AI Experimentation: Ad-hoc, Build or Buy?

AI technologies are evolving at a rapid pace, with many products and service options now available in the market for your use case. Teams are under pressure to showcase the benefits for their investments. Businesses are realising a controlled experimentation environment is needed for this to create accurate and reliable trials that can help support their initiatives and get endorsement. Trials that they can confidently base decision on and make wise investments into the future.?

Based on our experience, we’ve identified four common approaches organisations take when it comes to experimentation frameworks to run trials. Each has its advantages and challenges, but we believe buying off-the-shelf solutions offers the most efficient path to success. Let’s explore these options:

1. No Experimentation Framework Some organisations still rely on intuition and experience rather than formal experimentation. While this may work in stable, low-risk environments, it often results in missed opportunities, untested assumptions, and potential risks when scaling. The lack of data-driven validation limits growth, particularly in dynamic industries.
2. Ad-hoc Analysis Many companies start their experimentation journey with ad-hoc methods—running isolated experiments without a formal structure. While this approach can yield quick results, it lacks consistency and scalability. Each experiment often begins from scratch, making it costly, inefficient, and difficult to replicate. This fragmented approach can waste valuable time and effort.
3. Building a Framework In-house Some organisations opt to build their own customised experimentation frameworks. While this allows for flexibility and tailored solutions, it requires investment in terms of time, talent, and resources. Developing a system that is robust and flexible enough to meet all needs can be challenging, and the ongoing costs for maintenance can be high. However, this option can be very attractive for established data science teams that already have the expertise, experience, and capacity to develop.
4. Buying Off-the-Shelf Solutions The most efficient route for many organisations is to buy off-the-shelf experimentation platforms. These solutions offer faster implementation times, often include advanced features like machine learning techniques to auto select trial clusters, and can be integrated into existing systems. By purchasing a ready-made platform, companies relieve their internal teams from the burden of building and maintaining their own framework, accelerating their speed to value. The key to success lies in selecting a customisable platform that fits your unique business needs.

So, what’s the best option for you?

Choosing the right experimentation approach depends on your organisation’s priorities, resources, and goals. If speed to value and low maintenance is critical, buying an off-the-shelf solution is often the best option. These platforms help businesses stay agile, reduce risk, and maximise the return on AI investment. When adequate expertise and capacity are available, building your own may come at no incremental expense, but just be mindful of maintenance and ongoing costs needed to keep experiments going and up to date.??

It’s no surprise that although jahan.ai can help you in any step of the experimentation journey, we have developed our own tool jahanTestLab to help set up, configure and carry out trials at an enterprise level. If you would like to learn more about jahanTestLab or how we can help, contact us at [email protected].

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At jahan.ai, we build end-to-end AI twins for retail and supply chain businesses. We take the concept of a digital twin, a virtual replica of business processes, further with advanced AI, which not only mirrors these processes but also automatically optimises them. In this way, we support businesses in driving efficiency, going beyond their potential.

Reach out to jahan.ai to explore how we achieve this or how we can help optimise your business.