The Big AI Question – Buy or Build?

By Kasia Borowska, MD and Co-Founder of Brainpool AI

In a rush to capitalise on the AI hype, almost half of businesses leverage off-the-shelf solutions that promise quick wins, speedy deployment and lower up-front costs. But what if the businesses leveraging these technologies are trading long-term, sustainable AI for short-term, convenient cookie cutter solutions? 

This is why many businesses are struggling to answer the same question when looking to implement AI – should I buy or build? Building AI solutions around your business needs is the right long-term solution for many businesses, but the process can come with its own range of challenges. This is especially the case for businesses drowning in a mess of structured and unstructured data. 

So how does this approach compare to off-the-shelf solutions and where should businesses start with building their own AI models?.

Off-the-shelf solutions vs Agnostic AI

Pre-built solutions allow businesses to quickly implement AI without having to develop their own technology, which is why many have been initially attracted to this approach. However, these solutions create challenges for businesses, especially those looking to scale AI within their organisation. This is why businesses are turning to Agnostic AI. 

Agnostic AI is an approach which means businesses are not tied to a single vendor or framework – they are free to utilise different models and build a network of models to solve different business challenges. This method yields lower compute requirements, higher accuracy and provides businesses with a solution that evolves alongside technological advancements.

With Agnostic AI, businesses can tap into the most-effective LLM for each task. This allows businesses to tailor each use case to a specific domain to improve the overall effectiveness of the model. It also allows businesses to cut costs by only using services they need rather than paying for an off-the-shelf solution which includes tools they won’t use. 

By taking this approach, businesses can remain agile and keep up with changing market dynamics and regulatory requirements. This is because it provides businesses with the flexibility to switch models as regulations evolve to ensure they maintain their competitive advantage and remain consistently compliant.

Businesses that leverage off-the-shelf solutions will also have an increased risk of data and copyright breaches because they will have limited control over their data security and no freedom to customise their models to ensure compliance. In comparison, businesses that leverage Agnostic AI will be in control of their compliance and their data, therefore mitigating unnecessary security risks.

Finally, businesses that leverage off-the-shelf solutions will be forced to input all of their company data into a single vendor’s interface. During this process, these businesses are surrendering their IP which exposes them to long term risks and prevents them from being truly innovative. Business must remember it is your data, your context and it should therefore be your IP. To keep hold of all-important IP, businesses must take an agnostic approach to AI implementation.

Solving the biggest AI implementation challenge – data readiness

For businesses looking to build AI solutions  and take an agnostic approach, the biggest challenge they will face is data readiness. With 42% of leaders citing data quality as the biggest challenge they face when implementing AI, it’s no surprise that many businesses are utilising off-the-shelf solutions in an attempt to escape their unstructured data. 

For businesses that are looking to build their AI around their unique business needs and use cases, the first hurdle to overcome is ensuring your data is ready. Businesses must remember ML models are only as good as the data they are trained on, so although preparing your data for AI implementation can be time-consuming, it is arguably the most crucial step to developing effective AI models. 

To help prime data for effective AI implementation, businesses must leverage data processing techniques to understand the data’s lifecycle, source, significance and intended use. To understand the lifecycle of all data, businesses must enforce a data lineage and data change function which will allow organisations to track data throughout its entire lifecycle. This clear audit trail will allow businesses to monitor for any changes and truly understand the source of all data to help all ML models be consistently efficient. 

Businesses should also utilise semantic modelling to improve the quality of their data. Semantic modelling involves representing data in a way that captures its source, allowing businesses to understand its significance and intended use. This will allow businesses to make more accurate interpretations of all data to ensure it is leveraged and processed correctly to create effective ML models

In a race to harness AI’s full potential, businesses must resist the urge to jump in headfirst. Successful AI implementation requires a clear understanding of the technology and a strategy rooted in high-quality data. For those businesses looking to implement Agnostic AI – don’t forget that your models are only as good as the data that fuels them. To unlock AI which transforms your business rather than just supporting it – ensure your data is up to the task and reap the benefits that an agnostic approach has to offer.

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