaaS platform?
With Software-as-a-Service (SaaS), software is not installed on the company's own servers, but is used via the cloud. The provider takes care of operation, maintenance and updates so that no in-house IT infrastructure is required.
Compared to on-premise solutions, which require high investments and IT resources, SaaS is flexible, quickly ready for use and always up-to-date. Companies benefit from lower costs, automatic security updates and easy scalability - without having to worry about technical details.
What concerns do companies have when opting for a SaaS solution instead of in-house AI development? Are there any emotional hurdles or concerns that you often hear?
When deciding between in-house AI development and a SaaS solution, every company is faced with the question: am I really outsourcing a core competence or am I just outsourcing a specific technology? An insurer, for example, has expertise in assessing and settling claims - not necessarily in developing scalable AI products.
While in-house data science teams can drive innovation, it becomes critical when an AI prototype is to be transferred into a stable, production-ready core process. This is where many internal developments fail, because it's not just about training a model, but also about IT security, scalability, regulation and long-term operation.
A SaaS solution such as Buildsimple relieves companies of precisely this complexity without compromising their expertise. It ensures that incoming documents are automatically analyzed and processed in a structured manner, while the actual added value - such as the checks or customer interactions - remains with the company.
Many companies underestimate the fact that a functioning AI prototype is not automatically a viable solution for mass operation. Buildsimple, on the other hand, provides a market-ready solution that not only runs stably, but can also be flexibly integrated into existing processes - without companies having to set up and operate the entire AI infrastructure themselves.
So Buildsimple does not intervene in core processes, but helps to optimize sub-processes, for example processing document volumes in the insurance industry. Is that correct?
We are an essential part of the core process, which is why absolute stability has top priority. Our solution must be available at all times, must not fail and must not have any security gaps. A failure would be a serious problem - which is why we are constantly optimizing our systems to make them even more stable and secure. To date, there has not been a single incident.
Why SaaS platforms are the better choice
Instead of investing heavily in an in-house AI solution, SaaS platforms offer a ready-to-use, scalable and cost-effective alternative. The most important advantages at a glance at a glance:
- Immediate use: No development time, can be integrated directly into existing processes.
- Scalability: Adapts flexibly to company requirements and document volumes.
- Constant updating: Latest AI technologies without in-house development effort.
- Cost efficiency: No need for an in-house development team, transparent usage-dependent costs.
So Buildsimple does not intervene in core processes, but helps to optimize sub-processes, for example processing document volumes in the insurance industry. Is that correct?
Technology will become more diverse and there will be different solutions. It is crucial to have a platform that keeps pace with these developments.
The market has already thinned out considerably. Many companies have tried and failed because implementation is more complex than expected. A prototype is often developed first, followed by the realization that it is more difficult than expected, and in the end many opt for an existing solution.
At the same time, the volume to be processed will decrease in the long term as more digital and structured data is created. Nevertheless, there will be specialized providers. Companies will rely on them instead of building up their own expertise.
For providers, this means further developing their business model. The use of AI must go beyond document analysis and support other business processes. Even in five years' time, not everything will work automatically. Companies will want to continue to optimize and structure more and more information. Document analysis will remain a key issue - standing still would be fatal.
Conclusion: Companies benefit from AI without hurdles with SaaS
Implementing your own AI solutions is often associated with high costs, complexity and a shortage of specialists. Companies that want to use AI document processing efficiently should therefore rely on proven SaaS platforms. They offer modern AI technologies, are immediately ready for use and relieve companies of the time-consuming development of their own systems. This allows companies to completely focus on their core business and still benefit from the advantages of artificial intelligence. Ultimately, SaaS is a future-proof and economically viable solution for utilizing the benefits of AI efficiently and cost-effectively.
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