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Complementing Data Science & IT

What the task of AI implementation means for smaller companies.


In this article, we’re diving into the meaning of AI implementation from the perspective of technical departments.

We’ll explore who is responsible for what tasks and what challenges these tasks present. The process differs significantly between large corporations and smaller companies, and we’ll discuss these differences throughout the piece.

The decision to strongly integrate AI and focus on its developments often stems from the top levels of company leadership. This decision is usually driven by two main factors: a) the fear of falling behind competitors and becoming less relevant, and b) the potential economic benefits that AI can offer. Leaders see AI not just as a technology trend, but as a crucial step towards future-proofing their business and guaranteeing economic success.

While the strategic direction to pursue AI is set by senior management, the responsibility to execute this strategy typically falls on the shoulders of the Data Science and IT departments. These teams are tasked with the hands-on work of integrating AI technologies into existing systems, ensuring that they align with the company’s goals and operational needs.

Before we proceed, here’s a quick heads-up for smaller companies: Don’t be discouraged by the challenges we’re about to discuss. We have dedicated sections later in this article (section 4 and 5) where we’ll present solutions, simplifications, and support mechanisms designed specifically for smaller businesses facing these hurdles.

1. The role of Data Science

In their core roles, Data Scientists focus on enhancing a company’s analytical capabilities. This involves analyzing large datasets to extract valuable insights that can influence business decisions. They often employ statistical methods or machine learning models to perform tasks such as classification, clustering, or prediction of relevant business metrics, and present these results visually, such as in graphs.

However, the landscape of data science demands has evolved significantly. Data scientists who once primarily delivered graphs (we know this is strongly simplified) are now tasked to develop more sophisticated AI applications. These include creating chatbots that can interact with users, developing systems that automatically generate documents based on various inputs, and enhancing complex business workflows with a mix of automated prompts and actions. Moreover, they are involved in fine-tuning AI models and managing ongoing machine learning operations, known as MLOps.

This shift has brought new skill requirements, that were traditionally not within the core skillset of many data scientists. 

2. The role of IT

Also IT departments face several new challenges as they are tasked with the integration of AI models into their operations.

First, there is a significant need to upgrade infrastructure, if a company decided to host these large AI models by themselves (i.e. due to the compliance reasons of large enterprises). Deploying AI systems requires powerful servers and specialized hardware, along with better cloud resources and storage solutions for handling large data volumes.

IT departments are also tasked with enhancing data management practices to ensure the data is secure and efficiently processed.

Additionally, it’s important for AI systems to be scalable and reliable. IT departments implement strategies like load balancing and redundancy plans to support large-scale AI operations.

Finally, as the AI technology landscape grows, IT departments must carefully evaluate and choose suitable AI tools and platforms. This involves understanding the strengths and limitations of various AI tools to ensure they meet strategic needs effectively.

3. AI implementation in enterprises

In large enterprises, the presence of established IT and Data Science departments, along with the option to hire external specialists, equips these companies to pursue complex AI implementation strategies.

While implementing AI is by no means a simple task even for them, larger companies generally have access to more resources to better facilitate this process.

Additionally, the larger scale of these enterprises often makes AI solutions more scalable and economically attractive. They can therefore invest larger investment sums in order to benefit from AI technologies, leveraging their size and capacity to maximize the impact of AI on their operations.

4. AI implementation in SMBs

In smaller companies, the scale and scope of technical teams can vary significantly.

Some may have modest IT and data science departments, while others might only have a general IT team that handles data science tasks as well. In more constrained scenarios, one or two technical staff might be responsible for all IT-related functions, and in some cases, there may be no technical employees at all. With such varied setups, the prospect of implementing AI can seem overwhelming given the challenges involved.

Fortunately, there are more accessible ways for smaller companies to integrate AI without needing to host models on their own or hire specialized experts.

And this is the most important advantage of smaller companies: They typically have simpler needs. They do not have enormous compliance requirements, they can experiment with much more freedom and are potentially able to pursue their AI journey much faster.

Many can turn to ready-made cloud services and AI models that remove the necessity for self-hosting, significantly lowering the barrier to entry. Additionally, general AI toolkits like ValueFlow, offer functionality and solutions tailored to the needs of smaller businesses.

Such tools are designed to be user-friendly and require minimal technical expertise, enabling smaller companies to adopt AI solutions more easily and start enjoying first benefits quickly.

5. ValueFlow's support

In our opinion, smaller companies should avoid trying to develop everything in-house and setting overly ambitious goals from the start.

A practical starting point is to adopt a department-independent, general AI toolkit like ValueFlow. This kind of tool offers a complete application suite that includes a user-friendly frontend, chat capabilities, user management, and flow creation functionality.

These features are designed to be accessible without requiring deep technical knowledge, making it easier for smaller teams to get started.

When it comes to AI implementation, our key recommendations for smaller companies are straightforward:

For IT departments, we advice to avoid reinventing the wheel. There are numerous pre-built functions and tools available that can save time and resources, so there’s no need to develop everything from scratch.

For data science departments, our strong recommendation is to not attempt to (unconsciously) transform yourself into classical IT specialists. Instead, focus on leveraging complementary tools that enhance your capabilities and streamline your workflows.

This approach helps small companies optimize their strengths without overextending their capacities.


In conclusion, implementing AI in smaller companies comes with its own set of challenges, but it also offers a pathway to significant advantages.

While the resources and technical teams in smaller companies may not match those of larger enterprises, practical tools and strategies can bridge this gap. The significant advantage for smaller companies is, that the requirement profile of AI is typically much simpler that for their larger counterparts.

By utilizing ready-made cloud services, general AI toolkits like ValueFlow, and leveraging existing solutions, small businesses can effectively implement AI to enhance their operations.

The key is for data scientists to use complementary IT tools and not leave their own core skillset too much, just as IT employees of smaller companies should avoid to reinvent the wheel, acknowledge limited IT capacities and adapt to it accordingly.

April 2024 | ValueFlow team

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